A framework for automatic clustering of

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Gene Ontology: tool for the unification of biologyThe Gene Ontology Consortium**Michael Ashburner 1, Catherine A. Ball 3, Judith A. Blake 4, David Botstein 3, Heather Butler 1, J. Michael Cherry 3, Allan P. Davis 4, Kara Dolinski 3, Selina S.Dwight 3, Janan T. Eppig 4, Midori A. Harris 3, David P. Hill 4, Laurie Issel-Tarver 3, Andrew Kasarskis 3, Suzanna Lewis 2, John C. Matese 3, Joel E. Richardson 4,Martin Ringwald 4, Gerald M. Rubin 2& Gavin Sherlock 31FlyBase (http://www.fl). 2Berkeley Drosophila Genome Project (http://fruitfl). 3Saccharomyces Genome Database (). 4Mouse Genome Database and Gene Expression Database (). Correspondence should be addressed to J.M.C. (e-mail: cherry@) and D.B. (e-mail: botstein@), Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.The accelerating availability of molecular sequences, particularly the sequences of entire genomes, has transformed both the the-ory and practice of experimental biology. Where once bio-chemists characterized proteins by their diverse activities and abundances, and geneticists characterized genes by the pheno-types of their mutations, all biologists now acknowledge that there is likely to be a single limited universe of genes and proteins,many of which are conserved in most or all living cells. This recognition has fuelled a grand unification of biology; the infor-mation about the shared genes and proteins contributes to our understanding of all the diverse organisms that share them.Knowledge of the biological role of such a shared protein in one organism can certainly illuminate, and often provide strong inference of, its role in other organisms.Progress in the way that biologists describe and conceptualize the shared biological elements has not kept pace with sequencing.For the most part, the current systems of nomenclature for genes and their products remain divergent even when the experts appre-ciate the underlying similarities. Interoperability of genomic data-bases is limited by this lack of progress, and it is this major obstacle that the Gene Ontology (GO) Consortium was formed to address.Functional conservation requires a common language for annotationNowhere is the impact of the grand biological unification more evident than in the eukaryotes, where the genomic sequences of three model systems are already available (budding yeast, Sac-charomyces cerevisiae , completed in 1996 (ref. 1); the nematode worm Caenorhabditis elegans , completed in 1998 (ref. 2); and the fruitfly Drosophila melanogaster , completed earlier this year 3) and two more (the flowering plant Arabidopsis thaliana 4and fission yeast S chizosaccharomyces pombe ) are imminent. The complete genomic sequence of the human genome is expected in a year or two, and the sequence of the mouse (Mus musculus )will likely follow shortly thereafter.The first comparison between two complete eukaryotic genomes (budding yeast and worm 5) revealed that a surpris-ingly large fraction of the genes in these two organisms dis-played evidence of orthology. About 12% of the worm genes (∼18,000) encode proteins whose biological roles could be inferred from their similarity to their putative orthologues in yeast, comprising about 27% of the yeast genes (∼5,700). Most of these proteins have been found to have a role in the ‘core bio-logical processes’ common to all eukaryotic cells, such as DNA replication, transcription and metabolism. A three-way com-parison among budding yeast, worm and fruitfly shows that this relationship can be extended; the same subset of yeast genes generally have recognizable homologues in the fly genome 6.Estimates of sequence and functional conservation between the genes of these model systems and those of mammals are less reliable, as no mammalian genome sequence is yet known in its entirety. Nevertheless, it is clear that a high level of sequence and functional conservation will extend to all eukaryotes, with the likelihood that genes and proteins that carry out the core biological processes will again be probable orthologues. Fur-thermore, since the late 1980s, many experimental confirma-tions of functional conservation between mammals and model organisms (commonly yeast) have been published 7–12.This astonishingly high degree of sequence and functional conservation presents both opportunities and challenges. The main opportunity lies in the possibility of automated transfer of biological annotations from the experimentally tractable model organisms to the less tractable organisms based on gene and protein sequence similarity. Such information can be used to improve human health or agriculture. The challenge lies in meeting the requirements for a largely or entirely computa-tional system for comparing or transferring annotation among different species. Although robust methods for sequence comparison are at hand 13–15, many of the other ele-ments for such a system remain to be developed.Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web () are being constructed: biologicalprocess, molecular function and cellular component.© 2000 N a t u r e A m e r i c a I n c . • h t t p ://g e n e t i c s .n a t u r e .c o mA dynamic gene ontologyThe GO Consortium is a joint project of three model organism databases: FlyBase 16,Mouse Genome Informatics 17,18(MGI) and the Saccharomyces Genome Database 19(SGD). It is expected that other organism databases will join in the near future. The goal of the Consortium is to produce a structured, precisely defined, common, con-trolled vocabulary for describing the roles of genes and gene products in any organism.Early considerations of the problems posed by the diversity of activities that characterize the cells of yeast, flies and mice made it clear that extensions of standard indexing meth-ods (for example, keywords) are likely to be both unwieldy and, in the end, unworkable.Although these resources remain essential,and our proposed system will continue to link to and depend on them, they are not sufficient in themselves to allow automatic transfers of annotation.Each node in the GO ontologies will be linked to other kinds of information, includ-ing the many gene and protein keyword databases such as SwissPROT (ref. 20), Gen-Bank (ref. 21), EMBL (ref. 22), DDBJ (ref.23), PIR (ref. 24), MIPS (ref. 25), YPD &WormPD (ref. 26), Pfam (ref. 27), SCOP (ref. 28) and ENZYME (ref. 29). One reason for this is that the state of biological knowl-edge of what genes and proteins do is very incomplete and changing rapidly. Discover-ies that change our understanding of the roles of gene products in cells are published on a daily basis. To illustrate this, consider annotating two different proteins. One is known to be a transmembrane receptor ser-ine/threonine kinase involved in p53-induced apoptosis; the other is known only to be a membrane-bound protein. In one case, the knowledge about the protein is sub-stantial, whereas in the other it is minimal.© 2000 N a t u r e A m e r i c a I n c . • h t t p ://g e n e t i c s .n a t u r e .c o mWe need to be able to organize, describe, query and visualize bio-logical knowledge at vastly different stages of completeness. Any system must be flexible and tolerant of this constantly changing level of knowledge and allow updates on a continuing basis.Similar considerations suggested that a static hierarchical sys-tem, such as the Enzyme Commission 30(EC) hierarchy, although computationally tractable, was also likely to be inadequate to describe the role of a gene or a protein in biology in a manner that would be either intuitive or helpful for biologists. The hier-archical EC numbering system for enzymes is the standard resource for classifying enzymatic chemical reactions. The EC system does not address the classification of non-enzymatic pro-teins or the ability to describe the role of a gene product within a cell; also, the system has little facility for describing diverse pro-tein interactions. The vagueness of the term ‘function’ when applied to genes or proteins emerged as a particular problem, as this term is colloquially used to describe biochemical activities,biological goals and cellular structure. It is commonplace today to refer to the function of a protein such as tubulin as ‘GTPase’ or ‘constituent of the mitotic spindle’. For all these reasons, we are constructing three independent ontologies.Three categories of GOBiological process refers to a biological objective to which the gene or gene product contributes. A process is accomplished via one or more ordered assemblies of molecular functions.Processes often involve a chemical or physical transformation,in the sense that something goes into a process and something different comes out of it. Examples of broad (high level) bio-logical process terms are ‘cell growth and maintenance’ or ‘sig-nal transduction’. Examples of more specific (lower level)process terms are ‘translation’, ‘pyrimidine metabolism’ or ‘cAMP biosynthesis’.Molecular function is defined as the biochemical activity (including specific binding to ligands or structures) of a gene product. This definition also applies to the capability that a gene product (or gene product complex) carries as a potential. It describes only what is done without specifying where or when the event actually occurs. Examples of broad functional terms are ‘enzyme’, ‘transporter’ or ‘ligand’. Examples of narrower func-tional terms are ‘adenylate cyclase’ or ‘Toll receptor ligand’.Cellular component refers to the place in the cell where a gene product is active. These terms reflect our understanding of eukaryotic cell structure. As is true for the other ontologies, not all terms are applicable to all organisms; the set of terms is meant to be inclusive. Cellular component includes such terms as ‘ribo-some’ or ‘proteasome’, specifying where multiple gene products would be found. It also includes terms such as ‘nuclear mem-brane’ or ‘Golgi apparatus’.Ontologies have long been used in an attempt to describe all entities within an area of reality and all relationships between those entities. An ontology comprises a set of well-defined terms with well-defined relationships. The structure itself reflects the current representation of biological knowledge as well as serving as a guide for organizing new data. Data can be annotated to varying levels depending on the amount and completeness of available information. This flexibility also allows users to narrow or widen the focus of queries. Ultimately, an ontology can be a vital tool enabling researchers to turn data into knowledge. Com-puter scientists have made significant contributions to linguistic formalisms and computational tools for developing complex vocabulary systems using reason-based structures, and we hope that our ontologies will be useful in providing a well-developed data set for this community to test their systems. The Molecular Biology Ontology Working Group (/projects/bio-ontology/) is actively attempting to develop standards in this general field.Biological process, molecular function and cellular component are all attributes of genes, gene products or gene-product groups.Each of these may be assigned independently and, indeed, we believe that simply recognizing that biological process, molecular function and cellular location represent independent attributes is by itself clarifying in many situations, as in the annotation of gene-expression data. The relationships between a gene product (or gene-product group) to biological process, molecular func-tion and cellular component are one-to-many, reflecting the bio-logical reality that a particular protein may function in several processes, contain domains that carry out diverse molecular© 2000 N a t u r e A m e r i c a I n c . • h t t p ://g e n e t i c s .n a t u r e .c o mfunctions, and participate in multiple alternative interactions with other proteins, organelles or locations in the cell.The ontologies are developed for a generic eukaryotic cell;accordingly, specialized organs or body parts are not represented.Full integration of the ontologies with anatomical structures will occur as the ontologies are incorporated into each species’ data-base and are related to anatomical data within each database. GO terms are connected into nodes of a network, thus the connec-tions between its parents and children are known and form what are technically described as directed acyclic graphs. The ontolo-gies are dynamic, in the sense that they exist as a network that is changed as more information accumulates, but have sufficient uniqueness and precision so that databases based on the ontolo-gies can automatically be updated as the ontologies mature. The ontologies are flexible in another way, so that they can reflect the many differences in the biology of the diverse organisms, such as the breakdown of the nucleus during mitosis. In this way the GO Consortium has built up a system that supports a common lan-guage with specific, agreed-on terms with definitions and sup-porting documentation (the GO ontologies) that can be understood and used by a wide biological community.Examples of GO annotationAs one example, consider DNA metabolism, a biological process carried out by largely (but not entirely) shared elements in eukaryotes. The part of the process ontology (with selected gene names from S. cerevisiae , Drosophila and M. musculus ) shown is largely one parent to many children (Fig. 1a ). One notable excep-tion is the process of DNA ligation, which is a child of three processes, DNA replication, DNA repair and DNA recombina-tion. The yeast gene product Cdc9p is able to carry out the ligation step for all three processes, whereas it is uncertain whether the same enzyme is used in the other species. From the point of view of the ontology, it matters not, and a computer (or a human searcher) will find the appropriate nodes in either case using as the query either the enzyme, the gene name(s) or the GO term (or, if available, the unique GO identifier, in this case, GO:0003910).Also shown are the molecular function ontology for the MCM protein complex members that are known to regulate initiation of DNA replication in the three organisms (Fig. 1b ), and a por-tion of the cellular component ontology for these proteins (Fig.1c ). These ontologies reflect the finding that Mcm2–7 proteins are components of the pre-replicative complex in several model organisms, as well as sometimes localizing to the cytoplasm 30.The ontology supports both biological realities, and yet the mole-cular functions and the biological processes of the MCM homo-logues are conserved nevertheless.The usefulness of the GO ontologies for annotation received its first major test in the annotation of the recently completed sequence of the Drosophila genome. Little human intervention was required to annotate 50% of the genes to the molecular function and biological process ontologies using the GO method. Another use for GO ontologies that is gaining rapid adherence is the anno-tation of gene-expression data, especially after these have been clustered by similarities in pattern of gene expression 32,33. The results of clustering about 100 yeast experiments (of which about half are shown; Fig. 2) grouped together a subset of genes which, by name alone, convey little to most biologists. When the full short GO annotations for process, molecular function and location are added, however, the biological reason and import of the co-expres-sion of these genes becomes evident.The GO project is currently using a flat file format to store the ontologies, definitions of terms and gene associations. The ontologies, gene associations, definitions and documentation are available from the GO web site (),which also describes the principles and objectives used by the pro-ject. The ontologies are by no means complete. They are being expanded during the association of gene products from the col-laborating databases and we expect them to continue to evolve for many years. GO requires that all gene associations to the ontolo-gies must be attributed to the literature; for each citation the type of evidence will be encoded. As of early April 2000 there were 1,923, 2,094 and 490 nodes in the process, function and compo-nent ontologies, respectively. The three organism databases have made substantial progress to link gene products. Thus far the process, function and component ontologies have associations with 1,624, 1,602 and 1,577 yeast genes; 741, 2,334 and 1,061 fly genes; and 1,933, 2,896 and 1,696 mouse genes, respectively. A running table of these statistics can be found at the web site.The GO concept is intended to make possible, in a flexible and dynamic way, the annotation of homologous gene and protein sequences in multiple organisms using a common vocabulary that results in the ability to query and retrieve genes and proteins based on their shared biology. The GO ontologies produce a con-trolled vocabulary that can be used for dynamic maintenance and interoperability between genome databases. The ontologies are a work in progress. They can be consulted at any time on the World-Wide Web; indeed, their availability to human and machine alike is essential to maintain their flexibility and allow their evolution along with increased understanding of the under-lying biology. It is hoped that the GO concepts, especially the dis-tinctions between biological process, molecular function and cellular component, will find favour among biologists so that we can all facilitate, in our writing as well as our thinking, the grand unification of biology that the genome sequences portend.AcknowledgementsWe thank K. Fasman and M. Rebhan for useful discussions, and Astra Zeneca for financial support. SGD is supported by a P41, National Resources, grant from National Human Genome Research Institute (NHGRI) grantHG01315; MGD by a P41 from NHGRI grant HG00330; GXD by National Institute of Child Health and Human Development grant HD33745; and FlyBase by a P41 from NHGRI grant HG00739 and the Medical Research Council, London.Received 20 March; accepted 5 April 2000.© 2000 N a t u r e A m e r i c a I n c . • h t t p ://g e n e t i c s .n a t u r e .c o m1.Goffeau, A. et al.Life with 6000 genes. Science 274, 546 (1996).2.Worm Sequencing Consortium. Genome sequence of the nematode C. elegans : a platform for investigating biology. The C. elegans Sequencing Consortium.Science 282, 2012–2018 (1998).3.Adams, M.D. et al.The genome sequence of Drosophila melanogaster . Science 287, 2185–2195 (2000).4.Meinke, D.W. et al . Ar abidopsis thaliana : a model plant for genome analysis.Science 282, 662–682 (1998).5.Chervitz, S.A. et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res . 27, 74–78 (1999).6.Rubin, G.M. et parative genomics of the eukaryotes. Science 287,2204–2215 (2000).7.Tang, Z., Kuo, T., Shen, J. & Lin, R.J. Biochemical and genetic conservation of fission yeast Dsk1 and human SR protein-specific kinase 1. Mol. Cell. Biol . 20,816–824 (2000).8.Vajo, Z. et al . Conservation of the Caenorhabditis elegans timing gene clk-1 from yeast to human: a gene required for ubiquinone biosynthesis with potential implications for aging . Mamm. Genome 10, 1000–1004 (1999).9.Ohi, R. et al . Myb-related Schizosaccharomyces pombe cdc5p is structurally and functionally conserved in eukaryotes. Mol. Cell. Biol.18, 4097–4108 (1998).10.Bassett, D.E. Jr et al . Genome cross-referencing and XREFdb: implications for the identification and analysis of genes mutated in human disease. Nature Genet.15,339–344 (1997).11.Kataoka T. et al . Functional homology of mammalian and yeast RAS genes. Cell 40,19–26 (1985).12.Botstein, D. & Fink, G.R. Yeast: an experimental organism for modern biology.Science 240, 1439–1443 (1988).13.Tatusov, R.L., Galperin, M.Y ., Natale, D.A. & Koonin, E.V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res .28, 33–36 (2000).14.Andrade, M.A. et al . Automated genome sequence analysis and annotation.Bioinfor matics 15, 391–412 (1999).15.Fleischmann, W., Moller, S., Gateau, A. & Apweiler, R. A novel method for automatic functional annotation of proteins. Bioinformatics 15, 228–233 (1999).16.The FlyBase Consortium. The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res . 27, 85–88 (1999).17.Blake, J.A. et al . The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res . 28, 108–111 (2000).18.Ringwald, M. et al . GXD: a gene expression database for the laboratorymouse current status and recent enhancements. Nucleic Acids Res . 28,115–119 (2000).19.Ball, C.A. et al . Integrating functional genomic information into theSaccha r omyces Genome Database. Nucleic Acids Res . 28, 77–80 (2000).20.Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and itssupplement TrEMBL in 2000. Nucleic Acids Res.28, 45–48 (2000).21.Benson, D.A. et al . GenBank. Nucleic Acids Res.28, 15–18 (2000).22.Baker, W. et al . The EMBL Nucleotide Sequence Database. Nucleic Acids Res.28,19–23 (2000).23.Tateno, Y. et al . DNA Data Bank of J apan (DDBJ ) in collaboration with masssequencing teams. Nucleic Acids Res.28, 24–26 (2000).24.Barker, W.C. et al . The Protein Information Resource (PIR). Nucleic Acids Res.28,41–44 (2000).25.Mewes, H.W. et al . MIPS: a database for genomes and protein sequences.Nucleic Acids Res.28, 37–40 (2000).26.Costanzo, M.C. et al . The Yeast Proteome Database (YPD) and Caenorhabditiselegans Proteome Database (WormPD): comprehensive resources for the organization and comparison of model organism protein information. Nucleic Acids Res.28, 73–76 (2000).27.Bateman, A. et al . The Pfam protein families database. Nucleic Acids Res.28,263–266 (2000).28.Lo Conte, L. et al . SCOP: a structural classification of proteins database. NucleicAcids Res.28, 257–259 (2000).29.Bairoch, A. The ENZYME database in 2000. Nucleic Acids Res.28, 304–305(2000).30.Enzyme Nomenclature. Recommendations of the Nomenclature Committee ofthe Inte national Union of Biochemist y and Molecula Biology on the Nomenclature and Classification of Enyzmes. NC-IUBMB.(Academic, New York,1992).31.Tye, B.K. MCM proteins in DNA replication. Annu. Rev. Biochem.68, 649–686(1999).32.Eisen, M., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and displayof genome-wide expression patterns . Proc. Natl Acad. Sci. USA 95, 14863–14868(1998).33.Spellman, P.T. et al . Comprehensive identification of cell cycle-regulated genesof the yeast Sacchar omyces cer evisiae by microarray hybridization. Mol. Biol.Cell 9, 3273–3297 (1998).© 2000 N a t u r e A m e r i c a I n c . • h t t p ://g e n e t i c s .n a t u r e .c o m。

大数据和人工智能在足球项目比赛分析中的应用王星北京体育大学摘要：比赛表现分析是指通过分析比赛中收集得到的数据来探索不同环境因素和比赛层次状态下运动员和队伍决策行为和比赛表现，具体涉及运动员的技术运用、战术发挥、体能与生理反应、心理状态等相关因素对于特定比赛事件和比赛结果的影响。

随着人工智能、计算机视觉和传感器技术，比赛中的追踪数据得以被记录保留。

大数据分析和人工智能得以在比赛分析领域得到使用，尤其是在商业化程度最高的足球比赛中，全球各大高水平联赛的球队都配备了各自的比赛表现分析团队。

基于海量追踪数据，大数据和人工智能技术在足球领域的运用主要有两个部分——足球项目中比赛行为的识别和比赛结果的胜率模型。

基于这两部分研究，下一阶段对于足球比赛表现分析将深入探究赛场上发生的每一个比赛事件对于比赛结果的实时影响。

关键词：大数据；人工智能；足球项目比赛；应用比赛表现分析是通过分析影响比赛结果的因素来理解和提升团队运动表现的重要工具[1]。

比赛表现分析主要探索不同环境因素和比赛层次状态下运动员和队伍决策行为和比赛表现，具体涉及运动员的技术运用、战术发挥、体能与生理反应、心理状态等相关因素对于特定比赛事件和比赛结果的影响[2,3]。

尤其是在团队项目中，技术与战术的使用对决策结果和比赛评估起到决定性的作用[4]。

现如今比赛表现分析已经成为职业运动俱乐部、协会、高水平教练团队中的常规工作，而这其中的核心就是数据的收集和处理。

越来越多的专业数据软件被应用在各大高水平联赛。

通过分析这些数据，可以提供关键的技术指标帮助教练员和运动员提升赛场上的运动表现[5-8]。

例如此前就有研究发现助攻、射正球门、触球次数、传球、界外球和高吊球与比赛结果高度相关[9]。

但是由于这些数据都是一些观察指标，在收集和分析的过程中会丢失掉大量的细节信息[10]。

例如，我们通过射门指标可以知道是哪一脚射门完成了得分，但是无法知道是射门前的哪一脚传球在这次进球中起到关键性的作用。

IndexAactuation layer, 132average brightness,102-103adaptive control, 43Badaptive cruise control, 129backpropagation algorithm, 159adaptive FLC, 43backward driving mode,163,166,168-169adaptive neural networks,237adaptive predictive model, 283Baddeley-Molchanov average, 124aerial vehicles, 240 Baddeley-Molchanov fuzzy set average, 120-121, 123aerodynamic forces,209aerodynamics analysis, 208, 220Baddeley-Molchanov mean,118,119-121alternating filter, 117altitude control, 240balance position, 98amplitude distribution, 177bang-bang controller,198analytical control surface, 179, 185BCFPI, 61-63angular velocity, 92,208bell-shaped waveform,25ARMAX model, 283beta distributions,122artificial neural networks,115Bezier curve, 56, 59, 63-64association, 251Bezier Curve Fuzzy PI controller,61attitude angle,208, 217Bezier function, 54aumann mean,118-120bilinear interpolation, 90, 300,302automated manual transmission,145,157binary classifier,253Bo105 helicopter, 208automatic formation flight control,240body frame,238boiler following mode,280,283automatic thresholding,117border pixels, 101automatic transmissions,145boundary layer, 192-193,195-198autonomous robots,130boundary of a fuzzy set,26autonomous underwater vehicle, 191braking resistance, 265AUTOPIA, 130bumpy control surface, 55autopilot signal, 228Index 326CCAE package software, 315, 318 calibration accuracy, 83, 299-300, 309, 310, 312CARIMA models, 290case-based reasoning, 253center of gravity method, 29-30, 32-33centroid defuzzification, 7 centroid defuzzification, 56 centroid Method, 106 characteristic polygon, 57 characterization, 43, 251, 293 chattering, 6, 84, 191-192, 195, 196, 198chromosomes, 59circuit breaker, 270classical control, 1classical set, 19-23, 25-26, 36, 254 classification, 106, 108, 111, 179, 185, 251-253classification model, 253close formation flight, 237close path tracking, 223-224 clustering, 104, 106, 108, 251-253, 255, 289clustering algorithm, 252 clustering function, 104clutch stroke, 147coarse fuzzy logic controller, 94 collective pitch angle, 209 collision avoidance, 166, 168 collision avoidance system, 160, 167, 169-170, 172collision avoidance system, 168 complement, 20, 23, 45 compressor contamination, 289 conditional independence graph, 259 confidence thresholds, 251 confidence-rated rules, 251coning angle, 210constant gain, 207constant pressure mode, 280 contrast intensification, 104 contrast intensificator operator, 104 control derivatives, 211control gain, 35, 72, 93, 96, 244 control gain factor, 93control gains, 53, 226control rules, 18, 27, 28, 35, 53, 64, 65, 90-91, 93, 207, 228, 230, 262, 302, 304-305, 315, 317control surfaces, 53-55, 64, 69, 73, 77, 193controller actuator faulty, 289 control-weighting matrix, 207 convex sets, 119-120Coordinate Measurement Machine, 301coordinate measuring machine, 96 core of a fuzzy set, 26corner cube retroreflector, 85 correlation-minimum, 243-244cost function, 74-75, 213, 282-283, 287coverage function, 118crisp input, 18, 51, 182crisp output, 7, 34, 41-42, 51, 184, 300, 305-306crisp sets, 19, 21, 23crisp variable, 18-19, 29critical clearing time, 270 crossover, 59crossover probability, 59-60cruise control, 129-130,132-135, 137-139cubic cell, 299, 301-302, 309cubic spline, 48cubic spline interpolation, 300 current time gap, 136custom membership function, 294 customer behav or, 249iDdamping factor, 211data cleaning, 250data integration, 250data mining, 249, 250, 251-255, 259-260data selection, 250data transformation, 250d-dimensional Euclidean space, 117, 124decision logic, 321 decomposition, 173, 259Index327defuzzification function, 102, 105, 107-108, 111 defuzzifications, 17-18, 29, 34 defuzzifier, 181, 242density function, 122 dependency analysis, 258 dependency structure, 259 dependent loop level, 279depth control, 202-203depth controller, 202detection point, 169deviation, 79, 85, 185-188, 224, 251, 253, 262, 265, 268, 276, 288 dilation, 117discriminated rules, 251 discrimination, 251, 252distance function, 119-121 distance sensor, 167, 171 distribution function, 259domain knowledge, 254-255 domain-specific attributes, 251 Doppler frequency shift, 87 downhill simplex algorithm, 77, 79 downwash, 209drag reduction, 244driver’s intention estimator, 148 dutch roll, 212dynamic braking, 261-262 dynamic fuzzy system, 286, 304 dynamic tracking trajectory, 98Eedge composition, 108edge detection, 108 eigenvalues, 6-7, 212electrical coupling effect, 85, 88 electrical coupling effects, 87 equilibrium point, 207, 216 equivalent control, 194erosion, 117error rates, 96estimation, 34, 53, 119, 251, 283, 295, 302Euler angles, 208evaluation function, 258 evolution, 45, 133, 208, 251 execution layer, 262-266, 277 expert knowledge, 160, 191, 262 expert segmentation, 121-122 extended sup-star composition, 182 Ffault accommodation, 284fault clearing states, 271, 274fault detection, 288-289, 295fault diagnosis, 284fault durations, 271, 274fault isolation, 284, 288fault point, 270-271, 273-274fault tolerant control, 288fault trajectories, 271feature extraction, 256fiber glass hull, 193fin forces, 210final segmentation, 117final threshold, 116fine fuzzy controller, 90finer lookup table, 34finite element method, 318finite impulse responses, 288firing weights, 229fitness function, 59-60, 257flap angles, 209flight aerodynamic model, 247 flight envelope, 207, 214, 217flight path angle, 210flight trajectory, 208, 223footprint of uncertainty, 176, 179 formation geometry, 238, 247 formation trajectory, 246forward driving mode, 163, 167, 169 forward flight control, 217 forward flight speed, 217forward neural network, 288 forward velocity, 208, 214, 217, 219-220forward velocity tracking, 208 fossil power plants, 284-285, 296 four-dimensional synoptic data, 191 four-generator test system, 269 Fourier filter, 133four-quadrant detector, 79, 87, 92, 96foveal avascular zone, 123fundus images, 115, 121, 124 fuselage, 208-210Index 328fuselage axes, 208-209fuselage incidence, 210fuzz-C, 45fuzzifications, 18, 25fuzzifier, 181-182fuzzy ACC controller, 138fuzzy aggregation operator, 293 fuzzy ASICs, 37-38, 50fuzzy binarization algorithm, 110 fuzzy CC controller, 138fuzzy clustering algorithm, 106, 108 fuzzy constraints, 286, 291-292 fuzzy control surface, 54fuzzy damage-mitigating control, 284fuzzy decomposition, 108fuzzy domain, 102, 106fuzzy edge detection, 111fuzzy error interpolation, 300, 302, 305-306, 309, 313fuzzy filter, 104fuzzy gain scheduler, 217-218 fuzzy gain-scheduler, 207-208, 220 fuzzy geometry, 110-111fuzzy I controller, 76fuzzy image processing, 102, 106, 111, 124fuzzy implication rules, 27-28 fuzzy inference system, 17, 25, 27, 35-36, 207-208, 302, 304-306 fuzzy interpolation, 300, 302, 305- 307, 309, 313fuzzy interpolation method, 309 fuzzy interpolation technique, 300, 309, 313fuzzy interval control, 177fuzzy mapping rules, 27fuzzy model following control system, 84fuzzy modeling methods, 255 fuzzy navigation algorithm, 244 fuzzy operators, 104-105, 111 fuzzy P controller, 71, 73fuzzy PD controller, 69fuzzy perimeter, 110-111fuzzy PI controllers, 61fuzzy PID controllers, 53, 64-65, 80 fuzzy production rules, 315fuzzy reference governor, 285 Fuzzy Robust Controller, 7fuzzy set averages, 116, 124-125 fuzzy sets, 7, 19, 22, 24, 27, 36, 45, 115, 120-121, 124-125, 151, 176-182, 184-188, 192, 228, 262, 265-266fuzzy sliding mode controller, 192, 196-197fuzzy sliding surface, 192fuzzy subsets, 152, 200fuzzy variable boundary layer, 192 fuzzyTECH, 45Ggain margins, 207gain scheduling, 193, 207, 208, 211, 217, 220gas turbines, 279Gaussian membership function, 7 Gaussian waveform, 25 Gaussian-Bell waveforms, 304 gear position decision, 145, 147 gear-operating lever, 147general window function, 105 general-purpose microprocessors, 37-38, 44genetic algorithm, 54, 59, 192, 208, 257-258genetic operators, 59-60genetic-inclined search, 257 geometric modeling, 56gimbal motor, 90, 96global gain-scheduling, 220global linear ARX model, 284 global navigation satellite systems, 141global position system, 224goal seeking behaviour, 186-187 governor valves80, 2HHamiltonian function, 261, 277 hard constraints, 283, 293 heading angle, 226, 228, 230, 239, 240-244, 246heading angle control, 240Index329heading controller, 194, 201-202 heading error rate, 194, 201 heading speed, 226heading velocity control, 240 heat recovery steam generator, 279 hedges, 103-104height method, 29helicopter, 207-212, 214, 217, 220 helicopter control matrix, 211 helicopter flight control, 207 Heneghan method, 116-117, 121-124heuristic search, 258 hierarchical approaches, 261 hierarchical architecture, 185 hierarchical fuzzy processors, 261 high dimensional systems, 191 high stepping rates, 84hit-miss topology, 119home position, 96horizontal tail plane, 209 horizontal tracker, 90hostile, 223human domain experts, 255 human visual system, 101hybrid system framework, 295 hyperbolic tangent function, 195 hyperplane, 192-193, 196 hysteresis thres olding, 116-117hIIF-THEN rule, 27-28image binarization, 106image complexity, 104image fuzzification function, 111 image segmentation, 124image-expert, 122-123indicator function, 121inert, 223inertia frame, 238inference decision methods, 317 inferential conclusion, 317 inferential decision, 317 injection molding process, 315 inner loop controller, 87integral time absolute error, 54 inter-class similarity, 252 internal dependencies, 169 interpolation property, 203 interpolative nature, 262 intersection, 20, 23-24, 31, 180 interval sets, 178interval type-2 FLC, 181interval type-2 fuzzy sets, 177, 180-181, 184inter-vehicle gap, 135intra-class similarity, 252inverse dynamics control, 228, 230 inverse dynamics method, 227 inverse kinema c, 299tiJ - Kjoin, 180Kalman gain, 213kinematic model, 299kinematic modeling, 299-300 knowledge based gear position decision, 148, 153knowledge reasoning layer, 132 knowledge representation, 250 knowledge-bas d GPD model, 146eLlabyrinths, 169laser interferometer transducer, 83 laser tracker, 301laser tracking system, 53, 63, 65, 75, 78-79, 83-85, 87, 98, 301lateral control, 131, 138lateral cyclic pitch angle, 209 lateral flapping angle, 210 leader, 238-239linear control surface, 55linear fuzzy PI, 61linear hover model, 213linear interpolation, 300-301, 306-307, 309, 313linear interpolation method, 309 linear optimal controller, 207, 217 linear P controller, 73linear state feedback controller, 7 linear structures, 117linear switching line, 198linear time-series models, 283 linguistic variables, 18, 25, 27, 90, 102, 175, 208, 258Index 330load shedding, 261load-following capabilities, 288, 297 loading dock, 159-161, 170, 172 longitudinal control, 130-132 longitudinal cyclic pitch angle, 209 longitudinal flapping angle, 210 lookup table, 18, 31-35, 40, 44, 46, 47-48, 51, 65, 70, 74, 93, 300, 302, 304-305lower membership functions, 179-180LQ feedback gains, 208LQ linear controller, 208LQ optimal controller, 208LQ regulator, 208L-R fuzzy numbers, 121 Luenburger observer, 6Lyapunov func on, 5, 192, 284tiMMamdani model, 40, 46 Mamdani’s method, 242 Mamdani-type controller, 208 maneuverability, 164, 207, 209, 288 manual transmissions, 145 mapping function, 102, 104 marginal distribution functions, 259 market-basket analysis, 251-252 massive databases, 249matched filtering, 115 mathematical morphology, 117, 127 mating pool, 59-60max member principle, 106max-dot method, 40-41, 46mean distance function, 119mean max membership, 106mean of maximum method, 29 mean set, 118-121measuring beam, 86mechanical coupling effects, 87 mechanical layer, 132median filter, 105meet, 7, 50, 139, 180, 183, 302 membership degree, 39, 257 membership functions, 18, 25, 81 membership mapping processes, 56 miniature acrobatic helicopter, 208 minor steady state errors, 217 mixed-fuzzy controller, 92mobile robot control, 130, 175, 181 mobile robots, 171, 175-176, 183, 187-189model predictive control, 280, 287 model-based control, 224 modeless compensation, 300 modeless robot calibration, 299-301, 312-313modern combined-cycle power plant, 279modular structure, 172mold-design optimization, 323 mold-design process, 323molded part, 318-321, 323 morphological methods, 115motor angular acceleration, 3 motor plant, 3motor speed control, 2moving average filter, 105 multilayer fuzzy logic control, 276 multimachine power system, 262 multivariable control, 280 multivariable fuzzy PID control, 285 multivariable self-tuning controller, 283, 295mutation, 59mutation probability, 59-60mutual interference, 88Nnavigation control, 160neural fuzzy control, 19, 36neural networks, 173, 237, 255, 280, 284, 323neuro-fuzzy control, 237nominal plant, 2-4nonlinear adaptive control, 237non-linear control, 2, 159 nonlinear mapping, 55nonlinear switching curve, 198-199 nonlinear switching function, 200 nonvolatile memory, 44 normalized universe, 266Oobjective function, 59, 74-75, 77, 107, 281-282, 284, 287, 289-291,Index331295obstacle avoidance, 166, 169, 187-188, 223-225, 227, 231 obstacle avoidance behaviour, 187-188obstacle sensor, 224, 228off-line defuzzification, 34off-line fuzzy inference system, 302, 304off-line fuzzy technology, 300off-line lookup tables, 302 offsprings, 59-60on-line dynamic fuzzy inference system, 302online tuning, 203open water trial, 202operating point, 210optical platform, 92optimal control table, 300optimal feedback gain, 208, 215-216 optimal gains, 207original domain, 102outer loop controller, 85, 87outlier analysis, 251, 253output control gains, 92 overshoot, 3-4, 6-7, 60-61, 75-76, 94, 96, 193, 229, 266Ppath tracking, 223, 232-234 pattern evaluation, 250pattern vector, 150-151PD controller, 4, 54-55, 68-69, 71, 74, 76-77, 79, 134, 163, 165, 202 perception domain, 102 performance index, 60, 207 perturbed plants, 3, 7phase margins, 207phase-plan mapping fuzzy control, 19photovoltaic power systems, 261 phugoid mode, 212PID, 1-4, 8, 13, 19, 53, 61, 64-65, 74, 80, 84-85, 87-90, 92-98, 192 PID-fuzzy control, 19piecewise nonlinear surface, 193 pitch angle, 202, 209, 217pitch controller, 193, 201-202 pitch error, 193, 201pitch error rate, 193, 201pitch subsidence, 212planetary gearbox, 145point-in-time transaction, 252 polarizing beam-splitter, 86 poles, 4, 94, 96position sensor detectors, 84 positive definite matrix, 213post fault, 268, 270post-fault trajectory, 273pre-defined membership functions, 302prediction, 251, 258, 281-283, 287, 290predictive control, 280, 282-287, 290-291, 293-297predictive supervisory controller, 284preview distance control, 129 principal regulation level, 279 probabilistic reasoning approach, 259probability space, 118Problem understanding phases, 254 production rules, 316pursuer car, 136, 138-140 pursuer vehicle, 136, 138, 140Qquadrant detector, 79, 92 quadrant photo detector, 85 quadratic optimal technology, 208 quadrilateral ob tacle, 231sRradial basis function, 284 random closed set, 118random compact set, 118-120 rapid environment assessment, 191 reference beam, 86relative frame, 240relay control, 195release distance, 169residual forces, 217retinal vessel detection, 115, 117 RGB band, 115Riccati equation, 207, 213-214Index 332rise time, 3, 54, 60-61, 75-76road-environment estimator, 148 robot kinematics, 299robot workspace, 299-302, 309 robust control, 2, 84, 280robust controller, 2, 8, 90robust fuzzy controller, 2, 7 robustness property, 5, 203roll subsidence, 212rotor blade flap angle, 209rotor blades, 210rudder, 193, 201rule base size, 191, 199-200rule output function, 191, 193, 198-199, 203Runge-Kutta m thod, 61eSsampling period, 96saturation function, 195, 199 saturation functions, 162scaling factor, 54, 72-73scaling gains, 67, 69S-curve waveform, 25secondary membership function, 178 secondary memberships, 179, 181 selection, 59self-learning neural network, 159 self-organizing fuzzy control, 261 self-tuning adaptive control, 280 self-tuning control, 191semi-positive definite matrix, 213 sensitivity indices, 177sequence-based analysis, 251-252 sequential quadratic programming, 283, 292sets type-reduction, 184setting time, 54, 60-61settling time, 75-76, 94, 96SGA, 59shift points, 152shift schedule algorithms, 148shift schedules, 152, 156shifting control, 145, 147shifting schedules, 146, 152shift-schedule tables, 152sideslip angle, 210sigmoidal waveform, 25 sign function, 195, 199simplex optimal algorithm, 80 single gimbal system, 96single point mass obstacle, 223 singleton fuzzification, 181-182 sinusoidal waveform, 94, 300, 309 sliding function, 192sliding mode control, 1-2, 4, 8, 191, 193, 195-196, 203sliding mode fuzzy controller, 193, 198-200sliding mode fuzzy heading controller, 201sliding pressure control, 280 sliding region, 192, 201sliding surface, 5-6, 192-193, 195-198, 200sliding-mode fuzzy control, 19 soft constraints, 281, 287space-gap, 135special-purpose processors, 48 spectral mapping theorem, 216 speed adaptation, 138speed control, 2, 84, 130-131, 133, 160spiral subsidence, 212sporadic alternations, 257state feedback controller, 213 state transition, 167-169state transition matrix, 216state-weighting matrix, 207static fuzzy logic controller, 43 static MIMO system, 243steady state error, 4, 54, 79, 90, 94, 96, 98, 192steam turbine, 279steam valving, 261step response, 4, 7, 53, 76, 91, 193, 219stern plane, 193, 201sup operation, 183supervisory control, 191, 280, 289 supervisory layer, 262-264, 277 support function, 118support of a fuzzy set, 26sup-star composition, 182-183 surviving solutions, 257Index333swing curves, 271, 274-275 switching band, 198switching curve, 198, 200 switching function, 191, 194, 196-198, 200switching variable, 228system trajector192, 195y,Ttail plane, 210tail rotor, 209-210tail rotor derivation, 210Takagi-Sugeno fuzzy methodology, 287target displacement, 87target time gap, 136t-conorm maximum, 132 thermocouple sensor fault, 289 thickness variable, 319-320three-beam laser tracker, 85three-gimbal system, 96throttle pressure, 134throttle-opening degree, 149 thyristor control, 261time delay, 63, 75, 91, 93-94, 281 time optimal robust control, 203 time-gap, 135-137, 139-140time-gap derivative, 136time-gap error, 136time-invariant fuzzy system, 215t-norm minimum, 132torque converter, 145tracking error, 79, 84-85, 92, 244 tracking gimbals, 87tracking mirror, 85, 87tracking performance, 84-85, 88, 90, 192tracking speed, 75, 79, 83-84, 88, 90, 92, 97, 287trajectory mapping unit, 161, 172 transfer function, 2-5, 61-63 transient response, 92, 193 transient stability, 261, 268, 270, 275-276transient stability control, 268 trapezoidal waveform, 25 triangular fuzzy set, 319triangular waveform, 25 trim, 208, 210-211, 213, 217, 220, 237trimmed points, 210TS fuzzy gain scheduler, 217TS fuzzy model, 207, 290TS fuzzy system, 208, 215, 217, 220 TS gain scheduler, 217TS model, 207, 287TSK model, 40-41, 46TS-type controller, 208tuning function, 70, 72turbine following mode, 280, 283 turn rate, 210turning rate regulation, 208, 214, 217two-DOF mirror gimbals, 87two-layered FLC, 231two-level hierarchy controllers, 275-276two-module fuzzy logic control, 238 type-0 systems, 192type-1 FLC, 176-177, 181-182, 185- 188type-1 fuzzy sets, 177-179, 181, 185, 187type-1 membership functions, 176, 179, 183type-2 FLC, 176-177, 180-183, 185-189type-2 fuzzy set, 176-180type-2 interval consequent sets, 184 type-2 membership function, 176-178type-reduced set, 181, 183-185type-reduction,83-1841UUH-1H helicopter, 208uncertain poles, 94, 96uncertain system, 93-94, 96 uncertain zeros, 94, 96underlying domain, 259union, 20, 23-24, 30, 177, 180unit control level, 279universe of discourse, 19-24, 42, 57, 151, 153, 305unmanned aerial vehicles, 223 unmanned helicopter, 208Index 334unstructured dynamic environments, 177unstructured environments, 175-177, 179, 185, 187, 189upper membership function, 179Vvalve outlet pressure, 280vapor pressure, 280variable structure controller, 194, 204velocity feedback, 87vertical fin, 209vertical tracker, 90vertical tracking gimbal, 91vessel detection, 115, 121-122, 124-125vessel networks, 117vessel segmentation, 115, 120 vessel tracking algorithms, 115 vision-driven robotics, 87Vorob’ev fuzzy set average, 121-123 Vorob'ev mean, 118-120vortex, 237 WWang and Mendel’s algorithm, 257 WARP, 49weak link, 270, 273weighing factor, 305weighting coefficients, 75 weighting function, 213weld line, 315, 318-323western states coordinating council, 269Westinghouse turbine-generator, 283 wind–diesel power systems, 261 Wingman, 237-240, 246wingman aircraft, 238-239 wingman veloc y, 239itY-ZYager operator, 292Zana-Klein membership function, 124Zana-Klein method, 116-117, 121, 123-124zeros, 94, 96µ-law function, 54µ-law tuning method, 54。

人工智能基础(习题卷9)第1部分：单项选择题，共53题，每题只有一个正确答案,多选或少选均不得分。

1.[单选题]由心理学途径产生，认为人工智能起源于数理逻辑的研究学派是（ ）A)连接主义学派B)行为主义学派C)符号主义学派答案:C解析:2.[单选题]一条规则形如：，其中“←"右边的部分称为(___)A)规则长度B)规则头C)布尔表达式D)规则体答案:D解析:3.[单选题]下列对人工智能芯片的表述，不正确的是（）。

A)一种专门用于处理人工智能应用中大量计算任务的芯片B)能够更好地适应人工智能中大量矩阵运算C)目前处于成熟高速发展阶段D)相对于传统的CPU处理器，智能芯片具有很好的并行计算性能答案:C解析:4.[单选题]以下图像分割方法中，不属于基于图像灰度分布的阈值方法的是( )。

A)类间最大距离法B)最大类间、内方差比法C)p-参数法D)区域生长法答案:B解析:5.[单选题]下列关于不精确推理过程的叙述错误的是（ ）。

A)不精确推理过程是从不确定的事实出发B)不精确推理过程最终能够推出确定的结论C)不精确推理过程是运用不确定的知识D)不精确推理过程最终推出不确定性的结论答案:B解析:6.[单选题]假定你现在训练了一个线性SVM并推断出这个模型出现了欠拟合现象，在下一次训练时，应该采取的措施是（）0A)增加数据点D)减少特征答案:C解析:欠拟合是指模型拟合程度不高，数据距离拟合曲线较远，或指模型没有很好地捕 捉到数据特征，不能够很好地拟合数据。

可通过增加特征解决。

7.[单选题]以下哪一个概念是用来计算复合函数的导数？A)微积分中的链式结构B)硬双曲正切函数C)softplus函数D)劲向基函数答案:A解析:8.[单选题]相互关联的数据资产标准，应确保()。

数据资产标准存在冲突或衔接中断时，后序环节应遵循和适应前序环节的要求，变更相应数据资产标准。

A)连接B)配合C)衔接和匹配D)连接和配合答案:C解析:9.[单选题]固体半导体摄像机所使用的固体摄像元件为( )。

一种大规模流式数据聚类方法在交通热点分析中的应用牟向伟;陈燕;曹妍【摘要】In order to improve the efficiency of traffic hotspots analysis in large-scale streaming data environment,a two-phase flow data clustering framework is proposed,in the first stage,this framework produces macro cluster based on improved Canopy algorithm,in the second K-means clustering algorithm uses the cluster center from macro cluster to generate more accurate clustering results.In the experiment,this framework was programed to analyze a dataset of Beijing taxi GPS positioning data which had been simulated as streaming data.Clustering results was used to analyze traffic hotspot and shown in the heat map and WebGIS,which can directly find relatively taxi activity hot spots and road,the results of hot spots are consistent with daily experience.These experiment results show that the two-phase flow data clustering framework can real-time cluster the streaming data and are available for the user query in any time window.the two-phase flow data clustering framework application in real-time traffic hotspot analysis provides theory value and application significance for real time traffic analysis,traffic planning and congestion management.%为了提高在大规模流式数据环境下交通热点区域分析的算法效率,提出了一种流式数据两阶段方法;该方法在第一阶段使用基于改进Canopy算法进行粗聚类并产生宏簇,在第二阶段使用K-means算法进行细聚类;并以粗聚类产生的宏簇个数和类簇中心位置为指导产生更加准确的微簇聚类结果.在试验中,使用流式数据两阶段方法对北京市出租车的定位数据进行了聚类分析;并结合热力图和电子地图对聚类结果进行可视化表达,在最终的热力分析结果中可以直观地发现出租车活动较为频繁的热点区域和线路,且与日常出行经验相符合.试验结果表明该算法能够实时地对流式数据进行聚类分析,产生的数据结果可供用户在任意时间窗口范围进行查询分析,有助于为交通活动情况实时分析、交通规划和拥堵治理等方面提供有价值的理论参考依据.【期刊名称】《科学技术与工程》【年(卷),期】2017(017)015【总页数】8页(P260-267)【关键词】流式数据聚类;实时计算;交通热点分析;Canopy;K-means【作者】牟向伟;陈燕;曹妍【作者单位】大连海事大学交通运输管理学院,大连116026;大连海事大学交通运输管理学院,大连116026;大连海事大学交通运输管理学院,大连116026【正文语种】中文【中图分类】TP391.4交通热点区域是指在一定时间范围内能够持续带来大规模、高频次交通活动的区域，交通热点区域往往对周边路造成区域性交通拥堵影响，因此交通热点区域的发现有助于及时对交通拥堵情况进行分析，是改善交通状况的关键技术与前提条件之一，可以为交通规划、资源调度、拥堵治理、政府决策等方面提供有价值的理论参考依据。

自动聚类算法确定cluster数目的方法
from
/bittnt/item/857a51e404e8b2b72f140b1 9
1. 通过cross-validation方式。

就是在每个可能的聚类数目K下都对数据进行聚类，然后通过某种度量方式判断哪个K下的聚类更“好”，目前常见的度量方式包括cluster stability, gap statistic等等。

2. 利用Dirichlet Process来动态的自动确定cluster数目。

用Chinese restaurant process来讲比较直观，也就是当有一个新的instance（客人）来的时候，会通过概率判断新开一桌（也就是新的聚类）还是将这个 instance放到某个已经有的桌子上。

这是Bayesian 派的来法。

3. 利用reversible-jump技术自动确定cluster数目。

其思想比较类似于DP，不过在技术上是通过实施trans-dimensional变换来实现自动确定聚类个数。

据说是属于Frequentist派的来法。

此外，也有一些以自动判定聚类数目作为卖点的聚类方法，例如Affinity Propagation等，本质上是将聚类问题纳入到factor graph 框架下来解决。

通俗讲解dirichlet 聚类英文回答：Dirichlet clustering, also known as Dirichlet process mixture modeling, is a probabilistic clustering algorithm that allows for the automatic determination of the number of clusters in a dataset. It is named after the Dirichlet distribution, which is used to model the distribution of cluster assignments.In Dirichlet clustering, each data point is assigned to one of the clusters, and the cluster assignments are determined based on the similarity between data points. However, unlike traditional clustering algorithms,Dirichlet clustering does not require the number ofclusters to be specified in advance. Instead, it uses anon-parametric Bayesian approach to automatically determine the number of clusters based on the data.The Dirichlet process is a stochastic process thatallows for an infinite number of clusters. It is characterized by two parameters: a concentration parameter, which controls the number of clusters, and a base distribution, which specifies the distribution of cluster assignments. The concentration parameter determines the probability of creating a new cluster when a new data point is encountered, while the base distribution determines the distribution of data points within each cluster.To perform Dirichlet clustering, we start with an empty set of clusters and iteratively assign data points to clusters. At each iteration, we calculate the probability of assigning a data point to each existing cluster, as well as the probability of creating a new cluster. The data point is then assigned to the cluster with the highest probability. If the probability of creating a new clusteris higher than the probability of assigning the data point to any existing cluster, a new cluster is created.The process continues until all data points have been assigned to clusters. The resulting clusters can then be used for further analysis or visualization.中文回答：Dirichlet聚类，也被称为Dirichlet过程混合建模，是一种概率聚类算法，可以自动确定数据集中的聚类数量。

[1]卢汉清，刘静.基于图学习的自动图像标注[J]. 计算机学报,2008,31（9）:1630-1645.[2]李志欣，施智平，李志清，史忠植.融合语义主题的图像自动标注[J].软件学报，2011,22(4):801-812.[3]Minyi Ke, Shuaihao Li, Yong Sun, Shengjun Cao . Research on similarity comparison by quantifying grey histogram based on multi-feature in CBIR [J]// Proceedings of the 3th International Conference on Education Technology and Training .IEEE Press.2010:422-424.[4] Wang Ke-Gang, Qi Li-Ying. Classification and Application of Images Based on Color Texture Feature[C]// Proceedings of 4th IEEE International Conference on Computer Science and Information Technology .IEEE Press. 2011:284-290.[5]Mohamed Maher Ben Ismail. Image Database Categorization based on a Novel Possibilistic Clustering and Feature Weighting Algorithm[C] // Proceedings of 2012 International Conference on Network and Computational Intelligence. 2012:122-127.[6]Du Gen-yuana, Miao Fang, Tian Sheng-li, Liu Ye.A modified fuzzy C-means algorithm in remote sensing image segmentation[C]// Proceedings of Environmental Science and Information Application Technology. 2009: 447-450.[7]Jeon J., Lavrenko V., Manmatha R. Automatic image annotation and retrieval using cross- media relevance models［C］// ACM SIGIR.ACM Press，2003:119- 126．[8]苗晓光,袁平波,何芳,俞能海. 一种新颖的自动图像标注方法[C].// 第十三届中国图象图形学术会议.2006:581-584参考文献正解[1] Smeulders A W M, Worring M, Santini S, et al. Content-based image retrieval at the end ofthe early years[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,22(12),2000: 1349-1380.[2] Datta R, Joshi D, Li J, et al. Image retrieval: ideas, influences, and trends of the new age[J].ACM Computing Surveys (CSUR),40(2),2008: 5.[3] Mller H, Mller W, Squire D M G, et al. Performance evaluation in content-based imageretrieval: overview and proposals[J]. Pattern Recognition Letters,22(5),2001: 593-601.[4]Müller H, SO H E S. Text-based (image) retrieval[J]. HES SO//Valais, Sierre, Switzerland[Online] http://thomas. deselaers. de/teaching/files/tutorial_icpr08/03text Based Retrieval. pdf [Accessed 25 July 2010], 2007.[5]Zhao R, Grosky W I. Narrowing the semantic gap-improved text-based web document retrievalusing visual features[J]. Multimedia, IEEE Transactions on, 2002, 4(2): 189-200.[6]卢汉清，刘静.基于图学习的自动图像标注[J]. 计算机学报,2008,31（9）:1630-1645.[7]李志欣，施智平，李志清.史忠植.融合语义主题的图像自动标注[J].软件学报，2011,22(4):801-812.[8] Li J, Wang JZ. Automatic linguistic indexing of pictures by a statistical modeling approach.IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003,25(9):1075−1088. [doi:10.1109/TPAMI.2003.1227984][9] Chang E, Goh K, Sychay G, Wu G. CBSA: Content-Based soft annotation for multimodalimage retrieval using Bayes point machines. IEEE Trans. on Circuits and Systems for Video Technolo gy, 2003,13(1):26− 38. [doi: 10.1109/TCSVT.2002.808079][10]Carneiro G, Chan AB, Moreno PJ, Vasconcelos N. Supervised learning of semantic classes forimage annotation and retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2007,29(3):394 − 410. [doi: 10.1109/TPAMI.2007.61][11]Blei DM, Jordan MI. Modeling annotated data. In: Proc. of the 26th Int’l ACM SIGIR Conf.on Research and Development in Information Retrieval. New York: ACM Press, 2003. 127− 134. [doi: 10.1145/860435.860460][12]Barnard K, Duygulu P, Forsyth D, de Freitas N, Blei DM, Jordan MI. Matching words andpictures. Journal of Machine Learning Research, 2003,3(2):1107 − 1135. [doi:10.1162/153244303322533214][13] LA VRENKO V, JEON J. Automatic image annotation and retrieval using cross-mediarelevance models. [C]//Proceeding of the 26th ACM SIGIR Conf. on Research and Development in Information Retrieval . New York: ACM, 2003: 119 − 126.[14]MINVI KE, SHUAIHAO LI, YONG SUN, SHENGJUN CAO. Research on similaritycomparison by quantifying grey histogram based on multi-feature in CBIR [C]//Proceeding of the 3rd International Conference on Education Technology and Training .IEEE,2010:422-424.[15] WANGKE GANG, QILI YING. Classification and application of images based on colortexture feature[C]// Proceedings of 4th IEEE International Conference on Computer Science and Information Technology .IEEE, 2011:284-290.[16]Mohamed Maher Ben Ismail. Image Database Categorization based on a Novel PossibilisticClustering and Feature Weighting Algorithm[C] // Proceedings of 2012 International Conference on Network and Computational Intelligence. 2012:122-127.[17]Du Gen-yuana, Miao Fang, Tian Sheng-li, Liu Ye.A modified fuzzy C-means algorithm inremote sensing image segmentation[C]// Proceedings of Environmental Science and Information Application Technology. 2009: 447-450.[18] Wang Ke-Gang, Qi Li-Ying. Classification and Application of Images Based on ColorTexture Feature[C]// Proceedings of 4th IEEE International Conference on Computer Science and Information Technology .IEEE Press. 2011:284-290.[19]Jeon J., Lavrenko V., Manmatha R. Automatic image annotation and retrieval using cross-media relevance models［C］// ACM SIGIR.ACM Press，2003:119- 126.[20]苗晓光,袁平波,何芳,俞能海. 一种新颖的自动图像标注方法[C].// 第十三届中国图象图形学术会议.2006:581-584.[21] Duygulu P, Barnard K, de Freitas J F G, et al. Object recognition as machine translation:Learning a lexicon for a fixed image vocabulary[M]//Computer Vision—ECCV 2002.Springer Berlin Heidelberg, 2002: 97-112.[22]王科平，王小捷，钟义信．加权特征自动图像标注方法［Ｊ］．北京邮电大学学报，.2011:34(5):6-9.[23] Chen K, Li J, Ye L. Automatic Image Annotation Based on Region Feature[M]//Multimediaand Signal Processing. Springer Berlin Heidelberg, 2012: 137-145.[24]刘丽, 匡纲要. 图像纹理特征提取方法综述[J]. 中国图象图形学报, 2009, 14(4): 622-635.[25] 杨红菊, 张艳, 曹付元. 一种基于颜色矩和多尺度纹理特征的彩色图像检索方法[J]. 计算机科学, 2009, 36(9): 274-277.[26]Minyi Ke, Shuaihao Li, Yong Sun, Shengjun Cao . Research on similarity comparison byquantifying grey histogram based on multi-feature in CBIR [J]// Proceedings of the 3th International Conference on Education Technology and Training .IEEE Press.2010:422-424 [27] Mohamed Maher Ben Ismail. Image Database Categorization based on a Novel Possibilistic Clustering and Feature Weighting Algorithm[C] // Proceedings of 2012 International Conference on Network and Computational Intelligence. 2012:122-127.[28]Khalid Y I A, Noah S A. A framework for integrating DBpedia in a multi-modality ontologynews image retrieval system[C]//Semantic Technology and Information Retrieval (STAIR).IEEE, 2011: 144-149.[29]Celik T, Tjahjadi T. Bayesian texture classification and retrieval based on multiscale featurevector[J]. Pattern recognition letters, 2011, 32(2): 159-167.[30]Min R, Cheng H D. Effective image retrieval using dominant color descriptor and fuzzysupport vector machine[J]. Pattern Recognition, 2009, 42(1): 147-157.[31]Feng H, Shi R, Chua T S. A bootstrapping framework for annotating and retrieving WWWimages[C]//Proceedings of the 12th annual ACM international conference on Multimedia.ACM, 2004: 960-967.[22]Ke X, Chen G. Automatic Image Annotation Based on Multi-scale SalientRegion[M]//Unifying Electrical Engineering and Electronics Engineering.New York, 2014: 1265-1273.[33]Wartena C, Brussee R, Slakhorst W. Keyword extraction using wordco-occurrence[C]//Database and Expert Systems Applications (DEXA).IEEE, 2010: 54-58. [34]刘松涛, 殷福亮. 基于图割的图像分割方法及其新进展[J]. 自动化学报, 2012, 38(6):911-922.[35]陶文兵, 金海. 一种新的基于图谱理论的图像阈值分割方法[J]. 计算机学报, 2007, 30(1):110-119.[36]谭志明. 基于图论的图像分割及其嵌入式应用研究[D][J]. 博士学位论文) 上海交通大学,2007.[37] Shi J, Malik J. Normalized cuts and image segmentation[J]. Pattern Analysis and MachineIntelligence,2000, 22(8): 888-905.[38] Huang Z C, Chan P P K, Ng W W Y, et al. Content-based image retrieval using color momentand Gabor texture feature[C]//Machine Learning and Cybernetics (ICMLC), 2010 International Conference on. IEEE, 2010, 2: 719-724.[39]王涛, 胡事民, 孙家广. 基于颜色-空间特征的图像检索[J]. 软件学报, 2002, 13(10).[40] 朱兴全, 张宏江. iFind: 一个结合语义和视觉特征的图像相关反馈检索系统[J]. 计算机学报,2002, 25(7): 681-688.[41]Sural S, Qian G, Pramanik S. Segmentation and histogram generation using the HSV colorspace for image retrieval[C]//Image Processing. 2002. Proceedings. 2002 International Conference on. IEEE, 2002, 2: II-589-II-592 vol. 2.[42]Ojala T, Rautiainen M, Matinmikko E, et al. Semantic image retrieval with HSVcorrelograms[C]//Proceedings of the Scandinavian conference on Image Analysis. 2001: 621-627.[43]Yu H, Li M, Zhang H J, et al. Color texture moments for content-based imageretrieval[C]//Image Processing. 2002. Proceedings. 2002 International Conference on. IEEE, 2002, 3: 929-932.[44]Sun L, Ge H, Yoshida S, et al. Support vector description of clusters for content-based imageannotation[J]. Pattern Recognition, 2014, 47(3): 1361-1374.[45]Hiremath P S, Pujari J. Content based image retrieval using color, texture and shapefeatures[C]//Advanced Computing and Communications, 2007. ADCOM 2007.International Conference on. IEEE, 2007: 780-784.[46]Zhang D, Lu G. Generic Fourier descriptor for shape-based image retrieval[C]//Multimediaand Expo, 2002. ICME'02. Proceedings. 2002 IEEE International Conference on. IEEE, 2002, 1: 425-428.[47]Gevers T, Smeulders A W M. Pictoseek: Combining color and shape invariant features forimage retrieval[J]. Image Processing, IEEE Transactions on, 2000, 9(1): 102-119.[48] Bailloeul T, Zhu C, Xu Y. Automatic image tagging as a random walk with priors on thecanonical correlation subspace[C]//Proceedings of the 1st ACM international conference on Multimedia information retrieval. ACM, 2008: 75-82.[16]MOHAMED MAHER, BEN ISMAIL. Image database categorization based on a novelprobability clustering and feature weighting algorithm[C] // Proceedings of 2012 International Conference on Network and Computational Intelligence, 2012:122-127.[17]DU GENYUANA, TIAN SHENGLI, LIU YE.A modified fuzzy c-means algorithm in remotesensing image segmentation[C]// Proceedings of Environmental Science and Information Application Technology, 2009: 447-450.[18]SIVIC J, RUSSELL BC. Discovering objects and their location in images.[C] //Proceedingsof the 10th IEEE Int’l Conf. on Computer Vision .IEEE Computer Society, 2005:370 − 377.[19] DUYGULU P, BARNARD K, FORSYTH D. Object recognition as machine translation [J].//Learning a lexicon for a fixed image vocabulary In: HEYDEN A, NIELSEN M, JOHANSEN P, eds. Lecture Notes in Computer Science 2353,2002,45(1): 97−112.[20]JEON J, MANMA THA R. Automatic image annotation and retrieval using cross- mediarelevance models[C]// ACM SIGIR.ACM，2003:119- 126.[21]G K RAMANI and T ASSUDANI. Tag recommendation for photos[J].In Stanford CS229Class Project, 2009,23(1):130 − 145.[22] D. ZHANG and G. LU. A review on automatic image annotation techniques[J]. PatternRecognition, 2011,145(1):346–362.[23]K.BARNARD,P.DUGGULU,N FREITAS,D FORSYTH, D BLEI. Matching words andpictures [J].Journal of Machine Learning Research,2003,132(2):1107-1135.[24]苗晓光,袁平波,何芳,俞能海. 一种新颖的自动图像标注方法[C].// 第十三届中国图象图形学术会议.2006:581-584.[25] 王科平,王小捷, 钟义信.加权特征自动图像标注方法[J].北京邮电大学学报,2011,34(5):6−9.[26] JIN R, KANG F, SUKTHANKAR R. Correlated label propagation with application tomulti-label learning [C]. //Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2006:119-126.[27]YANG C.B, DONG, M, HUA J. Region-based image annotation using asymmetrical supportvector machine-based multiple-instance learning[C].// Proceeding of the CVPR.2006:2057–2063.[28] CARNEIRO G, V ASCONCELOS N. A database centric view of semantic image annotationand retrieval[C].// Proceeding of ACM SIGIR. 2005:559–566.[29] CUSANO C, CIOCCA G, SCHETTINI R. Image annotation using SVM[C].// Proceeding ofthe Internet Imaging, 2004: 330–338.[30]R Y AN, A HAUPTMANN, R JIN. Multimedia search with pseudo-relevancefeedback[C].//Proceeding of IEEE conference on Content-based Image and Video Retrieval.2007:238-247.[31] J W ANG, S KUMAR, S CHANG. Semi-supervised hashing for scalable imageretrieval[C].//Proceeding of IEEE conference on Computer Vision.2009:1-8.[32]M KOKARE, B CHATTERJI, P BISWAS. Comparison of similarity metrics for texture imageretrieval[C].//Proceeding of IEEE conference on Convergent Technologies for Asia-Pacific Region.2003:571-575.[33]EDWARD CHANG, KINGSHY GOH, GERARD SYCHAY, GANG WU. Content-base softannotation for multimodal image retrieval using bays point machines[J].CirSysVideo,2003,13(1):26-38.[34]SHI J, MALIK J. Normalized cuts and image segmentation[J].IEEE Transactions on PatternAnalysis and Machine Intelligence,2000,22(8):888-905.[35]ZHOU D, BOUSQUET O. Learning with local and global consistency[C].//Proceeding ofAdvances in Neural Information Proceeding Systems,2004:321-328.[36]Minyi Ke, Shuaihao Li, Yong Sun, Shengjun Cao . Research on similarity comparison byquantifying gray histogram based on multi-feature in CBIR [J]// Proceedings of the 3th International Conference on Education Technology and Training .IEEE Press.2010:422-424 [37] Wang Ke-Gang, Qi Li-Ying. Classification and Application of Images Based on ColorTexture Feature[C]// Proceedings of 4th IEEE International Conference on Computer Science and Information Technology .IEEE Press. PP:284-290 ,2011[38] Chen K, Li J, Ye L. Automatic Image Annotation Based on Region Feature[M]//Multimediaand Signal Processing. Springer Berlin Heidelberg, 2012: 137-145.[39]Lu Z, Ip H H S. Generalized relevance models for automatic image annotation [M]//Advancesin Multimedia Information Processing-PCM 2009. Springer Berlin Heidelberg, 2009: 245-255.[40]Fournier J, Cord M. A Flexible Search-by-Similarity Algorithm for Content-Based ImageRetrieval[C]//JCIS. 2002: 672-675.论文3[1]Zhang C, Chai J Y, Jin R. User term feedback in interactive text-based image retrieval[C]//Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2005: 51-58.[2]Akgül C B, Rubin D L, Napel S, et al. Content-based image retrieval in radiology: currentstatus and future directions[J]. Journal of Digital Imaging, 2011, 24(2): 208-222.[7]Zhang D, Islam M, Lu G. Structural image retrieval using automatic image annotation and region based inverted file[J]. Journal of Visual Communication and Image Representation, 2013, 24(7): 1087-1098.[8]Datta R, Joshi D, Li J, et al. Image retrieval: Ideas, influences, and trends of the new age[J]. ACM Computing Surveys (CSUR), 2008, 40(2): 111-115.[9]Li ZX, Shi ZP, Li ZQ, Shi ZZ. A survey of semantic mapping in image retrieval[J]. Journal of Computer-Aided Design and Computer Graphics, 2008,20(8):1085−1096 (in Chinese with English abstract).[10]Zhang D, Islam M M, Lu G. A review on automatic image annotation techniques[J]. Pattern Recognition, 2012, 45(1): 346-362.[11] Li J, Wang J Z. Automatic linguistic indexing of pictures by a statistical modeling approach[J]. Pattern Analysis and Machine Intelligence,2003, 25(9): 1075-1088.[12] Chang E, Goh K, Sychay G, et al. CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines[J]. Circuits and Systems for Video Technology,2003, 13(1): 26-38.[13]Jeon J, Lavrenko V, Manmatha R. Automatic image annotation and retrieval using cross-media relevance models[C]//Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval. ACM,2003: 119−126.[14] Lavrenko V, Manmatha R, Jeon J. A Model for Learning the Semantics of Pictures[C]//NIPS. 2003: 11-18.[15]Feng S L, Manmatha R, Lavrenko V. Multiple bernoulli relevance models for image and video annotation[C]//Proceedings of the IEEE Computer Society conference onComputer Vision and Pattern Recognition.IEEE, 2005: 51-58.[16]Tian D, Zhao X, Shi Z. An Efficient Refining Image Annotation Technique by Combining Probabilistic Latent Semantic Analysis and Random Walk Model[J]. Intelligent Automation & Soft Computing, 2014 (ahead-of-print): 1-11.。

二、（国外）英文参考资料1、网上文献2、国际会议文章（英文）[C1]Afzal S, Sezgin T.M, Yujian Gao, Robinson P. Perception of emotional expressions in different representations using facial feature points. In: Affective Computing and Intelligent Interaction and Workshops, Amsterdam,Holland, 2009 Page(s): 1 - 6[C2]Yuwen Wu, Hong Liu, Hongbin Zha. Modeling facial expression space for recognition In:Intelligent Robots and Systems,Edmonton,Canada,2005: 1968 – 1973[C3]Y u-Li Xue, Xia Mao, Fan Zhang. Beihang University Facial Expression Database and Multiple Facial Expression Recognition. In: Machine Learning and Cybernetics, Dalian,China,2006: 3282 – 3287[C4] Zhiguo Niu, Xuehong Qiu. Facial expression recognition based on weighted principal component analysis and support vector machines. In: Advanced Computer Theory and Engineering (ICACTE), Chendu,China,2010: V3-174 - V3-178[C5] Colmenarez A, Frey B, Huang T.S. A probabilistic framework for embedded face and facial expression recognition. In: Computer Vision and Pattern Recognition, Ft. Collins, CO, USA, 1999:[C6] Yeongjae Cheon, Daijin Kim. A Natural Facial Expression Recognition Using Differential-AAM and k-NNS. In: Multimedia（ISM 2008）,Berkeley, California, USA,2008: 220 - 227[C7]Jun Ou, Xiao-Bo Bai, Yun Pei,Liang Ma, Wei Liu. Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis. In: Computer Modeling and Simulation, Sanya, China, 2010: 215 - 218[C8]Dae-Jin Kim, Zeungnam Bien, Kwang-Hyun Park. Fuzzy neural networks (FNN)-based approach for personalized facial expression recognition with novel feature selection method. In: Fuzzy Systems, St.Louis,Missouri,USA,2003: 908 - 913[C9] Wei-feng Liu, Shu-juan Li, Yan-jiang Wang. Automatic Facial Expression Recognition Based on Local Binary Patterns of Local Areas. In: Information Engineering, Taiyuan, Shanxi, China ,2009: 197 - 200[C10] Hao Tang, Hasegawa-Johnson M, Huang T. Non-frontal view facial expression recognition based on ergodic hidden Markov model supervectors.In: Multimedia and Expo (ICME), Singapore ,2010: 1202 - 1207[C11] Yu-Jie Li, Sun-Kyung Kang,Young-Un Kim, Sung-Tae Jung. Development of a facial expression recognition system for the laughter therapy. In: Cybernetics and Intelligent Systems (CIS), Singapore ,2010: 168 - 171[C12] Wei Feng Liu, ZengFu Wang. Facial Expression Recognition Based on Fusion of Multiple Gabor Features. In: Pattern Recognition, HongKong, China, 2006: 536 - 539[C13] Chen Feng-jun, Wang Zhi-liang, Xu Zheng-guang, Xiao Jiang. Facial Expression Recognition Based on Wavelet Energy Distribution Feature and Neural Network Ensemble. In: Intelligent Systems, XiaMen, China, 2009: 122 - 126[C14] P. Kakumanu, N. Bourbakis. A Local-Global Graph Approach for Facial Expression Recognition. In: Tools with Artificial Intelligence, Arlington, Virginia, USA,2006: 685 - 692[C15] Mingwei Huang, Zhen Wang, Zilu Ying. Facial expression recognition using Stochastic Neighbor Embedding and SVMs. In: System Science and Engineering (ICSSE), Macao, China, 2011: 671 - 674[C16] Junhua Li, Li Peng. Feature difference matrix and QNNs for facial expression recognition. In: Control and Decision Conference, Yantai, China, 2008: 3445 - 3449[C17] Yuxiao Hu, Zhihong Zeng, Lijun Yin,Xiaozhou Wei, Jilin Tu, Huang, T.S. A study of non-frontal-view facial expressions recognition. In: Pattern Recognition, Tampa, FL, USA,2008: 1 - 4[C18] Balasubramani A, Kalaivanan K, Karpagalakshmi R.C, Monikandan R. Automatic facial expression recognition system. In: Computing, Communication and Networking, St. Thomas,USA, 2008: 1 - 5[C19] Hui Zhao, Zhiliang Wang, Jihui Men. Facial Complex Expression Recognition Based on Fuzzy Kernel Clustering and Support Vector Machines. In: Natural Computation, Haikou,Hainan,China,2007: 562 - 566[C20] Khanam A, Shafiq M.Z, Akram M.U. Fuzzy Based Facial Expression Recognition. In: Image and Signal Processing, Sanya, Hainan, China,2008: 598 - 602[C21] Sako H, Smith A.V.W. Real-time facial expression recognition based on features' positions and dimensions. In: Pattern Recognition, Vienna,Austria, 1996: 643 - 648 vol.3[C22] Huang M.W, Wang Z.W, Ying Z.L. A novel method of facial expression recognition based on GPLVM Plus SVM. In: Signal Processing (ICSP), Beijing, China, 2010: 916 - 919[C23] Xianxing Wu; Jieyu Zhao; Curvelet feature extraction for face recognition and facial expression recognition. In: Natural Computation (ICNC), Yantai, China, 2010: 1212 - 1216[C24]Xu Q Z, Zhang P Z, Yang L X, et al.A facial expression recognition approach based on novel support vector machine tree. In Proceedings of the 4th International Symposium on Neural Networks, Nanjing, China, 2007: 374-381.[C25] Wang Y B, Ai H Z, Wu B, et al. Real time facial expression recognition with adaboost.In: Proceedings of the 17th International Conference on Pattern Recognition , Cambridge,UK, 2004: 926-929.[C26] Guo G, Dyer C R. Simultaneous feature selection and classifier training via linear programming: a case study for face expression recognition. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, W isconsin, USA, 2003,1: 346-352.[C27] Bourel F, Chibelushi C C, Low A A. Robust facial expression recognition using a state-based model of spatially-localised facial dynamics. In: Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, USA, 2002: 113-118·[C28] Buciu I, Kotsia I, Pitas I. Facial expression analysis under partial occlusion. In: Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA, 2005,V: 453-456.[C29] ZHAN Yong-zhao，YE Jing-fu，NIU De-jiao，et al．Facial expression recognition based on Gabor wavelet transformation and elastic templates matching. Proc of the 3rd International Conference on Image and Graphics．Washington DC, USA，2004:254-257．[C30] PRASEEDA L V，KUMAR S，VIDYADHARAN D S，et al．Analysis of facial expressions using PCA on half and full faces. Proc of ICALIP2008．2008:1379-1383．[C31]LEE J J，UDDIN M Z，KIM T S．Spatiotemporal human facial expression recognition using Fisher independent component analysis and Hidden Markov model［C］//Proc of the 30th Annual International Conference of IEEE Engineering in Medicine and Biology Society．2008:2546-2549．[C32] LITTLEWORT G,BARTLETT M,FASELL. Dynamics of facial expression extracted automatically from video. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Face Processing inVideo, Washington DC,USA,2006:80-81．[C33] Kotsia I, Nikolaidis N, Pitas I. Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant. In: Acoustics, Speech and Signal Processing, Honolulu, Hawaii, USA, 2007: II-585 - II-588[C34] Ruo Du, Qiang Wu, Xiangjian He, Wenjing Jia, Daming Wei. Facial expression recognition using histogram variances faces. In: Applications of Computer Vision (WACV), Snowbird, Utah, USA, 2009: 1 - 7[C35] Kobayashi H, Tange K, Hara F. Real-time recognition of six basic facial expressions. In: Robot and Human Communication, Tokyo , Japan,1995: 179 - 186[C36] Hao Tang, Huang T.S. 3D facial expression recognition based on properties of line segments connecting facial feature points. In: Automatic Face & Gesture Recognition, Amsterdam, The Netherlands, 2008: 1 - 6[C37] Fengjun Chen, Zhiliang Wang, Zhengguang Xu, Donglin Wang. Research on a method of facial expression recognition.in: Electronic Measurement & Instruments, Beijing,China, 2009: 1-225 - 1-229[C38] Hui Zhao, Tingting Xue, Linfeng Han. Facial complex expression recognition based on Latent DirichletAllocation. In: Natural Computation (ICNC), Yantai, Shandong, China, 2010: 1958 - 1960[C39] Qinzhen Xu, Pinzheng Zhang, Wenjiang Pei, Luxi Yang, Zhenya He. An Automatic Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree. In: Acoustics, Speech and Signal Processing, Honolulu, Hawaii, USA, 2007: I-625 - I-628[C40] Sung Uk Jung, Do Hyoung Kim, Kwang Ho An, Myung Jin Chung. Efficient rectangle feature extraction for real-time facial expression recognition based on AdaBoost.In: Intelligent Robots and Systems, Edmonton,Canada, 2005: 1941 - 1946[C41] Patil K.K, Giripunje S.D, Bajaj P.R. Facial Expression Recognition and Head Tracking in Video Using Gabor Filter .In: Emerging Trends in Engineering and Technology (ICETET), Goa, India, 2010: 152 - 157[C42] Jun Wang, Lijun Yin, Xiaozhou Wei, Yi Sun. 3D Facial Expression Recognition Based on Primitive Surface Feature Distribution.In: Computer Vision and Pattern Recognition, New York, USA,2006: 1399 - 1406[C43] Shi Dongcheng, Jiang Jieqing. The method of facial expression recognition based on DWT-PCA/LDA.IN: Image and Signal Processing (CISP), Yantai,China, 2010: 1970 - 1974[C44] Asthana A, Saragih J, Wagner M, Goecke R. Evaluating AAM fitting methods for facial expression recognition. In: Affective Computing and Intelligent Interaction and Workshops, Amsterdam,Holland, 2009:1-8[C45] Geng Xue, Zhang Youwei. Facial Expression Recognition Based on the Difference of Statistical Features.In: Signal Processing, Guilin, China, 2006[C46] Metaxas D. Facial Features Tracking for Gross Head Movement analysis and Expression Recognition.In: Multimedia Signal Processing, Chania,Crete,GREECE, 2007:2[C47] Xia Mao, YuLi Xue, Zheng Li, Kang Huang, ShanWei Lv. Robust facial expression recognition based on RPCA and AdaBoost.In: Image Analysis for Multimedia Interactive Services, London, UK, 2009: 113 - 116[C48] Kun Lu, Xin Zhang. Facial Expression Recognition from Image Sequences Based on Feature Points and Canonical Correlations.In: Artificial Intelligence and Computational Intelligence (AICI), Sanya,China, 2010: 219 - 223[C49] Peng Zhao-yi, Wen Zhi-qiang, Zhou Yu. Application of Mean Shift Algorithm in Real-Time Facial Expression Recognition.In: Computer Network and Multimedia Technology, Wuhan,China, 2009: 1 - 4[C50] Xu Chao, Feng Zhiyong, Facial Expression Recognition and Synthesis on Affective Emotions Composition.In: Future BioMedical InformationEngineering, Wuhan,China, 2008: 144 - 147[C51] Zi-lu Ying, Lin-bo Cai. Facial Expression Recognition with Marginal Fisher Analysis on Local Binary Patterns.In: Information Science and Engineering (ICISE), Nanjing,China, 2009: 1250 - 1253[C52] Chuang Yu, Yuning Hua, Kun Zhao. The Method of Human Facial Expression Recognition Based on Wavelet Transformation Reducing the Dimension and Improved Fisher Discrimination.In: Intelligent Networks and Intelligent Systems (ICINIS), Shenyang,China, 2010: 43 - 47[C53] Stratou G, Ghosh A, Debevec P, Morency L.-P. Effect of illumination on automatic expression recognition: A novel 3D relightable facial database .In: Automatic Face & Gesture Recognition and Workshops (FG 2011), Santa Barbara, California,USA, 2011: 611 - 618[C54] Jung-Wei Hong, Kai-Tai Song. Facial expression recognition under illumination variation.In: Advanced Robotics and Its Social Impacts, Hsinchu, Taiwan,2007: 1 - 6[C55] Ryan A, Cohn J.F, Lucey S, Saragih J, Lucey P, De la Torre F, Rossi A. Automated Facial Expression Recognition System.In: Security Technology, Zurich, Switzerland, 2009: 172 - 177[C56] Gokturk S.B, Bouguet J.-Y, Tomasi C, Girod B. Model-based face tracking for view-independent facial expression recognition.In: Automatic Face and Gesture Recognition, Washington, D.C., USA, 2002: 287 - 293[C57] Guo S.M, Pan Y.A, Liao Y.C, Hsu C.Y, Tsai J.S.H, Chang C.I. A Key Frame Selection-Based Facial Expression Recognition System.In: Innovative Computing, Information and Control, Beijing,China, 2006: 341 - 344[C58] Ying Zilu, Li Jingwen, Zhang Youwei. Facial expression recognition based on two dimensional feature extraction.In: Signal Processing, Leipzig, Germany, 2008: 1440 - 1444[C59] Fengjun Chen, Zhiliang Wang, Zhengguang Xu, Jiang Xiao, Guojiang Wang. Facial Expression Recognition Using Wavelet Transform and Neural Network Ensemble.In: Intelligent Information Technology Application, Shanghai,China,2008: 871 - 875[C60] Chuan-Yu Chang, Yan-Chiang Huang, Chi-Lu Yang. Personalized Facial Expression Recognition in Color Image.In: Innovative Computing, Information and Control (ICICIC), Kaohsiung,Taiwan, 2009: 1164 - 1167 [C61] Bourel F, Chibelushi C.C, Low A.A. Robust facial expression recognition using a state-based model of spatially-localised facial dynamics. In: Automatic Face and Gesture Recognition, Washington, D.C., USA, 2002: 106 - 111[C62] Chen Juanjuan, Zhao Zheng, Sun Han, Zhang Gang. Facial expression recognition based on PCA reconstruction.In: Computer Science and Education (ICCSE), Hefei,China, 2010: 195 - 198[C63] Guotai Jiang, Xuemin Song, Fuhui Zheng, Peipei Wang, Omer A.M.Facial Expression Recognition Using Thermal Image.In: Engineering in Medicine and Biology Society, Shanghai,China, 2005: 631 - 633[C64] Zhan Yong-zhao, Ye Jing-fu, Niu De-jiao, Cao Peng. Facial expression recognition based on Gabor wavelet transformation and elastic templates matching.In: Image and Graphics, Hongkong,China, 2004: 254 - 257[C65] Ying Zilu, Zhang Guoyi. Facial Expression Recognition Based on NMF and SVM. In: Information Technology and Applications, Chengdu,China, 2009: 612 - 615[C66] Xinghua Sun, Hongxia Xu, Chunxia Zhao, Jingyu Yang. Facial expression recognition based on histogram sequence of local Gabor binary patterns. In: Cybernetics and Intelligent Systems, Chengdu,China, 2008: 158 - 163[C67] Zisheng Li, Jun-ichi Imai, Kaneko M. Facial-component-based bag of words and PHOG descriptor for facial expression recognition.In: Systems, Man and Cybernetics, San Antonio,TX,USA,2009: 1353 - 1358[C68] Chuan-Yu Chang, Yan-Chiang Huang. Personalized facial expression recognition in indoor environments.In: Neural Networks (IJCNN), Barcelona, Spain, 2010: 1 - 8[C69] Ying Zilu, Fang Xieyan. Combining LBP and Adaboost for facial expression recognition.In: Signal Processing, Leipzig, Germany, 2008: 1461 - 1464[C70] Peng Yang, Qingshan Liu, Metaxas, D.N. RankBoost with l1 regularization for facial expression recognition and intensity estimation.In: Computer Vision, Kyoto,Japan, 2009: 1018 - 1025[C71] Patil R.A, Sahula V, Mandal A.S. Automatic recognition of facial expressions in image sequences: A review.In: Industrial and Information Systems (ICIIS), Mangalore, India, 2010: 408 - 413[C72] Iraj Hosseini, Nasim Shams, Pooyan Amini, Mohammad S. Sadri, Masih Rahmaty, Sara Rahmaty. Facial Expression Recognition using Wavelet-Based Salient Points and Subspace Analysis Methods.In: Electrical and Computer Engineering, Ottawa, Canada, 2006: 1992 - 1995[C73][C74][C75][C76][C77][C78][C79]3、英文期刊文章[J1] Aleksic P.S., Katsaggelos A.K. Automatic facial expression recognition using facial animation parameters and multistream HMMs.IEEE Transactions on Information Forensics and Security, 2006, 1(1):3-11 [J2] Kotsia I,Pitas I. Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines. IEEE Transactions on Image Processing, 2007, 16(1):172 – 187[J3] Mpiperis I, Malassiotis S, Strintzis M.G. Bilinear Models for 3-D Face and Facial Expression Recognition. IEEE Transactions on Information Forensics and Security, 2008,3(3) : 498 - 511[J4] Sung J, Kim D. Pose-Robust Facial Expression Recognition Using View-Based 2D+3D AAM. IEEE Transactions on Systems and Humans, 2008 , 38 (4): 852 - 866[J5]Yeasin M, Bullot B, Sharma R. Recognition of facial expressions and measurement of levels of interest from video. IEEE Transactions on Multimedia, 2006, 8(3): 500 - 508[J6] Wenming Zheng, Xiaoyan Zhou, Cairong Zou, Li Zhao. Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Transactions on Neural Networks, 2006, 17(1): 233 - 238 [J7]Pantic M, Patras I. Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006, 36(2): 433 - 449[J8] Mingli Song, Dacheng Tao, Zicheng Liu, Xuelong Li, Mengchu Zhou. Image Ratio Features for Facial Expression Recognition Application. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(3): 779 - 788[J9] Dae Jin Kim, Zeungnam Bien. Design of “Personalized” Classifier Using Soft Computing Techniques for “Personalized” Facial Expression Recognition. IEEE Transactions on Fuzzy Systems, 2008, 16(4): 874 - 885 [J10] Uddin M.Z, Lee J.J, Kim T.-S. An enhanced independent component-based human facial expression recognition from video. IEEE Transactions on Consumer Electronics, 2009, 55(4): 2216 - 2224[J11] Ruicong Zhi, Flierl M, Ruan Q, Kleijn W.B. Graph-Preserving Sparse Nonnegative Matrix Factorization With Application to Facial Expression Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2011, 41(1): 38 - 52[J12] Chibelushi C.C, Bourel F. Hierarchical multistream recognition of facial expressions. IEE Proceedings - Vision, Image and Signal Processing, 2004, 151(4): 307 - 313[J13] Yongsheng Gao, Leung M.K.H, Siu Cheung Hui, Tananda M.W. Facial expression recognition from line-based caricatures. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2003, 33(3): 407 - 412[J14] Ma L, Khorasani K. Facial expression recognition using constructive feedforward neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(3): 1588 - 1595[J15] Essa I.A, Pentland A.P. Coding, analysis, interpretation, and recognition of facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 757 - 763[J16] Anderson K, McOwan P.W. A real-time automated system for the recognition of human facial expressions. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006, 36(1): 96 - 105[J17] Soyel H, Demirel H. Facial expression recognition based on discriminative scale invariant feature transform. Electronics Letters 2010, 46(5): 343 - 345[J18] Fei Cheng, Jiangsheng Yu, Huilin Xiong. Facial Expression Recognition in JAFFE Dataset Based on Gaussian Process Classification. IEEE Transactions on Neural Networks, 2010, 21(10): 1685 – 1690[J19] Shangfei Wang, Zhilei Liu, Siliang Lv, Yanpeng Lv, Guobing Wu, Peng Peng, Fei Chen, Xufa Wang. A Natural Visible and Infrared Facial Expression Database for Expression Recognition and Emotion Inference. IEEE Transactions on Multimedia, 2010, 12(7): 682 - 691[J20] Lajevardi S.M, Hussain Z.M. Novel higher-order local autocorrelation-like feature extraction methodology for facial expression recognition. IET Image Processing, 2010, 4(2): 114 - 119[J21] Yizhen Huang, Ying Li, Na Fan. Robust Symbolic Dual-View Facial Expression Recognition With Skin Wrinkles: Local Versus Global Approach. IEEE Transactions on Multimedia, 2010, 12(6): 536 - 543[J22] Lu H.-C, Huang Y.-J, Chen Y.-W. Real-time facial expression recognition based on pixel-pattern-based texture feature. Electronics Letters 2007, 43(17): 916 - 918[J23]Zhang L, Tjondronegoro D. Facial Expression Recognition Using Facial Movement Features. IEEE Transactions on Affective Computing, 2011, pp(99): 1[J24] Zafeiriou S, Pitas I. Discriminant Graph Structures for Facial Expression Recognition. Multimedia, IEEE Transactions on 2008,10(8): 1528 - 1540[J25]Oliveira L, Mansano M, Koerich A, de Souza Britto Jr. A. Selecting 2DPCA Coefficients for Face and Facial Expression Recognition. Computingin Science & Engineering, 2011, pp(99): 1[J26] Chang K.I, Bowyer W, Flynn P.J. Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression. Pattern Analysis and Machine Intelligence, IEEE Transactions on2006, 28(10): 1695 - 1700 [J27] Kakadiaris I.A, Passalis G, Toderici G, Murtuza M.N, Yunliang Lu, Karampatziakis N, Theoharis T. Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(4): 640 - 649[J28] Guoying Zhao, Pietikainen M. Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 915 - 928[J29] Chakraborty A, Konar A, Chakraborty U.K, Chatterjee A. Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2009, 39(4): 726 - 743[J30] Pantic M, RothkrantzL J.M. Facial action recognition for facial expression analysis from static face images. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(3): 1449 - 1461 [J31] Calix R.A, Mallepudi S.A, Bin Chen, Knapp G.M. Emotion Recognition in Text for 3-D Facial Expression Rendering. IEEE Transactions on Multimedia, 2010, 12(6): 544 - 551[J32]Kotsia I, Pitas I, Zafeiriou S, Zafeiriou S. Novel Multiclass Classifiers Based on the Minimization of the Within-Class Variance. IEEE Transactions on Neural Networks, 2009, 20(1): 14 - 34[J33]Cohen I, Cozman F.G, Sebe N, Cirelo M.C, Huang T.S. Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(12): 1553 - 1566[J34] Zafeiriou S. Discriminant Nonnegative Tensor Factorization Algorithms. IEEE Transactions on Neural Networks, 2009, 20(2): 217 - 235 [J35] Zafeiriou S, Petrou M. Nonlinear Non-Negative Component Analysis Algorithms. IEEE Transactions on Image Processing, 2010, 19(4): 1050 - 1066[J36] Kotsia I, Zafeiriou S, Pitas I. A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems. IEEE Transactions on Information Forensics and Security, 2007, 2(3): 588 - 595[J37] Irene Kotsia, Stefanos Zafeiriou, Ioannis Pitas. Texture and shape information fusion for facial expression and facial action unit recognition. Pattern Recognition, 2008, 41(3): 833-851[J38]Wenfei Gu, Cheng Xiang, Y.V. Venkatesh, Dong Huang, Hai Lin. Facial expression recognition using radial encoding of local Gabor features andclassifier synthesis.Pattern Recognition, In Press, Corrected Proof, Available online 27 May 2011[J39] F Dornaika, E Lazkano, B Sierra. Improving dynamic facial expression recognition with feature subset selection.Pattern Recognition Letters, 2011, 32(5): 740-748[J40] Te-Hsun Wang, Jenn-Jier James Lien. Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation. Pattern Recognition, 2009, 42(5): 962-977[J41] Hyung-Soo Lee, Daijin Kim.Expression-invariant face recognition by facial expression transformations.Pattern Recognition Letters, 2008, 29(13): 1797-1805[J42] Guoying Zhao, Matti Pietikäinen. Boosted multi-resolution spatiotemporal descriptors for facial expression recognition. Pattern Recognition Letters, 2009, 30(12): 1117-1127[J43] Xudong Xie, Kin-Man Lam. Facial expression recognition based on shape and texture. Pattern Recognition, 2009, 42(5):1003-1011[J44] Peng Yang, Qingshan Liu, Dimitris N. Metaxas Boosting encoded dynamic features for facial expression recognition. Pattern Recognition Letters, 2009,30(2): 132-139[J45] Sungsoo Park, Daijin Kim. Subtle facial expression recognition using motion magnification. Pattern Recognition Letters, 2009, 30(7): 708-716[J46] Chathura R. De Silva, Surendra Ranganath, Liyanage C. De Silva. Cloud basis function neural network: A modified RBF network architecture for holistic facial expression recognition.Pattern Recognition, 2008, 41(4): 1241-1253[J47] Do Hyoung Kim, Sung Uk Jung, Myung Jin Chung. Extension of cascaded simple feature based face detection to facial expression recognition.Pattern Recognition Letters, 2008, 29(11): 1621-1631[J48] Y. Zhu, L.C. De Silva, C.C. Ko. Using moment invariants and HMM in facial expression recognition. Pattern Recognition Letters, 2002, 23(1-3): 83-91[J49] Jun Wang, Lijun Yin. Static topographic modeling for facial expression recognition and puter Vision and Image Understanding, 2007, 108(1-2): 19-34[J50] Caifeng Shan, Shaogang Gong, Peter W. McOwan. Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing, 2009, 27(6): 803-816[J51] Xue-wen Chen, Thomas Huang. Facial expression recognition: A clustering-based approach. Pattern Recognition Letters, 2003, 24(9-10): 1295-1302[J52] Irene Kotsia, Ioan Buciu, Ioannis Pitas.An analysis of facial expression recognition under partial facial image occlusion. Image and Vision Computing, 2008, 26(7): 1052-1067[J53] Shuai Liu, Qiuqi Ruan. Orthogonal Tensor Neighborhood Preserving Embedding for facial expression recognition.Pattern Recognition, 2011, 44(7):1497-1513[J54] Eszter Székely, Henning Tiemeier, Lidia R. Arends, Vincent W.V. Jaddoe, Albert Hofman, Frank C. Verhulst, Catherine M. Herba. Recognition of Facial Expressions of Emotions by 3-Year-Olds. Emotion, 2011, 11(2): 425-435[J55] Kathleen M. Corcoran, Sheila R. Woody, David F. Tolin.Recognition of facial expressions in obsessive–compulsive disorder. Journal of Anxiety Disorders, 2008, 22(1): 56-66[J56] Bouchra Abboud, Franck Davoine, MôDang. Facial expression recognition and synthesis based on an appearance model. Signal Processing: Image Communication, 2004, 19(8): 723-740[J57] Teng Sha, Mingli Song, Jiajun Bu, Chun Chen, Dacheng Tao. Feature level analysis for 3D facial expression recognition. Neurocomputing, 2011, 74(12-13) :2135-2141[J58] S. Moore, R. Bowden. Local binary patterns for multi-view facial expression recognition. Computer Vision and Image Understanding, 2011, 15(4):541-558[J59] Rui Xiao, Qijun Zhao, David Zhang, Pengfei Shi. Facial expression recognition on multiple manifolds.Pattern Recognition, 2011, 44(1):107-116[J60] Shyi-Chyi Cheng, Ming-Yao Chen, Hong-Yi Chang, Tzu-Chuan Chou. Semantic-based facial expression recognition using analytical hierarchy process.Expert Systems with Applications, 2007, 33(1): 86-95[J71] Carlos E. Thomaz, Duncan F. Gillies, Raul Q. Feitosa. Using mixture covariance matrices to improve face and facial expression recognitions. Pattern Recognition Letters, 2003, 24(13): 2159-2165[J72]Wen G，Bo C，Shan Shi-guang，et al. The CAS-PEAL large-scale Chinese face database and baseline evaluations.IEEE Transactions on Systems，Man and Cybernetics，part A：Systems and Hu-mans，2008，38（1）：149-161. [J73] Yongsheng Gao，Leung ，M.K.H. Face recognition using line edge map.IEEE Transactions on Pattern Analysis and Machine Intelligence，2002，24：764-779.[J74] Hanouz M，Kittler J，Kamarainen J K，et al. Feature-based affine-invariant localization of faces.IEEE Transactions on Pat-tern Analysis and Machine Intelligence，2005，27：1490-1495.[J75] WISKOTT L，FELLOUS J M，KRUGER N，et al．Face recognition by elastic bunch graph matching．IEEE Trans on Pattern Analysis and Machine Intelligence，1997，19(7):775-779．[J76] Belhumeur P.N, Hespanha J.P, Kriegman D.J. Eigenfaces vs. fischerfaces: recognition using class specific linear projection．IEEE Trans on Pattern Analysis and Machine Intelligence，1997，15(7):711-720 [J77] MA L,KHORASANI K．Facial Expression Recognition Using ConstructiveFeedforward Neural Networks. IEEE Transactions on Systems, Man and Cybernetics, Part B,2007,34(3):1588-1595．[J78][J79][J80][J81][J82][J83][J84][J85][J86][J87][J88][J89][J90]。

人工智能英汉Aβα-Pruning, βα-剪枝, (2) Acceleration Coefficient, 加速系数, (8) Activation Function, 激活函数, (4) Adaptive Linear Neuron, 自适应线性神经元,(4)Adenine, 腺嘌呤, (11)Agent, 智能体, (6)Agent Communication Language, 智能体通信语言, (11)Agent-Oriented Programming, 面向智能体的程序设计, (6)Agglomerative Hierarchical Clustering, 凝聚层次聚类, (5)Analogism, 类比推理, (5)And/Or Graph, 与或图, (2)Ant Colony Optimization (ACO), 蚁群优化算法, (8)Ant Colony System (ACS), 蚁群系统, (8) Ant-Cycle Model, 蚁周模型, (8)Ant-Density Model, 蚁密模型, (8)Ant-Quantity Model, 蚁量模型, (8)Ant Systems, 蚂蚁系统, (8)Applied Artificial Intelligence, 应用人工智能, (1)Approximate Nondeterministic Tree Search (ANTS), 近似非确定树搜索, (8) Artificial Ant, 人工蚂蚁, (8)Artificial Intelligence (AI), 人工智能, (1) Artificial Neural Network (ANN), 人工神经网络, (1), (3)Artificial Neural System, 人工神经系统，(3) Artificial Neuron, 人工神经元, (3) Associative Memory, 联想记忆, (4) Asynchronous Mode, 异步模式, (4) Attractor, 吸引子, (4)Automatic Theorem Proving, 自动定理证明,(1)Automatic Programming, 自动程序设计, (1) Average Reward, 平均收益, (6) Axon, 轴突, (4)Axon Hillock, 轴突丘, (4)BBackward Chain Reasoning, 逆向推理, (3) Bayesian Belief Network, 贝叶斯信念网, (5) Bayesian Decision, 贝叶斯决策, (3) Bayesian Learning, 贝叶斯学习, (5) Bayesian Network贝叶斯网, (5)Bayesian Rule, 贝叶斯规则, (3)Bayesian Statistics, 贝叶斯统计学, (3) Biconditional, 双条件, (3)Bi-Directional Reasoning, 双向推理, (3) Biological Neuron, 生物神经元, (4) Biological Neural System, 生物神经系统, (4) Blackboard System, 黑板系统, (8)Blind Search, 盲目搜索, (2)Boltzmann Machine, 波尔兹曼机, (3) Boltzmann-Gibbs Distribution, 波尔兹曼-吉布斯分布, (3)Bottom-Up, 自下而上, (4)Building Block Hypotheses, 构造块假说, (7) CCell Body, 细胞体, (3)Cell Membrane, 细胞膜, (3)Cell Nucleus, 细胞核, (3)Certainty Factor, 可信度, (3)Child Machine, 婴儿机器, (1)Chinese Room, 中文屋, (1) Chromosome, 染色体, (6)Class-conditional Probability, 类条件概率,(3), (5)Classifier System, 分类系统, (6)Clause, 子句, (3)Cluster, 簇, (5)Clustering Analysis, 聚类分析, (5) Cognitive Science, 认知科学, (1) Combination Function, 整合函数, (4) Combinatorial Optimization, 组合优化, (2) Competitive Learning, 竞争学习, (4) Complementary Base, 互补碱基, (11) Computer Games, 计算机博弈, (1) Computer Vision, 计算机视觉, (1)Conflict Resolution, 冲突消解, (3) Conjunction, 合取, (3)Conjunctive Normal Form (CNF), 合取范式,(3)Collapse, 坍缩, (11)Connectionism, 连接主义, (3) Connective, 连接词, (3)Content Addressable Memory, 联想记忆, (4) Control Policy, 控制策略, (6)Crossover, 交叉, (7)Cytosine, 胞嘧啶, (11)DData Mining, 数据挖掘, (1)Decision Tree, 决策树, (5) Decoherence, 消相干, (11)Deduction, 演绎, (3)Default Reasoning, 默认推理（缺省推理）,(3)Defining Length, 定义长度, (7)Rule (Delta Rule), 德尔塔规则, 18(3) Deliberative Agent, 慎思型智能体, (6) Dempster-Shafer Theory, 证据理论, (3) Dendrites, 树突, (4)Deoxyribonucleic Acid (DNA), 脱氧核糖核酸, (6), (11)Disjunction, 析取, (3)Distributed Artificial Intelligence (DAI), 分布式人工智能, (1)Distributed Expert Systems, 分布式专家系统,(9)Divisive Hierarchical Clustering, 分裂层次聚类, (5)DNA Computer, DNA计算机, (11)DNA Computing, DNA计算, (11) Discounted Cumulative Reward, 累计折扣收益, (6)Domain Expert, 领域专家, (10) Dominance Operation, 显性操作, (7) Double Helix, 双螺旋结构, (11)Dynamical Network, 动态网络, (3)E8-Puzzle Problem, 八数码问题, (2) Eletro-Optical Hybrid Computer, 光电混合机, (11)Elitist strategy for ant systems (EAS), 精化蚂蚁系统, (8)Energy Function, 能量函数, (3) Entailment, 永真蕴含, (3) Entanglement, 纠缠, (11)Entropy, 熵, (5)Equivalence, 等价式, (3)Error Back-Propagation, 误差反向传播, (4) Evaluation Function, 评估函数, (6) Evidence Theory, 证据理论, (3) Evolution, 进化, (7)Evolution Strategies (ES), 进化策略, (7) Evolutionary Algorithms (EA), 进化算法, (7) Evolutionary Computation (EC), 进化计算,(7)Evolutionary Programming (EP), 进化规划,(7)Existential Quantification, 存在量词, (3) Expert System, 专家系统, (1)Expert System Shell, 专家系统外壳, (9) Explanation-Based Learning, 解释学习, (5) Explanation Facility, 解释机构, (9)FFactoring, 因子分解, (11)Feedback Network, 反馈型网络, (4) Feedforward Network, 前馈型网络, (1) Feasible Solution, 可行解, (2)Finite Horizon Reward, 横向有限收益, (6) First-order Logic, 一阶谓词逻辑, (3) Fitness, 适应度, (7)Forward Chain Reasoning, 正向推理, (3) Frame Problem, 框架问题, (1)Framework Theory, 框架理论, (3)Free-Space Optical Interconnect, 自由空间光互连, (11)Fuzziness, 模糊性, (3)Fuzzy Logic, 模糊逻辑, (3)Fuzzy Reasoning, 模糊推理, (3)Fuzzy Relation, 模糊关系, (3)Fuzzy Set, 模糊集, (3)GGame Theory, 博弈论, (8)Gene, 基因, (7)Generation, 代, (6)Genetic Algorithms, 遗传算法, (7)Genetic Programming, 遗传规划（遗传编程）,(7)Global Search, 全局搜索, (2)Gradient Descent, 梯度下降, (4)Graph Search, 图搜索, (2)Group Rationality, 群体理性, (8) Guanine, 鸟嘌呤, (11)HHanoi Problem, 梵塔问题, (2)Hebbrian Learning, 赫伯学习, (4)Heuristic Information, 启发式信息, (2) Heuristic Search, 启发式搜索, (2)Hidden Layer, 隐含层, (4)Hierarchical Clustering, 层次聚类, (5) Holographic Memory, 全息存储, (11) Hopfield Network, 霍普菲尔德网络, (4) Hybrid Agent, 混合型智能体, (6)Hype-Cube Framework, 超立方体框架, (8)IImplication, 蕴含, (3)Implicit Parallelism, 隐并行性, (7) Individual, 个体, (6)Individual Rationality, 个体理性, (8) Induction, 归纳, (3)Inductive Learning, 归纳学习, (5) Inference Engine, 推理机, (9)Information Gain, 信息增益, (3)Input Layer, 输入层, (4)Interpolation, 插值, (4)Intelligence, 智能, (1)Intelligent Control, 智能控制, (1) Intelligent Decision Supporting System (IDSS), 智能决策支持系统，(1) Inversion Operation, 倒位操作, (7)JJoint Probability Distribution, 联合概率分布,(5) KK-means, K-均值, (5)K-medoids, K-中心点, (3)Knowledge, 知识, (3)Knowledge Acquisition, 知识获取, (9) Knowledge Base, 知识库, (9)Knowledge Discovery, 知识发现, (1) Knowledge Engineering, 知识工程, (1) Knowledge Engineer, 知识工程师, (9) Knowledge Engineering Language, 知识工程语言, (9)Knowledge Interchange Format (KIF), 知识交换格式, (8)Knowledge Query and ManipulationLanguage (KQML), 知识查询与操纵语言，(8)Knowledge Representation, 知识表示, (3)LLearning, 学习, (3)Learning by Analog, 类比学习, (5) Learning Factor, 学习因子, (8)Learning from Instruction, 指导式学习, (5) Learning Rate, 学习率, (6)Least Mean Squared (LSM), 最小均方误差,(4)Linear Function, 线性函数, (3)List Processing Language (LISP), 表处理语言, (10)Literal, 文字, (3)Local Search, 局部搜索, (2)Logic, 逻辑, (3)Lyapunov Theorem, 李亚普罗夫定理, (4) Lyapunov Function, 李亚普罗夫函数, (4)MMachine Learning, 机器学习, (1), (5) Markov Decision Process (MDP), 马尔科夫决策过程, (6)Markov Chain Model, 马尔科夫链模型, (7) Maximum A Posteriori (MAP), 极大后验概率估计, (5)Maxmin Search, 极大极小搜索, (2)MAX-MIN Ant Systems (MMAS), 最大最小蚂蚁系统, (8)Membership, 隶属度, (3)Membership Function, 隶属函数, (3) Metaheuristic Search, 元启发式搜索, (2) Metagame Theory, 元博弈理论, (8) Mexican Hat Function, 墨西哥草帽函数, (4) Migration Operation, 迁移操作, (7) Minimum Description Length (MDL), 最小描述长度, (5)Minimum Squared Error (MSE), 最小二乘法,(4)Mobile Agent, 移动智能体, (6)Model-based Methods, 基于模型的方法, (6) Model-free Methods, 模型无关方法, (6) Modern Heuristic Search, 现代启发式搜索,(2)Monotonic Reasoning, 单调推理, (3)Most General Unification (MGU), 最一般合一, (3)Multi-Agent Systems, 多智能体系统, (8) Multi-Layer Perceptron, 多层感知器, (4) Mutation, 突变, (6)Myelin Sheath, 髓鞘, (4)(μ+1)-ES, (μ+1) -进化规划, (7)(μ+λ)-ES, (μ+λ) -进化规划, (7) (μ,λ)-ES, (μ,λ) -进化规划, (7)NNaïve Bayesian Classifiers, 朴素贝叶斯分类器, (5)Natural Deduction, 自然演绎推理, (3) Natural Language Processing, 自然语言处理,(1)Negation, 否定, (3)Network Architecture, 网络结构, (6)Neural Cell, 神经细胞, (4)Neural Optimization, 神经优化, (4) Neuron, 神经元, (4)Neuron Computing, 神经计算, (4)Neuron Computation, 神经计算, (4)Neuron Computer, 神经计算机, (4) Niche Operation, 生态操作, (7) Nitrogenous base, 碱基, (11)Non-Linear Dynamical System, 非线性动力系统, (4)Non-Monotonic Reasoning, 非单调推理, (3) Nouvelle Artificial Intelligence, 行为智能,(6)OOccam’s Razor, 奥坎姆剃刀, (5)(1+1)-ES, (1+1) -进化规划, (7)Optical Computation, 光计算, (11)Optical Computing, 光计算, (11)Optical Computer, 光计算机, (11)Optical Fiber, 光纤, (11)Optical Waveguide, 光波导, (11)Optical Interconnect, 光互连, (11) Optimization, 优化, (2)Optimal Solution, 最优解, (2)Orthogonal Sum, 正交和, (3)Output Layer, 输出层, (4)Outer Product, 外积法, 23(4)PPanmictic Recombination, 混杂重组, (7) Particle, 粒子, (8)Particle Swarm, 粒子群, (8)Particle Swarm Optimization (PSO), 粒子群优化算法, (8)Partition Clustering, 划分聚类, (5) Partitioning Around Medoids, K-中心点, (3) Pattern Recognition, 模式识别, (1) Perceptron, 感知器, (4)Pheromone, 信息素, (8)Physical Symbol System Hypothesis, 物理符号系统假设, (1)Plausibility Function, 不可驳斥函数（似然函数）, (3)Population, 物种群体, (6)Posterior Probability, 后验概率, (3)Priori Probability, 先验概率, (3), (5) Probability, 随机性, (3)Probabilistic Reasoning, 概率推理, (3) Probability Assignment Function, 概率分配函数, (3)Problem Solving, 问题求解, (2)Problem Reduction, 问题归约, (2)Problem Decomposition, 问题分解, (2) Problem Transformation, 问题变换, (2) Product Rule, 产生式规则, (3)Product System, 产生式系统, (3) Programming in Logic (PROLOG), 逻辑编程, (10)Proposition, 命题, (3)Propositional Logic, 命题逻辑, (3)Pure Optical Computer, 全光计算机, (11)QQ-Function, Q-函数, (6)Q-learning, Q-学习, (6)Quantifier, 量词, (3)Quantum Circuit, 量子电路, (11)Quantum Fourier Transform, 量子傅立叶变换, (11)Quantum Gate, 量子门, (11)Quantum Mechanics, 量子力学, (11) Quantum Parallelism, 量子并行性, (11) Qubit, 量子比特, (11)RRadial Basis Function (RBF), 径向基函数,(4)Rank based ant systems (ASrank), 基于排列的蚂蚁系统, (8)Reactive Agent, 反应型智能体, (6) Recombination, 重组, (6)Recurrent Network, 循环网络, (3) Reinforcement Learning, 强化学习, (3) Resolution, 归结, (3)Resolution Proof, 归结反演, (3) Resolution Strategy, 归结策略, (3) Reasoning, 推理, (3)Reward Function, 奖励函数, (6) Robotics, 机器人学, (1)Rote Learning, 机械式学习, (5)SSchema Theorem, 模板定理, (6) Search, 搜索, (2)Selection, 选择, (7)Self-organizing Maps, 自组织特征映射, (4) Semantic Network, 语义网络, (3)Sexual Differentiation, 性别区分, (7) Shor’s algorithm, 绍尔算法, (11)Sigmoid Function, Sigmoid 函数（S型函数）,(4)Signal Function, 信号函数, (3)Situated Artificial Intelligence, 现场式人工智能, (1)Spatial Light Modulator (SLM), 空间光调制器, (11)Speech Act Theory, 言语行为理论, (8) Stable State, 稳定状态, (4)Stability Analysis, 稳定性分析, (4)State Space, 状态空间, (2)State Transfer Function, 状态转移函数,(6)Substitution, 置换, (3)Stochastic Learning, 随机型学习, (4) Strong Artificial Intelligence (AI), 强人工智能, (1)Subsumption Architecture, 包容结构, (6) Superposition, 叠加, (11)Supervised Learning, 监督学习, (4), (5) Swarm Intelligence, 群智能, (8)Symbolic Artificial Intelligence (AI), 符号式人工智能（符号主义）, (3) Synapse, 突触, (4)Synaptic Terminals, 突触末梢, (4) Synchronous Mode, 同步模式, (4)TThreshold, 阈值, (4)Threshold Function, 阈值函数, (4) Thymine, 胸腺嘧啶, (11)Topological Structure, 拓扑结构, (4)Top-Down, 自上而下, (4)Transfer Function, 转移函数, (4)Travel Salesman Problem, 旅行商问题, (4) Turing Test, 图灵测试, (1)UUncertain Reasoning, 不确定性推理, (3)Uncertainty, 不确定性, (3)Unification, 合一, (3)Universal Quantification, 全称量词, (4) Unsupervised Learning, 非监督学习, (4), (5)WWeak Artificial Intelligence (Weak AI), 弱人工智能, (1)Weight, 权值, (4)Widrow-Hoff Rule, 维德诺-霍夫规则, (4)。

K-means后数据聚类的50年发展Anil K.Jain 密歇根州立大学计算机科学与工程系高丽大学大脑与认知工程系翻译人徐天宇专业班级自动化1104 .摘要：数据进行合理的聚群是理解和学习最基本的模式之一。

例如，一个常见的科学分类将生物归类为如下的类别体系：域、界、门、纲、目等。

聚类分析是根据对象的可测得的或可感知的本质特征或相似度来对其进行聚群或聚类的方法和算法的正式研究。

聚类分析并不使用种类标签，即通过如类标这样已有的标示符来标识对象。

类别信息的缺失将数据聚类（无监督学习）和分类或判别分析（有监督学习）。

聚类的目标是寻找数据的结构，因此是对自然的一种探索。

聚类在不同的科学领域里面都有着悠久而丰富的历史。

1955年第一次发表的K-means算法是最受欢迎的简单聚类算法之一。

事实上，尽管K-means算法已经提出了50多年，而且从那时起发表了数以千计的其它聚类算法，K-means仍然有着广泛的运用。

这说明设计一个有广泛适用性的聚类算法的困难以及聚类本身是一个病态问题。

我们对聚类进行了简要的综述，总结了有名的聚类方法，讨论了设计聚类算法主要挑战和核心问题，指出了部分新兴和有用的研究方向包括半监督聚类、集成聚类、在数据聚类时同时进行特征选择以及大规模数据聚类。

关键词：数据聚类、用户困境、历史发展、聚类的前景、傅京孙奖1. 引言传感和存储技术的进步以及像互联网搜索、数字成像、视频监控等技术应用的迅猛发展产生了大量的高维数据集。

据估计2007年数据全球数据使用量为281艾字节，预计2011年这个数字将增长10倍（1艾大约是1018B或1,000,000TB）。

大部分的数据数字化的存储在电子介质中，因此给自动化数据分析、分类和检索技术的发展提供了巨大的可能。

可利用的数据除了量的增长，类型也增多了（文本、图像、视频）。

并不昂贵的数字摄影机产生了大量的图像和视频。

由于无线射频识别标签和收发机低价和小尺寸，它们得以普及并导致了成千上万的能有规律传输数据的传感器的部署。

正确答案：A、B 你选对了Quizzes for Chapter 11 单选(1 分)图灵测试旨在给予哪一种令人满意的操作定义得分/ 5 多选(1 分)选择下列计算机系统中属于人工智能的实例得分/总分总分A. Web搜索引擎A. 人类思考B.超市条形码扫描器B. 人工智能C.声控电话菜单该题无法得分/1.00C.机器智能 1.00/1.00D.智能个人助理该题无法得分/1.00正确答案：A、D 你错选为C、DD.机器动作正确答案： C 你选对了6 多选(1 分)选择下列哪些是人工智能的研究领域得分/总分2 多选(1 分)选择以下关于人工智能概念的正确表述得分/总分A.人脸识别0.33/1.00A. 人工智能旨在创造智能机器该题无法得分/1.00B.专家系统0.33/1.00B. 人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/1.00C.图像理解C.人工智能将其定义为人类智能体的研究该题无法D.分布式计算得分/1.00正确答案：A、B、C 你错选为A、BD.人工智能是为了开发一类计算机使之能够完成通7 多选(1 分)考察人工智能(AI) 的一些应用，去发现目前下列哪些任务可以通过AI 来解决得分/总分常由人类所能做的事该题无法得分/1.00正确答案：A、B、D 你错选为A、B、C、DA.以竞技水平玩德州扑克游戏0.33/1.003 多选(1 分)如下学科哪些是人工智能的基础？得分/总分B.打一场像样的乒乓球比赛A. 经济学0.25/1.00C.在Web 上购买一周的食品杂货0.33/1.00B. 哲学0.25/1.00D.在市场上购买一周的食品杂货C.心理学0.25/1.00正确答案：A、B、C 你错选为A、CD.数学0.25/1.008 填空(1 分)理性指的是一个系统的属性，即在_________的环境下正确答案：A、B、C、D 你选对了做正确的事。

得分/总分正确答案：已知4 多选(1 分)下列陈述中哪些是描述强AI (通用AI )的正确答案？得1 单选(1 分)图灵测试旨在给予哪一种令人满意的操作定义得分/ 分/总分总分A. 指的是一种机器，具有将智能应用于任何问题的A.人类思考能力0.50/1.00B.人工智能B. 是经过适当编程的具有正确输入和输出的计算机，因此有与人类同样判断力的头脑0.50/1.00C.机器智能 1.00/1.00C.指的是一种机器，仅针对一个具体问题D.机器动作正确答案： C 你选对了D.其定义为无知觉的计算机智能，或专注于一个狭2 多选(1 分)选择以下关于人工智能概念的正确表述得分/总分窄任务的AIA. 人工智能旨在创造智能机器该题无法得分/1.00B.专家系统0.33/1.00B. 人工智能是研究和构建在给定环境下表现良好的C.图像理解智能体程序该题无法得分/1.00D.分布式计算C.人工智能将其定义为人类智能体的研究该题无法正确答案：A、B、C 你错选为A、B得分/1.00 7 多选(1 分)考察人工智能(AI) 的一些应用，去发现目前下列哪些任务可以通过AI 来解决得分/总分D.人工智能是为了开发一类计算机使之能够完成通A.以竞技水平玩德州扑克游戏0.33/1.00常由人类所能做的事该题无法得分/1.00正确答案：A、B、D 你错选为A、B、C、DB.打一场像样的乒乓球比赛3 多选(1 分)如下学科哪些是人工智能的基础？得分/总分C.在Web 上购买一周的食品杂货0.33/1.00A. 经济学0.25/1.00D.在市场上购买一周的食品杂货B. 哲学0.25/1.00正确答案：A、B、C 你错选为A、CC.心理学0.25/1.008 填空(1 分)理性指的是一个系统的属性，即在_________的环境下D.数学0.25/1.00 做正确的事。

设计地图英语知识点总结1. Geographic Information Systems (GIS):GIS is a framework for gathering, managing, and analyzing geographic data. It integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. Knowledge of GIS is essential for designing a map, as it enables the collection and visualization of spatial data.2. Cartography:Cartography is the practice of creating maps or visual representations of geographic areas. It involves the utilization of design principles, such as scale, symbolization, and representation, to communicate spatial information effectively. Understanding the principles of cartography is crucial for creating clear and informative maps.3. Spatial Analysis:Spatial analysis is the process of examining the locations, attributes, and relationships of features in geographic data. It involves the use of techniques such as overlay, proximity analysis, and spatial statistics to gain insights into the patterns and trends of spatial data. A solid grasp of spatial analysis is important for designing maps that provide valuable insights into geographic phenomena.4. Data Visualization:Data visualization is the graphical representation of information and data. It involves the use of visual elements such as charts, graphs, and maps to communicate data patterns and trends. Knowledge of data visualization is critical for creating maps that effectively convey complex spatial information to the intended audience.5. Design Principles:Design principles, such as balance, contrast, and hierarchy, play a crucial role in map design. Understanding these principles helps in creating maps that are visually appealing and easy to interpret. Additionally, knowledge of typography, color theory, and layout design is important for creating maps that are aesthetically pleasing and functional.6. Spatial Data Management:Spatial data management involves the organization, storage, and retrieval of geographic data. It includes techniques such as data capture, data storage, and data manipulation, which are essential for managing the spatial data used in map design. Proficiency in spatial data management is necessary for handling large volumes of geographic data efficiently. 7. Geospatial Technologies:Geospatial technologies, such as GPS, remote sensing, and geographic information systems, are used to capture, analyze, and visualize spatial data. Understanding these technologies is important for incorporating various data sources and creating accurate and up-to-date maps.8. Map Projection:Map projection is the process of transforming the 3-dimensional surface of the Earth into a 2-dimensional map. It involves the use of mathematical formulas to represent the Earth's curved surface on a flat map. Knowledge of map projection is crucial for producing maps that accurately represent geographic features and spatial relationships.9. Spatial Analysis Techniques:Spatial analysis techniques, such as clustering, interpolation, and network analysis, are used to analyze the spatial patterns and relationships within geographic data. Understanding these techniques is important for identifying spatial trends, patterns, and anomalies, and for making informed decisions based on spatial data.10. Spatial Data Visualization Tools:There are a variety of spatial data visualization tools, such as Geographic Information Systems (GIS), web mapping platforms, and data visualization software, that are used to create and display maps. Familiarity with these tools is essential for designing maps that are interactive, dynamic, and accessible to a wide audience.In conclusion, designing a map requires a diverse set of skills and knowledge, including geographic information systems, cartography, spatial analysis, data visualization, design principles, spatial data management, geospatial technologies, map projection, spatial analysis techniques, and spatial data visualization tools. By mastering these knowledge points, map designers can create maps that effectively communicate spatial information and insights to a wide audience.。

大数据理论考试(习题卷12)说明：答案和解析在试卷最后第1部分：单项选择题，共64题，每题只有一个正确答案,多选或少选均不得分。

1.[单选题]（）试图学得一个属性的线性组合来进行预测的函数。

A)决策树B)贝叶斯分类器C)神经网络D)线性模2.[单选题]随机试验所有可能出现的结果，称为（）A)基本事件B)样本C)全部事件D)样本空间3.[单选题]DWS实例中，下列哪项不是主备配置的：A)CMSB)GTMC)OMSD)coordinato4.[单选题]数据科学家可能会同时使用多个算法（模型）进行预测，并且最后把这些算法的结果集成起来进行最后的预测（集成学习），以下对集成学习说法正确的是（）。

A)单个模型之间具有高相关性B)单个模型之间具有低相关性C)在集成学习中使用“平均权重”而不是“投票”会比较好D)单个模型都是用的一个算法5.[单选题]下面算法属于局部处理的是（）。

A)灰度线性变换B)二值化C)傅里叶变换D)中值滤6.[单选题]中文同义词替换时，常用到Word2Vec，以下说法错误的是（）。

A)Word2Vec基于概率统计B)Word2Vec结果符合当前预料环境C)Word2Vec得到的都是语义上的同义词D)Word2Vec受限于训练语料的数量和质7.[单选题]一位母亲记录了儿子3～9岁的身高，由此建立的身高与年龄的回归直线方程为y=7.19x+73.93，据此可以预测这个孩子10岁时的身高，则正确的叙述是（）。

A)身高一定是145.83cmB)身高一定超过146.00cmC)身高一定高于145.00cmD)身高在145.83cm左右8.[单选题]有关数据仓库的开发特点,不正确的描述是（）。

A)数据仓库开发要从数据出发;B)数据仓库使用的需求在开发出去就要明确;C)数据仓库的开发是一个不断循环的过程,是启发式的开发;D)在数据仓库环境中,并不存在操作型环境中所固定的和较确切的处理流,数据仓库中数据分析和处理更灵活,且没有固定的模式9.[单选题]由于不同类别的关键词对排序的贡献不同，检索算法一般把查询关键词分为几类，以下哪一类不属于此关键词类型的是（）。

大宽表英文缩写The abbreviation for a large data table in English is DKT (Data Knowledge Table).A DKT is an essential component in big data analysis and processing, as it houses an extensive collection of structured and unstructured data, allowing for comprehensive exploration and insights.The emergence of big data has led to the creation of large data tables, commonly referred to as wide or large tables. These tables store a vast amount of data, often spanning multiple variables or dimensions, making it challenging to manage and analyze effectively. To address this challenge, the concept of a DKT has gained popularity among analysts and data scientists.A DKT is known for its wide structure, often consisting of numerous columns or fields that capture various attributes or characteristics of the data. It serves as a central repository, allowing users to conduct exploratory data analysis, discover patterns, and extract valuable insights. The DKT acts as a foundation for various data mining techniques, such as association rule mining, clustering, and predictive modeling.The benefits of utilizing a DKT are numerous. Firstly, it provides a structured framework that organizes large datasets, facilitating efficient data retrieval and manipulation. Analysts can quickly locate and access specific subsets of the data for further analysis, saving time and effort. Additionally, the DKT allows for flexibility in data processing, enabling users to combine multiple sources of data seamlessly.Furthermore, a DKT enhances data exploration capabilities. With its wide structure, analysts can expand their analysis beyond simple aggregations and delve into more complex relationships among variables. The large number of columns in a DKT allows for detailed feature engineering, maximizing the potential for discovering hidden patterns and trends.Moreover, a DKT promotes collaboration and knowledge sharing within an organization. As a centralized resource, it facilitates data integration from variousdepartments or teams, promoting a unified understanding of the data. Analysts can share their findings and insights derived from the DKT, fostering a data-driven culture that supports informed decision-making.To effectively utilize a DKT, organizations must implement robust data governance and data management strategies. This involves establishing data quality standards, ensuring data integrity, and implementing security measures to protect sensitive information. Additionally, organizations should invest in advanced analytics tools and technologies that can handle the scale and complexity of large data tables.In conclusion, a DKT, or Data Knowledge Table, is a vital component in big data analysis. It represents a large data table with a wide structure, enabling analysts to explore, analyze, and derive insights from vast amounts of structured and unstructured data. With its benefits in efficient data retrieval, advanced data exploration, and collaborative decision-making, the DKT plays a crucial role in leveraging the power of big data for organizations.。

英语专业八级考试TEM-8阅读理解练习册（1）(英语专业2012级)UNIT 1Text AEvery minute of every day, what ecologist生态学家James Carlton calls a global ―conveyor belt‖, redistributes ocean organisms生物.It’s planetwide biological disruption生物的破坏that scientists have barely begun to understand.Dr. Carlton —an oceanographer at Williams College in Williamstown,Mass.—explains that, at any given moment, ―There are several thousand marine species traveling… in the ballast water of ships.‖ These creatures move from coastal waters where they fit into the local web of life to places where some of them could tear that web apart. This is the larger dimension of the infamous无耻的，邪恶的invasion of fish-destroying, pipe-clogging zebra mussels有斑马纹的贻贝.Such voracious贪婪的invaders at least make their presence known. What concerns Carlton and his fellow marine ecologists is the lack of knowledge about the hundreds of alien invaders that quietly enter coastal waters around the world every day. Many of them probably just die out. Some benignly亲切地，仁慈地—or even beneficially — join the local scene. But some will make trouble.In one sense, this is an old story. Organisms have ridden ships for centuries. They have clung to hulls and come along with cargo. What’s new is the scale and speed of the migrations made possible by the massive volume of ship-ballast water压载水— taken in to provide ship stability—continuously moving around the world…Ships load up with ballast water and its inhabitants in coastal waters of one port and dump the ballast in another port that may be thousands of kilometers away. A single load can run to hundreds of gallons. Some larger ships take on as much as 40 million gallons. The creatures that come along tend to be in their larva free-floating stage. When discharged排出in alien waters they can mature into crabs, jellyfish水母, slugs鼻涕虫，蛞蝓, and many other forms.Since the problem involves coastal species, simply banning ballast dumps in coastal waters would, in theory, solve it. Coastal organisms in ballast water that is flushed into midocean would not survive. Such a ban has worked for North American Inland Waterway. But it would be hard to enforce it worldwide. Heating ballast water or straining it should also halt the species spread. But before any such worldwide regulations were imposed, scientists would need a clearer view of what is going on.The continuous shuffling洗牌of marine organisms has changed the biology of the sea on a global scale. It can have devastating effects as in the case of the American comb jellyfish that recently invaded the Black Sea. It has destroyed that sea’s anchovy鳀鱼fishery by eating anchovy eggs. It may soon spread to western and northern European waters.The maritime nations that created the biological ―conveyor belt‖ should support a coordinated international effort to find out what is going on and what should be done about it. (456 words)1.According to Dr. Carlton, ocean organism‟s are_______.A.being moved to new environmentsB.destroying the planetC.succumbing to the zebra musselD.developing alien characteristics2.Oceanographers海洋学家are concerned because_________.A.their knowledge of this phenomenon is limitedB.they believe the oceans are dyingC.they fear an invasion from outer-spaceD.they have identified thousands of alien webs3.According to marine ecologists, transplanted marinespecies____________.A.may upset the ecosystems of coastal watersB.are all compatible with one anotherC.can only survive in their home watersD.sometimes disrupt shipping lanes4.The identified cause of the problem is_______.A.the rapidity with which larvae matureB. a common practice of the shipping industryC. a centuries old speciesD.the world wide movement of ocean currents5.The article suggests that a solution to the problem__________.A.is unlikely to be identifiedB.must precede further researchC.is hypothetically假设地，假想地easyD.will limit global shippingText BNew …Endangered‟ List Targets Many US RiversIt is hard to think of a major natural resource or pollution issue in North America today that does not affect rivers.Farm chemical runoff残渣, industrial waste, urban storm sewers, sewage treatment, mining, logging, grazing放牧,military bases, residential and business development, hydropower水力发电,loss of wetlands. The list goes on.Legislation like the Clean Water Act and Wild and Scenic Rivers Act have provided some protection, but threats continue.The Environmental Protection Agency (EPA) reported yesterday that an assessment of 642,000 miles of rivers and streams showed 34 percent in less than good condition. In a major study of the Clean Water Act, the Natural Resources Defense Council last fall reported that poison runoff impairs损害more than 125,000 miles of rivers.More recently, the NRDC and Izaak Walton League warned that pollution and loss of wetlands—made worse by last year’s flooding—is degrading恶化the Mississippi River ecosystem.On Tuesday, the conservation group保护组织American Rivers issued its annual list of 10 ―endangered‖ and 20 ―threatened‖ rivers in 32 states, the District of Colombia, and Canada.At the top of the list is the Clarks Fork of the Yellowstone River, whereCanadian mining firms plan to build a 74-acre英亩reservoir水库，蓄水池as part of a gold mine less than three miles from Yellowstone National Park. The reservoir would hold the runoff from the sulfuric acid 硫酸used to extract gold from crushed rock.―In the event this tailings pond failed, the impact to th e greater Yellowstone ecosystem would be cataclysmic大变动的，灾难性的and the damage irreversible不可逆转的.‖ Sen. Max Baucus of Montana, chairman of the Environment and Public Works Committee, wrote to Noranda Minerals Inc., an owner of the ― New World Mine‖.Last fall, an EPA official expressed concern about the mine and its potential impact, especially the plastic-lined storage reservoir. ― I am unaware of any studies evaluating how a tailings pond尾矿池，残渣池could be maintained to ensure its structural integrity forev er,‖ said Stephen Hoffman, chief of the EPA’s Mining Waste Section. ―It is my opinion that underwater disposal of tailings at New World may present a potentially significant threat to human health and the environment.‖The results of an environmental-impact statement, now being drafted by the Forest Service and Montana Department of State Lands, could determine the mine’s future…In its recent proposal to reauthorize the Clean Water Act, the Clinton administration noted ―dramatically improved water quality since 1972,‖ when the act was passed. But it also reported that 30 percent of riverscontinue to be degraded, mainly by silt泥沙and nutrients from farm and urban runoff, combined sewer overflows, and municipal sewage城市污水. Bottom sediments沉积物are contaminated污染in more than 1,000 waterways, the administration reported in releasing its proposal in January. Between 60 and 80 percent of riparian corridors (riverbank lands) have been degraded.As with endangered species and their habitats in forests and deserts, the complexity of ecosystems is seen in rivers and the effects of development----beyond the obvious threats of industrial pollution, municipal waste, and in-stream diversions改道to slake消除the thirst of new communities in dry regions like the Southwes t…While there are many political hurdles障碍ahead, reauthorization of the Clean Water Act this year holds promise for US rivers. Rep. Norm Mineta of California, who chairs the House Committee overseeing the bill, calls it ―probably the most important env ironmental legislation this Congress will enact.‖ (553 words)6.According to the passage, the Clean Water Act______.A.has been ineffectiveB.will definitely be renewedC.has never been evaluatedD.was enacted some 30 years ago7.“Endangered” rivers are _________.A.catalogued annuallyB.less polluted than ―threatened rivers‖C.caused by floodingD.adjacent to large cities8.The “cataclysmic” event referred to in paragraph eight would be__________.A. fortuitous偶然的，意外的B. adventitious外加的，偶然的C. catastrophicD. precarious不稳定的，危险的9. The owners of the New World Mine appear to be______.A. ecologically aware of the impact of miningB. determined to construct a safe tailings pondC. indifferent to the concerns voiced by the EPAD. willing to relocate operations10. The passage conveys the impression that_______.A. Canadians are disinterested in natural resourcesB. private and public environmental groups aboundC. river banks are erodingD. the majority of US rivers are in poor conditionText CA classic series of experiments to determine the effects ofoverpopulation on communities of rats was reported in February of 1962 in an article in Scientific American. The experiments were conducted by a psychologist, John B. Calhoun and his associates. In each of these experiments, an equal number of male and female adult rats were placed in an enclosure and given an adequate supply of food, water, and other necessities. The rat populations were allowed to increase. Calhoun knew from experience approximately how many rats could live in the enclosures without experiencing stress due to overcrowding. He allowed the population to increase to approximately twice this number. Then he stabilized the population by removing offspring that were not dependent on their mothers. He and his associates then carefully observed and recorded behavior in these overpopulated communities. At the end of their experiments, Calhoun and his associates were able to conclude that overcrowding causes a breakdown in the normal social relationships among rats, a kind of social disease. The rats in the experiments did not follow the same patterns of behavior as rats would in a community without overcrowding.The females in the rat population were the most seriously affected by the high population density: They showed deviant异常的maternal behavior; they did not behave as mother rats normally do. In fact, many of the pups幼兽，幼崽, as rat babies are called, died as a result of poor maternal care. For example, mothers sometimes abandoned their pups,and, without their mothers' care, the pups died. Under normal conditions, a mother rat would not leave her pups alone to die. However, the experiments verified that in overpopulated communities, mother rats do not behave normally. Their behavior may be considered pathologically 病理上，病理学地diseased.The dominant males in the rat population were the least affected by overpopulation. Each of these strong males claimed an area of the enclosure as his own. Therefore, these individuals did not experience the overcrowding in the same way as the other rats did. The fact that the dominant males had adequate space in which to live may explain why they were not as seriously affected by overpopulation as the other rats. However, dominant males did behave pathologically at times. Their antisocial behavior consisted of attacks on weaker male,female, and immature rats. This deviant behavior showed that even though the dominant males had enough living space, they too were affected by the general overcrowding in the enclosure.Non-dominant males in the experimental rat communities also exhibited deviant social behavior. Some withdrew completely; they moved very little and ate and drank at times when the other rats were sleeping in order to avoid contact with them. Other non-dominant males were hyperactive; they were much more active than is normal, chasing other rats and fighting each other. This segment of the rat population, likeall the other parts, was affected by the overpopulation.The behavior of the non-dominant males and of the other components of the rat population has parallels in human behavior. People in densely populated areas exhibit deviant behavior similar to that of the rats in Calhoun's experiments. In large urban areas such as New York City, London, Mexican City, and Cairo, there are abandoned children. There are cruel, powerful individuals, both men and women. There are also people who withdraw and people who become hyperactive. The quantity of other forms of social pathology such as murder, rape, and robbery also frequently occur in densely populated human communities. Is the principal cause of these disorders overpopulation? Calhoun’s experiments suggest that it might be. In any case, social scientists and city planners have been influenced by the results of this series of experiments.11. Paragraph l is organized according to__________.A. reasonsB. descriptionC. examplesD. definition12．Calhoun stabilized the rat population_________.A. when it was double the number that could live in the enclosure without stressB. by removing young ratsC. at a constant number of adult rats in the enclosureD. all of the above are correct13.W hich of the following inferences CANNOT be made from theinformation inPara. 1?A. Calhoun's experiment is still considered important today.B. Overpopulation causes pathological behavior in rat populations.C. Stress does not occur in rat communities unless there is overcrowding.D. Calhoun had experimented with rats before.14. Which of the following behavior didn‟t happen in this experiment?A. All the male rats exhibited pathological behavior.B. Mother rats abandoned their pups.C. Female rats showed deviant maternal behavior.D. Mother rats left their rat babies alone.15. The main idea of the paragraph three is that __________.A. dominant males had adequate living spaceB. dominant males were not as seriously affected by overcrowding as the otherratsC. dominant males attacked weaker ratsD. the strongest males are always able to adapt to bad conditionsText DThe first mention of slavery in the statutes法令，法规of the English colonies of North America does not occur until after 1660—some forty years after the importation of the first Black people. Lest we think that existed in fact before it did in law, Oscar and Mary Handlin assure us, that the status of B lack people down to the 1660’s was that of servants. A critique批判of the Handlins’ interpretation of why legal slavery did not appear until the 1660’s suggests that assumptions about the relation between slavery and racial prejudice should be reexamined, and that explanation for the different treatment of Black slaves in North and South America should be expanded.The Handlins explain the appearance of legal slavery by arguing that, during the 1660’s, the position of white servants was improving relative to that of black servants. Thus, the Handlins contend, Black and White servants, heretofore treated alike, each attained a different status. There are, however, important objections to this argument. First, the Handlins cannot adequately demonstrate that t he White servant’s position was improving, during and after the 1660’s; several acts of the Maryland and Virginia legislatures indicate otherwise. Another flaw in the Handlins’ interpretation is their assumption that prior to the establishment of legal slavery there was no discrimination against Black people. It is true that before the 1660’s Black people were rarely called slaves. But this shouldnot overshadow evidence from the 1630’s on that points to racial discrimination without using the term slavery. Such discrimination sometimes stopped short of lifetime servitude or inherited status—the two attributes of true slavery—yet in other cases it included both. The Handlins’ argument excludes the real possibility that Black people in the English colonies were never treated as the equals of White people.The possibility has important ramifications后果，影响.If from the outset Black people were discriminated against, then legal slavery should be viewed as a reflection and an extension of racial prejudice rather than, as many historians including the Handlins have argued, the cause of prejudice. In addition, the existence of discrimination before the advent of legal slavery offers a further explanation for the harsher treatment of Black slaves in North than in South America. Freyre and Tannenbaum have rightly argued that the lack of certain traditions in North America—such as a Roman conception of slavery and a Roman Catholic emphasis on equality— explains why the treatment of Black slaves was more severe there than in the Spanish and Portuguese colonies of South America. But this cannot be the whole explanation since it is merely negative, based only on a lack of something. A more compelling令人信服的explanation is that the early and sometimes extreme racial discrimination in the English colonies helped determine the particular nature of the slavery that followed. (462 words)16. Which of the following is the most logical inference to be drawn from the passage about the effects of “several acts of the Maryland and Virginia legislatures” (Para.2) passed during and after the 1660‟s?A. The acts negatively affected the pre-1660’s position of Black as wellas of White servants.B. The acts had the effect of impairing rather than improving theposition of White servants relative to what it had been before the 1660’s.C. The acts had a different effect on the position of white servants thandid many of the acts passed during this time by the legislatures of other colonies.D. The acts, at the very least, caused the position of White servants toremain no better than it had been before the 1660’s.17. With which of the following statements regarding the status ofBlack people in the English colonies of North America before the 1660‟s would the author be LEAST likely to agree?A. Although black people were not legally considered to be slaves,they were often called slaves.B. Although subject to some discrimination, black people had a higherlegal status than they did after the 1660’s.C. Although sometimes subject to lifetime servitude, black peoplewere not legally considered to be slaves.D. Although often not treated the same as White people, black people,like many white people, possessed the legal status of servants.18. According to the passage, the Handlins have argued which of thefollowing about the relationship between racial prejudice and the institution of legal slavery in the English colonies of North America?A. Racial prejudice and the institution of slavery arose simultaneously.B. Racial prejudice most often the form of the imposition of inheritedstatus, one of the attributes of slavery.C. The source of racial prejudice was the institution of slavery.D. Because of the influence of the Roman Catholic Church, racialprejudice sometimes did not result in slavery.19. The passage suggests that the existence of a Roman conception ofslavery in Spanish and Portuguese colonies had the effect of _________.A. extending rather than causing racial prejudice in these coloniesB. hastening the legalization of slavery in these colonies.C. mitigating some of the conditions of slavery for black people in these coloniesD. delaying the introduction of slavery into the English colonies20. The author considers the explanation put forward by Freyre andTannenbaum for the treatment accorded B lack slaves in the English colonies of North America to be _____________.A. ambitious but misguidedB. valid有根据的but limitedC. popular but suspectD. anachronistic过时的，时代错误的and controversialUNIT 2Text AThe sea lay like an unbroken mirror all around the pine-girt, lonely shores of Orr’s Island. Tall, kingly spruce s wore their regal王室的crowns of cones high in air, sparkling with diamonds of clear exuded gum流出的树胶; vast old hemlocks铁杉of primeval原始的growth stood darkling in their forest shadows, their branches hung with long hoary moss久远的青苔;while feathery larches羽毛般的落叶松,turned to brilliant gold by autumn frosts, lighted up the darker shadows of the evergreens. It was one of those hazy朦胧的, calm, dissolving days of Indian summer, when everything is so quiet that the fainest kiss of the wave on the beach can be heard, and white clouds seem to faint into the blue of the sky, and soft swathing一长条bands of violet vapor make all earth look dreamy, and give to the sharp, clear-cut outlines of the northern landscape all those mysteries of light and shade which impart such tenderness to Italian scenery.The funeral was over,--- the tread鞋底的花纹/ 踏of many feet, bearing the heavy burden of two broken lives, had been to the lonely graveyard, and had come back again,--- each footstep lighter and more unconstrained不受拘束的as each one went his way from the great old tragedy of Death to the common cheerful of Life.The solemn black clock stood swaying with its eternal ―tick-tock, tick-tock,‖ in the kitchen of the brown house on Orr’s Island. There was there that sense of a stillness that can be felt,---such as settles down on a dwelling住处when any of its inmates have passed through its doors for the last time, to go whence they shall not return. The best room was shut up and darkened, with only so much light as could fall through a little heart-shaped hole in the window-shutter,---for except on solemn visits, or prayer-meetings or weddings, or funerals, that room formed no part of the daily family scenery.The kitchen was clean and ample, hearth灶台, and oven on one side, and rows of old-fashioned splint-bottomed chairs against the wall. A table scoured to snowy whiteness, and a little work-stand whereon lay the Bible, the Missionary Herald, and the Weekly Christian Mirror, before named, formed the principal furniture. One feature, however, must not be forgotten, ---a great sea-chest水手用的储物箱,which had been the companion of Zephaniah through all the countries of the earth. Old, and battered破旧的，磨损的, and unsightly难看的it looked, yet report said that there was good store within which men for the most part respect more than anything else; and, indeed it proved often when a deed of grace was to be done--- when a woman was suddenly made a widow in a coast gale大风，狂风, or a fishing-smack小渔船was run down in the fogs off the banks, leaving in some neighboring cottage a family of orphans,---in all such cases, the opening of this sea-chest was an event of good omen 预兆to the bereaved丧亲者;for Zephaniah had a large heart and a large hand, and was apt有…的倾向to take it out full of silver dollars when once it went in. So the ark of the covenant约柜could not have been looked on with more reverence崇敬than the neighbours usually showed to Captain Pennel’s sea-chest.1. The author describes Orr‟s Island in a(n)______way.A.emotionally appealing, imaginativeB.rational, logically preciseC.factually detailed, objectiveD.vague, uncertain2.According to the passage, the “best room”_____.A.has its many windows boarded upB.has had the furniture removedC.is used only on formal and ceremonious occasionsD.is the busiest room in the house3.From the description of the kitchen we can infer that thehouse belongs to people who_____.A.never have guestsB.like modern appliancesC.are probably religiousD.dislike housework4.The passage implies that_______.A.few people attended the funeralB.fishing is a secure vocationC.the island is densely populatedD.the house belonged to the deceased5.From the description of Zephaniah we can see thathe_________.A.was physically a very big manB.preferred the lonely life of a sailorC.always stayed at homeD.was frugal and saved a lotText BBasic to any understanding of Canada in the 20 years after the Second World War is the country' s impressive population growth. For every three Canadians in 1945, there were over five in 1966. In September 1966 Canada's population passed the 20 million mark. Most of this surging growth came from natural increase. The depression of the 1930s and the war had held back marriages, and the catching-up process began after 1945. The baby boom continued through the decade of the 1950s, producing a population increase of nearly fifteen percent in the five years from 1951 to 1956. This rate of increase had been exceeded only once before in Canada's history, in the decade before 1911 when the prairies were being settled. Undoubtedly, the good economic conditions of the 1950s supported a growth in the population, but the expansion also derived from a trend toward earlier marriages and an increase in the average size of families; In 1957 the Canadian birth rate stood at 28 per thousand, one of the highest in the world. After the peak year of 1957, thebirth rate in Canada began to decline. It continued falling until in 1966 it stood at the lowest level in 25 years. Partly this decline reflected the low level of births during the depression and the war, but it was also caused by changes in Canadian society. Young people were staying at school longer, more women were working; young married couples were buying automobiles or houses before starting families; rising living standards were cutting down the size of families. It appeared that Canada was once more falling in step with the trend toward smaller families that had occurred all through theWestern world since the time of the Industrial Revolution. Although the growth in Canada’s population had slowed down by 1966 (the cent), another increase in the first half of the 1960s was only nine percent), another large population wave was coming over the horizon. It would be composed of the children of the children who were born during the period of the high birth rate prior to 1957.6. What does the passage mainly discuss?A. Educational changes in Canadian society.B. Canada during the Second World War.C. Population trends in postwar Canada.D. Standards of living in Canada.7. According to the passage, when did Canada's baby boom begin?A. In the decade after 1911.B. After 1945.C. During the depression of the 1930s.D. In 1966.8. The author suggests that in Canada during the 1950s____________.A. the urban population decreased rapidlyB. fewer people marriedC. economic conditions were poorD. the birth rate was very high9. When was the birth rate in Canada at its lowest postwar level?A. 1966.B. 1957.C. 1956.D. 1951.10. The author mentions all of the following as causes of declines inpopulation growth after 1957 EXCEPT_________________.A. people being better educatedB. people getting married earlierC. better standards of livingD. couples buying houses11.I t can be inferred from the passage that before the IndustrialRevolution_______________.A. families were largerB. population statistics were unreliableC. the population grew steadilyD. economic conditions were badText CI was just a boy when my father brought me to Harlem for the first time, almost 50 years ago. We stayed at the hotel Theresa, a grand brick structure at 125th Street and Seventh avenue. Once, in the hotel restaurant, my father pointed out Joe Louis. He even got Mr. Brown, the hotel manager, to introduce me to him, a bit punchy强力的but still champ焦急as fast as I was concerned.Much has changed since then. Business and real estate are booming. Some say a new renaissance is under way. Others decry责难what they see as outside forces running roughshod肆意践踏over the old Harlem. New York meant Harlem to me, and as a young man I visited it whenever I could. But many of my old haunts are gone. The Theresa shut down in 1966. National chains that once ignored Harlem now anticipate yuppie money and want pieces of this prime Manhattan real estate. So here I am on a hot August afternoon, sitting in a Starbucks that two years ago opened a block away from the Theresa, snatching抓取，攫取at memories between sips of high-priced coffee. I am about to open up a piece of the old Harlem---the New York Amsterdam News---when a tourist。

Team-Centered Perspective for Adaptive Automation DesignLawrence J.PrinzelLangley Research Center, Hampton, VirginiaAbstractAutomation represents a very active area of human factors research. Thejournal, Human Factors, published a special issue on automation in 1985.Since then, hundreds of scientific studies have been published examiningthe nature of automation and its interaction with human performance.However, despite a dramatic increase in research investigating humanfactors issues in aviation automation, there remain areas that need furtherexploration. This NASA Technical Memorandum describes a new area ofIt discussesautomation design and research, called “adaptive automation.”　the concepts and outlines the human factors issues associated with the newmethod of adaptive function allocation. The primary focus is onhuman-centered design, and specifically on ensuring that adaptiveautomation is from a team-centered perspective. The document showsthat adaptive automation has many human factors issues common totraditional automation design. Much like the introduction of other new technologies and paradigm shifts, adaptive automation presents an opportunity to remediate current problems but poses new ones forhuman-automation interaction in aerospace operations. The review here isintended to communicate the philosophical perspective and direction ofadaptive automation research conducted under the Aerospace OperationsSystems (AOS), Physiological and Psychological Stressors and Factors (PPSF)project.Key words:Adaptive Automation; Human-Centered Design; Automation;Human FactorsIntroduction"During the 1970s and early 1980s...the concept of automating as much as possible was considered appropriate. The expected benefit was a reduction inpilot workload and increased safety...Although many of these benefits have beenrealized, serious questions have arisen and incidents/accidents that have occurredwhich question the underlying assumptions that a maximum availableautomation is ALWAYS appropriate or that we understand how to designautomated systems so that they are fully compatible with the capabilities andlimitations of the humans in the system."---- ATA, 1989The Air Transport Association of America (ATA) Flight Systems Integration Committee(1989) made the above statement in response to the proliferation of automation in aviation. They noted that technology improvements, such as the ground proximity warning system, have had dramatic benefits; others, such as the electronic library system, offer marginal benefits at best. Such observations have led many in the human factors community, most notably Charles Billings (1991; 1997) of NASA, to assert that automation should be approached from a "human-centered design" perspective.The period from 1970 to the present was marked by an increase in the use of electronic display units (EDUs); a period that Billings (1997) calls "information" and “management automation." The increased use of altitude, heading, power, and navigation displays; alerting and warning systems, such as the traffic alert and collision avoidance system (TCAS) and ground proximity warning system (GPWS; E-GPWS; TAWS); flight management systems (FMS) and flight guidance (e.g., autopilots; autothrottles) have "been accompanied by certain costs, including an increased cognitive burden on pilots, new information requirements that have required additional training, and more complex, tightly coupled, less observable systems" (Billings, 1997). As a result, human factors research in aviation has focused on the effects of information and management automation. The issues of interest include over-reliance on automation, "clumsy" automation (e.g., Wiener, 1989), digital versus analog control, skill degradation, crew coordination, and data overload (e.g., Billings, 1997). Furthermore, research has also been directed toward situational awareness (mode & state awareness; Endsley, 1994; Woods & Sarter, 1991) associated with complexity, coupling, autonomy, and inadequate feedback. Finally, human factors research has introduced new automation concepts that will need to be integrated into the existing suite of aviationautomation.Clearly, the human factors issues of automation have significant implications for safetyin aviation. However, what exactly do we mean by automation? The way we choose to define automation has considerable meaning for how we see the human role in modern aerospace s ystems. The next section considers the concept of automation, followed by an examination of human factors issues of human-automation interaction in aviation. Next, a potential remedy to the problems raised is described, called adaptive automation. Finally, the human-centered design philosophy is discussed and proposals are made for how the philosophy can be applied to this advanced form of automation. The perspective is considered in terms of the Physiological /Psychological Stressors & Factors project and directions for research on adaptive automation.Automation in Modern AviationDefinition.Automation refers to "...systems or methods in which many of the processes of production are automatically performed or controlled by autonomous machines or electronic devices" (Parsons, 1985). Automation is a tool, or resource, that the human operator can use to perform some task that would be difficult or impossible without machine aiding (Billings, 1997). Therefore, automation can be thought of as a process of substituting the activity of some device or machine for some human activity; or it can be thought of as a state of technological development (Parsons, 1985). However, some people (e.g., Woods, 1996) have questioned whether automation should be viewed as a substitution of one agent for another (see "apparent simplicity, real complexity" below). Nevertheless, the presence of automation has pervaded almost every aspect of modern lives. From the wheel to the modern jet aircraft, humans have sought to improve the quality of life. We have built machines and systems that not only make work easier, more efficient, and safe, but also give us more leisure time. The advent of automation has further enabled us to achieve this end. With automation, machines can now perform many of the activities that we once had to do. Our automobile transmission will shift gears for us. Our airplanes will fly themselves for us. All we have to dois turn the machine on and off. It has even been suggested that one day there may not be aaccidents resulting from need for us to do even that. However, the increase in “cognitive”　faulty human-automation interaction have led many in the human factors community to conclude that such a statement may be premature.Automation Accidents. A number of aviation accidents and incidents have been directly attributed to automation. Examples of such in aviation mishaps include (from Billings, 1997):DC-10 landing in control wheel steering A330 accident at ToulouseB-747 upset over Pacific DC-10 overrun at JFK, New YorkB-747 uncommandedroll,Nakina,Ont. A320 accident at Mulhouse-HabsheimA320 accident at Strasbourg A300 accident at NagoyaB-757 accident at Cali, Columbia A320 accident at BangaloreA320 landing at Hong Kong B-737 wet runway overrunsA320 overrun at Warsaw B-757 climbout at ManchesterA310 approach at Orly DC-9 wind shear at CharlotteBillings (1997) notes that each of these accidents has a different etiology, and that human factors investigation of causes show the matter to be complex. However, what is clear is that the percentage of accident causes has fundamentally shifted from machine-caused to human-caused (estimations of 60-80% due to human error) etiologies, and the shift is attributable to the change in types of automation that have evolved in aviation.Types of AutomationThere are a number of different types of automation and the descriptions of them vary considerably. Billings (1997) offers the following types of automation:?Open-Loop Mechanical or Electronic Control.Automation is controlled by gravity or spring motors driving gears and cams that allow continous and repetitive motion. Positioning, forcing, and timing were dictated by the mechanism and environmental factors (e.g., wind). The automation of factories during the Industrial Revolution would represent this type of automation.?Classic Linear Feedback Control.Automation is controlled as a function of differences between a reference setting of desired output and the actual output. Changes a re made to system parameters to re-set the automation to conformance. An example of this type of automation would be flyball governor on the steam engine. What engineers call conventional proportional-integral-derivative (PID) control would also fit in this category of automation.?Optimal Control. A computer-based model of controlled processes i s driven by the same control inputs as that used to control the automated process. T he model output is used to project future states and is thus used to determine the next control input. A "Kalman filtering" approach is used to estimate the system state to determine what the best control input should be.?Adaptive Control. This type of automation actually represents a number of approaches to controlling automation, but usually stands for automation that changes dynamically in response to a change in state. Examples include the use of "crisp" and "fuzzy" controllers, neural networks, dynamic control, and many other nonlinear methods.Levels of AutomationIn addition to “types ”　of automation, we can also conceptualize different “levels ”　of automation control that the operator can have. A number of taxonomies have been put forth, but perhaps the best known is the one proposed by Tom Sheridan of Massachusetts Institute of Technology (MIT). Sheridan (1987) listed 10 levels of automation control:1. The computer offers no assistance, the human must do it all2. The computer offers a complete set of action alternatives3. The computer narrows the selection down to a few4. The computer suggests a selection, and5. Executes that suggestion if the human approves, or6. Allows the human a restricted time to veto before automatic execution, or7. Executes automatically, then necessarily informs the human, or8. Informs the human after execution only if he asks, or9. Informs the human after execution if it, the computer, decides to10. The computer decides everything and acts autonomously, ignoring the humanThe list covers the automation gamut from fully manual to fully automatic. Although different researchers define adaptive automation differently across these levels, the consensus is that adaptive automation can represent anything from Level 3 to Level 9. However, what makes adaptive automation different is the philosophy of the approach taken to initiate adaptive function allocation and how such an approach may address t he impact of current automation technology.Impact of Automation TechnologyAdvantages of Automation . Wiener (1980; 1989) noted a number of advantages to automating human-machine systems. These include increased capacity and productivity, reduction of small errors, reduction of manual workload and mental fatigue, relief from routine operations, more precise handling of routine operations, economical use of machines, and decrease of performance variation due to individual differences. Wiener and Curry (1980) listed eight reasons for the increase in flight-deck automation: (a) Increase in available technology, such as FMS, Ground Proximity Warning System (GPWS), Traffic Alert andCollision Avoidance System (TCAS), etc.; (b) concern for safety; (c) economy, maintenance, and reliability; (d) workload reduction and two-pilot transport aircraft certification; (e) flight maneuvers and navigation precision; (f) display flexibility; (g) economy of cockpit space; and (h) special requirements for military missions.Disadvantages o f Automation. Automation also has a number of disadvantages that have been noted. Automation increases the burdens and complexities for those responsible for operating, troubleshooting, and managing systems. Woods (1996) stated that automation is "...a wrapped package -- a package that consists of many different dimensions bundled together as a hardware/software system. When new automated systems are introduced into a field of practice, change is precipitated along multiple dimensions." As Woods (1996) noted, some of these changes include: ( a) adds to or changes the task, such as device setup and initialization, configuration control, and operating sequences; (b) changes cognitive demands, such as requirements for increased situational awareness; (c) changes the roles of people in the system, often relegating people to supervisory controllers; (d) automation increases coupling and integration among parts of a system often resulting in data overload and "transparency"; and (e) the adverse impacts of automation is often not appreciated by those who advocate the technology. These changes can result in lower job satisfaction (automation seen as dehumanizing human roles), lowered vigilance, fault-intolerant systems, silent failures, an increase in cognitive workload, automation-induced failures, over-reliance, complacency, decreased trust, manual skill erosion, false alarms, and a decrease in mode awareness (Wiener, 1989).Adaptive AutomationDisadvantages of automation have resulted in increased interest in advanced automation concepts. One of these concepts is automation that is dynamic or adaptive in nature (Hancock & Chignell, 1987; Morrison, Gluckman, & Deaton, 1991; Rouse, 1977; 1988). In an aviation context, adaptive automation control of tasks can be passed back and forth between the pilot and automated systems in response to the changing task demands of modern aircraft. Consequently, this allows for the restructuring of the task environment based upon (a) what is automated, (b) when it should be automated, and (c) how it is automated (Rouse, 1988; Scerbo, 1996). Rouse(1988) described criteria for adaptive aiding systems:The level of aiding, as well as the ways in which human and aidinteract, should change as task demands vary. More specifically,the level of aiding should increase as task demands become suchthat human performance will unacceptably degrade withoutaiding. Further, the ways in which human and aid interact shouldbecome increasingly streamlined as task demands increase.Finally, it is quite likely that variations in level of aiding andmodes of interaction will have to be initiated by the aid rather thanby the human whose excess task demands have created a situationrequiring aiding. The term adaptive aiding is used to denote aidingconcepts that meet [these] requirements.Adaptive aiding attempts to optimize the allocation of tasks by creating a mechanism for determining when tasks need to be automated (Morrison, Cohen, & Gluckman, 1993). In adaptive automation, the level or mode of automation can be modified in real time. Further, unlike traditional forms of automation, both the system and the pilot share control over changes in the state of automation (Scerbo, 1994; 1996). Parasuraman, Bahri, Deaton, Morrison, and Barnes (1992) have argued that adaptive automation represents the optimal coupling of the level of pilot workload to the level of automation in the tasks. Thus, adaptive automation invokes automation only when task demands exceed the pilot's capabilities. Otherwise, the pilot retains manual control of the system functions. Although concerns have been raised about the dangers of adaptive automation (Billings & Woods, 1994; Wiener, 1989), it promises to regulate workload, bolster situational awareness, enhance vigilance, maintain manual skill levels, increase task involvement, and generally improve pilot performance.Strategies for Invoking AutomationPerhaps the most critical challenge facing system designers seeking to implement automation concerns how changes among modes or levels of automation will be accomplished (Parasuraman e t al., 1992; Scerbo, 1996). Traditional forms of automation usually start with some task or functional analysis and attempt to fit the operational tasks necessary to the abilities of the human or the system. The approach often takes the form of a functional allocation analysis (e.g., Fitt's List) in which an attempt is made to determine whether the human or the system is better suited to do each task. However, many in the field have pointed out the problem with trying to equate the two in automated systems, as each have special characteristics that impede simple classification taxonomies. Such ideas as these have led some to suggest other ways of determining human-automation mixes. Although certainly not exhaustive, some of these ideas are presented below.Dynamic Workload Assessment.One approach involves the dynamic assessment o fmeasures t hat index the operators' state of mental engagement. (Parasuraman e t al., 1992; Rouse,1988). The question, however, is what the "trigger" should be for the allocation of functions between the pilot and the automation system. Numerous researchers have suggested that adaptive systems respond to variations in operator workload (Hancock & Chignell, 1987; 1988; Hancock, Chignell & Lowenthal, 1985; Humphrey & Kramer, 1994; Reising, 1985; Riley, 1985; Rouse, 1977), and that measures o f workload be used to initiate changes in automation modes. Such measures include primary and secondary-task measures, subjective workload measures, a nd physiological measures. T he question, however, is what adaptive mechanism should be used to determine operator mental workload (Scerbo, 1996).Performance Measures. One criterion would be to monitor the performance of the operator (Hancock & Chignel, 1987). Some criteria for performance would be specified in the system parameters, and the degree to which the operator deviates from the criteria (i.e., errors), the system would invoke levels of adaptive automation. For example, Kaber, Prinzel, Clammann, & Wright (2002) used secondary task measures to invoke adaptive automation to help with information processing of air traffic controllers. As Scerbo (1996) noted, however,"...such an approach would be of limited utility because the system would be entirely reactive."Psychophysiological M easures.Another criterion would be the cognitive and attentional state of the operator as measured by psychophysiological measures (Byrne & Parasuraman, 1996). An example of such an approach is that by Pope, Bogart, and Bartolome (1996) and Prinzel, Freeman, Scerbo, Mikulka, and Pope (2000) who used a closed-loop system to dynamically regulate the level of "engagement" that the subject had with a tracking task. The system indexes engagement on the basis of EEG brainwave patterns.Human Performance Modeling.Another approach would be to model the performance of the operator. The approach would allow the system to develop a number of standards for operator performance that are derived from models of the operator. An example is Card, Moran, and Newell (1987) discussion of a "model human processor." They discussed aspects of the human processor that could be used to model various levels of human performance. Another example is Geddes (1985) and his colleagues (Rouse, Geddes, & Curry, 1987-1988) who provided a model to invoke automation based upon system information, the environment, and expected operator behaviors (Scerbo, 1996).Mission Analysis. A final strategy would be to monitor the activities of the mission or task (Morrison & Gluckman, 1994). Although this method of adaptive automation may be themost accessible at the current state of technology, Bahri et al. (1992) stated that such monitoring systems lack sophistication and are not well integrated and coupled to monitor operator workload or performance (Scerbo, 1996). An example of a mission analysis approach to adaptive automation is Barnes and Grossman (1985) who developed a system that uses critical events to allocate among automation modes. In this system, the detection of critical events, such as emergency situations or high workload periods, invoked automation.Adaptive Automation Human Factors IssuesA number of issues, however, have been raised by the use of adaptive automation, and many of these issues are the same as those raised almost 20 years ago by Curry and Wiener (1980). Therefore, these issues are applicable not only to advanced automation concepts, such as adaptive automation, but to traditional forms of automation already in place in complex systems (e.g., airplanes, trains, process control).Although certainly one can make the case that adaptive automation is "dressed up" automation and therefore has many of the same problems, it is also important to note that the trend towards such forms of automation does have unique issues that accompany it. As Billings & Woods (1994) stated, "[i]n high-risk, dynamic environments...technology-centered automation has tended to decrease human involvement in system tasks, and has thus impaired human situation awareness; both are unwanted consequences of today's system designs, but both are dangerous in high-risk systems. [At its present state of development,] adaptive ("self-adapting") automation represents a potentially serious threat ... to the authority that the human pilot must have to fulfill his or her responsibility for flight safety."The Need for Human Factors Research.Nevertheless, such concerns should not preclude us from researching the impact that such forms of advanced automation are sure to have on human performance. Consider Hancock’s (1996; 1997) examination of the "teleology for technology." He suggests that automation shall continue to impact our lives requiring humans to co-evolve with the technology; Hancock called this "techneology."What Peter Hancock attempts to communicate to the human factors community is that automation will continue to evolve whether or not human factors chooses to be part of it. As Wiener and Curry (1980) conclude: "The rapid pace of automation is outstripping one's ability to comprehend all the implications for crew performance. It is unrealistic to call for a halt to cockpit automation until the manifestations are completely understood. We do, however, call for those designing, analyzing, and installing automatic systems in the cockpit to do so carefully; to recognize the behavioral effects of automation; to avail themselves of present andfuture guidelines; and to be watchful for symptoms that might appear in training andoperational settings." The concerns they raised are as valid today as they were 23 years ago.However, this should not be taken to mean that we should capitulate. Instead, becauseobservation suggests that it may be impossible to fully research any new Wiener and Curry’ｓtechnology before implementation, we need to form a taxonomy and research plan tomaximize human factors input for concurrent engineering of adaptive automation.Classification of Human Factors Issues. Kantowitz and Campbell (1996)identified some of the key human factors issues to be considered in the design of advancedautomated systems. These include allocation of function, stimulus-response compatibility, andmental models. Scerbo (1996) further suggested the need for research on teams,communication, and training and practice in adaptive automated systems design. The impactof adaptive automation systems on monitoring behavior, situational awareness, skilldegradation, and social dynamics also needs to be investigated. Generally however, Billings(1997) stated that the problems of automation share one or more of the followingcharacteristics: Brittleness, opacity, literalism, clumsiness, monitoring requirement, and dataoverload. These characteristics should inform design guidelines for the development, analysis,and implementation of adaptive automation technologies. The characteristics are defined as: ?Brittleness refers to "...an attribute of a system that works well under normal or usual conditions but that does not have desired behavior at or close to some margin of its operating envelope."?Opacity reflects the degree of understanding of how and why automation functions as it does. The term is closely associated with "mode awareness" (Sarter & Woods, 1994), "transparency"; or "virtuality" (Schneiderman, 1992).?Literalism concern the "narrow-mindedness" of the automated system; that is, theflexibility of the system to respond to novel events.?Clumsiness was coined by Wiener (1989) to refer to automation that reduced workload demands when the demands are already low (e.g., transit flight phase), but increases them when attention and resources are needed elsewhere (e.g., descent phase of flight). An example is when the co-pilot needs to re-program the FMS, to change the plane's descent path, at a time when the co-pilot should be scanning for other planes.?Monitoring requirement refers to the behavioral and cognitive costs associated withincreased "supervisory control" (Sheridan, 1987; 1991).?Data overload points to the increase in information in modern automated contexts (Billings, 1997).These characteristics of automation have relevance for defining the scope of humanfactors issues likely to plague adaptive automation design if significant attention is notdirected toward ensuring human-centered design. The human factors research communityhas noted that these characteristics can lead to human factors issues of allocation of function(i.e., when and how should functions be allocated adaptively); stimulus-response compatibility and new error modes; how adaptive automation will affect mental models,situation models, and representational models; concerns about mode unawareness and-of-the-loop” performance problem; situation awareness decay; manual skill decay and the “outclumsy automation and task/workload management; and issues related to the design of automation. This last issue points to the significant concern in the human factors communityof how to design adaptive automation so that it reflects what has been called “team-centered”;that is, successful adaptive automation will l ikely embody the concept of the “electronic team member”. However, past research (e.g., Pilots Associate Program) has shown that designing automation to reflect such a role has significantly different requirements than those arising in traditional automation design. The field is currently focused on answering the questions,does that definition translate into“what is it that defines one as a team member?” and “howUnfortunately, the literature also shows that the designing automation to reflect that role?”　answer is not transparent and, therefore, adaptive automation must first tackle its own uniqueand difficult problems before it may be considered a viable prescription to currenthuman-automation interaction problems. The next section describes the concept of the electronic team member and then discusses t he literature with regard to team dynamics, coordination, communication, shared mental models, and the implications of these foradaptive automation design.Adaptive Automation as Electronic Team MemberLayton, Smith, and McCoy (1994) stated that the design of automated systems should befrom a team-centered approach; the design should allow for the coordination betweenmachine agents and human practitioners. However, many researchers have noted that automated systems tend to fail as team players (Billings, 1991; Malin & Schreckenghost,1992; Malin et al., 1991;Sarter & Woods, 1994; Scerbo, 1994; 1996; Woods, 1996). Thereason is what Woods (1996) calls “apparent simplicity, real complexity.”Apparent Simplicity, Real Complexity.Woods (1996) stated that conventional wisdomabout automation makes technology change seem simple. Automation can be seen as simply changing the human agent for a machine agent. Automation further provides for more optionsand methods, frees up operator time to do other things, provides new computer graphics and interfaces, and reduces human error. However, the reality is that technology change has often。