ANN-based modelling and estimation of daily global solar

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~N 0, 2 ,
~N 0, 2
. runmlwin y cons x1 x2 x3, ///
level3(community: cons) ///
level2(mother: cons) ///
level1(child:) ///
discrete(d(binomial) l(logit) denom(cons)) ///
Rasbash et al. (2009) and others
•
4059 students nested within 65 schools
= 0 + +
~N 0, 2
~N 0, 2
The runmlwin command syntax
Multilevel modelling in MLwiN
1.
Estimation of multilevel models for continuous, binary, ordered categorical,
unordered categorical and count data
2.
Fast estimation via classical and Bayesian methods
7
0.99 1.00 1.00 0.99 0.98 0.99 1.00 1.00 1.00
MULTILEVEL MODELS FOR
BINARY RESPONSES
Guatemalan immunization
campaign
•
Child immunization data
•
First analysed by Pebley, Goldman and Rodriguez (1996)

名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域 二级分类 三级分类。

Package‘dccmidas’October13,2022Type PackageTitle DCC Models with GARCH-MIDAS Specifications in the UnivariateStepVersion0.1.0Description Estimates a variety of Dynamic Conditional Correlation(DCC)models.More in de-tail,the'dccmidas'package allows the estimation of the cor-rected DCC(cDCC)of Aielli(2013)<doi:10.1080/07350015.2013.771027>,the DCC-MIDAS of Colacito et al.(2011)<doi:10.1016/j.jeconom.2011.02.013>,the Asymmet-ric DCC of Cappiello et al.<doi:10.1093/jjfinec/nbl005>,and the Dynamic Equicorrela-tion(DECO)of Engle and Kelly(2012)<doi:10.1080/07350015.2011.652048>.'dccmidas'of-fers the possibility of including standard GARCH<doi:10.1016/0304-4076(86)90063-1>,GARCH-MIDAS<doi:10.1162/REST_a_00300>and Double Asymmetric GARCH-MIDAS<doi:10.1016/j.econmod.2018.07.025>models in the univariate estimation.Fi-nally,the package calculates also the var-cov matrix under two non-parametric models:the Mov-ing Covariance and the RiskMetrics specifications.License GPL-3LinkingTo Rcpp,RcppArmadilloEncoding UTF-8LazyData trueRoxygenNote7.1.1RdMacros RdpackDepends R(>=4.0.0)Imports Rcpp(>=1.0.5),maxLik(>=1.3-8),rumidas(>=0.1.1),rugarch(>=1.4-4),roll(>=1.1.4),xts(>=0.12.0),tseries(>=0.10.47),Rdpack(>=1.0.0),lubridate(>=1.7.9),zoo(>=1.8.8),stats(>=4.0.2),utils(>=4.0.2)Suggests knitr,rmarkdownNeedsCompilation yesAuthor Vincenzo Candila[aut,cre]Maintainer Vincenzo Candila<****************************>Repository CRANDate/Publication2021-03-1510:00:07UTC12cov_eval R topics documented:cov_eval (2)dcc_fit (3)Det (7)ftse100 (7)indpro (8)Inv (9)nasdaq (9)plot_dccmidas (10)sp500 (11)Index13 cov_eval Var-cov matrix evaluationDescriptionEvaluates the estimated var-cov matrix H_t with respect to a covariance proxy,under different robust loss functions(Laurent et al.2013).The losses considered are also used in Amendola et al.(2020).Usagecov_eval(H_t,cov_proxy=NULL,r_t=NULL,loss="FROB")ArgumentsH_t Estimated covariance matrix,formatted as arraycov_proxy optional Covariance matrix,formatted as arrayr_t optional List of daily returns used to calculate H_t.If parameter’cov_proxy’is not provided,then r_t must be included.In this case,a(noise)proxy will beautomatically usedloss Robust loss function to use.Valid choices are:"FROB"for Frobenius(by de-fault),"SFROB"for Squared Frobenius,"EUCL"for Euclidean,"QLIKE"forQLIKE and"RMSE"for Root Mean Squared ErrorsValueThe value of the loss for each tReferencesAmendola A,Braione M,Candila V,Storti G(2020).“A Model Confidence Set approach to the combination of multivariate volatility forecasts.”International Journal of Forecasting,36(3),873-891.doi:10.1016/j.ijforecast.2019.10.001.Laurent S,Rombouts JV,Violante F(2013).“On loss functions and ranking forecasting perfor-mances of multivariate volatility models.”Journal of Econometrics,173(1),1–10.doi:10.1016/ j.jeconom.2012.08.004.Examplesrequire(xts)#open to close daily log-returnsr_t_s<-log(sp500[ 2010/2019 ][,3])-log(sp500[ 2010/2019 ][,1])r_t_n<-log(nasdaq[ 2010/2019 ][,3])-log(nasdaq[ 2010/2019 ][,1])r_t_f<-log(ftse100[ 2010/2019 ][,3])-log(ftse100[ 2010/2019 ][,1])db_m<-merge.xts(r_t_s,r_t_n,r_t_f)db_m<-db_m[complete.cases(db_m),]colnames(db_m)<-c("S&P500","NASDAQ","FTSE100")#list of returnsr_t<-list(db_m[,1],db_m[,2],db_m[,3])#estimationK_c<-144N_c<-36cdcc_est<-dcc_fit(r_t,univ_model="sGARCH",distribution="norm",corr_model="DCCMIDAS",N_c=N_c,K_c=K_c)cov_eval(cdcc_est$H_t,r_t=r_t)[(K_c+1):dim(cdcc_est$H_t)[3]]dcc_fit DCCfit(first and second steps)DescriptionObtains the estimation of a variety of DCC models,using as univariate models both GARCH and GARCH-MIDAS specifications.Usagedcc_fit(r_t,univ_model="sGARCH",distribution="norm",MV=NULL,K=NULL,corr_model="cDCC",lag_fun="Beta",N_c=NULL,K_c=NULL)Argumentsr_t List of daily returns on which estimate a DCC model.Each daily return must bean’xts’object.Note that the sample period of the returns should be the same.Otherwise,a merge is performeduniv_model Specification of the univariate model.Valid choices are:some of the specifica-tions used in the rugarch(ugarchspec)and rumidas(ugmfit)packages.Morein detail,the models coming from rugarch are:model Valid models(currentlyimplemented)are’sGARCH’,’eGARCH’,’gjrGARCH’,’iGARCH’,and’cs-GARCH’.The models implemented from rumidas are:’GM_skew’,’GM_noskew’,’DAGM_skew’,and’DAGM_noskew’distribution optional Distribution chosen for the univariate estimation.Valid choices are:"norm"(by default)and"std",respectively,for the Normal and Student’s t dis-tributionsMV optional MIDAS variable to include in the univariate estimation,if the modelspecificied is a GARCH-MIDAS(GM,Engle et al.(2013))or a Double Asym-metric GM(DAGM,Engle et al.(2013)).In the case of MIDAS-based models,please provide a list of the MIDAS variables obtained from the mv_into_matfunction.If the same MV variable is used,then provide always a list,with thesame(transformed)variable repeatedK optional The number of lagged realization of MV variable to use,if’univ_model’has a MIDAS termcorr_model Correlation model used.Valid choices are:"cDCC"(the corrected DCC ofAielli(2013)),"aDCC"(the asymmetric DCC model of Cappiello et al.(2006)),"DECO"(Dynamic equicorrelation of Engle and Kelly(2012)),and"DCCMI-DAS"(the DCC-MIDAS of Colacito et al.(2011)).By detault,it is"cDCC"lag_fun g function to use.Valid choices are"Beta"(by default)and"Al-mon",for the Beta and Exponential Almon lag functions,respectively,if’univ_model’has a MIDAS term and/or if’corr_model’is"DCCMIDAS"N_c optional Number of(lagged)realizations to use for the standarized residualsforming the long-run correlation,if’corr_model’is"DCCMIDAS"K_c optional Number of(lagged)realizations to use for the long-run correlation,if’corr_model’is"DCCMIDAS"DetailsFunction dcc_fit implements the two-steps estimation of the DCC models.In thefirst step,a variety of univariate models are considered.These models can be selected using for the pa-rameter’univ_model’one of the following choices:’sGARCH’(standard GARCH of Bollerslev (1986)),’eGARCH’of Nelson(1991),’gjrGARCH’of Glosten et al.(1993),’iGARCH’(Integrated GARCH of Engle and Bollerslev(1986)),’csGARCH’(the Component GARCH of Engle and Lee (1999)),’GM_noskew’and’GM_skew’(the GARCH-MIDAS model of Engle et al.(2013),respec-tively,without and with the asymmetric term in the short-run component),and’DAGM_noskew’and’DAGM_skew’(the Double Asymmetric GARCH-MIDAS model of Amendola et al.(2019), respectively,without and with the asymmetric term in the short-run component).Valuedcc_fit returns an object of class’dccmidas’.The function summary.dccmidas can be used to print a summary of the results.Moreover,an object of class’dccmidas’is a list containing the following components:•assets:Names of the assets considered.•model:Univariate model used in thefirst step.•est_univ_model:List of matrixes of estimated coefficients of the univariate model,with the QML(Bollerslev and Wooldridge1992)standard errors.•corr_coef_mat:Matrix of estimated coefficients of the correlation model,with the QML stan-dard errors.•mult_model:Correlation model used in the second step.•obs:The number of daily observations used for the estimation.•period:The period of the estimation.•H_t:Conditional covariance matrix,reported as an array.•R_t:Conditional correlation matrix,reported as an array.•R_t_bar:Conditional long-run correlation matrix,reported as an array,if the correlation ma-trix includes a MIDAS specification.•est_time:Time of estimation.•Days:Days of the sample period.•llk:The value of the log-likelihood(for the second step)at the maximum.ReferencesAielli GP(2013).“Dynamic conditional correlation:on properties and estimation.”Journal of Business&Economic Statistics,31(3),282–299.doi:10.1080/07350015.2013.771027.Amendola A,Candila V,Gallo GM(2019).“On the asymmetric impact of macro–variables on volatility.”Economic Modelling,76,135–152.doi:10.1016/j.econmod.2018.07.025.Bollerslev T(1986).“Generalized autoregressive conditional heteroskedasticity.”Journal of Econo-metrics,31(3),307–327.doi:10.1016/03044076(86)900631.Bollerslev T,Wooldridge JM(1992).“Quasi-maximum likelihood estimation and inference in dy-namic models with time-varying covariances.”Econometric Reviews,11,143–172.doi:10.1080/ 07474939208800229.Cappiello L,Engle RF,Sheppard K(2006).“Asymmetric dynamics in the correlations of global equity and bond returns.”Journal of Financial Econometrics,4(4),537–572.doi:10.1093/jjfinec/ nbl005.Colacito R,Engle RF,Ghysels E(2011).“A component model for dynamic correlations.”Journalof Econometrics,164(1),45–59.doi:10.1016/j.jeconom.2011.02.013.Engle R,Kelly B(2012).“Dynamic equicorrelation.”Journal of Business&Economic Statis-tics,30(2),212–228.doi:10.1080/07350015.2011.652048.Engle RF,Bollerslev T(1986).“Modelling the persistence of conditional variances.”Econometric Reviews,5(1),1–50.doi:10.1080/07474938608800095.Engle RF,Ghysels E,Sohn B(2013).“Stock market volatility and macroeconomic fundamen-tals.”Review of Economics and Statistics,95(3),776–797.doi:10.1162/REST_a_00300.Engle RF,Lee GJ(1999).“A Long-run and Short-run Component Model of Stock Return V olatil-ity.”In Engle RF,White H(eds.),Cointegration,Causality,and Forecasting:A Festschrift in Honor of Clive W.J.Granger,475–497.Oxford University Press,Oxford.Glosten LR,Jagannathan R,Runkle DE(1993).“On the relation between the expected value and the volatility of the nominal excess return on stocks.”The Journal of Finance,48(5),1779–1801.doi:10.1111/j.15406261.1993.tb05128.x.Nelson DB(1991).“Conditional heteroskedasticity in asset returns:A new approach.”Econo-metrica,59,347–370.doi:10.2307/2938260.Examplesrequire(xts)#open to close daily log-returnsr_t_s<-log(sp500[ 2005/2008 ][,3])-log(sp500[ 2005/2008 ][,1])r_t_n<-log(nasdaq[ 2005/2008 ][,3])-log(nasdaq[ 2005/2008 ][,1])r_t_f<-log(ftse100[ 2005/2008 ][,3])-log(ftse100[ 2005/2008 ][,1])db_m<-merge.xts(r_t_s,r_t_n,r_t_f)db_m<-db_m[complete.cases(db_m),]colnames(db_m)<-c("S&P500","NASDAQ","FTSE100")#list of returnsr_t<-list(db_m[,1],db_m[,2],db_m[,3])#MV transformation(same MV for all the stocks)require(rumidas)mv_m<-mv_into_mat(r_t[[1]],diff(indpro),K=12,"monthly")#list of MVMV<-list(mv_m,mv_m,mv_m)#estimationK_c<-144N_c<-36dccmidas_est<-dcc_fit(r_t,univ_model="GM_noskew",distribution="norm",MV=MV,K=12,corr_model="DCCMIDAS",N_c=N_c,K_c=K_c)dccmidas_estsummary.dccmidas(dccmidas_est)Det7 Det Matrix determinantDescriptionCalculates the determinant of a numeric matrix.UsageDet(x)Argumentsx a numeric matrixValueThe determinant of x.Examplesx<-matrix(sample(1:25,25,replace=TRUE),ncol=5)Det(x)ftse100FTSE100dataDescriptionDaily data on FTSE100collected from the realized library of the Oxford-Man Institute(Heber et al.2009).Usagedata(ftse100)FormatAn object of class"xts".Detailsftse100includes the open price(open_price),the realized variance(rv5),and the close price(close_price).The realized variance has been calculated using intradaily intervals offive minutes(Andersen and Bollerslev1998).8indproSourceRealized library of the Oxford-Man InstituteReferencesAndersen TG,Bollerslev T(1998).“Answering the Skeptics:Yes,Standard V olatility Models do Provide Accurate Forecasts.”International Economic Review,39,885–905.doi:10.2307/2527343.Heber G,Lunde A,Shephard N,Sheppard K(2009).“OMI’s realised library,version0.1.”Oxford–Man Institute,University of Oxford.Exampleshead(ftse100)summary(ftse100)indpro Monthly U.S.Industrial ProductionDescriptionMonthly data on the U.S.Industrial Production index(IP,index2012=100,seasonally adjusted) collected from the Federal Reserve Economic Data(FRED)archive.The IP has been used as MIDAS term in different contributions(see,for instance,Engle et al.(2013),Conrad and Loch (2015),and Amendola et al.(2017)).Usagedata(indpro)FormatAn object of class"xts".SourceArchive of the Federal Reserve Economic Data(FRED)ReferencesAmendola A,Candila V,Scognamillo A(2017).“On the influence of US monetary policy on crude oil price volatility.”Empirical Economics,52(1),155–178.doi:10.1007/s0018101610695.Conrad C,Loch K(2015).“Anticipating Long-Term Stock Market V olatility.”Journal of Applied Econometrics,30(7),1090–1114.doi:10.1002/jae.2404.Engle RF,Ghysels E,Sohn B(2013).“Stock market volatility and macroeconomic fundamentals.”Review of Economics and Statistics,95(3),776–797.doi:10.1162/REST_a_00300.Inv9Exampleshead(indpro)summary(indpro)plot(indpro)Inv Inverse of a matrixDescriptionCalculates the inverse of a numeric matrixUsageInv(x)Argumentsx a numeric matrixValueThe inverse of x.Examplesx<-matrix(sample(1:25,25,replace=TRUE),ncol=5)Inv(x)nasdaq NASDAQ dataDescriptionDaily data on NASDAQ collected from the realized library of the Oxford-Man Institute(Heber et al.2009).Usagedata(nasdaq)FormatAn object of class"xts".10plot_dccmidas Detailsnasdaq includes the open price(open_price),the realized variance(rv5),and the close price(close_price).The realized variance has been calculated using intradaily intervals offive minutes(Andersen and Bollerslev1998).SourceRealized library of the Oxford-Man InstituteReferencesAndersen TG,Bollerslev T(1998).“Answering the Skeptics:Yes,Standard V olatility Models do Provide Accurate Forecasts.”International Economic Review,39,885–905.doi:10.2307/2527343.Heber G,Lunde A,Shephard N,Sheppard K(2009).“OMI’s realised library,version0.1.”Oxford–Man Institute,University of Oxford.Exampleshead(nasdaq)summary(nasdaq)plot_dccmidas Plot method for’dccmidas’classDescriptionPlots of the conditional volatilities on the main diagonal and of the conditional correlations on the extra-diagonal elements.Usageplot_dccmidas(x,K_c=NULL,vol_col="black",long_run_col="red",cex_axis=0.75,LWD=2,asset_sub=NULL)sp50011Argumentsx An object of class’dccmidas’,that is the result of a call to dcc_fit.K_c optional Number of(lagged)realizations to use for the long-run correlation,,if ’corr_model’is"DCCMIDAS"vol_col optional Color of the volatility and correlation plots."black"by defaultlong_run_col optional Color of the long-run correlation plots,if present."red"by default cex_axis optional Size of the x-axis.Default to0.75LWD optional Width of the plotted lines.Default to2asset_sub optional Numeric vector of selected assets to consider for the plot.NULL by defaultValueNo return value,called for side effectsExamplesrequire(xts)#open to close daily log-returnsr_t_s<-log(sp500[ 2010/2019 ][,3])-log(sp500[ 2010/2019 ][,1])r_t_n<-log(nasdaq[ 2010/2019 ][,3])-log(nasdaq[ 2010/2019 ][,1])r_t_f<-log(ftse100[ 2010/2019 ][,3])-log(ftse100[ 2010/2019 ][,1])db_m<-merge.xts(r_t_s,r_t_n,r_t_f)db_m<-db_m[complete.cases(db_m),]colnames(db_m)<-c("S&P500","NASDAQ","FTSE100")#list of returnsr_t<-list(db_m[,1],db_m[,2],db_m[,3])#estimationK_c<-144N_c<-36cdcc_est<-dcc_fit(r_t,univ_model="sGARCH",distribution="norm",corr_model="DCCMIDAS",N_c=N_c,K_c=K_c)plot_dccmidas(cdcc_est,K_c=144)sp500S&P500dataDescriptionDaily data on S&P500collected from the realized library of the Oxford-Man Institute(Heber et al.2009).Usagedata(sp500)12sp500FormatAn object of class"xts".Detailssp500includes the open price(open_price),the realized variance(rv5),and the close price(close_price).The realized variance has been calculated using intradaily intervals offive minutes(Andersen and Bollerslev1998).SourceRealized library of the Oxford-Man InstituteReferencesAndersen TG,Bollerslev T(1998).“Answering the Skeptics:Yes,Standard V olatility Models do Provide Accurate Forecasts.”International Economic Review,39,885–905.doi:10.2307/2527343.Heber G,Lunde A,Shephard N,Sheppard K(2009).“OMI’s realised library,version0.1.”Oxford–Man Institute,University of Oxford.Exampleshead(sp500)summary(sp500)Index∗datasetsftse100,7indpro,8nasdaq,9sp500,11cov_eval,2dcc_fit,3,11Det,7ftse100,7indpro,8Inv,9mv_into_mat,4nasdaq,9plot_dccmidas,10sp500,11summary.dccmidas,5ugarchspec,4ugmfit,413。

The ITU-T published J.144, a measurement of quality of service, for the transmission of television and other multimedia digital signals over cable networks. This defines the relationship between subjective assessment of video by a person and objective measurements taken from the network.The correlation between the two are defined by two methods:y Full Reference (Active) – A method applicable when the full reference video signal is available, and compared with the degraded signal as it passes through the network.y No Reference (Passive) – A method applicable when no reference video signal or informationis available.VIAVI believes that a combination of both Active and Passive measurements gives the correct blendof analysis with a good trade off of accuracy and computational power. T eraVM provides both voice and video quality assessment metrics, active and passive, based on ITU-T’s J.144, but are extended to support IP networks.For active assessment of VoIP and video, both the source and degraded signals are reconstituted from ingress and egress IP streams that are transmitted across the Network Under T est (NUT).The VoIP and video signals are aligned and each source and degraded frame is compared to rate the video quality.For passive measurements, only the degraded signal is considered, and with specified parameters about the source (CODEC, bit-rate) a metric is produced in real-time to rate the video quality.This combination of metrics gives the possibility of a ‘passive’ but lightweight Mean Opinion Score (MOS) per-subscriber for voice and video traffic, that is correlated with CPU-expensive but highly-accurate ‘active’ MOS scores.Both methods provide different degrees of measurement accuracy, expressed in terms of correlation with subjective assessment results. However, the trade off is the considerable computation resources required for active assessment of video - the algorithm must decode the IP stream and reconstitute the video sequence frame by frame, and compare the input and outputnframesto determine its score. The passive method is less accurate, but requires less computing resources. Active Video AnalysisThe active video assessment metric is called PEVQ– Perceptual Evaluation of Video Quality. PEVQ provides MOS estimates of the video quality degradation occurring through a network byBrochureVIAVITeraVMVoice, Video and MPEG Transport Stream Quality Metricsanalysing the degraded video signal output from the network. This approach is based on modelling the behaviour of the human visual tract and detecting abnormalities in the video signal quantified by a variety of KPIs. The MOS value reported, lies within a range from 1 (bad) to 5 (excellent) and is based on a multitude of perceptually motivated parameters.T o get readings from the network under test, the user runs a test with an video server (T eraVM or other) and an IGMP client, that joins the stream for a long period of time. The user selects the option to analysis the video quality, which takes a capture from both ingress and egress test ports.Next, the user launches the T eraVM Video Analysis Server, which fetches the video files from the server, filters the traffic on the desired video channel and converts them into standard video files. The PEVQ algorithm is run and is divided up into four separate blocks.The first block – pre-processing stage – is responsible for the spatial and temporal alignment of the reference and the impaired signal. This process makes sure, that only those frames are compared to each other that also correspond to each other.The second block calculates the perceptual difference of the aligned signals. Perceptual means that only those differences are taken into account which are actually perceived by a human viewer. Furthermore the activity of the motion in the reference signal provides another indicator representing the temporal information. This indicator is important as it takes into account that in frame series with low activity the perception of details is much higher than in frame series with quick motion. The third block in the figure classifies the previously calculated indicators and detects certain types of distortions.Finally, in the fourth block all the appropriate indicators according to the detected distortions are aggregated, forming the final result ‒ the mean opinion score (MOS). T eraVM evaluates the quality of CIF and QCIF video formats based on perceptual measurement, reliably, objectively and fast.In addition to MOS, the algorithm reports:y D istortion indicators: For a more detailed analysis the perceptual level of distortion in the luminance, chrominance and temporal domain are provided.y D elay: The delay of each frame of the test signal related to the reference signal.y Brightness: The brightness of the reference and degraded signal.y Contrast: The contrast of the distorted and the reference sequence.y P SNR: T o allow for a coarse analysis of the distortions in different domains the PSNR is provided for theY (luminance), Cb and Cr (chrominance) components separately.y Other KPIs: KPIs like Blockiness (S), Jerkiness, Blurriness (S), and frame rate the complete picture of the quality estimate.Passive MOS and MPEG StatisticsThe VQM passive algorithm is integrated into T eraVM, and when required produces a VQM, an estimation of the subjective quality of the video, every second. VQM MOS scores are available as an additional statistic in the T eraVM GUI and available in real time. In additionto VQM MOS scores, MPEG streams are analysed to determine the quality of each “Packet Elementary Stream” and exports key metrics such as Packets received and Packets Lost for each distinct Video stream within the MPEG Transport Stream. All major VoIP and Video CODECs are support, including MPEG 2/4 and the H.261/3/3+/4.2 TeraVM Voice, Video and MPEG Transport Stream Quality Metrics© 2020 VIAVI Solutions Inc.Product specifications and descriptions in this document are subject to change without notice.tvm-vv-mpeg-br-wir-nse-ae 30191143 900 0620Contact Us +1 844 GO VIAVI (+1 844 468 4284)To reach the VIAVI office nearest you, visit /contacts.VIAVI SolutionsVoice over IP call quality can be affected by packet loss, discards due to jitter, delay , echo and other problems. Some of these problems, notably packet loss and jitter, are time varying in nature as they are usually caused by congestion on the IP path. This can result in situations where call quality varies during the call - when viewed from the perspective of “average” impairments then the call may appear fine although it may have sounded severely impaired to the listener. T eraVM inspects every RTP packet header, estimating delay variation and emulating the behavior of a fixed or adaptive jitter buffer to determine which packets are lost or discarded. A 4- state Markov Model measures the distribution of the lost and discarded packets. Packet metrics obtained from the Jitter Buffer together with video codec information obtained from the packet stream to calculate a rich set of metrics, performance and diagnostic information. Video quality scores provide a guide to the quality of the video delivered to the user. T eraVM V3.1 produces call quality metrics, includinglistening and conversational quality scores, and detailed information on the severity and distribution of packet loss and discards (due to jitter). This metric is based on the well established ITU G.107 E Model, with extensions to support time varying network impairments.For passive VoIP analysis, T eraVM v3.1 emulates a VoIP Jitter Buffer Emulator and with a statistical Markov Model accepts RTP header information from the VoIP stream, detects lost packets and predicts which packets would be discarded ‒ feeding this information to the Markov Model and hence to the T eraVM analysis engine.PESQ SupportFinally , PESQ is available for the analysis of VoIP RTP Streams. The process to generate PESQ is an identical process to that of Video Quality Analysis.。

This report should be referred to as: Etman, L.F.P., `Design and analysis of computer experiments: The method of Sacks et al.', Engineering Mechanics report WFW 94.098, Eindhoven University of Technology, 1994.
Contents
1 Introduction 2 Theorem
2.1 2.2 2.3 2.4 2.5 2.6 3.1 3.2 3.3 3.4 3.5 Modelling, estimation and prediction . . . . . . . . . . . . . . Correlation functions . . . . . . . . . . . . . . . . . . . . . . . Experimental design . . . . . . . . . . . . . . . . . . . . . . . Accuracy and design variable e ects . . . . . . . . . . . . . . Sacks et al. versus response-surface in structural optimization Moving least-squares approximations . . . . . . . . . . . . . . Response prediction and accuracy . . . . . . . . . . . . Maximum likelihood behaviour . . . . . . . . . . . . . Two bar truss . . . . . . . . . . . . . . . . . . . . . . . Modelling the acoustical damping of the hum of a bell Ten bar truss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5 6 7 9 10

国际 会议
1 月 8 日-10 日 1 月 8 日-10 日 2 月 13 日-13 日
The 96th Annual Meeting of the Transportation Research Board
The 96th Annual Meeting of the Transportation Research Board 全国道路交通安全
论文宣读“Analysis of Fatigued Driving Lateral Performance
Considering Highway Horizontal Alignment Design-A Field Study
in China” 大会报告“Comparison between
Lattice-Gas Model and Agent-Based Continuous Model
Tests Data”
上海 英国
邀请报告“Driving mode decision-making method for
automated vehicles with human-vehicle cooperation ”
论文宣读“Experimental
序 姓名
号
32. 严新平 33. 严新平
34. 吕能超 35. 2
6 月 21-23 日
TRANSNAV 2017
国际 会议
6 月 21 日-23 日
TRANSNAV 2017
国际 会议
6 月 25 日-30 日
第二十七届国际海洋 与极地工程大会 （ISOPE 2017）
国际 会议
6 月 25 日-30 日 7 月 6 日-9 日 7 月 6 日-9 日

一种齿轮时变啮合刚度的通用计算方法李大磊; 李安民; 张二亮【期刊名称】《《重庆理工大学学报（自然科学版）》》【年(卷),期】2019(033)010【总页数】6页(P61-66)【关键词】时变啮合刚度; 势能法; 有限元法; 装配误差【作者】李大磊; 李安民; 张二亮【作者单位】郑州大学机械工程学院郑州450001【正文语种】中文【中图分类】TH132.4齿轮箱是现代机械传动系统中的重要部件之一，由于其恒定的传动比、大功率及高效率等特点，被广泛应用于各类工程机械。

振动与噪声是评价齿轮箱工作性能的主要指标，齿轮在传动过程中啮合刚度的时变特性是引起齿轮箱产生振动与噪声的主要原因[1]。

因此，齿轮时变啮合刚度的计算研究得到了国内外学者的广泛关注。

齿轮时变啮合刚度的计算方法主要分为3类：基于势能原理的解析法[2-7]、基于有限元的数值计算方法[8-11]和解析有限元法[12-15]。

势能解析法的基本思想是视轮齿为基圆上的变截面悬臂梁，并根据弹性力学理论，推导齿轮啮合过程中储存在轮齿中的应变能，进而计算齿轮啮合刚度。

Yang等[2]考虑了轮齿的弯曲势能、径向压缩势能以及轮齿接触赫兹能，计算了齿轮的啮合刚度；Wu等[3]进一步考虑了齿轮啮合时的剪切变形能，完善了解析算法；Wan和Xiang等[4-5]考虑了轮齿基体的应变能和齿轮基圆与齿根圆不重合的情况，提高了解析法的计算精度，但也增加了算法的复杂度。

随着有限元软件几何建模能力的日益增强，基于数值分析的齿轮啮合刚度计算也得到了重视和发展，其主要途径有：① 通过有限元方法分析计算轮齿接触部位沿啮合方向的位移量，提取啮合轮齿的法向载荷，根据刚度的定义计算齿轮的啮合刚度，如：唐进元等[8]应用有限元分析软件构建螺旋锥齿轮模型并计算出法向接触力和综合弹性变形量，得到单齿啮合刚度和多齿综合啮合刚度；Song等[9]利用有限元法研究了渐开线行星轮系的扭转刚度。

② 在准静态条件下(转速很低)，通过计算齿轮副的传递误差，获得齿轮的啮合刚度。

遥感类英文期刊、与GIS相关的SCI（EI）期刊遥感类英文期刊[1]. REMOTE SENSING OF ENVIRONMENTISSN： 0034-4257版本： SCI-CDE出版频率： Monthly出版社： ELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY, 10010-1710出版社网址：http://www.elsevier.nl/期刊网址：http://www.elsevier.nl/inca/publications/store/5/0/5/7/3/3/index.htt影响因子： 1.697(2001),1.992(2002)主题范畴： REMOTE SENSING; ENVIRONMENTAL SCIENCES; IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY[2]. International Journal of Remote Sensing国际遥感杂志,这是英国出版的一本专业遥感杂志，由Taylor & Francis出版社出版，我看得不多，不好多说，大家可以到/上查该杂志目录和摘要，全文可以到系资料室复印。

Taylor&Francis注册邮箱为cumtlp#.[3]. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSINGISSN： 0099-1112版本： SCI-CDE出版频率： Monthly出版社： AMER SOC PHOTOGRAMMETRY, 5410 GROSVENOR LANE, SUITE 210, BETHESDA, MD, 20814-2160出版社网址：/期刊网址：/publications.html影响因子： 0.841(2001);1.176(2002)主题范畴： GEOGRAPHY, PHYSICAL; GEOSCIENCES, MULTIDISCIPLINARY; REMOTE SENSING; PATHOLOGY美国摄影测量与遥感协会的会刊，摄影测量工程与遥感(Photogrametry Engineering and remote sensing)杂志,网址，网上可看论文摘要，全文可去系图书室复印，该杂志南大很全，过去50年的都有,同时你可以发现测绘学报、遥感学报、武测学报甚至国外的文章中参考文献许多来自该刊，在2000年SCI地学类杂志排名中该刊名列第三，相当地不容易，南大校友美国 berkeley加州大学副教授宫鹏就曾三次获得该刊的年度最佳论文奖，由此奠定其在国际遥感学界的地位。

电力系统英文书籍推荐unication&control in power system 电力系统通讯与控制2.electric power systems: analysis and control 电力系统: 分析与控制3.Electrical Energy System 电能系统4.embedded generation 嵌入式发电5.fundamentals of power system economics 电力系统经济学基础6.Handbook of Electric Power Calculations 电力系统计算手册7.market operations in electric power systems 电力系统市场运行8.POWER QUALITY 电能质量9.Risk assessment of power systems 电力系统风险评估10.Switching Power Supply Design 开关供电设计11.understanding electric power systems 电力系统学习12.understanding Power Quality problems 电能质量问题学习13.electric energy economic methods 电能经济方法14.FACTS Modelling and Simulation in Power Networks 灵活交流输电: 在电网中的仿真与模拟15.HVDC.and.FACTS.Controllers.Applications.of.Static.Converters.in.Power.System s 高压直流和灵活交流控制器在电力系统中应用16.LOAD-FLOW ANALYSIS IN POWER SYSTEMS 电力系统潮流分析17.Operation of Market-oriented Power Systems 市场化电力系统运营18.Power Generation Operation and Control 发电运行和控制19.Power system economics 电力系统经济学20.power system harmonics 电力系统谐波21.Power System Operations and Electricity Markets 电力系统运行和电力市场22.Power System Restructuring and Deregulation 电力系统改制和放松管制(即电力市场)23.voltage stability of electric power systems 电力系统电压稳定24.Transients in Power Systems 电力系统(电磁)暂态25.transient stability of power systems电力系统暂态稳定26.Wind Energy Handbook 风电手册27.distrbuted generation-the power paradigmfor the new millennium分布式发电28.electric power distribution handbook 配电手册29.electric power engineering handbook 电力工程手册30.spatial load forecasting(空间)电力负荷预测31.power transer-principles and applications 电力变压器-原理和应用32.electric power transer engineering 电力系统变压器工程33.wind and solar power system 风电和太阳能发电34.Electric Power Distribution Reliability 配电网可靠性35.Aging power delivery infrastrutures 送电结构36.Renewable and Efficient Electric Power Systems 可再生与高效电力系统37.probabilityconcepts in electric power systems 电力系统概率应用38.Short Circuits in Power Systems 电力系统短路39.VOLTAGE STABILITY ASSESSMENT,PROCEDURES AND GUIDES 电压稳定性评估,措施和导则40.electric systems, dynamics and stability with AI application 电力系统动态和稳定性: 人工智能应用41.electric power system application of optimiztion 电力系统优化应用42.protective relaying theory and application 继电保护理论与应用43.vehicular electric power systems 车辆电力系统44.electric power quality control techniques 电能质量控制技术45.reliability assessment of electric power systems using monte carlo methods 利用蒙特卡罗方法进行电力系统可靠性评估petitive Electricity Markets 竞争性电力市场47.power quality enhancement using customer power devices 用户电力设备与电能质量提高48.power system harmonics: computer modelling and analysis 电力系统谐波:计算机仿真与分析49.Analysis of Faulted Power Systems 故障电力系统分析50.Dynamic and control of large power system 大电力系统动态与控制51.Distributed power generation: planning and evaluation分布式发电(规划与评估)52.AC-DC power system analysis 交直流电力系统分析53.FACTS (flexible AC transmission system) 灵活交流输电系统54.Power system in emergencies 紧急状态下的电力系统55.Power system restoration 电力系统恢复56.Electric power system quality 电能质量57.Energy Management Systems (EMS) 能量管理系统58.Automatic learning techniques in power systems 自学习技术在电力系统中的应用59.Power system protection 1-4 电力系统保护1-4册 (electricity association 培训教程)60 electrical power system protection 电力系统保护61.elements of power system analysis 电力系统分析基础62.AC power system handbook 交流电力系统手册63. Wind turbine operation in electric power systems: advanced modelling 风力发电(机)在电力系统运行64. Power system control and stability 电力系统控制与稳定性( 不是那本stability and control)65. Analysis of subsynchronous resonance in power system 电力系统次同步谐振分析putationalmethods for large sparse power systems: a object orientedapproach 大稀疏电力系统计算方法: 面向对象的途径67. Power system oscillation 电力系统振荡68. Power system restructuring: engineering and economics 电力系统市场化: 工程和经济69. Distribution system modelling and analysis 配电系统建模与分析70. Electric power engineering 电力工程71. Subsynchronous resonance in power systems 电力系统中的次同步谐振72. Computer modelling of electrical power system 电力系统计算机建模73. High Voltage Direct Current Transmission 高压直流输电74. Electricitydistribution network design (2nd)配电网规划设计75. Industrial power distribution 工业配电76. Protection ofelectricity distribution networks 配电网保护77. Energy function analysis for power system stability 电力系统稳定性的能量函数分析78. Power system commission and maintenance practice电力系统试验(调试)与检修(维护)实践79. Statistical techniques for high-voltage engineering 高电压工程中的统计技术80. Digital protection for power system电力系统数字保护81. Power system protection 电力系统(继电)保护82. Voltage quality in electrical power systems 电力系统电压质量83.Electric power applications of fuzzy systems 模糊系统的电力应用84. Artificial intelligence techniques in power system 电力系统中的人工智能技术85. Insulators in high voltages 高压绝缘体86. Electrical safety供电安全87. High voltageengineering and testing 高电压工程与试验88. Reactive power control in electric systems 电力系统无功(功率)控制93. Electric power system电力系统教程94. Computer-Aided Power systems analysis 计算机辅助电力系统分析99. Reliability evaluation of power system 电力系统可靠性评估106. Power system stability handbook 电力系统稳定性手册109. Reliability assessment of large electric power systems 大电力系统可靠性评估112. HVDC power transmission systems 高压直流输电系统128. Electric Machinery and power system fundamentals 电机与电力系统基础(MATLAB 辅助)129. Intelligent system applications in power engineering (EP and ANN) 智能系统在电力工程中应用(进化计算和神经网)130. Thyristor-based FACTS controllers for electrical transmission systems 基于晶闸管的灵活交流输电系统控制器131. The economics of power system reliability and planning 电力系统可靠性与规划的经济学132. Computational Intelligence Applications to Power systems 计算智能在电力系统中的应用133. Environmental Impact of Power Generation 发电的环境影响134. Operation and Maintenance of Large Turbo-Generators 大型涡轮发电机组运行与检修135. Power system simulation 电力系统仿真136. Advanced load dispatch for power systems 电力系统高级调度137. The development of electric power transmission 电力传输进展138. Renewable Energy Sources 可再生发电源139. Power system dynamics andstablity 电力系统动态与稳定性140. Practical electrical network automation and communication systems 电力系统自动化与通信系统实践141. Electrical power and controls 电力与控制142. Deregulation of Electric Utilities 电力企业放松管制(市场改革)143. Computational Auction Mechanisms for restructured power industry operation 电力市场运行的(计算)投标机理144. Finanicial and economic evaluation of projects in the electricity supply industry 电力工程项目的金融与经济评价145. Electricity economics and planning 电力经济与规划146. Computational Methods for electric power systems 电力系统计算方法147. Power system relaying 电力系统继电保护148. Computer relaying for power systems 电力系统计算机保护149. Modern power system planning 现代电力系统规划150. High Voltage Engineering (2nd) 高电压工程151. Operation of restructured power systems 市场化电力系统运行152. Transer and Inductor Design Handbook变压器和电感设计手册(04增强版)153. Modern power system analysis (matlab supported) 现代电力系统分析(03年含MATLAB版)154. Power distribution planning reference book 配电规划参考手册155. Understanding FACTS 理解灵活交流输电系统156. Power system analysis :short-circuit load flow and harmonics 电力系统分析: 短路潮流和谐波157. Power systems electromagnetic transients simulation 电力系统电磁暂态仿真158. Power electronic control in electrical systems 电力系统中的电力电子控制159. Protection devices and systems for high-voltage applications保护装置和系统的高压应用160. Small signal analysis of power systems 电力系统小信号分析161. Electrical power cable engineering 电力线缆工程162. Power System State Estimation: Theory and Implementation 电力系统状态估计: 理论和实现163. Dielectrics in Electric Fields电场中的电介质(绝缘体)164. spacecraft power system 航天器电力系统165. Grid integration of wind energy conversion systems 风能转换系统的电网整合(接入)166. Power loss: the origins of deregulation and restructuring in the American electricutility system网损:美国电力系统放松管制和市场化的根源167. High Voltage Circuit Breakers: Design and Applications 高压断路器:设计与应用168. Power system capacitors 电力系统电容器169. Energy Management Systems & Direct Digitial Control 能量管理系统(EMS)及直接数字控制170. Pricing in Competitive Electricity Market 电力市场电价171. Designing Competitive Electricity Markets 电力市场设计172. Power system dynamics and stability 电力系统动态与稳定性(美国)173. Theory and problems of electric power systems 电力系统的理论和问题174. Insulation coordinationfor power systems 电力系统绝缘配合175. Modal analysis of large interconnected power systems 大互联电力系统的模式分析176. Making competition work in electricity 电力市场竞争177. Power system operation 电力系统运行178. Transmission line reliability and security 输电线路安全可靠性179. Computer analysis of power systems 电力系统计算机分析89. Electical distribution engineering配电网工程90. Power systemplanning电力系统规划91. Uniquepower system problems 电力系统问题92. Tranmission and Distribution ofElectrical Energy 电力系统输配电95. Electric powertransmission system 输电系统96. Reliability Modelling in Electric power systems电力系统可靠性建模97. High voltage engineering in power system 电力系统高电压工程98. Extra High voltage AC transmission engineering 超高压交流输电工程100. Computation of power system transients 电力系统暂态计算101.Piecewise methods and application to power systems 分段法及其在电力系统中应用103. Analysis and protection of electrical power systems 电力系统分析与保护104. Power systems engineering and mathematicas电力系统工程与数学105. Stability of large power systems 大电力系统稳定性107. Power system reliability evaluation电力系统可靠性评估108.Electric power system dynamics 电力系统动态110. Power system analysis and planning 电力系统分析与规划111. Electric transmission line fundamental 输电线(工程)基础113. Transient Processes in electrical power systems 电力系统暂态过程114.Discrete Fourier transation and its applications to power system 离散傅立叶变换及其在电力系统中的应用115. Electrical Transients inpower system 电力系统暂态116. Optimal economic operation of electric power system 电力系统优化经济调度运行117.High power switching 大功率开关118. power plant engineering 电厂工程119. power plant system design 电厂系统设计120. power plant evaluation and design reference guide 电厂评估和设计参考导则121. planning engineering, and construction of electric power generationfacilities 发电设备的规划和建设工程122. Elements electrical power station design 电站设计基础123.Optimal control applications in electric power systems 电力系统最优控制应用124. applied protected relaying应用继电保护125. power station and substation maintenance 电厂与变电站维修126. Power system operation 电力系统运行127. power system reliability,safety and management 电力系统可靠性,安全与管理。

控制类外文期刊SCI,EI比较好投的有哪些作者: yanwei123456 发布日期: 2007-12-05控制类的SCI的都很难发，除非内容很好！如果不错就试试SIAM Journal on Control and Optimization.作者: mqlong 发布日期: 2007-12-05你研究具体哪方面？可以交流一下。

作者: Hongtan 发布日期: 2007-12-05International Journal of systems science, Internatioanl Journal of control, 应该要好中一点，atuomatica，IEEE AC, systems and control letters一般比较难中作者: ertuo104 发布日期: 2007-12-05ddfdd作者: qpl001 发布日期: 2007-12-05我现在主要做智能控制，研究随机梯度及投影算法有一段时间，研究了一种他们的快速收敛的方法，想发到国外的期刊上，速度快一点的作者: yanwei123456 发布日期: 2007-12-05建议找计算数学类的，applied numerical mathematics, Journal of computational and applied mathematics, applied mathematics and computation等等！不过现在数学类（包括控制类的）没有很快的，一般都得小半年才有审稿意见！系统与控制类投稿期刊整理AI MagazineAmerican Association for Artificial Intelligence (AAAI) PublicationsApplied Mathematical ModellingApplied Mathematics and ComputationArtificial IntelligenceArtificial Intelligence in EngineeringAutomatic control and computer sciences. 0146-4116Automatica. 0005-1098Automation and control. 0110-6295Automation and remote control. 0005-1179Chemical Design Automation NewsComputational Mathematics and Mathematical Physics Computational Statistics & Data AnalysisComputer Aided Geometric DesignComputer-Aided DesignComputers & Electrical EngineeringComputers & GraphicsComputers & Mathematics with Applications Computers in EngineeringComputers in IndustryControl ApplicationsControl Engineering PracticeControl, Optimisation and Calculus of Variations. Computing & control engineering journal.Control and instrumentation. 0010-8022Control engineering. 0010-8049Data & Knowledge EngineeringDecision Support SystemsDiscrete Applied MathematicsDiscrete MathematicsEcological ModellingEngineering Analysis with Boundary Elements Engineering Applications of Artificial Intelligence Expert Systems with ApplicationsElectronic Journal of Linear Algebra: Germany Electronic Journal of Linear Algebra: Israel Electronic Journal of Linear Algebra: Portugal Electronic Journal of Linear Algebra: USA Electronic Transactions On Numerical Analysis Future Generation Computer SystemsFuzzy Sets and SystemsIEE Computing & Control Engineering JournalIEE Proc. Control theory and applications 0143-7054 IEEE Control Systems MagazineIEEE Robotics and Automation MagazineIEEE Trans. Control Systems Technology. 1063-6536 IEEE SpectrumIEEE Transactions on Automatic ControlIEEE Transactions on Robotics and AutomationIFAC PublicationsImage and Vision ComputingIndustrial and Systems Control EngineeringInformation SystemsInstrumentation & control systemsInternational journal of adaptive control and signal processing. International journal of control.Information and Software TechnologyIntegration, the VLSI JournalInteracting with ComputersInternational journal of robotics & automation.International journal of systems automation, research & applications.Journal of Computational and Applied MathematicsJournal of Systems ArchitectureJournal of Systems and SoftwareJournal of Dynamic Systems, Measurement and ControlJournal of Dynamical and Control Systems:IsraelJournal of Dynamical and Control Systems:USAJournal of Mathematical Systems, Estimation, and ControlJournal of Process ControlJournal of Robotics and Autonomous systemsJournal of automation and information sciences.Knowledge-Based SystemsLinear Algebra and Its ApplicationsMathematics and Computers in SimulationMathematics and Computers in Simulation with Applied Numerical Math. Microprocessors and MicrosystemsMathematics of Control, Signals, and Systems:Netherlands Mathematics of Control, Signals, and Systems:USAMechatronicsModeling, Identification, and ControlNeural NetworksNeurocomputingParallel ComputingPattern RecognitionPattern Recognition LettersPerformance EvaluationRobotics and Autonomous SystemsSIAM Journal on Control and OptimizationSimulation Practice and TheorySystems and Control LettersTransactions of ASME. J. dynamic systems, measurement & control.SCI、EI收录的自动控制类期刊。

未来土地利用变化情景模拟模型(GeoSOS-FLUS)软件用户手册1.模型与软件简述FLUS模型是用于模拟人类活动与自然影响下的土地利用变化以及未来土地利用情景的模型。

该模型的原理源自元胞自动机（CA），并在传统元胞自动机的基础上做了较大的改进。

首先，FLUS模型采用神经网络算法（ANN）从一期土地利用数据与包含人为活动与自然效应的多种驱动力因子（气温、降水、土壤、地形、交通、区位、政策等方面）获取各类用地类型在研究范围内的适宜性概率。

其次，FLUS模型采用从一期土地利用分布数据中采样的方式，能较好的避免误差传递的发生。

另外，在土地变化模拟过程中，FLUS模型提出一种基于轮盘赌选择的自适应惯性竞争机制，该机制能有效处理多种土地利用类型在自然作用与人类活动共同影响下发生相互转化时的不确定性与复杂性，使得FLUS模型具有较高的模拟精度并且能获得与现实土地利用分布相似的结果。

GeoSOS-FLUS软件是根据FLUS模型的原理开发的多类土地利用变化情景模拟软件，是在其前身------地理模拟与优化系统GeoSOS的基础上的发展与传承。

GeoSOS-FLUS软件为用户提供进行空间土地利用变化模拟的功能，在对未来土地利用变化进行模拟时，需要用户先应用其他方法（系统动力学模型，或马尔科夫链）或使用预设情景来确定未来土地利用变化的数量作为GeoSOS-FLUS 的输入。

GeoSOS-FLUS软件是在Visual Studio 2010平台上基于C++语言及一系列C++开源库开发的软件。

软件的输入输出采用遥感影像处理底层库GDAL 1.9.2 (/)，因而软件可以读入各种格式的遥感影像数据及其投影坐标，并输出带坐标和投影的tiff影像模拟结果；软件界面采用Qt 4.8.5 (https://www.qt.io/download/)，能实时显示模拟区域的土地利用变化过程，方便用户的使用；软件采用的神经网络算法来自强大的Shark 3.1.0库(http://image.diku.dk/shark/)，能较快的获得各类土地分布的适宜性概率。

———————————————————————作者简介:梅志恒（1992-），男，湖北武汉人，工程师，硕士，主要从事电力建设安全管理研究；肖振航（通讯作者）（1997-），男，湖北黄石人，博士研究生，主要从事安全风险管理研究。

0引言我国垃圾焚烧发电建设规模将加快增长，垃圾焚烧发电具有占地面积小、处理快速、社会效益好等优势，但其恶臭污染问题也越发受到公众关注。

垃圾焚烧发电会产生大量恶臭物质，不同工艺流程产生的恶臭污染物浓度和组成都有很大的差异。

这些恶臭气体不但会污染空气也会产生二次污染物，威胁垃圾焚烧发电厂从业人员及周边居民生命健康[1-2]。

为合理评估垃圾焚烧发电厂臭气扩散对周边环境影响范围及程度，需掌握臭气扩散时空特征。

衡量恶臭污染危害程度的指标是臭气强度，恶臭气体的臭气强度与其浓度成正比。

恶臭污染物中氨和硫化氢占比大[3]，而在日常监测中，氨的排放浓度远高于硫化氢。

因此，本文以氨作为特征恶臭污染物，研究其大气扩散规律，为我国垃圾焚烧发电厂恶臭污染控制、推进垃圾焚烧发电建设绿色健康发展提供一定的科学指导。

大气扩散模型用于研究排放进入大气的空气污染物的扩散、转化、迁移和清除的规律[4]。

目前描述大气扩散规律的模型主要有三类[5]：高斯烟羽模型、欧拉模型和拉格朗日模型。

选择空气污染物扩散模型应考虑合适的时空尺度[6]，在探究中小尺度研究区域的空气污染物扩散规律时，高斯模型简单高效[7]，而高斯模型中的AERMOD （AMS/EPA Regulatory Model ，AERMOD ），预测值与监测值具有很好的一致性[8-9]，AERMOD 预测效果受多因素影响[10-11]，适用于模拟平坦地形污染物的迁移扩散[12]，已有学者应用AERMOD 研究垃圾焚烧发电厂、钢铁厂、燃气轮机发电厂等烟气特征污染物的扩散特性[13-16]，获得小尺度下污染物浓度的精准模拟值，然而目前鲜有垃圾焚烧发电厂恶臭气体时空特性方面的研究。

Video sensor networks (VSNs) has become the recent research focus due to the rich information it provides to address various data-hungry applications. However, VSNimplementations face stringent constraints of limited communication bandwidth, processing capability, and power supply. In-network processing has been proposed as efficient means toaddress these problems. The key component of in-network processing, task mapping and scheduling problem, is investigated in this paper. Although task mapping and scheduling in wired networks of processors has been extensively studied, their application to VSNs remains largely unexplored. Existing algorithms cannot be directly implemented in VSNs due to limited resource availability and shared wireless communication medium. In this work, an application-independent task mapping and scheduling solution in multi-hop VSNs is presented that provides real-time guarantees to process video feeds. The processed data is smaller in volume which further releases the burden on the end-to-end communication. Using a novel multi-hop channel model and a communication scheduling algorithm, computation tasks and associated communication events are scheduled simultaneously with a dynamic critical-path scheduling algorithm. Dynamic voltage scaling (DVS) mechanism isimplemented to further optimize energy consumption. According to the simulation results, the proposed solution outperforms existing mechanisms in terms of guaranteeing application deadlines with minimum energy consumption.Article Outline1. Introduction2. Preliminaries2.1. Network assumptions2.2. Application and energy consumption model 2.3. Problem statement3. The proposed scheduling solution3.1. Hyper-DAG extension and multi-hop channel modeling 3.2. Communication scheduling algorithms 3.3. Scheduling with DCTMS algorithmPurchase$ 31.503.3.1. DCOS procedure 3.3.2. OSTA procedure 3.4. The DVS algorithm 4. Simulation results 4.1. Simulation parameters4.2. Simulation with a real-life application example 4.3.Simulation with randomly generated DAG 4.3.1. Effect of the application deadlines 4.3.2. Effect of the cluster size 4.3.3. Effect of the number of tasks 4.3.4. Comparison with EbTA 5. Conclusion Acknowledgements References170Evaluation of service management algorithms in adistributed web search system Original Research ArticleComputer Standards & Interfaces , Volume 29, Issue 2,February 2007, Pages 152-160Ahmed Patel, Muhammad J. KhanClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThe World Wide Web interconnected through the internet today offers numerous specialist topic-oriented or regional search engines and systems in a largely federated heterogeneous environment. Old ones continue to exist and new ones appear in spite of the tremendous progress achieved by their generic Web-wide rival competitors, because they produce better results in their areas of specialisation. However, finding and choosing the best specialised search engines or systems for a particular information need is difficult. This is made even more complicated by the fact that these enginesPurchase $ 31.50and systems would want to carve out a niche market that generates maximum revenue for themselves. The ADSA (Adaptive Distributed Search and Advertising) Web research project has investigated the problem at some depth and had put forward a search architecture which allows many search engines to be independently owned and controlled, offering advantages over existing centralised architectures. One aspect of the architecture has been to evaluate the service management algorithms that were designed to support competing autonomous systems in a cooperative marketplace. Here we present ADSA economic model and the service management strategies that can lead to maximum revenue generation, by making informative and intelligent decisions on search price adjustments of key quantitative parameters, as well as the results of evaluation experiments and briefly discuss the need for standardised interfaces which are required if this concept is to ease development and implementation of such a marketplace in a large scale.Article Outline1. Introduction2. ADSA economic model and pricing function2.1. The pricing function3. Evaluation3.1. Tests3.2. Performance criteria4. Experimental results5. Need for standard interfaces for administrative service management6. Conclusions and future workReferencesVitae171 Geophysical wavelet library: Applications of the continuous wavelet transform to the polarization anddispersion analysis of signals Original Research ArticleComputers & Geosciences , Volume 34, Issue 12,December 2008, Pages 1732-1752M. Kulesh, M. Holschneider, M.S. DialloClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractIn the present paper, we consider and summarize applications of the continuous wavelet transform to 2C and 3C polarization analysis and filtering, modeling the dispersed and attenuated wave propagation in thetime –frequency domain, and estimation of the phase and group velocity and the attenuation from a seismogram. Along with a mathematical overview of each of the presented methods, we show that all these algorithms are logically combined into one software package “Geophysical Wavelet Library” developed by the authors. The novelty of this package is that we incorporate the continuous wavelet transform into the library, where the kernel is the time –frequency polarization and dispersion analysis. This library has a wide range of potential applications in the field of signal analysis and may be particularly suitable in geophysical problems that we illustrate by analyzing synthetic, geomagnetic and real seismic data.Article Outline1. Introduction2. GWL structure and implementation technology3. The CWT3.1. The direct wavelet transform 3.2. Wavelets3.3. The inverse wavelet transform 3.4. Wavelet transform of a complex signalPurchase $ 19.954. Polarization properties of two-component data and polarization filtering 4.1. Polarization analysis in the time domain 4.2. Complex trace method in the wavelet domain 4.3. Two-component polarization filter4.4. Application to the analysis of geomagnetic data5. Polarization analysis and filtering of 3C data 5.1. CWT-based polarization properties of an ellipse5.2. CWT-based polarization properties of an ellipsoid 5.3. Application to the analysis and filtering of seismic signals6. Modeling of wave propagation using a diffeomorphism in wavelet space 6.1. Linear diffeomorphism of the wavelet spectrum6.2. Describing wave dispersion and attenuation with non-linear diffeomorphism6.3. The case for non-linear frequency-dependent attenuation6.4. Modeling wave propagation in the space of polarization parameters 6.5. Parametrization of dispersion and attenuation7. An estimate of phase and group velocities and attenuation 7.1. Wavelet-based frequency –velocity analysis7.2. Inversion for the dispersion and attenuation characteristics of the medium 7.3. Application to experimental data 8. Conclusion Acknowledgements Appendix A. List of symbols References172A software framework for model predictive control with GenOpt Original Research ArticleEnergy and Buildings , Volume 42, Issue 7, July 2010, Pages 1084-1092Brian Coffey, Fariborz Haghighat, Edward Morofsky, Edward KutrowskiPurchase$ 35.95Close preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThere is a growing interest in integrated control strategies for building systems with numerous responsive elements, such as solar shading devices, thermal storage and hybrid ventilation systems, both for energy efficiency and for demand response. Model predictive control is a promising way of approaching this challenge. This paper presents a flexible software framework for model predictive control using GenOpt, along with a modified genetic algorithm developed for use within it, and applies it to a case study of demand response by zone temperature ramping in an office space. Various areas for further research and development using this framework are discussed.Article Outline1. Introduction2. Background2.1. Model predictive control in buildings research2.2. Simulation, optimization and controls software3. Framework3.1. Generic problem definition3.2. Framework overview3.3. The organizational layer3.4. Virtual testing environment4. Example algorithm4.1. Basic genetic algorithm description4.2. Using the optimization instructions file5. Example application study5.1. Demand response with zone temperature ramping5.2. Case study description 5.3. Results6.Discussion6.1. Appropriate complexity level for application studies 6.2. Improving optimization results6.3. Imperfect models, initializations and predictions6.4. Other potential research areas with SimCon7. Conclusions Acknowledgements References173Design and implementation of an environmentaldecision support systemEnvironmental Modelling & Software , Volume 16, Issue 5,July 2001, Pages 453-458W. G. Booty, D. C. L. Lam, I. W. S. Wong, P. SiconolfiClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractAn environmental decision support system is a specific version of an environmental information system that is designed to help decision makers, managers, and advisors locate relevant information and carry out optimal solutions to problems using special tools and knowledge. The RAISON (Regional Analysis by Intelligent Systems ON microcomputers) for Windows decision support system has been developed at the National Water Research Institute, Environment Canada, over the last 10 years. It integrates data, text, maps, satellite images, pictures, video and other knowledge input. A library of software functions and tools are available for selective extraction of spatial and temporal data that can be analysed using spatial algorithms, models, statistics, expert systems, neural networks, and other information technologies. ThePurchase $ 19.95system is of a modular design which allows for flexibility in modification of the system to meet the demands of a wide range of applications. System design and practical experiences learned in the development of a decision support system for toxic chemicals in the Great Lakes of North America are discussed. Article Outline1. Introduction2. RAISON for Windows decision support system3. Great Lakes toxic chemical decision support system3.1. Databases3.1.1. Parameters3.1.2. Site_Info3.1.3. SiteDesc3.2. Modelling3.3. Neural network3.4. Expert system3.5. Optimization3.6. Data visualization4. DiscussionAcknowledgementsReferences174 DSP instrument for transient monitoring Original Research Article Computer Standards & Interfaces, Volume 33, Issue 2, February 2011, Pages 182-190Tomasz Tarasiuk, Mariusz Szweda应届生-算法工程师)|公司行业： 通信/电信运营、增值服务 计算机软件公司性质： 国企公司规模： 150-500人比比你的竞争力发布日期：2011-03-10工作地点：北京 招聘人数：2工作年限：应届毕业生 语言要求： 英语 熟练 日语 熟练学 历：硕士职位描述算法工程师招聘人数：2人工作地点：北京、上海职位描述：负责交通信息预测和海量数据分析。

剖宫产术后子宫瘢痕憩室分级诊断与月经异常相关性研究滕伟,彭萍*,万淑梅,王善林(解放军南部战区总医院妇产科,广东省广州市510010) 摘要:目的探讨剖宫产术后子宫瘢痕憩室(PCSD )TOWER 分级诊断与月经异常相关性㊂方法选取2015年3月至2017年9月在我科就诊的具有剖宫产手术史患者325例,采用超声检查对PCSD 患者进行诊断(137例),符合纳入标准的PCSD 患者108例,按照TOWER 分级标准,将其分为Ⅰ级㊁Ⅱ级㊁Ⅲ级三组,比较三组月经异常的情况㊂结果PCSD 总的检出率为42.15%,108例PCSD 患者按TOWER 分级诊断,Ⅰ级40例中月经异常6例,发病率为15%;Ⅱ级54例中月经异常30例,发病率为55.5%;Ⅲ级14例中月经异常12例,发病率为85.7%㊂其差异具有统计学意义(P <0.01)㊂结论PCSD 分级与月经异常发生呈正相关,分级越高发生月经异常比率越高㊁月经持续时间越长㊂ 关键词:　剖宫产;　子宫瘢痕憩室;　月经异常;　TOWER 分级 中图分类号:R719.8 文献标志码:A 文章编号:1001-5248(2020)11-0035-03基金项目:广东省医学科学技术研究基金项目(No.20181030224714423)作者简介:滕伟(1982-),女,硕士研究生,医师㊂从事围产优生工作㊂*通信作者E-mail:pengping1728@ 剖宫产术后子宫瘢痕憩室又称剖宫产术后子宫切口瘢痕缺损(previous cesarean scar defect,PCSD),发生率为19.4~88.0%〔1-3〕,其发生率差别较大是由于近年对PCSD 的认识提高和检测技术的提升,因此成为剖宫产术后常见远期并发症㊂PCSD 常伴发月经异常㊁痛经或腹痛㊁继发性不孕等一系列远期并发症,严重影响妇女的生殖健康㊂本文通过采用国外Tower 等〔4〕应用阴道超声或子宫输卵管造影的检测并结合临床症状制定的PCSD 严重程度的分级评分系统对PCSD 患者进行分级诊断,研究不同分级与月经异常的相关性㊂1　对象与方法1.1　研究对象　选取2015年3月至2017年9月在我院就诊的具有剖宫产手术史患者,年龄在20~45岁之间,距末次剖宫产手术时间大于6个月,存在月经天数延长大于7天或月经量较前增多1/2以上,排除其他原因引起的子宫异常出血患者㊂1.2　超声检查设备及方法1.2.1　超声检查设备:采用GE VOLUSON E8超声诊断仪和低机械指数CCI 技术,探头为RIC5-9-D,造影中心频率5.0~10.0MHz,机械指数为0.14㊂1.2.2　经阴道超声检查方法:探头置于阴道内做子宫纵切㊁横切或多角度检查,子宫纵切面上观察子宫前壁下段切口情况,PCSD 诊断标准参考Mah 等〔5-6〕提出的标准:子宫前壁下段切口后方子宫肌层内凹,产生扩张或囊状突出的无回声区;无回声区与宫腔相通㊂发现子宫瘢痕处缺损形成,即测量缺损各径线:瘢痕缺损深度㊁残存肌层厚度㊁邻近肌层厚度㊂1.3　研究方法　对已诊断的PCSD 患者,参照TOWER 分级标准进行分级诊断,分为Ⅰ级(轻度)㊁Ⅱ级(中度)㊁Ⅲ级(重度)三组㊂收集三组患者月经资料,包括异常子宫出血的发生率及异常出血的持续时间㊂1.4　统计分析方法　采用统计软件SPSS 22.0版进行统计分析,计量资料采用(⎺x ±s)表示,组间比较采用t 检验或单因素方差分析或似然比检验,计数资料用百分率表示,采用卡方检验㊂检验水平α=0.05,P <0.05为差异具有统计学意义㊂2　结果2.1　三组月经情况比较　108例PCSD 患者,按TOWER 分级诊断,Ⅰ级组:40例中月经异常6例,均为月经期延长,月经淋漓不净,持续时间为10~14天,发病率为15%;Ⅱ级组:54例中月经异常30例,30例中24例为月经期延长,月经淋漓不净,持续时间为7~20天;5例为月经增多,1例为突发阴道大量出血,发病率为55.5%㊂Ⅲ级组:14例中月经异常12例,12例中10例为月经期延长,月经淋漓不净,持续时间为7~22天,1例为突发阴道大量出血,1例为月经增多,发病率为85.7%㊂三组比较,月经异常发生率及月经持续时间的差异具有统计学意义(P <0.05),但月经周期时间的差异无统计学意义(P>0.05)(见表1)㊂2.2　PCSD各径线与月经异常的关系　月经异常患者:PCSD上下径范围为1.8~13mm,平均值6.31±2.93mm;左右径范围为1.2~20mm,平均值9.58±4.94mm;深度范围为2~12mm,平均值6.17±2.22mm;残余肌层厚度范围为1~7mm,平均值3.55±1.65mm㊂月经正常患者:PCSD上下径范围为1.3~14mm,平均值6.25±2.66mm;左右径范围为2~17mm,平均值8.80±3.08mm;深度范围为1.5~8mm,平均值4.74±1.70mm;残余肌层厚度范围为1~7mm,平均值3.42±1.22mm㊂与正常月经组相应径线比较,月经异常与PCSD深度的相关性具有统计学意义(P<0.01),但月经异常与PCSD上下径㊁左右径及残余肌层厚度的相关性无统计学意义(P>0.05)(见表2)㊂表1　三组月经情况的比较组别例数(例)月经异常(例%)月经周期时间(天)月经持续时间(天)Ⅰ级406(15)29.3±2.417.13±2.51Ⅱ级5430(55.5)29.66±2.149.22±4.48Ⅲ级1412(85.7)30.00±4.0012.85±4.90 P<0.01>0.05<0.05表2　PCSD各径线与月经异常关系PCSD各径线(mm)月经正常月经异常t P值上下径 6.38±2.71 6.15±2.890.2940.77左右径9.17±3.669.14±4.530.0280.978深度 4.68±1.7 6.30±2.16-3.0660.003残余肌层厚度 3.51±1.29 3.45±1.610.1520.883　讨论 月经异常作为PCSD最主要的临床并发症,其可能的发生机制包括:(1)异常出血来源于宫腔:切口的下缘纤维组织增生,形成活瓣样结构,憩室像一个 蓄水池”,经血积聚在缺损处,月经期过后缓慢排出导致阴道异常出血;Thurmond等〔7〕提出缺损局部积血是由于瘢痕局部肌肉缺乏收缩力引起;(2)异常出血来源于缺损本身:Morris等〔8〕在1995年对51例因剖宫产后月经异常引起全宫切除的子宫标本进行病理检查发现瘢痕处解剖结构异常,包括子宫下段变宽㊁变形㊁局部内膜充血㊁毛细血管扩张和淋巴细胞浸润,因此不排除经后异常出血来源于:①瘢痕局部慢性炎症引起的出血;②血液供应较差,创面修复较慢,造成经期延长;③异位的子宫内膜出血㊂已有研究表明PCSD月经异常与憩室的容积㊁形态㊁各径线等相关,其中高晓艳等〔9〕对56例月经周期正常,但经期延长,淋漓不尽的PCSD患者研究发现月经异常与憩室的深度和体积相关,而与憩室形态㊁憩室的长度和宽度无明显相关;施连东等〔10〕研究发现月经异常组瘢痕憩室的宽度㊁高度㊁长度大于月经正常组,提示瘢痕的宽度㊁高度及长度与异常子宫出血有关,切口憩室越大,症状更明显㊁月经淋漓不尽的时间就越长;van der Voet等〔11〕研究发现月经异常与残存肌层厚度与邻近肌层厚的比值相关,比值小于50%更易发生月经异常㊂本研究分析PCSD分级与月经异常的相关性,提示Ⅰ级发生月经异常比率为15%,Ⅱ级发生月经异常比率为55.5%,Ⅲ级发生月经异常比率为85.7%,其差异有统计学意义(P<0.01),并且分级的高低与月经异常呈中等强度相关,分级越高发生月经异常的比率越高;并且Ⅲ级月经持续时间较Ⅰ级㊁Ⅱ级长,其差异亦具有统计学意义(P<0.05)㊂比较月经异常组与月经正常组PCSD的相应径线,发现仅深度的差异有统计学意义(P<0.01),行相关分析亦显示月经异常与憩室深度有明显相关,与上下径㊁左右径㊁残余肌层厚度无相关,提示憩室越深越容易出现月经异常㊂综上所述,PCSD的分级与月经异常症状明显相关,但目前子宫切口憩室的治疗较棘手,单纯的药物治疗效果不佳,且易复发㊂有报道可经宫腹腔镜联合修补PCSD〔13〕㊁经阴道切除PCSD〔14〕等方法治疗部分有效,但同时也可能造成新的创伤㊂因此,目前PCSD尚无一个疗效肯定切实可行的治疗方法,唯有降低剖宫产率㊁从源头上减少PCSD发生才是解决问题的根本㊂参考文献:〔1〕　WANG CB,CHIU WW,LEE CY,et al.Cesarean scar 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1.1. Principle of friction extraction
The interaction of strand shell surface and copper mould can be interpreted as mould friction. This mould strand interfacial force affects slab surface quality and caster productivity. Friction is generated in both unloaded conditions (interface in the drive
பைடு நூலகம்
Mould friction between mould and strand is one of the most important parameters that can be used to describe frictional behavior and mechanical interaction between the strand and the mould. Friction between mould and strand of a continuous casting machine plays a great role in defining the surface quality of a steel slab. Since long time, friction between mould and Presently continuous casting of steel is playing a great role for the productivity and quality of steel industries. Producing a defect free product is most important in this process as the defect may propagate to the end product. Oscillation of mould in a caster allowed higher casting speed with less frequency of sticker breakouts. Inadequate oscillation parameters as well as defective oscillation may greatly affect the surface quality of cast 1 product and therefore to be considered as critical. Development of a reliable monitoring technique to assess the friction between the mould and strand is very important for continuous casting process not only to detect product quality but also for process 2, 3 optimization. So it is necessary to enhance process monitoring to stabilize the operation, and control casting defects. By continuously monitoring the friction, all variables affecting the surface quality of the cast product can be evaluated. The productivity and quality of a continuous caster depend mainly on various process parameters, i.e. casting speed, superheat, liquid steel composition, etc. Computer science, robotics and artificial intelligence techniques are being introduced for the optimisation of manufacturing process, reduction of costs and quality maximisation of the final product. Continuous casting of steel is one among the number of industrial processes which is subjected to perturbation during continuous casting operation. The optimisation of different process parameters can be done using Artificial Intelligence (AI) 4 techniques. AI technique is a research field between psychology, cognitive science and computer science with the common goal to improve the productivity of any system. The usage of AI techniques such as

130城市建筑Urbanism and Architecture / 2023.14空间的影响提供更为科学系统的实验研究。

2高密度街区光环境模拟研究2.1建模平台选择及模型建立城市形态是影响城市微气候的重要参数，其中天空开阔度（sky vIew factor, SVF）是最具代表性的参数之一[7]，在过去的几十年里，单点SVF 的计算方法已经成熟[8]，SVF 数值的获取可以通过Rayman 模型[9]，也可以通过全景图像的转化[10]，本文为了实验的便捷性和准确性，采用第三方平台计算方法来获得。

目前流行的环境模拟主要由两大常用软件作为核心系统展开，一个是Autodesk Ecotect，在采光研究方面一般是与RADIANCE 和DAYSIM 组合使用；另一个是以Rhino 建模平台作为构架核心，基于Grasshopper 量化平台开发的一系列的环境分析插件，其中以Ladybug、Honeybee、Butterfly 为典型代表，三个工具包通过将抽象的编程语言转化为Grasshopper 的电池组进行串联，从而在Rhino 里实现可视化建模及各类环境分析。

与Ecotect 相比，Grasshopper 平台下可以通过电池组的自由组合排列对统一模型进行SVF 及DA 的模拟测试，极大提升了模拟效率。

本实验将西安市高新区CBD 作为模拟空间（见图1），利用Ladybug 中的电池组对SVF 进行逐点计算，利用Honeybee 对高密度街区整体空间及逐点测试点的光环境进行模拟，通过图表分析找到高密度街区光环境与天空开阔度之间的变化关系。

2.2 实验过程及评价方法模型网格设置为20，测试时间为8:00—18:00，将高新区CBD 两条东西朝向街道及四条南北朝向街道以5 m 为步长，分别添加100和74个测试点（见图2），在Grasshopper 平台下对每个测试点的SVF、DF 及DA 进行模拟，由于运算量较大，本文只将个别测试点计算结果列在表1中（由于全自然采光比的临界阈值为300 lx，而室外光环境绝大部分时候都远高于此数值，因此本实验将此数值定为10 000 l x，这也是直射光表面和阴影区域照度临界值）。