Operant conditioning learning automatic and its application on robot balance control

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。

人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? 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Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. 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Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. 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A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting[J]. Engineering Applications of Artificial Intelligence,2020,92.[118]Qingsong Ruan,Zilin Wang,Yaping Zhou,Dayong Lv. A new investor sentiment indicator ( ISI ) based on artificial intelligence: A powerful return predictor in China[J]. Economic Modelling,2020,88.[119]Mohamed Abdel-Basset,Weiping Ding,Laila Abdel-Fatah. The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model[J]. Information Fusion,2020,61.[120]Federico Caobelli. Artificial intelligence in medical imaging: Game over for radiologists?[J]. European Journal of Radiology,2020,126.以上就是关于人工智能参考文献的分享，希望对你有所帮助。

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无监督学习在自然语言生成中的应用第一章引言1.1 研究背景自然语言生成(Natural Language Generation，NLG)是人工智能领域重要的研究方向之一。

随着深度学习技术的快速发展，特别是无监督学习(Unsupervised Learning)方法的引入，自然语言生成取得了显著的进展。

本文将重点探讨无监督学习在自然语言生成中的应用。

1.2 研究目的和意义自然语言生成是人机交互、智能问答、游戏设计等领域的重要基础技术，在实际应用中具有广泛的应用前景。

本文旨在探讨无监督学习在自然语言生成中的应用，以期为后续研究提供参考，并推动自然语言生成技术的进一步发展。

第二章无监督学习概述2.1 无监督学习简介无监督学习是指从未标记的数据中学习模式或结构的机器学习方法。

相比于有监督学习，无监督学习不需要标记的数据，能够自主地发现数据中的隐藏模式，因此具有更广泛的应用场景。

2.2 无监督学习方法在自然语言生成中，常用的无监督学习方法包括聚类、降维和生成模型。

聚类算法将相似的数据点归为一类，对语料库进行划分，发现数据的潜在类别。

降维算法则通过保留关键信息，将高维数据映射到低维空间，以便更好地处理和分析。

生成模型则是通过从数据中学习其分布，进而生成与原始数据相似的样本。

第三章无监督学习在自然语言生成中的应用3.1 主题发现与文本摘要主题发现是聚类算法在自然语言处理中的重要应用之一。

通过聚类算法，可以将文本数据划分为不同的主题，从而实现对大规模文本数据的自动归纳和处理。

在这个基础上，可以进一步提取关键词和生成文本摘要，为用户提供更快速、更精确的信息。

3.2 语言模型的生成语言模型的生成是自然语言生成的重要任务之一，可以用于机器翻译、对话系统和文本生成等应用中。

无监督学习可以通过生成模型来对大规模文本数据进行建模，发现其中的语言规律和结构。

生成模型能够学习到数据的分布，从而生成与原始数据相似的新样本。

3.3 词嵌入和语义表示词嵌入是将离散的词语映射到连续的向量空间中，以便更好地捕捉词语之间的语义相似性。

研究NLP100篇必读的论⽂---已整理可直接下载100篇必读的NLP论⽂⾃⼰汇总的论⽂集，已更新链接：提取码：x7tnThis is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field should probably know about and read.这是100篇重要的⾃然语⾔处理(NLP)论⽂的列表，认真的学⽣和研究⼈员在这个领域应该知道和阅读。

This list is compiled by .本榜单由编制。

I welcome any feedback on this list. 我欢迎对这个列表的任何反馈。

This list is originally based on the answers for a Quora question I posted years ago: .这个列表最初是基于我多年前在Quora上发布的⼀个问题的答案:[所有NLP学⽣都应该阅读的最重要的研究论⽂是什么?]( -are-the-most-important-research-paper -which-all-NLP-students-should- definitread)。

I thank all the people who contributed to the original post. 我感谢所有为原创⽂章做出贡献的⼈。

This list is far from complete or objective, and is evolving, as important papers are being published year after year.由于重要的论⽂年复⼀年地发表，这份清单还远远不够完整和客观，⽽且还在不断发展。

Experiential Learning Theory:Previous Research and New DirectionsDavid A. KolbRichard E. BoyatzisCharalampos MainemelisDepartment of Organizational BehaviorWeatherhead School of ManagementCase Western Reserve University10900 Euclid Avenue,Cleveland, OH 44106PH: (216) 368 -2050FAX: (216) 368-4785dak5,@August 31, 1999The revised paper appears in:R. J. Sternberg and L. F. Zhang (Eds.), Perspectives on cognitive, learning, and thinking styles. NJ: Lawrence Erlbaum, 2000.Experiential Learning Theory: Previous Research and New Directions Experiential Learning Theory (ELT) provides a holistic model of the learning process and a multilinear model of adult development, both of which are consistent with what we know about how people learn, grow, and develop. The theory is called “Experiential Learning” to emphasize the central role that experience plays in the learning process, an emphasis that distinguishes ELT from other learning theories. The term “experiential” is used therefore to differentiate ELT both from cognitive learning theories, which tend to emphasize cognition over affect, and behavioral learning theories that deny any role for subjective experience in the learning process.Another reason the theory is called “experiential” is its intellectual origins in the experiential works of Dewey, Lewin, and Piaget. Taken together, Dewey’s philosophical pragmatism, Lewin’s social psychology, and Piaget’s cognitive-developmental genetic epistemology form a unique perspective on learning and development. (Kolb, 1984).The Experiential Learning Model and Learning Styles Experiential learning theory defines learning as "the process whereby knowledge is created through the transformation of experience. Knowledge results from the combination of grasping and transforming experience"(Kolb 1984, p. 41). The ELT model portrays two dialectically related modes of graspingexperience -- Concrete Experience (CE) and Abstract Conceptualization (AC) -- and two dialectically related modes of transforming experience -- Reflective Observation (RO) and Active Experimentation (AE). According to the four-stage learning cycle depicted in Figure 1, immediate or concrete experiences are the basis for observations and reflections. These reflections are assimilated and distilled into abstract concepts from which new implications for action can be drawn. These implications can be actively tested and serve as guides in creating new experiences.-------------------------------Insert Figure 1 about here-------------------------------A closer examination of the ELT learning model suggests that learning requires abilities that are polar opposites, and that the learner must continually choose which set of learning abilities he or she will use in a specific learning situation. In grasping experience some of us perceive new information through experiencing the concrete, tangible, felt qualities of the world, relying on our senses and immersing ourselves in concrete reality. Others tend to perceive, grasp, or take hold of new information through symbolic representation or abstract conceptualization – thinking about, analyzing, or systematically planning, rather than using sensation as a guide. Similarly, in transforming or processing experience some of us tend to carefully watch others who are involved in theexperience and reflect on what happens, while others choose to jump right in and start doing things. The watchers favor reflective observation, while the doers favor active experimentation.Each dimension of the learning process presents us with a choice. Since it is virtually impossible, for example, to simultaneously drive a car (Concrete Experience) and analyze a driver’s manual about the car’s functioning (Abstract Conceptualization), we resolve the conflict by choosing. Because of our hereditary equipment, our particular past life experiences, and the demands of our present environment, we develop a preferred way of choosing. We resolve the conflict between concrete or abstract and between active or reflective in some patterned, characteristic ways. We call these patterned ways “learning styles.”The Learning Style Inventory and the Four Basic Learning StylesIn 1971 David Kolb developed the Learning Style Inventory (LSI) to assess individual learning styles. While individuals tested on the LSI show many different patterns of scores, research on the instrument has identified four statistically prevalent learning styles -- Diverging, Assimilating, Converging, and Accommodating (Figure 1). The following summary of the four basic learning styles is based on both research and clinical observation of these patterns of LSI scores (Kolb, 1984, 1999a, 1999b).Diverging. The Diverging style’s dominant learning abilities are Concrete Experience (CE) and Reflective Observation (RO). People with this learning style are best at viewing concrete situations from many different points of view. It is labeled “Diverging” because a person with it performs better in situations that call for generation of ideas, such as a “brainstorming” session. People with a Diverging learning style have broad cultural interests and like to gather information. Research shows that they are interested in people, tend to be imaginative and emotional, have broad cultural interests, and tend to specialize in the arts. In formal learning situations, people with the Diverging style prefer to work in groups, listening with an open mind and receiving personalized feedback.Assimilating. The Assimilating style’s dominant learning abilities are Abstract Conceptualization (AC) and Reflective Observation (RO). People with this learning style are best at understanding a wide range of information and putting into concise, logical form. Individuals with an Assimilating style are less focused on people and more interested in ideas and abstract concepts. Generally, people with this style find it more important that a theory have logical soundness than practical value. The Assimilating learning style is important for effectiveness in information and science careers. In formal learning situations, people with this style prefer readings, lectures, exploring analytical models, and having time to think things through.Converging. The Converging style’s dominant learning abilities are Abstract Conceptualization (AC) and Active Experimentation (AE). People with this learning style are best at finding practical uses for ideas and theories. They have the ability to solve problems and make decisions based on finding solutions to questions or problems. Individuals with a Converging learning style prefer to deal with technical tasks and problems rather than with social issues and interpersonal issues. These learning skills are important for effectiveness in specialist and technology careers. In formal learning situations, people with this style prefer to experiment with new ideas, simulations, laboratory assignments, and practical applications.Accommodating. The Accommodating style’s dominant learning abilities are Concrete Experience (CE) and Active Experimentation (AE). People with this learning style have the ability to learn from primarily “hand-on” experience. They enjoy carrying out plans and involving themselves in new and challenging experiences. Their tendency may be to act on “gut” feelings rather than on logical analysis. In solving problems, individuals with an Accommodating learning style rely more heavily on people for information than on their own technical analysis. This learning style is important for effectiveness in action-oriented careers such as marketing or sales. In formal learning situations, people with the Accommodating learning style prefer to work with others to get assignmentsdone, to set goals, to do field work, and to test out different approaches to completing a project.Factors that Shape and Influence Learning StylesThe above patterns of behavior associated with the four basic learning styles are shown consistently at various levels of behavior. During the last three decades researchers have examined the characteristics of learning styles at five particular levels of behavior: Personality types, early educational specialization, professional career, current job role, and adaptive competencies. We summarize briefly these research findings in Table 1 and discuss them below.--------------------------------Insert Table 1 about here--------------------------------Personality Types. ELT follows Carl Jung in recognizing that learning styles result from individuals’ preferred ways for adapting in the world. Jung’s Extraversion/Introversion dialectical dimension as measured by the Myers-Briggs Type Indicator (MBTI) correlates with the Active/Reflective dialectic of ELT as measured by the LSI; and the MBTI Feeling/Thinking dimension correlates with the LSI Concrete Experience/ Abstract Conceptualization dimension. The MBTI Sensing type is associated with the LSI Accommodating learning style and the MBTI Intuitive type with the LSI Assimilating style. MBTI Feeling typescorrespond to LSI Diverging learning styles and Thinking types to Converging styles.The above discussion implies that the Accommodating learning style is the Extraverted Sensing type, and the Converging style the Extraverted Thinking type. The Assimilating learning style corresponds to the Introverted Intuitive personality type and the Diverging style to the Introverted Feeling type. Myers (1962) descriptions of these MBTI types are very similar to the corresponding LSI learning styles as described by ELT (see also Kolb, 1984, pp: 83-85).Educational Specialization. Early educational experiences shape people’s individual learning styles by instilling positive attitudes toward specific sets of learning skills and by teaching students how to learn. Although elementary education is generalized, there is an increasing process of specialization that begins at high school and becomes sharper during the college years. This specialization in the realms of social knowledge influences individuals’ orientations toward learning, resulting to particular relations between learning styles and early training in an educational specialty or discipline.People with undergraduate majors in the Arts, History, Political science, English, and Psychology tend to have Diverging learning styles, while those majoring in more abstract and applied areas like Physical Sciences and Engineering have Converging learning styles. Individuals with Accommodatingstyles have educational backgrounds in Business and Management, and those with Assimilating styles in Economics, Mathematics, Sociology, and Chemistry.Professional Career Choice. A third set of factors that shape learning styles stems from professional careers. One’s professional career choice not only exposes one to a specialized learning environment, but it also involves a commitment to a generic professional problem, such as social service, that requires a specialized adaptive orientation. In addition, one becomes a member of a reference group of peers who share a professional mentality, and a common set of values and beliefs about how one should behave professionally. This professional orientation shapes learning style through habits acquired in professional training and through the more immediate normative pressures involved in being a competent professional.Research over the years has shown that social service (i.e., psychology, nursing, social work, public policy) and arts and communications professions (i.e., theater, literature, design, journalism, media) comprise people who are heavily or primarily Diverging in their learning style. Professions in the sciences (i.e., biology, mathematics, physical sciences) and information or research (i.e., educational research, sociology, law, theology) have people with an Assimilating learning style. The Converging learning styles tends to be dominant among professionals in the fields of technology (i.e., engineering, computer sciences, medical technology), economics, and environment science (i.e., farming,forestry). Finally, the Accommodating learning style characterizes people with careers in organizations (i.e., management, public finance, educational administration) and business (i.e., marketing, government, human resources).Current Job Role. The fourth level of factors influencing learning style is the person’s current job role. The task demands and pressures of a job shape a person’s adaptive orientation. Executive jobs, such as general management, that require a strong orientation to task accomplishment and decision making in uncertain emergent circumstances require an Accommodating learning style. Personal jobs, such as counseling and personnel administration, that require the establishment of personal relationships and effective communication with other people demand a Diverging learning style. Information jobs, such as planningand research, that require data gathering and analysis, as well as conceptual modeling, have an Assimilating learning style requirement. Technical jobs, suchas bench engineering and production that require technical and problem-solving skills require a convergent learning orientation.Adaptive competencies. The fifth and most immediate level of forces that shapes learning style is the specific task or problem the person is currentlyworking on. Each task we face requires a corresponding set of skills for effective performance. The effective matching of task demands and personal skills resultsin an adaptive competence. The Accommodative learning style encompasses a setof competencies that can best be termed Acting skills: Leadership, Initiative, andAction. The Diverging learning style is associated with Valuing skills: Relationship, Helping others, and Sense-making. The Assimilating learning style is related to Thinking skills: Information-gathering, Information-analysis, and Theory building. Finally, the Converging learning style is associated with Decision skills like Quantitative Analysis, Use of Technology, and Goal-setting Kolb, 1984).An Overview of Research on ELT and the LSI: 1971-1999 What has been the impact of ELT and the LSI on scholarly research? Since ELT is a holistic theory of learning that identifies learning style differences among different academic specialties, it is not surprising to see that ELT/LSI research is highly interdisciplinary, addressing learning and educational issues in several fields. Since the first publications in 1971 (Kolb, 1971; Kolb, Rubin & McIntyre, 1971) there have been many studies of the ELT and LSI. The most recent update of the Bibliography of Research on Experiential Learning Theory and The Learning Style Inventory (Kolb & Kolb, 1999) includes 990 entries.Table 2 shows the distribution of these studies by field and publication period. The field classification categories are: Education (including k-12, higher education, and adult learning), Management, Computer/Information Science, Psychology, Medicine, Nursing, Accounting, and Law. Studies were also classified as early (1971-1984) or recent (1985-1999). In addition to being themid-point of the 28 1/2 year history of the work, the division makes sense in that the most comprehensive statement of ELT, Experiential Learning, was published in 1984, and the original LSI was first revised in 1985.-------------------------------Insert Table 2 about here-------------------------------Table 2 also shows the distribution of the 990 studies according to the publication type. More than 50% of the studies were published in journals and another approximately 20% were doctoral dissertations. 10% of the studies were either books or book chapters, and the remaining 150 studies were conference presentations, technical manuals, working papers, and master theses. Numbers should be considered approximate since a few recent citations have yet to be verified by abstract or full text. Also, classification by field is not easy because many studies are interdisciplinary. However, the Bibliography does probably give a fair representation of the scope, topics and trends in ELT/LSI research. The following is a brief overview of research activity in the various fields.EducationThe education category includes the largest number of ELT/LSI studies. The bulk of studies in education are in higher education (excluding professional education in the specific fields identified below). K-12 education accounts for arelatively small number, as does adult learning alone. However, in many cases adult learning is integrated with higher education. A number of studies in the education category have been done in other cultures--UK, Canada, Australia, Finland, Israel, Thailand, China, Melanesia, Spain, Malta, and American Indian.Many of the studies in higher education use ELT and the LSI as a framework for educational innovation. These include research on the matching of learning style with instructional method and teaching style and curriculum and program design using ELT (e.g., Claxton & Murrell, 1987). A number of publications assess the learning style of various student, faculty and other groups.Other work includes theoretical contributions to ELT, ELT construct validation, LSI psychometrics and comparison of different learning style assessment tools. In adult learning there are a number of publications on ELT and adult development, moral development, and career development. The work of Sheckley and colleagues on adult learning at the University of Connecticut is noteworthy here (e.g., Allen, Sheckley, & Keeton 1992; Travers, 1998). K-12 education research has been primarily focused on the use of ELT as a framework for curriculum design, particularly in language and science. (e.g., McCarthy, 1996; Hainer, 1992)ManagementELT/LSI research was first published in management and there has continued to be substantial interest in the topic in the management literature. Studies can be roughly grouped into four categories--management and organizational processes, innovation in management education, theoretical contributions to ELT including critique, and psychometric studies of the LSI. Cross-cultural ELT/LSI research has been done in Poland, New Zealand, Australia, Canada, UK, and Singapore. In the management/organization area, organizational learning is a hot topic. Dixon’s (1999) new book The Organizational Learning Cycle is an excellent example.Another group of studies has examined the relationship between learning style and management style, decision-making, and problem solving. Other work has measured work related learning environments and investigated the effect of a match between learning style and learning environment on job satisfaction and performance. ELT has been used as a framework for innovation in management education including research on matching learning styles and learning environments, program design and experiential learning in computerized business games (e.g., Boyatzis, Cowen, & Kolb, 1995; Lengnick-Hall & Sanders, 1997).Other education work has been on training design, management development and career development. Another area of research has been on the development and critique of ELT. Most psychometric studies of the LSI in theearly period were published in management, while recent psychometric studies have been published in psychology journals. Hunsaker reviewed the early studies in management and concluded, "The LSI does not demonstrate sufficient reliability to grant it the predictive reliability that such a measurement instrument requires. The underlying model, however, appears to receive enough support to merit further use and development." (1981, p. 151)Computer and Information ScienceThe LSI has been used widely in computer and information science particularly to study end-user software use and end-user training (e.g., Bostrom, Olfman, & Sein, 1990; Davis & Bostrom, 1993). Of particular interest for this book on individual differences in cognitive and learning styles is the debate about whether these differences are sufficiently robust to be taken in account in the design of end-user software and end user computer training. Other studies have examined the relationship between learning style and problem solving and decision making, on line search behavior, and performance in computer training and computer assisted instruction.PsychologyStudies in psychology have shown a large increase over time, with 77% of the studies in the recent period. Many of these recent studies were on LSIpsychometrics. The first version of the LSI was released in 1976 and received wide support for its strong face validity and independence of the two ELT dimensions of the learning process (Marshall & Meritt, 1985; Katz, 1986). Although early critique of the instrument focused on the internal consistency of scales and test-retest reliability, a study by Ferrell (1983) showed that the LSI version 1 was the most psychometrically sound among four learning instruments of that time. In 1985 version 2 of the LSI was released and improved the internal consistency of the scales (Veres, Sims, & Shake, 1987; Sims, Veres, Watson, & Buckner, 1986). Critiques of this version focused their attention on the test-retest reliability of the instrument, but a study by Veres, Sims, and Locklear (1991) showed that randomizing the order of the LSI version 2 items results in dramatic improvement of test-retest reliability. This finding led to an experimental research and finally to the latest LSI revision, LSI Version 3 (Kolb 1999a). The LSI version 3 has significantly improved psychometric properties, especially test-retest reliability (see Kolb, 1999b).Other research includes factor analytic studies of the LSI, construct validation studies of ELT using the LSI, and comparison of the LSI with other learning style and cognitive style measures. Another line of work uses ELT as a model for personal growth and development, including examination of counselor/client learning style match and its impact on counseling outcomes.Notable here is the work of Hunt and his colleagues at the Ontario Institute for Studies in Education (Hunt, 1992,1987).MedicineThe majority of studies in medicine focus on learning style analysis in many medical education specialties--residency training, anesthesia education, family medicine, surgical training, and continuing medical education. Of significance here is the program of research by Baker and associates (e.g., Baker, Cooke, Conroy, Bromley, Hollon, & Alpert, 1988; Baker, Reines, & Wallace, 1985). Also Curry (1999) has done a number of studies comparing different measures of learning styles. Other research has examined clinical supervision and patient/physician relationships, learning style and student performance on examinations, and the relationship between learning style and medical specialty career choice.NursingELT/LSI research has also increased dramatically with 81% of the nursing studies in the recent period. In 1990 Laschinger reviewed the experiential learning research in nursing and concluded, "Kolb's theory of experiential learning has been tested extensively in the nursing population. Researchers have investigated relationships between learning style and learning preferences,decision-making skills, educational preparation, nursing roles, nursing specialty, factors influencing career choices and diagnostic abilities. As would be expected in a human service profession, nursing learning environments have been found to have a predominantly concrete learning press, matching the predominating concrete styles of nurses…Kolb's cycle of learning which requires the use of a variety of learning modalities appears to be a valid and useful model for instructional design in nursing education" (p. 991).AccountingThere has been considerable interest in ELT/LSI research in accounting education, where there have been two streams of research activity. One is the comparative assessment of learning style preferences of accounting majors and practitioners, including changes in learning style over the stages of career in accounting and the changing learning style demands of the accounting profession primarily due to the introduction of computers. Other research has been focused on using ELT to design instruction in accounting and studying relationships between learning style and performance in accounting courses.In 1991 Stout and Ruble reviewed ELT/LSI research in accounting education. Reviewing the literature on predicting the learning styles of accounting students they found mixed results and concluded that low predictive and classification validity for the LSI was a result of weak psychometric qualitiesof the original LSI and response set problems in the LSI 1985. They tentatively recommended the use of the randomized version proposed by Veres, Sims, and Locklear (1991). They write, "researchers who wish to use the LSI for predictive and classification purposes should consider using a scrambled version of the instrument", and note, "…it is important to keep in mind that assessing the validity of the underlying theoretical model (ELT) is separate from assessing the validity of the measuring instrument (LSI). Thus, for example, the theory may be valid even though the instrument has psychometric limitations. In such a case, sensitivity to differences in learning styles in instructional design may be warranted, even though assessment of an individual's learning style is problematic" (p. 50).LawWe are now seeing the beginning of significant research programs in legal education, for example the program developed by Reese (1998) using learning style interventions to improve student learning at the University of Denver Law School.Evaluation of ELT and the LSIThere have been two recent comprehensive reviews of the ELT/LSI literature, one qualitative and one quantitative. In 1991 Hickox extensivelyreviewed the theoretical origins of ELT and qualitatively analyzed 81 studies in accounting and business education, helping professions, medical professions, post-secondary education and teacher education. She concluded that overall 61.7% of the studies supported ELT, 16.1% showed mixed support, and 22.2% did not support ELT.In 1994 Iliff conducted a meta-analysis of 101 quantitative studies culled from 275 dissertations and 624 articles that were qualitative, theoretical, and quantitative studies of ELT and the LSI. Using Hickox's evaluation format he found that 49 studies showed strong support for the LSI, 40 showed mixed support and 12 studies showed no support. About half of the 101 studies reported sufficient data on the LSI scales to compute effect sizes via meta-analysis. Most studies reported correlations he classified as low (<.5) and effect sizes fell in the weak (.2) to medium (.5) range for the LSI scales. In conclusion Iliff suggests that the magnitude of these statistics is not sufficient to meet standards of predictive validity.Most of the debate and critique in the ELT/LSI literature has centered on the psychometric properties of the LSI. Results from this research have been of great value in revising the LSI in 1985 and again in 1999. Other critique, particularly in professional education, has questioned the predictive validity of the LSI. Iliff correctly notes that the LSI was not intended to be a predictive psychological test like IQ, GRE or GMAT. The LSI was originally developed asa self-assessment exercise and later used as a means of construct validation for ELT. Tests designed for predictive validity typically begin with a criterion like academic achievement and work backward in an a-theoretical way to identify items or tests with high criterion correlations. Even so, even the most sophisticated of these tests rarely rises above a .5 correlation with the criterion. For example, while Graduate Record Examination Subject Test scores are better predictors of first-year graduate school grades than either the General Test score or undergraduate GPA, the combination of these three measures only produces multiple correlations with grades ranging from .4 to .6 in various fields (Anastasi & Urbina, 1997). While researchers in the professions are understandably searching for measures with high predictive validity to aid in decision-making, a more realistic approach than relying on any single measure is to rely on prediction from new multi-trait multi-method techniques such as structural equation modeling (e.g. White & Manolis, 1997; Coover 1993; Travers, 1998).Construct validation is not focused on an outcome criterion, but on the theory or construct the test measures. Here the emphasis is on the pattern of convergent and discriminant theoretical predictions made by the theory. Failure to confirm predictions calls into question the test and the theory. "However, even if each of the correlations proved to be quite low, their cumulative effect would be to support the validity of the test and the underlying theory" (Selltiz, Jahoda, Deutsch, & Cook, 1960, p. 160). Judged by the standards of construct validity。

本文网址：/cn/article/doi/10.19693/j.issn.1673-3185.03122期刊网址：引用格式：宋利飞, 许传毅, 郝乐, 等. 基于改进DDPG 算法的无人艇自适应控制[J]. 中国舰船研究, 2024, 19(1): 137–144.SONG L F, XU C Y, HAO L, et al. Adaptive control of unmanned surface vehicle based on improved DDPG algorithm[J].Chinese Journal of Ship Research, 2024, 19(1): 137–144 (in Chinese).基于改进DDPG 算法的无人艇自适应控制扫码阅读全文宋利飞1,2，许传毅1,2，郝乐1,2，郭荣1,2，柴威*1,21 武汉理工大学 高性能船舶技术教育部重点实验室，湖北 武汉 4300632 武汉理工大学 船海与能源动力工程学院，湖北 武汉 430063摘 要:［目的］针对水面无人艇（USV ）在干扰条件下航行稳定性差的问题，提出一种基于深度强化学习（DRL ）算法的智能参数整定方法，以实现对USV 在干扰情况下的有效控制。

［方法］首先，建立USV 动力学模型，结合视线（LOS ）法和PID 控制器对USV 进行航向控制；其次，引入DRL 理论，设计智能体环境状态、动作和奖励函数在线调整PID 参数；然后，针对深度确定性策略梯度 （DDPG ）算法收敛速度慢和训练时容易出现局部最优的情况，提出改进DDPG 算法，将原经验池分离为成功经验池和失败经验池；最后，设计自适应批次采样函数，优化经验池回放结构。

［结果］仿真实验表明，所改进的算法迅速收敛。

同时，在训练后期条件下，基于改进DDPG 算法控制器的横向误差和航向角偏差均显著减小，可更快地贴合期望路径后保持更稳定的路径跟踪。

［结论］改进后的DDPG 算法显著降低了训练时间成本，不仅增强了智能体训练后期的稳态性能，还提高了路径跟踪精度。

利用AI算法优化大气化学模型在当今的科技时代，人类面临着越来越多的环境问题，其中空气质量问题日益严重。

空气质量监测中的大气化学模型是非常重要的一环，但由于其复杂性和模型参数不确定性，模型预测偏差较大，因此有必要采用现代机器学习工具优化大气化学模型，以提高预测精度和准确性。

AI算法类型AI（人工智能）是利用计算机模拟人类智能的一种技术。

机器学习（Machine Learning）是AI技术的重要分支之一。

机器学习是指让计算机通过学习数据和模式，来识别模式并进行决策和推断的过程。

在机器学习中，有许多不同类型的算法，其中包括：监督学习、非监督学习和强化学习。

利用AI算法优化大气化学模型的挑战大气化学模型是天气预报、气候变化和空气质量监测中的重要模型之一。

该模型是建立在对大气中的各种化学反应和物理过程的数学描述上的。

这个模型包括数千条方程和参数。

而这些参数的值通常是未知的，需要从实验数据中估计得出。

这就导致了模型预测的不确定性和偏差较大。

利用AI算法优化大气化学模型的优势利用AI算法来优化大气化学模型可以获得以下几个优势：1. 优化模型的精度和准确性。

由于大气化学模型参数估计的不确定性和计算过程的复杂性，模型预测的精度和准确性常常不理想。

利用AI算法来优化模型，可以在已知数据条件下，自动寻找最优参数组合，提高模型精度和准确性。

2. 缩短模型训练和调整的时间。

大气化学模型的复杂性导致训练和调整过程的时间常常很长。

而利用AI算法优化模型可以提高训练和调整的效率，并且通常可以在较短时间内完成。

3. 可以处理大量数据。

利用AI算法来处理大气化学模型的数据可以处理大量数据，提高数据处理效率。

利用AI算法优化大气化学模型的应用在大气化学模型的应用中，利用AI算法优化模型已经取得了很多成功的应用。

例如，机器学习可以被用于优化颗粒物大小分布分析和相对湿度分析，从而提高大气化学模型的预测准确性。

另外，机器学习还可以帮助我们了解甚至预测不同的大气化学现象，例如空气污染和气候变化。

第38卷第6期2023年12月安 徽 工 程 大 学 学 报J o u r n a l o fA n h u i P o l y t e c h n i cU n i v e r s i t y V o l .38N o .6D e c .2023文章编号:1672-2477(2023)06-0064-08收稿日期:2023-06-13基金项目:安徽省高校优秀青年基金项目(2023A H 030020)作者简介:徐文奇(1991-),男,浙江宁波人,助理实验师,硕士㊂通信作者:胡耀聪(1992-),男,安徽芜湖人,讲师,博士㊂基于面部多特征跨层融合网络的驾驶员疲劳检测方法徐文奇,胡耀聪*(安徽工程大学电气工程学院,安徽芜湖 241000)摘要:针对现有驾驶员疲劳检测很大程度依赖于局部疲劳相关信息提取而导致检测准确度不足的问题,本文提出了一种基于面部多特征融合的驾驶员疲劳检测算法,能够对整体面部疲劳状态进行特征学习,从而实现更精确的驾驶员疲劳状态检测㊂提出的驾驶员人脸疲劳检测算法包含3个步骤:首先使用M T C N N 网络检测面部关键点并截取脸部㊁眼部㊁嘴部图像区域;其次设计一种面部多特征跨层融合网络,实现不同面部区域之间的信息交互与疲劳相关特征提取,进而通过多标签分类对单帧图像面部疲劳相关属性进行识别;最后使用L S TM 对长时间序列进行建模,实现最终的驾驶员疲劳状态检测㊂本文提出的驾驶员疲劳检测算法在N T HU -D D D 数据集进行了测试,对比实验验证了该方法的可行性和有效性㊂关 键 词:疲劳相关信息;多特征跨层融合;多标签分类;长时间序列中图分类号:T P 391.41 文献标志码:A伴随公共交通的快速发展和车辆数量的指数级增长,交通安全已成为世界各地亟待解决的问题㊂世界卫生组织近来调查显示,全球由交通事故导致死亡人数每24秒新增1例,每年由车祸导致死亡人数超13万[1]㊂由司机长时间驾驶或睡眠不足导致的疲劳驾驶是造成交通事故死亡的重要原因之一㊂因此,驾驶员疲劳检测的研究对智能交通系统具有重要意义[2-4]㊂计算机视觉算法是基于视频的疲劳驾驶检测系统的核心技术㊂近期研究中,学者们已经提出了几种算法来实现疲劳驾驶检测㊂通常来说,一个完整的驾驶员疲劳检测框架主要包含以下3个步骤:(1)人脸检测:通过目标检测器逐帧检测驾驶员面部并对关键点进行定位;(2)特征提取:通过传统的特征描述子[5-7]或深度学习模型[8-12]来学习与疲劳驾驶相关的信息;(3)疲劳判定:依赖帧间信息判别驾驶员疲劳程度㊂面部特征提取是驾驶员疲劳检测的关键,然而现有的方法通常仅关注局部区域的疲劳相关属性,例如眼睑闭合时间(P e r c e n t a g e o f E y e l i dC l o s u r e ,P E R C L O S )[13]㊁嘴角张合比(M o u t hA s pe c tR a t i o ,MA R )[14]等,而忽略了全局面部特征表示,致使疲劳检测精度较低㊂为了解决这个问题,本文设计了面部多特征跨层融合网络来进行精确的驾驶员疲劳检测,首先使用MT C N N 网络检测面部关键点并截取脸部㊁眼部㊁嘴部图像区域;其次设计一种面部多特征跨层融合网络,实现不同面部区域之间的信息交互与疲劳相关特征提取,进而通过多标签分类对单帧图像的面部疲劳相关属性进行识别;最后使用L S T M 对长时间序列进行建模,实现最终的驾驶员疲劳状态检测㊂1 疲劳检测算法1.1 面部关键点检测本研究采用多任务级联卷积神经网络(M u l t i -t a s kC a s c a d eC o n v o l u t i o n a lN e u r a lN e t w o r k ,MT C N N )模型[15]进行人脸关键点检测,它包含3个子网络:P -N e t ㊁R -N e t 和O -N e t,模型结构如图2所示㊂具体来说,MT C N N 模型的推理流程包含以下步骤:(1)对输入图像进行缩放操作,设定缩放因子为γ,并将原始图像以{1,γ,γ2, ,γn}的比例进行缩放,从而生成一组不同尺度的图像㊂(2)P -N e t:采用全卷积神经网络结构,用于初步标定人脸边界框㊂通过3个浅层卷积提取面部特征,粗略地搜索人脸候选区域㊂(3)R -N e t :包含3个卷积层和1个全连接层,用于进一步剔除错误和重复的人脸框,其输入为P -N e t 检测出的候选区域,采用卷积层实现特征细化,最后通过回归判定出候选区域中是否包含人脸㊁人脸中心偏移量㊁人脸关键点坐标㊂(4)O -N e t :包含4个卷积层和1个全连接层,用于输出最终的人脸关键点检测结果㊂MT C -N N 模型可以准确地检测出视频图像中的人脸区域并定位出关键点,为后续的疲劳特征提取和疲劳状态检测打下了基础㊂图1 驾驶员人脸疲劳检测算法整体框架图2 M T C N N 模型结构示意图1.2 面部疲劳属性识别疲劳状态主要表现在面部全局纹理㊁眼睑闭合程度㊁嘴角张合程度等相关属性㊂面部多特征跨层融合网络模型如图3所示㊂由图3可知,此网络模型包含脸部特征提取网络分支F -b r a n c h ㊁眼部特征提取网络分支E -b r a n c h ㊁嘴部特征提取网络分支M -b r a n c h ,分别从人脸㊁眼睛和嘴巴3个图像区域学习疲劳相关信息,对应的输入尺寸为:128×128㊁64×64㊁64×64㊂特征提取网络分支F -b r a n c h ㊁E -b r a n c h 和M -b r a n c h借鉴了M o b i l e N e t -V 2网络结构,有效地平衡了模型性能与计算复杂度㊂具体来说,它采用了深度可分离卷积运算,即将标准的3×3卷积分操作拆分为逐点卷积(P o i n t w i s eC o n v o l u t i o n )和逐通道卷积(C h a n n e l -w i s eC o n v o l u t i o n ),降低了模型的参数量和浮点计算数;其次瓶颈层采用倒置残差模块(I n v e r t e dR e s i d u -a l sB l o c k )结构,即先通道升维,后通道降维的策略,在保持网络深度的同时,增加了特征图的维度;此外,倒置残差模块的最后1个卷积层使用线性激活函数代替R e L U 激活函数,用于解决特征丢失与梯度弥散问题㊂面部多特征跨层融合网络模型能够学习不同面部区域的疲劳相关信息,具体运算过程可以定义为:F (l )f a c e =F b r a n c h (F (l -1)f a c e |θ(l )f a c e )=θ(l )f a c e ×F (l -1)f a c e ,(1)F (l )e y e s =E b r a n c h (F (l -1)e y e s |θ(l )e y e s )=θ(l )e y e s ×F (l -1)e ye s ,(2)㊃56㊃第6期徐文奇,等:基于面部多特征跨层融合网络的驾驶员疲劳检测方法F (l )m o u t h =M b r a n c h (F (l -1)m o u t h |θ(l )m o u t h )=θ(l )m o u t h ×F (l -1)m o u t h ,(3)其中,F (l )f a c e ㊁F (l )e ye s ㊁F (l )m o u t h 分别表示面部㊁眼部㊁嘴部网络分支在第l 层提取的特征图;θ(l )f a c e ㊁θ(l )e ye s ㊁θ(l)m o u t h 表示其对应的相关参数㊂图3 面部多特征跨层融合网络模型需要注意的是,为了进一步促进不同面部区域的疲劳相关信息交互,面部多特征跨层融合网络采用了跨层连接单元,用于对不同网络分支的中间层特征图进行融合,即通过1×1卷积将E -b r a n c h 与M -b r a n c h 学习的眼部区域信息与嘴部区域信息进行特征映射,接着将其与F -b r a n c h 学习的全局面部特征进行拼接,并通过1×1卷积实现维度变换㊂此外,全局均值池化层用于将F -b r a n c h ㊁E -b r a n c h 与M -b r a n c h 网络分支中的最后一层(L 层)卷积特征图进行降维,具体可以定义为:^f =A v g P o o l i n g (F (L )f a c e ⊕F (L )e ye s ⊕F (L )m o u t h ),(4)其中,A v g P o o l i n g (㊃)表示全局均值池化操作;⊕表示特征通道合并;^f 表示融合后得到的全局疲劳状态表征㊂本文采用了多标签分类方法对单帧图像的面部疲劳相关属性进行判别,主要涉及全局属性(正常/垂头)㊁眼部属性(正常/闭眼)和嘴部属性(正常/打哈欠)㊂多标签分类损失具体可以定义如下:c jk =s o f t m a x (f |θk )=e x p (θj k ㊃f )∑j'e x p (θj 'k ㊃f ),(5)L c l s =-∑Jj =1∑Kk =1δk ㊃l k ㊃l o g (c j k ),(6)其中,c jk 表示s o f t m a x 分类器计算出的第k 个疲劳相关属性被分类为第j 个类别的概率;θk 为该分类器的相关参数;L c l s 为单个样本的损失;δk 表示第k 个疲劳相关属性的权重参数;l k 表示第k 个疲劳相关属性的真实值标签㊂1.3 面部疲劳状态检测疲劳是一种连续出现的长时间面部状态,因此仅仅依赖于单帧图像表现出的面部疲劳相关属性仍然难以实现精确的疲劳状态检测㊂基于此,本文采用长短期记忆网络模型(L o n g S h o r t -t e r m N e t w o r k ,L S T N )逐帧对面部疲劳相关属性进行编码,建模长时序信息,最终输出疲劳检测结果㊂L S T N 单元的输入门i (t )用于调制输入信号z (t ),记忆单元m (t )记录了当前的记忆状态,L S T N 单元的输出h (t )由遗忘门f (t )和输出门o (t )共同决定,面部多特征跨层融合网络疲劳逐帧计算疲劳相关属性c (t ),而双向长短期记忆网络以该属性作为输入,并输出每帧图像的疲劳得分,运算过程可表示为:i (t )=σ(W i c (t )+R i h (t -1)+b i ,(7)㊃66㊃安 徽 工 程 大 学 学 报第38卷f (t )=σ(W f c (t )+R f h (t -1)+b f ,(8)o (t )=σ(W o c (t )+R o h (t -1)+b o ,(9)z (t )=φ(W z c (t )+R z h (t -1)+b z ,(10)m (t )=i (t )⊗z (t )+f (t )⊗m (t -1),(11)h (t )=o (t )⊗φ(m (t )),(12)其中,W 表示当前状态输入的权重矩阵;R 表示上一个状态输出的权重矩阵;b 表示阈值项;σ为s i g m o i d 函数;φ为双正切函数;⊗表示元素内积㊂L S T N 单元的输出取决于当前时刻的疲劳相关属性和之前时刻的疲劳相关属性,实现了长时序信息融合㊂2 算法实现与结果2.1 实验环境本文在U b u n t u18.04操作系统下,通过P y -t o r c h 开源工具构建MT C N N 模型㊁面部多特征跨层融合网络模型与L S T N 网络模型,并应用于驾驶员疲劳识别任务中,实验相关配置与具体参数如表1所示㊂表1 实验平台相关配置与具体参数相关配置具体参数主机D e l l P o w e r E d g eT 440C P U I n t e l C o r e i 7-9700G P UN V I D I A G e f o r c eR T X 3090操作系统U b u n t u16.04P yt h o n 版本P yt h o n3.8P yt o r c h 版本P yt o r c h1.132.2 实验数据集N T HU -D D D 是一个公开的驾驶员疲劳识别数据集,由中国台湾清华大学发布㊂该数据集中的所有视频都是由带有主动红外L E D 的彩色摄像头拍摄的㊂录制视频的参与者在模拟驾驶环境中进行正常驾驶和疲劳驾驶,并可以分为五种场景条件:白天不佩戴眼镜㊁白天佩戴眼镜㊁白天佩戴墨镜㊁夜晚不佩戴眼镜㊁夜晚佩戴眼镜,如图4所示㊂录制的视频分辨率为640×480,每秒30帧㊂此外,N T HU -D D D 数据集包含每一帧图像的疲劳相关信息标注,涉及全局状态(正常/垂头)㊁眼睛(正常/闭眼)和嘴巴(正常/打哈欠)㊂本实验借鉴了文献[16]的数据处理方法,即采用滑动窗口对完整视频进行截取,截取片段的帧长设置为300帧㊂N T HU -D D D 数据集中的360段完整的训练视频可以被拆分为2390个时间片段,涉及1572个正常驾驶片段和818个疲劳驾驶片段;20段完整的测试视频可以被拆分为602个时间片段,涉及348个正常驾驶片段和254个疲劳驾驶片段㊂图4 N T HU -D D D 数据集示例样本2.3 实验评价指标本文实验的评价指标包括检测率(D e t e c t i o nR a t e ,D R )㊁误报率(F a l s eA l a r m R a t e ,F A R )㊁准确率(A c c u r a c y Ra t e ,A R ),分别可以定义为:㊃76㊃第6期徐文奇,等:基于面部多特征跨层融合网络的驾驶员疲劳检测方法D R =T pT p +F n ×100%,(13)F A R =F pT n +F p×100%,(14)D R =T p +T nT p +T n +F p +F n×100%,(15)其中,T p ㊁F p ㊁T n 和F n 分别表示真阳性㊁假阳性㊁真阴性㊁假阴性的样本数量㊂2.4 实验结果比较本实验主要采用检测率㊁误报率和准确率这3个指标评估面部多特征跨层融合网络模型的性能并与现有的驾驶员疲劳识别方法和模型做了比较㊂对比方法主要涉及三类:第一类是基于规则的疲劳检测算法,例如P E R C L O S [13]㊁MA R [14]等㊂第二类是结合传统的特征描述子和机器学习算法进行驾驶员疲劳检测,例如S L A F s -R F [17]㊁L B P T O P -S V M [18]等;第三类是基于深度学习的驾驶员疲劳识别方法,例如M S T N [19]㊁D D D -I A A [20]㊁3D C N N -F F [21]㊂本文所提出的基于面部多特征融合的驾驶员疲劳检测算法及其对比模型在相同的D e l lP o w e r E d g eT 440计算平台上进行训练与测试,模型训练过程中的超参数设置如表2所示㊂实验统一采用MT C N N 模型截取驾驶员人脸区域,通过不同模型提取疲劳相关特征,并最终判定驾驶员是否处于疲劳状态㊂表3列出了不同模型在N T HU -D D D 数据集上的识别精度对比结果㊂表2 模型训练的超参数设置超参数配置具体参数初始学习率0.001学习率下降间隔纪元数100学习率调整倍数0.1迭代纪元数500优化器类型A d a m 优化器批样本数16表3 各种疲劳识别算法在N T HU -D D D 数据集上的识别精度对比方法名称N T HU -D D D D R /%F A R /%A R /%P E R C L O S [13]65.419.873.9MA R[14]55.928.764.7S L A F s -R F [17]72.022.175.4L B P T O P -S VM [18]74.020.477.2M S T N[19]85.011.287.2D D D -I A A [2]81.919.081.43D C N N -C A L [21]80.321.579.2F B r a n c h -L S T N 79.513.583.6E B r a n c h -L S T N 78.723.077.9M B r a n c h -L S T N 67.724.772.1面部多特征跨层融合网络-L S T N (本文方法)89.48.690.5文献[13]和文献[14]分别将眼睑闭合时间(P E R C L O S )和嘴角张合比(MA R )作为规则用于评判驾驶员疲劳程度,这类方法在N T HU -D D D 数据集上的表现不佳,准确率分别为73.9%㊁70.6%㊂文献[17]和文献[18]结合了传统的特征描述子和机器学习算法判定驾驶员疲劳状态,其中,文献[17]融合了梯度方向特征和关键点运动矢量,接着随机森林分类器判定驾驶员是否处于疲劳状态;文献[18]使用了三维局部二值模式L B P -T O P 描述子提取面部动态纹理特征,并通过S VM 对提取的特征进行分类,进而检测驾驶员疲劳状态;实验结果表明,基于传统特征描述子的疲劳检测算法性能优于基于规则的疲劳检测算法㊂文献[19]㊁文献[20]和文献[21]构建了深度学习模型进行端到端的疲劳特征提取和疲劳检测㊂具体来说,文献[19]提出了一种多阶段时空网络模型(M u l t i s t a g eS p a t i a l -t e m p o r a lN e t w o r k ,M S T N ),其中C N N 模型从单帧图片中提取人脸疲劳相关特征,L S T N 模型用于建模长时序信息,并输出疲劳检测结果㊂文献[20]提出了D D D -I A A 驾驶员疲劳检测框架,其中A l e x N e t ㊁F l o w I m a g e N e t 和V G G F a c e 分别用于提取全局环㊃86㊃安 徽 工 程 大 学 学 报第38卷境信息㊁帧间动作信息和面部细节轮廓信息,最后采用分数融合检测驾驶员是否处于疲劳状态㊂文献[21]中提出了一种3D C N N -C A L 的疲劳检测框架,该框架首先使用三维卷积网络提取连续时间段的疲劳相关信息,接着借助条件自适应学习获取全局场景信息,最终通过特征融合识别驾驶员疲劳状态㊂实验结果显示文献[19]提出的M S T N 模型优于其他对比方法,该算法在N T HU -D D D 数据集上的检测率㊁误报率和准确率分别为85.0%㊁11.2%和87.2%㊂本文提出的面部多特征跨层融合网络包含3个网络分支:F -b r a n c h ㊁E -b r a n c h 与M -b r a n c h ,分别从人脸㊁眼睛和嘴巴3个图像区域学习疲劳相关信息,并通过跨层连接单元实现不同分支的信息交互㊂从实验结果可以看出,单独的脸部㊁眼部或嘴部特征提取网络分支在N T HU -D D D 数据集上的检测精度不高,而将3个网络分支进行跨层融合后,模型的性能得到了显著提升,检测率㊁误报率和准确率分别达到了89.4%㊁8.6%㊁90.5%,优于其他对比模型㊂面部多特征跨层融合网络模型在五种不同场景条件下的精度表现如表4所示,这五种场景包括白天不佩戴眼睛㊁白天佩戴眼镜㊁白天佩戴墨镜㊁夜间不佩戴眼镜㊁夜间佩戴眼镜㊂实验结果表明,本文提出的算法在白天条件下的检测精度要高于夜间条件下的检测精度,不佩戴眼镜情况下的检测精度要高于佩戴眼镜或佩戴墨镜情况下的检测精度㊂表4 本文提出的疲劳识别算法在N T HU -D D D 数据集中不同场景条件下的识别精度对比场景类型D R /%F A R /%A R /%白天不佩戴眼镜98.12.997.6白天佩戴眼镜94.05.494.3白天佩戴墨镜75.018.678.6夜间不佩戴眼镜92.38.791.7夜间佩戴眼镜85.59.288.5所有场景89.48.690.5图5 疲劳检测效果示意图本文提出的疲劳检测算法的效果示意图如图5所示,从图中可以看出MT C N N 网络可以准确地定位出人脸关键点,进而对脸部㊁眼部和嘴部区域进行截取;面部多特征跨层融合网络模型能够有效地判断出单帧图片的面部疲劳相关属性,而L S T N 网络结合了当前时刻与之前时刻的疲劳相关属性,并输出最终的疲劳检测结果㊂㊃96㊃第6期徐文奇,等:基于面部多特征跨层融合网络的驾驶员疲劳检测方法㊃07㊃安 徽 工 程 大 学 学 报第38卷3 结论本文针对现有驾驶员疲劳检测很大程度依赖于局部疲劳相关信息提取而导致检测准确度不足的问题,提出了一种基于面部多特征融合的驾驶员疲劳检测算法㊂首先使用MT C N 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u241000,C h i n a )A b s t r a c t :T h i s p a p e r p r o p o s e d a d r i v e r f a t i g u e d e t e c t i o n a l g o r i t h mb a s e do n f a c i a lm u l t i -f e a t u r e f u s i o n .I t c a n l e a r n f e a t u r e s f r o mt h e o v e r a l l f a c i a l f a t ig u e s t a t e .Th e c u r r e n t d ri v e r f a t i g u e d e t e c t i o nh e a v i l y re l i e d o n e x t r a c t i n g l o c a l d r o w s i n e s s r e l a t e d i nf o r m a t i o n ,r e s u l t i ng i n i n s u f f i c i e n t d e t e c t i o n a c c u r a c y.H o w e v e r ,t h em e t h o d c a na c h i e v em o r e a c c u r a t e d r i v e r f a t i g u e s t a t e d e t e c t i o n .T h e p r o p o s e dd r i v e r f a c e f a t i g u e d e -t e c t i o na l g o r i t h mc o n s i s t s o f t h r e e s t e p s .F i r s t l y ,MT C N Nn e t w o r kw a su s e d t od e t e c t f a c i a l k e yp o i n t s a n de x t r a c t f a c i a l ,e y e ,a n d m o u t h i m a g e r e g i o n s ;S e c o n d l y ,a f a c i a lm u l t i f e a t u r e c r o s s l a y e r f u s i o nn e t -w o r kw a s d e s i g n e d t o a c h i e v e i n f o r m a t i o n e x c h a n g e a n d f a t i g u e r e l a t e d -f e a t u r e e x t r a c t i o nb e t w e e n d i f f e r -e n tf a c i a l r eg i o n s ,a n dth e nr e c o g ni z e df a c i a l f a t i g u er e l a t e da t t r i b u t e s i ns i n g l ef r a m e i m a g e st h r o u gh m u l t i -l a b e l c l a s s i f i c a t i o n ;F i n a l l y ,L S T M w a su s e dt o m o d e l t h e l o n g t i m es e r i e sa n da c h i e v e dt h e f i n a l d e t e c t i o no f d r i v e r f a t i g u e s t a t u s .T h e p r o p o s e dd r i v e r f a t i g u e d e t e c t i o n a l g o r i t h m w a s t e s t e d o n t h eN T -HU -D D Dd a t a s e t ,a n d c o m p a r a t i v e e x p e r i m e n t s v e r i f i e d t h e f e a s i b i l i t y a n d e f f e c t i v e n e s s o f t h i sm e t h o d .K e y w o r d s :d r o w s i n e s s -r e l a t e d i n f o r m a t i o n ;c r o s s -l a y e r f e a t u r e i n t e r a c t i o n ;m u l t i -l a b e l c l a s s i f i c a t i o n ;l o n g -t i m e s e qu e n c e (上接第63页)I m p r o v e m e n t o f S L A M A l go r i t h mf o rP o i n t a n dL i n eV i s i o n B a s e d o nG r a d i e n tD e n s i t yL IM i n g h a o 1,2,C H E N M e n g y u a n 1,2*,G O N GP e n g h a o 1,2,L O N G H a i ya n 3(1.S c h o o l o fE l e c t r i c a l E n g i n e e r i n g ,A n h u i P o l y t e c h n i cU n i v e r s i t y,W u h u241000,C h i n a ;2.K e y L a b o r a t o r y o fA d v a n c e dP e r c e p t i o na n d I n t e l l i g e n tC o n t r o l o fH i g h -e n dE q u i p m e n t ,A n h u i P o l y t e c h n i cU n i v e r s i t y,W u h u241000,C h i n a ;3.S c h o o l o fE l e c t r i c a l a n dE l e c t r o n i cE n g i n e e r i n g ,A n h u i I n s t i t u t e o f I n f o r m a t i o nT e c h n o l o g y,W u h u241000,C h i n a )A b s t r a c t :A i m i n g a t t h e p r o b l e m st h a t t h ev i s u a l s y n c h r o n o u s l o c a l i z a t i o na n d m a p b u i l d i n g (S L AM )m e t h o d i s l i k e l y t o c a u s e i m a g eb l u r i n t h e r a pi d c a m e r am o v e m e n t ,a n d t h a t t h e e x t r a c t i o no f c e n t e r l i n e f e a t u r e s i nd e n s es c e n e s i s l i k e l y t oc a u s e i n f o r m a t i o nr e d u n d a n c y ,a ni m p r o v e d p o i n ta n dl i n ev i s u a l S L AMa l g o r i t h mi n t e g r a t i n g g r a d i e n t d e n s i t y i s p r o p o s e d .T h e a l go r i t h mf i r s t u s e s t h e n u m b e r o f f e a t u r e p o i n t sb e t w e e n t h e f r o n t a n db a c k i m a g e f r a m e s t o f i l t e r t h e b l u r r e d i m a ge ,a n d t h e nu s e sG a u s s i a nb l u r t oo p t i m i z e t h e p r o c e s s i n g t o o b t a i nb e t t e rm a t c h i n g i m a ge f r a m e s .T h e n ,t h e p o i n t f e a t u r e i n f o r m a t i o n i s u s e d t o j u d g ew h e t h e r t h e l i n e f e a t u r e i s i n t r o d u c e d ,a n d t h e i m a g e p i x e l d e n s i t y g r a d i e n t i s i n t r o d u c e d t o o p t i m i z e t h eL S D (l i n e s e g m e n t d e t e c t i o n )l i n e f e a t u r e f r o m m u l t i -d i m e n s i o n s ,a n d t h e s t a b l e l i n e f e a t u r e i s e x t r a c t e d t o i m p r o v e t h e s u b s e q u e n tm a t c h i n gq u a l i t y .F i n a l l y,t h e e r r o r f u n c t i o n i s c o n s t r u c t e db a s e d o n t h e p o i n t a n d l i n e c h a r a c t e r i s t i c e r r o r t om i n i m i z e t h e p r o j e c t i o ne r r o r a n d i m p r o v e t h e p o s e e s t i m a -t i o na c c u r a c y .T h e a l g o r i t h mi s t e s t e d i nT UMd a t a s e t ,a n d t h e e x p e r i m e n t a l r e s u l t s s h o wt h a t t h e a l g o -r i t h mc a ne f f e c t i v e l y i m p r o v et h er o b u s t n e s so f f e a t u r ee x t r a c t i o n ,t h e r e b y i m p r o v i n g t h ea c c u r a c y of c a m e r a p o s e e s t i m a t i o na n dm a p p i n g.K e y w o r d s :s y n c h r o n o u s p o s i t i o n i n g a n dm a p p i n g ;g r a d i e n t i n f o r m a t i o n ;l i n e f e a t u r e e x t r a c t i o n ;p o i n t a n d l i n e f u s i o n ;i n f o r m a t i o ne n t r o p y㊃17㊃第6期徐文奇,等:基于面部多特征跨层融合网络的驾驶员疲劳检测方法。