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Lecture Notes in Networks and systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

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Editorial Special issue on approximate dynamic programming and reinforcement learning

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控制理论与应用（英文版） 2011年第3期 309-309页

作者： Silvia Ferrari Jagannathan Sarangapani Frank L. Lewis Laboratory for Intelligent Systems and Control(LISC)Department of Mechanical Engineering & Materials Science Box 90300 Duke University Durham NC 27708-0005 Department of Electrical & Computer Engineering University of Missouri-Rolla MO 65401 U.S.A. Automation and Robotics Research Institute The University of Texas at Arlington 7300 Jack Newell Blvd. S Ft. Worth Texas 76118-7115 U.S.A.

We are extremely pleased to present this special issue of the Journal of control Theory and *** dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain environments over *** optimizes the sensing objectives accrued over a future time interval with respect to an adaptive control law,conditioned on prior knowledge of the system,its state,and uncertainties.A numerical search over the present value of the control minimizes a Hamilton-Jacobi-Bellman (HJB) equation providing a basis for real-time,approximate optimal control.

关键词： reinforcement learning dynamic programming optimal control effective approach prior knowledge control Theory real-time control law problems special search future ADP basis

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Persistence border collisions in a vibrating system excited by an unbalanced motor with a relay control

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AIP Conference Proceedings 2018年第1期1959卷

作者： Zhanybai T. Zhusubaliyev Viktor Avrutin Vasily G. Rubanov Dmitry A. Bushuev Dmitry V. Titov Olga O. Yanochkina 1Department of Computer Science Southwest State University 50 Years of October Str. 94 305040 Kursk Russia 2Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 70550 Stuttgart Germany 3Department of Engineering Cybernetics Belgorod State Technological University Kostyukov Str. 46 308012 Belgorod Russia

The paper describes a new scenario for the transition to complex dynamics in a vibrating system with an unbalanced rotor and a relay feedback control. We show that the transition from a regular dynamics without switching events in the relay element to an irregular dynamics which takes place completely in the hysteresis region occurs via a cascade of persistence border collisions.

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Corrigendum to “A pathway-based network analysis of hypertension-related genes” [Physica A 444 (2016) 928–939]

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Physica A: Statistical Mechanics and its Applications 2016年 447卷 569-570页

作者： Huan Wang Jing-Bo Hu Chuan-Yun Xu De-Hai Zhang Qian Yan Ming Xu Ke-Fei Cao Xu-Sheng Zhang School of Computer Science and Technology Baoji University of Arts and Sciences Baoji Shaanxi 721016 China Center for Nonlinear Complex Systems Department of Physics School of Physics Science and Technology Yunnan University Kunming Yunnan 650091 China School of Electronic and Electrical Engineering Baoji University of Arts and Sciences Baoji Shaanxi 721016 China School of Software Yunnan University Kunming Yunnan 650091 China School of Physics and Electronic Science Chuxiong Normal University Chuxiong Yunnan 675000 China School of Mathematical Sciences Kaili University Kaili Guizhou 556011 China Modelling and Economics Unit Centre for Infectious Disease Surveillance and Control Public Health England 61 Colindale Avenue London NW9 5EQ UK Medical Research Council Centre for Outbreak Analysis and Modelling Department of Infectious Disease Epidemiology School of Public Health Imperial College London Norfolk Place London W2 1PG UK

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Influence of human engineering on manning levels and human performance on ships

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NAVAL ENGINEERS JOURNAL 1997年第5期109卷 67-76页

作者： Anderson, DE Oberman, FR Malone, TB Baker, CC David E. Anderson:has a bachelor of science degree in environmental engineering from Florida Technological University and a master's degree in environmental engineering from the University of Central Florida. He is a graduate of the Naval Sea Systems Command's Engineer-In-Training (EIT) Program. Mr. Anderson was instrumental in introducing the collective protection system (CPS) in the U.S. Navy developing the initial forward-fit package for the USS Gunston Hall and the engineering change proposal (ECP) for the USS Wasp. In 1990 he joined the Human Systems Integration (HSI) Division (SEA 55W5) where he was task leader for auxiliary ships. He is currently the HSI manager for the future technology variant of the SeaLift ship and the future carrier. Association of Scientists and Engineers 33rdAnnual Technical Symposium 26 April 1996. Fred R. Oberman:has B.A. and M.A. degrees from the University of Chicago and Loyola University (experimental psychology) and an M.S. degree in industrial engineering and operations research from Virginia Polytechnic Institute (VPI). He has more than 30 years experience in HSI management planning research analysis design and testing in government and private sector positions. He is currently responsible for NavSea HSI generic research and tool development efforts. He is responsible for Human Engineering Specifications and Standards (Commercial Hypertext) and is the NavSea 03D7 representative on HSI in Performance Specifications and for integration of HSI within Integrated Logistic Support (ILS). He has served as DoD HSI SubTAG chair and member of the Simulation and Modeling Test and Evaluation Display and Control Systems Human Computer Interaction Specifications and Standards and Systems Design Sub Tags as a member of the Society of Naval Architects and Marine Engineers (SNAME) Systems Safety Panel and a member of NATO RSG 14 on man-machine analysis. Thomas B. Malone: CHFEP received a Ph.D. in experimental psychology from Fordham University in 1964. He is president of Carlow

The objectives of Human engineering (HE) are generally viewed as increasing human performance, reducing human error, enhancing personnel and equipment safety, and reducing training and related personnel costs. There are other benefits that are thoroughly consistent with the direction of the Navy of the future, chief among these is reduction of required numbers of personnel to operate and maintain Navy ships. The Naval Research Advisory Committee (NRAC) report on Man-Machine Technology in the Navy estimated that one of the benefits from increased application of man-machine technology to Navy ship design is personnel reduction as well as improving system availability, effectiveness, and safety The objective of this paper is to discuss aspects of the human engineering design of ships and systems that affect manning requirements, and impact human-performance and safety The paper will also discuss how the application of human engineering leads to improved performance, and crew safety, and reduced workload, all of which influence manning levels. Finally, the paper presents a discussion of tools and case studies of good human engineering design practices which reduce manning.

关键词： Human engineering

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Load frequency control of multi-area power system incorporated renewable energy considering electrical vehicle effect using modified cascaded controller tuned by BESSO algorithm

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Heliyon 2024年第11期10卷

作者： Rehana Ghafoor Lyu Guanghua Muhammad Majid Gulzar Rasmia Irfan Mohammed Alqahtani Muhammad Khalid Department of Electrical Engineering University of Engineering & Technology Lahore Pakistan Power China Hua Dong Engineering Corporation Ltd. Hangzhou 311100 2019 China Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Sustainability Energy Systems King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan Department of Industrial Engineering King Khalid University Abha 62529 Saudi Arabia Center for Engineering and Technology Innovations King Khalid University Abha 62529 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia

In power systems, load frequency control (LFC) matters significantly to achieve stability. Dealing with the fluctuations in the frequency of a multi-area power system becomes more challenging by incorporating additional energy resources. In this research, a multi-area power system is built by integrating thermal power systems with photovoltaic (PV) cells, wind turbines, and electric vehicles (EV). The addition of an electric vehicle "to a thermal power system which is integrated with a renewable energy source (RES)" increases the system productivity but also increases the system complexity, making it more problematic for LFC. Looking at the stability criteria for LFC, frequencies in two areas (Area-1 & Area-2) and tie-line power are considered for measurements. For the tuning of the proposed cascaded (1+PI)-PID controller, a new approach Bald Eagle Sparrow Search Optimization (BESSO) algorithm is implemented which is strongly inspired by nature. BESSO is a combination of bald eagle and sparrow searching techniques and performs comparatively better for fast convergence due to their strong food-seeking natural behavior to find the best solution for controller gains. controller effects on multi-area systems are compared with the addition of PV, wind, and EV and resulting measurements meet the stability criteria with high accuracy even with the complexity of the system and also undertake a stability analysis to prove the performance by minimizing undershoot, overshoot, steady-state error and settling time for system frequencies and tie-line power. Simulation results are examined at different load-changing conditions. In contrast with similar combinations of PID controller with proposed cascaded (1+PI)-PID controller, it is claimed that the effect of the proposed controller is much finer and more reliable, even with electric vehicles to avoid system blackout caused by frequency fluctuations in interconnected power system.

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Hot methanol extraction for the analysis of volatile organic chemicals in subsurface core samples from Dover Air Force Base, Delaware

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GROUND WATER MONITORING AND REMEDIATION 1997年第1期17卷 104-121页

作者： Ball, WP Xia, G Durfee, DP Wilson, RD Brown, MJ Mackay, DM William P. Ball is an assistant professor in the Department of Geography and environmental engineering at Johns Hopkins University (313 Ames Hall 3400 N. Charles St. Baltimore MD 21218). His research interests are in the physical and chemical processes that control contaminant fate in aqueous systems both in the natural environment and in engineered processes of treatment and remediation. Recent work has included both laboratory and computer modeling studies related to contaminant fate and transport in subsurface environments. Ball holds M.S. and Ph.D. degrees in environmental engineering and science from Stanford University and a B.S. degree in civil engineering from the University of Virginia. He is a registered pro-fessional engineer in the state of Virginia. Guoshou Xia is a Ph.D. candidate at Johns Hopkins University He was previously a lecturer in the Department of Civil Engineering at Southeast University in China. He received his B.S. degree in chemistry from Hefei University and his M.S. degree in environmental engineering from East China University of Chemical Technology. His current research interests are in the area ofsubsurface contaminant transport with emphasis on better understanding the nature of organic contaminant interactions with subsurface solids. Donald P. Durfee received his M.S. degree in environmental engineering from Johns Hopkins University in 1993 and is currently a Ph.D. student at Johns Hopkins. He is studying contaminant fate and transport in the subsurface and is also interested in site characterization and remediation issues. Ryan D. Wilson is a research associate and Ph.D. student in the Department of Earth Sciences at the University of Waterloo Waterloo Ontario. His research interests include field and modeling studies of semipassive remediation technologies and the behavior of gaseous tracers. Wilson received his B.Sc. in geology from the University of Alberta in 1987 and his M.Sc. in earth sciences (hydrogeology) from the University of Waterloo in 1993. Michael J. Br

The evaluation of contaminant concentrations in ground water and soil is an essential aspect of most hazardous waste remedial investigations. This paper describes methods applied toward obtaining, preserving, and analyzing subsurface samples for the determination of VOC concentrations in the saturated region of an unconfined coastal plain aquifer at Dover Air Force Base (DAFB), Delaware. The described protocol involved headspace-free subsampling of cores, field preservation of subsamples in methanol, and overnight extraction of the VOCs at elevated temperature (70 degrees C). Methanol-extracted compounds were subsequently transferred to hexane and analyzed by gas chromatography. The method was found to achieve quantitative extraction from the aquifer sands in a single step, although extraction from fine-grained and more strongly sorbing aquitard samples required multiple methanol extractions to achieve comparable recovery. An extensive set of DAFB results is presented as an indication of how these methods can be applied toward characterizing field-scale contamination with a high degree of resolution and accuracy. These data have enabled a quantitative estimate of the contaminant distribution at this site and offer valuable insight into processes of contaminant migration into the underlying aquitard. In these regards, the methods used are believed to have provided much more accurate results than could have been obtained using more commonly applied techniques.

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RETRACTED ARTICLE: Irreversibility of hybrid nano-powder within permeable tank with MHD

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Applied Nanoscience 2021年第1期13卷 553-553页

作者： Zhang, Ying-Fang Alawee, Wissam H. Issakhov, Alibek Selim, Mahmoud Mohamed School of Mathematics and Information Science Henan Polytechnic University Jiaozuo China Control and Systems Engineering Department University of Technology Baghdad Iraq Training and Workshops Center University of Technology Baghdad Iraq Department of Mathematical and Computer Modeling Al-Farabi Kazakh National University Almaty Kazakhstan Department of Mathematical and Computer Modeling Kazakh-British Technical University Almaty Kazakhstan Department of Mathematics Al-Aflaj College of Science and Humanities Studies Prince Sattam Bin Abdulaziz University Al-Aflaj Saudi Arabia Department of Mathematics Suez Faculty of Science Suez University Suez Egypt

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Workshop on imaging science development for cancer prevention and preemption

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Cancer Biomarkers 2007年第1期3卷 1-33页

作者： Kelloff, Gary J. Sullivan, Daniel C. Baker, Houston Clarke, Lawrence P. Nordstrom, Robert Tatum, James L. Dorfman, Gary S. Jacobs, Paula Berg, Christine D. Pomper, Martin G. Birrer, Michael J. Tempero, Margaret Higley, Howard R. Petty, Brenda Gumbs Sigman, Caroline C. Maley, Carlo Sharma, Prateek Wax, Adam Ginsberg, Gregory G. Dannenberg, Andrew J. Hawk, Ernest T. Messing, Edward M. Grossman, H. Barton Harisinghani, Mukesh Bigio, Irving J. Griebel, Donna Henson, Donald E. Fabian, Carol J. Ferrara, Katherine Fantini, Sergio Schnall, Mitchell D. Zujewski, Jo Anne Hayes, Wendy Klein, Eric A. DeMarzo, Angelo Ocak, Iclal Ketterling, Jeffrey A. Tempany, Clare Shtern, Faina Parnes, Howard L. Gomez, Jorge Srivastava, Sudhir Szabo, Eva Lam, Stephen Seibel, Eric J. Massion, Pierre McLennan, Geoffrey Cleary, Kevin Suh, Robert Burt, Randall W. Pfeiffer, Ruth M. Hoffman, John M. Roy, Hemant K. Wang, Thomas Limburg, Paul J. El-Deiry, Wafik S. Papadimitrakopoulou, Vali Hittelman, Walter N. MacAulay, Calum Veltri, Robert W. Solomonzz, Diane Jeronimo, Jose Richards-Kortum, Rebecca Johnson, Karen A. Viner, Jaye L. Stratton, Steven P. Rajadhyaksha, Milind Dhawan, Atam NIH/NCI/DCTD Cancer Imaging Program Bethesda MD United States NIH/NCI/DCTD Cancer Imaging Program NCI-Frederick Frederick MD United States NIH/NCI/DCP Early Detection Research Group Bethesda MD United States The Johns Hopkins University School of Medicine Departments of Radiology Pharmacology and Oncology Division of Neuroradiology Baltimore MD United States NIH/NCI/CCR Cell and Cancer Biology Branch Bethesda MD United States University of California San Francisco Department of Medicine Division of Medical Oncology San Francisco CA United States CCS Associates Inc. Mountain View CA United States Wistar Institute Molecular and Cellular Oncogenesis Program Philadelphia PA United States University of Kansas School of Medicine VA Hospital Kansas City MO United States Duke University Department of Biomedical Engineering Medical Physics Program Durham NC United States University of Pennsylvania School of Medicine Department of Gastroenterology Philadelphia PA United States New York Presbyterian Hospital Weill Medical College of Cornell University New York NY United States NIH/NCI/OD Office of Centers for Training and Resources Bethesda MD United States University of Rochester Medical Center Department of Urology Rochester NY United States University of Texas MD Anderson Cancer Center Department of Urology Houston TX United States Massachusetts General Hospital Abdominal Imaging/Interventional Radiology Boston MA United States Boston University Biomedical Engineering Electrical and Computer Engineering and Physics Boston MA United States NIH/NCI/DCP Gastrointestinal and Other Cancers Research Group Bethesda MD United States The George Washington University Cancer Institute Office of Cancer Prevention and Control Washington DC United States University of Kansas Cancer Center Clinical Oncology Kansas City KS United States University of California Davis Department of Biomedical Engineering Davis CA United States Tufts University De

The article provides information on a workshop on the development of imaging systems for cancer prevention and preemption convened by the U.S. National Cancer Institute (NCI) Cancer Imaging Program held in Gaithersburg, Maryland. Doctor Katherine Ferrara of the University of California discussed the function of ultrasound in breast tumor imaging. Ways to close the gap between development and application of imaging approaches were tackled in a roundtable discussion on cervix and skin cancer.

关键词： FORUMS (Discussion & debate) IMAGING systems in medicine CANCER prevention

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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