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Corrections to “Single-Processor Scheduling Problem With Dynamic Models of Task Release Dates” [ Mar 11 264-271]

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2016年第7期46卷 1017-1017页

作者： Adam Janiak Wladyslaw Janiak Institute of Computer Engineering Control and Robotics Wrocław University of Technology Wrocław Poland Faculty of Computer Science and Management Wrocław University of Technology Wrocław Poland

In the above paper, the acknowledgment for financial support is missing. The statement is provided in the footnote below.

关键词： Dynamic scheduling Approximation algorithms Processor scheduling Resource management

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User-configurable decision support system for clinical risk management 11th

User-configurable decision support system for clinical risk ...

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2020 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence: Eleventh Annual Meeting of the BICA Society, BICA*AI 2020

作者： Kleymenova, Elena Dronov, Mikhail Yashina, Liubov Matrosova, Elena Payushchik, Svetlana Nigmatkulova, Maria Otdelenov, Vitaly General Medical Center of the Bank of Russia Sevastopolsky pr.66 Moscow117593 Russia National Research Nuclear University MEPhI Moscow Engineering Physics Institute Kashirskoe shosse 31 Moscow115409 Russia Faculty of Physics M.V. Lomonosov Moscow State University Leninskie Gory GSP-1 Moscow119991 Russia Information Technology Department Moscow City University Vtoroy Selskohoziajstvenny proezd 4 Moscow129226 Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Vavilova str. 44 Moscow119333 Russia Russian Medical Academy of Continuous Professional Education Barrikadnaya st. 2/1 Moscow125993 Russia

The paper describes an application of relational structure-independent database for the design of user-configurable information system aimed at the intellectual support of clinical risk management in a hospital. The system represents a clinical decision support system (CDSS) and includes three modules: prevention of hospital-acquired adverse events (HA-AE), HA-AE prevention processes and audit of HA-AE cases. Thanks to flexible system configuration and graphic user interface, the clinical experts are able to compose complex knowledge artifacts directly working with the system, without engagement of knowledge engineers for supporting knowledge formalization or highly skilled professionals to ensure the physical layer performance. The successful practical approval of CDSS in a medical facility opens up prospects for a new level of cognitive support for diagnostic and treatment processes, patient safety improvement and healthcare costs reduction. © 2020 Elsevier B.V.. All rights reserved.

关键词： Decision support systems

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Optimizing Neural Network Image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： Image classification

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Transition from time-variant to static networks: Timescale separation in N-intertwined mean-field approximation of susceptible-infectious-susceptible epidemics

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Physical Review E 2024年第3期109卷 034308-034308页

作者： Robin Persoons Mattia Sensi Bastian Prasse Piet Van Mieghem Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology P.O. Box 5031 2600 GA Delft The Netherlands MathNeuro Team Inria at Université Côte d'Azur 2004 Rte des Lucioles 06410 Biot France Department of Mathematical Sciences “G. L. Lagrange” Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy European Centre for Disease Prevention and Control (ECDC) Gustav III's Boulevard 40 169 73 Solna Sweden

We extend the N-intertwined mean-field approximation (NIMFA) for the susceptible-infectious-susceptible (SIS) epidemiological process to time-varying networks. Processes on time-varying networks are often analyzed under the assumption that the process and network evolution happen on different timescales. This approximation is called timescale separation. We investigate timescale separation between disease spreading and topology updates of the network. We introduce the transition times T̲(r) and T¯(r) as the boundaries between the intermediate regime and the annealed (fast changing network) and quenched (static network) regimes, respectively, for a fixed accuracy tolerance r. By analyzing the convergence of static NIMFA processes, we analytically derive upper and lower bounds for T¯(r). Our results provide insights and bounds on the time of convergence to the steady state of the static NIMFA SIS process. We show that, under our assumptions, the upper-transition time T¯(r) is almost entirely determined by the basic reproduction number R0 of the network. The value of the upper-transition time T¯(r) around the epidemic threshold is large, which agrees with the current understanding that some real-world epidemics cannot be approximated with the aforementioned timescale separation.

关键词： Epidemic Spreading Erdos-Renyi graph Evolving networks Social networks

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Synergy Analysis of Industrial, Research and University Training Centers Collaboration: A Case Study in Thailand's Eastern Economic Corridor

Synergy Analysis of Industrial, Research and University Trai...

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Portland International Conference on Management of engineering and Technology (PICMET)

作者： Udom Lewlomphaisarl Ukrit Pungjitwisut Wassana Pullpol Kumpee Suksomboon Arnuphap Dowrueng Industrial IoT and Automation Research Group (IIARG) Sustainable Manufacturing Center (SMC) Eastern Economic Corridor of Innovation (EECi) Rayong Thailand EEC Automation Park Faculty of Engineering Burapha University Chonburi Thailand Intelligent SCADA Technology Research Team (IST) Advanced Control and Electronics Research Group (ACERG) National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

The Royal Thai Government has outlined a 20-year strategy to achieve high-income status by 2036. “Thailand 4.0” is guided by the concept of “Industry 4.0”, which aims to transform the country into an innovative, value-driven industry, with a focus on 12 areas such as Automation & Robotics, Aviation & Logistics, Biofuel & Biochemicals, and Digital. The development of Eastern Economic Corridor (EEC) lies at the heart of Thailand 4.0 scheme. The EEC is an area-based development initiative aimed at revitalizing the prestigious East Coast, where numerous business developers have experienced a rewarding investment journey and exceptional success over the past 30 *** paper introduces a synergy analysis of the 3-phase collaboration approach between the EEC Automation Park located on the campus of Burapha University as the main university in the EEC and the Sustainable Manufacturing Center (SMC) at Automation Robotics and Intelligent System (ARIPOLIS) in Eastern Economic Corridor of Innovation (EECi) with strong partners from industry e.g. Mitsubishi Electric Factory Automation (Thailand) Limited. This 3-phase approach:}{1. Build an island demonstrator, 2. Link production demonstration lines and 3. Transfer to 100 factories in the EEC helps managers realize possible difficulties and systematically prepare solutions in advance.

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Investigation on application of egg shell micro powder based epoxy composite for vibration isolation of MEMS sensor

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AIP Conference Proceedings 2023年第1期2976卷

作者： Nitin Satpute Atharva Joshi Kaustubh Utpat Nishant Kulkarni Siddharth Jabade Ludwin Molina Arias Ramesh Narina Marek Iwaniec Faculty of Science and Technology Vishwakarma University Pune India Department of Mechanical Engineering Vishwakarma Institute of Technology Pune India Department of Process Control AGH University of Science and Technology Krakow Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

In the presented work, the stiffness and damping properties of eggshell-based epoxy composites are investigated. The ingredients used for the preparation of the composite include 25 - 45 µm sized eggshell powder, polydimethylsiloxane (PDMS), hardener, and silane as a coupling agent. Experimentation on prototype samples has been conducted using a vibration shaker, accelerometer, and fast Fourier transform (FFT) analyzer, to investigate the influence of the addition of micro-powder on the damping factor and stiffness of the composite. The study reveals that there is an improvement of up to 201% in damping factor with the 2.5% addition of micro powder by weight. The experimental study indicates that the stiffness properties of the composite are not influenced by the addition of eggshell micro powder. Additionally, a case study is demonstrated with finite element simulation to validate the advantages of using the proposed epoxy composite for Microelectromechanical System (MEMS) sensor packaging. In case of a harmonic force of 0.2 N acting on the sensor within the frequency range of 40-60 Hz, the maximum acceleration of the MEMS sensor increases by 48%, whereas the force transmissibility decreases by 44%.

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2020年 1241 AISC卷 v-vi页

作者： Vittorini, Pierpaolo Di Mascio, Tania Tarantino, Laura Temperini, Marco Gennari, Rosella De la Prieta, Fernando Department of Life Health and Environmental Sciences University of L’Aquila L’Aquila Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Computer Science Faculty Free University of Bozen-Bolzano Bolzano Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Computer Science and Automation Control University of Salamanca Salamanca Spain

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Development and application of MCAL tools for marine radar instruction

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computer Applications in engineering Education 1995年第4期3卷 259-259页

作者： Guirguis, S. Korany, E. Abdel-Bary, A. Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Shawkat K. Guirguis:obtained the BSc and M.Sc. degrees in Computer Science and Automatic Control Faculty of Engineering Alexandria University in 1981 and 1984 respectively. In 1988 he obtained a PhD degree in Electronics and Communications Faculty of Engineering Cairo University co-supervised by the Imperial College of Science and Technology University of London where he spent two years as an academic visitor. His current research interests include software quality management multimedia automatic programming and decision support systems. He is currently a lecturer of Computer Science at the Institute of Graduate Studies & Research Alexandria University. Ezzat A. Korany:received the BS degree in Electrical Engineering (Electronics Section) from the University of Alexandria Egypt in 1971 the MS degree in Electrical Engineering (Computer Section) from Ain Shams University Cairo Egypt in 1977 and the PhD degree in Electrical Engineering (Digital Systems) from Florida Institute of Technology U.S.A. in 1982. He is currently an associate professor of Computer Science at the Institute of Graduate Studies & Research University of Alexandria. His research interests are in data communications and multimedia applications computer networks image processing and pattern recognition. Abdel-Latif A. Abdel-Bary:obtained the BEng degree in Electronics Technology from Arab Maritime Transport Academy in 1986. In 1995 he obtained the MSc degree in Information Technology from the Institute of Graduate Studies & Research Alexandria University. His current research interests include ICAL hypermedia and automatic programming.

In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional course information through various types of computer media as opposed to the classic CAL systems. The philosophy and techniques employed to design the system are investigated. Usage of the implemented system and its merits have been illustrated through its application to teach engineering students and technicians the theory and concepts of marine radar. System design, implementation, test, and revision phases are presented and discussed.

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset

A Horizontal Federated Learning Approach to IoT Malware Traf...

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International Conference on Advanced Communication Technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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