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The impact of industry 4.0 on automation and information technology

AIP Conference Proceedings 2024年第1期3102卷

作者： A. L. Zolkin T. G. Aygumov T. B. Matvievskaya A. S. Bityutskiy V. V. Dragulenko Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala Russia Department “International financial and management accounting” Russian University of Transport (MIIT) Moscow 127994 Russia "Invent Technology" LLP Almaty A10E5P4 Kazakhstan Department of tractors automobiles and technical mechanics Federal State Budgetary Educational Institution of Higher Education “Kuban State Agrarian University named after I.T. Trubilin” Kalinina Street 13 Krasnodar 350044 Russia

Industry 4.0 is transforming the manufacturing industry through the integration of advanced technologies such as automation and information technology. This paper provides a comprehensive review of the impact of Industry 4.0 on automation and information technology in industry. It examines the potential benefits of Industry 4.0, including improved efficiency, productivity, flexibility, and customization, as well as the challenges businesses face in implementing these technologies. The paper discusses the need for careful planning, collaboration, and investment to ensure that Industry 4.0 investments align with business goals and provide a return on investment. Also, this paper highlights the need to prioritize cyber security and training to mitigate the risks associated with integrating advanced technologies. It identifies the importance of reskilling and upskilling the workforce to prepare for new roles and responsibilities and highlights the need to prioritize cybersecurity in Industry 4.0 strategies. The paper highlights the opportunities that Industry 4.0 offers businesses to create new business models and transform their manufacturing operations. The use of advanced technologies such as 3D printing, artificial intelligence and the Internet of Things enables businesses to create products on demand and tailor them to specific customer needs. This can increase customer satisfaction and create new business opportunities. Alongside this, this paper presents an analysis of the impact of Industry 4.0 on various industries and the growth in adoption of Industry 4.0 technologies over time.

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Research of problems of computer networks expert systems

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IOP Conference Series: Materials Science and engineering 2021年第1期1047卷

作者： A L Zolkin A N Losev D V Gridina T G Aygumov Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics L.Tolstogo Street 23 Samara 443010 Russia Department of Applied Informatics Federal State Budgetary Educational Institution of Higher Education 'Russian Timiryazev State Agrarian University' Timiryazevskaya Street 49 Moscow 127550 Russia Department of legal and customs regulation of foreign trade Moscow Automobile and road construction State Technical University 64 Leningradsky Prospekt Moscow 125319 Russia Department of computer engineering software and automated systems Dagestan State Technical University I. Shamyl Street 70 Makhachkala 367000 Russia

The article is dedicated to the issues of development of expert systems in data-processing networks during web programming. The dependence of the processing, storage and transmission of information and web applications on the uniqueness and problematics of expert systems creating is observed. The process of development of a software control block of content (engine) as the own control system is considered in details; vulnerabilities and a set of necessary functions inherent in the specified engine are explored. Each new developed software engine can be exposed to unauthorized access and content theft, in this regard, sometimes it is worth considering the percentage of uniqueness of the development methodology and code. In this case, the control block (engine) must be written using its own unique variables and file names, which will help secure the site from third-party interference and will not require the purchase of more expensive hosting for its placement. In addition, the article investigates the problems associated with the threat posed by malicious programs in computer networks, such as direct DDOS attacks, identifies the features of such programs, and also provides ways to combat them.

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

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NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

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The use of public-private partnership in the machine-building industry

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AIP Conference Proceedings 2022年第1期2467卷

作者： Oksana Bondarskaya Tatyana Bondarskaya Sergey Pichugin Anna Drozdova Elena Avksentieva Sergey Yekimov 1Tambov State Technical University Tambov Russian 2S. P. Korolev Rocket and Space Corporation Energia Moscow Russia 3Faculty of Business Informatics and Integrated Systems Management Department of Industrial Economics and Management National Research Nuclear University MEPhI Moscow Russia 4Faculty of Software Engineering and Computer Systems ITMO University St. Peterburg Russia 5Publishing House “Education and Science” Prague The Czech Republic

The process of reengineering business processes, in our opinion, is an iterative procedure in the process of enterprise management. Each subsequent business process in the business process chain is a logical continuation of the previous one. The reengineering procedure is characterized by the presence of some feedback about the impact of each individual business process on the overall set of business processes. During the implementation of reengineering, traditional business approaches may undergo revision. Often, many business rules need to be improved and are outdated. The use of public-private partnerships, in our opinion, can be considered as elements of reengineering, since partnerships with the state can open up new business development prospects for an enterprise, and many business processes used at this enterprise can be rethought and updated. The production of high-quality and high-performance agricultural machinery can be of great importance for solving the problem of ensuring food security of the state. The use of various forms of public-private partnership allows us to speed up the solution of this problem. The implementation of economic modernization requires the full use of the economic potential of the state. This can be achieved only if public-private partnerships are effectively implemented, since they allow combining the resources of the state and private business to achieve common goals.

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Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

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Archives of Computational Methods in engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

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ERRATUM: 'THE ALLEN TELESCOPE ARRAY TWENTY-CENTIMETER SURVEY—A 690 DEG2, 12 EPOCH RADIO DATA SET. I. CATALOG AND LONG-DURATION TRANSIENT STATISTICS' (2010, ApJ, 719, 45)

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The Astrophysical Journal 2010年第1期724卷

作者： Steve Croft Geoffrey C. Bower Rob Ackermann Shannon Atkinson Don Backer Peter Backus WilliamC. Barott Amber Bauermeister Leo Blitz Douglas Bock Tucker Bradford Calvin Cheng Chris Cork Mike Davis Dave DeBoer Matt Dexter John Dreher Greg Engargiola Ed Fields Matt Fleming James R. Forster Colby Gutierrez-Kraybill Gerry Harp Tamara Helfer Chat Hull Jane Jordan Susanne Jorgensen Garrett Keating Tom Kilsdonk Casey Law Joeri van Leeuwen John Lugten Dave MacMahon Peter McMahon Oren Milgrome Tom Pierson Karen Randall John Ross Seth Shostak Andrew Siemion Ken Smolek Jill Tarter Douglas Thornton Lynn Urry Artyom Vitouchkine Niklas Wadefalk Jack Welch Dan Werthimer David Whysong Peter K. G. Williams Melvyn Wright Department of Astronomy University of California Berkeley 601 Campbell Hall #3411 Berkeley CA 94720 USA SETI Institute Mountain View CA 94043 USA Electrical Computer Software and Systems Engineering Department Embry-Riddle Aeronautical University Daytona Beach FL 32114 USA Minex Engineering Antioch CA 94509 USA CSIRO/ATNF Epping NSW 1710 Australia ASTRON 7990 AA Dwingeloo The Netherlands Lawrence Livermore National Laboratory Livermore CA 94550 USA Electrical Engineering Department Stanford University Stanford CA 94305 USA Department of Microtechnology and Nanoscience-MC2 Chalmers University of Technology SE-412 96 Göteborg Sweden

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is-rSNP: a novel technique for in silico regulatory SNP detection

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BMC Bioinformatics 2010年第10期11卷 1-1页

作者： Geoff Macintyre James Bailey Adam Kowalczyk Izhak Haviv Department of Computer Science and Software Engineering The University of Melbourne Victoria 3010 Australia NICTA Victoria Research Lab The University of Melbourne Victoria 3010 Australia Department of Biochemistry and Molecular Biology The University of Melbourne Victoria 3010 Australia Ian Potter Centre for Cancer Genomics and Predictive Medicine Peter MacCallum Cancer Centre East Melbourne Victoria Australia Bioinformatics and Systems Integration The Blood and DNA Profiling Facility Baker IDI heart and diabetes Institute 75 Commercial Rd Prahran 3004 Victoria Australia

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Scaled Background Swap: Video Augmentation for Action Quality Assessment with Background Debiasing

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Xin Zhang Hongzhi Feng M. Shamim Hossain Yinzhuo Chen Hongbo Wang Yuyu Yin Hangzhou Dianzi University China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China and Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Hangzhou Dianzi University China Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia Hangzhou Dianzi University China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China and Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China

Action Quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the dominance of the background in videos. Especially, the background bias tends to overshadow subtle foreground differences, which is crucial for precise action evaluation. To address the background bias issue, we propose a novel data augmentation method named Scaled Background Swap. Firstly, the background regions between different video samples are swapped to guide models focus toward the dynamic foreground regions and mitigate its sensitivity to the background during training. Secondly, the video’s foreground region is up-scaled to further enhance models’ attention to the critical foreground action information for AQA tasks. In particular, the proposed Scaled Background Swap method can effectively improve models’ accuracy and generalization by prioritizing foreground motion and swapping backgrounds. It can be flexibly applied with various video analysis models. Extensive experiments on AQA benchmarks demonstrate that Scaled Background Swap method achieves better performance than baselines. Specifically, the Spearman’s rank correlation on datasets AQA-7 and MTL-AQA reaches 0.8870 and 0.9526, respectively. The code is available at: https://***/Emy-cv/Scaled-Background Swap.

关键词： Background Swap Foreground Up-scale Data Augmentation Background Bias Action Quality Assessment

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Vehicle Dynamics and Interaction for Trajectory Prediction and Traffic Control

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ACM Transactions on Autonomous and Adaptive systems 1000年

作者： Jian Chen Shaorui Zhou Wei Wang Yuzhu Hu Jianqing Li Ben-Guo He Junxin Chen Marwan Omar Ali Kashif Bashir Xiping Hu School of Intelligent Systems Engineering Sun Yat-Sen University China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University China the School of Software Dalian University of Technology China Information Technology and Management Illinois Institute of Technology USA Department of Computing and Mathematics Manchester Metropolitan University UK Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.

关键词： Trajectory Prediction Vehicle Dynamics Interaction Perception Graph Convolution Attention Mechanism

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