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arXiv 2021年

作者： Alsamhi, Saeed H. Afghah, Fatemeh Sahal, Radhya Hawbani, Ammar Al-Qaness, A.A. Lee, B. Guizani, Mohsen Software Research Institute Athlone Institute of Technology Athlone Ireland IBB University Ibb Yemen School of Informatics Computing and Cyber Systems Northern Arizona University United States Hudaidah University Hudaida Yemen School of Computer Science and Technology University of Science and Technology of China Hefei China State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Department of Computer Science and Engineering Qatar University Qatar

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented. © 2021, CC BY.

关键词： Energy efficiency

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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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Development of a fuzzy authentication system for the automated line devices of a distributed internet of things using a hash-resistant algorithm

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AIP Conference Proceedings 2023年第1期2700卷

作者： A. L. Zolkin T. G. Aygumov A. N. Losev E. V. Alexandrova 1Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia 2Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala 367000 Russia 3Department of Applied Informatics Russian State Agrarian University - Moscow Timiryazev Agricultural Academy Moscow 127550 Russia 4Department of Information Technologies and Mathematics Orel State Agrarian University named after N.V. Parakhin Orel 302019 Russia

The cybernetic essence of the data extraction process at the level of end nodes (intermediate links of the information exchange path) is considered in the article. Authors consider and generalize the context-oriented problem of control action transferring to a specific activation point (transfer perceptron (response node)).The authors consider the subject area - distributed response networks. The role of addictive technologies in the process of formation of a complex control action or command and their transmitting is indicated. A simulation balance model is being developed for a routing model in the spatial field of an industrial computer network. It implements the method of filtering and disaggregation of automated technological process control lines according to a group criterion (by the method of sets determination).The introduction of hardware control of the model of the authentication system of automated line devices using the method of data multiplexing and the apparatus of fuzzy mathematics is given, formalized and considered, taking into account the use of a cryptographic flow limiter (unidirectional hash function, which has an integrated address controlled impact).Authors generalize a probabilistic model for calculation of the efficiency and expediency of the functioning of this system for a four-loop motion control line. Calculation of the standard deviation and variance for the emulating output of the considered perceptron is given. In conclusion, a block diagram of the developed device is presented.

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Mechanism of the use of the fixed assets of the mining industry enterprises

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AIP Conference Proceedings 2023年第1期2910卷

作者： Zh. Kenzhin T. Aygumov K. Nursapina A. Zolkin O. Saradzheva West Kazakhstan agrarian and technical University named after Zhangir Khan Uralsk 090009 Kazakhstan Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala Russia Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia Economic security controlling and audit department A.N. Kosygin Russian State University Moscow 115035 Russia

Orientation of manufacturers on the development of the consumer market, the growth of competition between enterprises of various forms of ownership and various sectors of the economy contribute to a sharp increase in the interest of researchers in the problem of the efficiency of use of fixed assets, identification of the causes of the decline in capital productivity, and quantitative assessment of factors that make an influence on it. All these factors predetermined the relevance of the chosen topic as well the purpose and objectives of the study. Issues of use of fixed assets at mining enterprises are considered in the article. Factors and tools for the efficient use of fixed assets are analysed, and proposals and recommendations for improvement of the efficiency of use of fixed assets at the enterprise are formulated.

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Energy loss calculation and voltage profile improvement for the rehabilitation of 0.4 kV low voltage distribution network (LVDN)

Measurement: Energy

引用

Measurement: Energy 2024年 4卷

作者： Hasham Khan Main Farhan Ullah Muhammad Saqib Ali Muhammad Shahzaib Shah Muhammad Adnan Khan Muhammad Waseem Ahmed Mohammed Saleh Government College of Technology Abbottabad Pakistan Elementary & Secondary Education Department Khyber Pakhtunkhwa Pakistan College of Electrical Engineering Zhejiang University 310027 China Department of Electrical Engineering University of Engineering and Technology Taxila Taxila 47050 Pakistan School of Electrical Engineering Chongqing University Chongqing 400044 China International Renewable and Energy Systems Integration Research Group (IRESI) Department of Electronic Engineering Maynooth University Co. Kildare Ireland Lero - Science Foundation Ireland Research Centre for Software School of Business Innovation Value Institute (IVI) Maynooth University Co. Kildare Ireland Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BME V1 Building Egry József u. 18 1111 Budapest Hungary

In recent decades, electrical utilities have made significant advancements in Electrical Power Distribution systems (EPDS). However, energy loss in distribution networks, particularly in 0.4 kV systems, remains a critical challenge. It is increasingly essential for countries to review and update their power loss policies to deliver electrical energy to consumers at the most feasible and economical rates. The performance of Low Voltage Distribution Networks (LVDN) often falls short, resulting in high voltage fluctuations and significant energy losses for end consumers in 0.4 kV systems. This research aims to address these issues by minimizing energy losses and enhancing the voltage profile of the 0.4 kV distribution network. Various rehabilitation techniques have been employed to significantly improve the efficiency of the LVDN. Since most energy losses are directly associated with the LVDN, two 0.4 kV distribution networks in the villages of New Kalyam and Upper Tharjial in the Mandra Sub-Division, under Islamabad Electricity Supply Corporation (IESCO), Pakistan, were selected as case studies. Field surveys were conducted in collaboration with the local utility staff to collect actual data. The analysis was performed using computer-aided tools, including the Computer-Aided Distribution Planning and Design (CADPAD) software and Energy Loss Reduction (ELR) programs. The proposed methodology is straightforward and practical. The case study results demonstrate significant improvements in the voltage profile and reductions in energy losses within the LVDN. These results have been validated and found to be within permissible standard limits, underscoring the effectiveness of the proposed approach.

关键词： CADPAD Distribution networks Distributed generation ELR Energy losses EPDS Financial saving LVDN Power loss Power quality Rehabilitation techniques Technical and non-technical losses Voltage profile improvement

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Analysis of the integration of robotic automation in production

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AIP Conference Proceedings 2024年第1期3102卷

作者： A. L. Zolkin T. G. Aygumov A. I. Adzhieva A. S. Bityutskiy Yu. N. Koval 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 of Accounting North Caucasus State Academy Karachay-Cherkess Republic Cherkessk 369001 Russia "Invent Technology" LLP Almaty A10E5P4 Kazakhstan FSBEI HE Siberian Fire and Rescue Academy EMERCOM of Russia Zheleznogorsk Krasnoyarsk Krai 662972 Russia

This scientific paper examines the integration of robotic automation in production and its potential benefits for businesses. The paper presents the results of a study that analyzed the impact of robotic automation on productivity, product quality, and cost savings for three different production lines. The study found that the implementation of robotic automation systems led to significant improvements in productivity and cost savings, as well as a decrease in defect rates. However, the study also highlights important factors that must be considered when deciding whether to integrate robotic automation into production, including the initial investment cost, the need for skilled workers, and the potential impact on workers. The paper concludes that while the integration of robotic automation in production is a complex issue that requires careful consideration, businesses that are able to successfully integrate these systems into their production processes can gain a competitive advantage in the marketplace, leading to increased profitability and long-term sustainability, but proper planning and implementation are key to ensuring success. for implementation.

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Multi-sector assessment framework – a new approach to analyse cybersecurity challenges and opportunities 1

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10th International Conference on Multimedia Communications, Services and Security, MCSS 2020

作者： Pappalardo, Salvatore Marco Niemiec, Marcin Bozhilova, Maya Stoianov, Nikolai Dziech, Andrzej Stiller, Burkhard CIRM Viale Zara 81 Milan Italy Software Engineering Italia S.r.l. Via Santa Sofia 64 Catania Italy University of Messina COSPECS PhD St via Concezione 8 Messina Italy AGH University of Science and Technology Mickiewicza 30 Krakow30-059 Poland Bulgarian Defence Institute Sofia Bulgaria Communication Systems Group CSG Department of Informatics IfI University of Zurich UZH Binzmühlestrasse 14 Zurich8050 Switzerland

ISBN: (数字)9783030590000

ISBN: (纸本)9783030589998

This paper presents a new approach to analyse cybersecurity challenges and opportunities, focused on the development of a new risk assessment and management framework. Such a multi-sector assessment framework should be able to evaluate and prioritize cybersecurity risks in trans-sectoral and inter-sectoral contexts. It leads toward proper resource allocations and mitigation actions. To achieve this goal, the analysis of existing risk assessment and management frameworks was performed. Also, an overview on common multi-sectoral technological challenges and opportunities were provided being derived from sector-specific use cases as well as transversal and inter-sectoral challenges and opportunities. As a result of this analysis the architecture of the ECHO Multi-sector Assessment Framework was proposed. The identified technological challenges and opportunities, multi-sector dependencies, and transversal aspects determine the input data for the new framework. The architecture is applicable in healthcare, energy, maritime transportation, and defence sectors, being extensible to others. The framework enables the definition of governance models or the design of cybersecurity technology roadmaps. © Springer Nature Switzerland AG 2020.

关键词： Risk assessment

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An empirical study on learning fairness metrics for COMPAS data with human supervision

arXiv

引用

arXiv 2019年

作者： Wang, Hanchen Grgić-Hlača, Nina Lahoti, Preethi Gummadi, Krishna P. Weller, Adrian Department of Engineering University of Cambridge Max Planck Institute for Software Systems Max Planck Institute for Informatics University of Cambridge Alan Turing Institute

The notion of individual fairness requires that similar people receive similar treatment. However, this is hard to achieve in practice since it is difficult to specify the appropriate similarity metric. In this work, we attempt to learn such similarity metrics from human annotated data. We gather a new dataset of human judgments on a criminal recidivism prediction (COMPAS) task. Assuming that people's judgments encode the fairness metric they adhere to, we leverage prior work on metric learning and attempt to learn people's similarity metrics from these judgments. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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The impact of industry 4.0 on automation and information technology

引用

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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