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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Shaharkar, Bhushan Mittal, Tanusha Sunkara, Krishna Chaitanya Shukla, Vishal Desai, Ishan Mookherjee, Ujal Walchand Institute of Technology Department of Information Technology Solapur India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Oracle Cloud Engineering Data Center Design & Modeling Raleigh United States Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rise of Internet of Things (IoT) networks has introduced new opportunities for innovation but also increased vulnerability to cyberattacks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection and mitigation mechanisms often struggle to handle the dynamic and distributed nature of IoT systems. In this study, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to detect and mitigate DDoS attacks in IoT networks in real-time. The framework consists of multiple collaborative agents, each deployed at different network layers or nodes, trained using deep reinforcement learning to identify attack patterns and take immediate mitigation actions. Through cooperative learning, these agents adapt to evolving attack strategies and optimize defense policies. Experimental evaluations demonstrate that the proposed MADRL framework outperforms traditional techniques by achieving higher detection accuracy, faster response times, and reduced false positive rates. Our results indicate that this framework can effectively secure IoT networks against complex DDoS attacks, maintaining network stability and availability. © 2024 IEEE.

关键词： Deep reinforcement learning

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum Access in Sensor Ad Hoc Networks 15

Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Geetha, B. Kansal, Amit Sudhamsu, Gadug Gawali, Piyush P. Amudha, A. Samrat, Bhavuk Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Research Center Quantum University Uttar Pradesh India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Cognitive Radio-primarily based Spectrum Sensing (CRSS) is a crucial generation for permitting dynamic spectrum access in sensor ad hoc networks (SAHNs). This era allows customers to successfully make use of to be had spectrum whilst keeping off interference from existing customers. CRSS makes use of sensing and optimization strategies to identify free frequency bands to enable verbal exchange in SAHNs. Moreover, the CRSS additionally consists of protocols and techniques to locate and defend against malicious behavior, which include spectrum jamming and false sensing reports. Subsequently, CRSS may be blended with superior technology, such as multi-agent structures and system gaining knowledge of, to create an efficient and sturdy gadget for dynamic spectrum access. © 2024 IEEE.

关键词： Frequency Generation Malicious Permitting Primarily Spectrum Techniques technology

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Multimedia Classification via Tensor Linear Discriminant Analysis

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IEEE Transactions on Broadcasting 2024年第4期70卷 1139-1152页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Yan, Kun Chih-Hao Huang, Scott Wu, Yiyan San Jose State University Department Of Applied Data Science San JoseCA95192 United States Louisiana State University School Of Electrical Engineering And Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center For Ai Applications Taoyuan32003 Taiwan Guilin University Of Electronic Technology Guangxi Key Laboratory Of Wireless Wideband Communication And Signal Processing Department Of Information And Telecommunication Guilin541004 China National Tsing Hua University Department Of Electrical Engineering Hsinchu300 Taiwan Western University Department Of Electrical And Computer Engineering LondonONN6A 3K7 Canada

Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet. © 1963-12012 IEEE.

关键词： Tensors

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Computing Paradigms in Smart Grids 2.0: Exploring Computing Models and Cybersecurity Challenges 19

Computing Paradigms in Smart Grids 2.0: Exploring Computing ...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Shahinzadeh, Hossein Mohammadali Zanjani, S. Gharehpetian, Gevork B. Poursaeid, Amir-Emad Etemadnia, Ehsan Mehrabani-Najafabadi, Saiedeh Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Najafabad Branch Islamic Azad University Department of Electrical Engineering Najafabad Iran Islamic Azad University Faculty of Computer Engineering Najafabad Branch Najafabad Iran

ISBN: (纸本)9798331507565

Smart Grid 2.0 represents a significant advancement in energy systems, propelled by innovations such as the Internet of Things (IoT), Blockchain technology, and 5G telecommunications. These technologies enable dynamic and intelligent energy management while introducing new challenges in data processing and cybersecurity. This paper examines the role of various computing models - Cloud, Fog, Edge, and Distributed computing - within the framework of Smart Grids 2.0, emphasizing their impact on system efficiency and security. We first define each computing paradigm and its specific applications in smart grids. Next, we assess the security risks and computational challenges associated with these models, offering a comparative analysis of their performance in addressing concerns such as latency, scalability, and susceptibility to cyber-attacks. Proposed solutions, including encryption techniques and real-time threat detection, are discussed to strengthen the resilience of smart grids. The paper concludes with recommendations for future research, particularly in advancing cybersecurity measures and optimizing data processing in an increasingly interconnected energy infrastructure. © 2024 IEEE.

关键词： Cybersecurity

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Design and Implementation of a 6.78-MHz Wireless Charger for E-bike Applications 6

Design and Implementation of a 6.78-MHz Wireless Charger for...

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6th International Future Energy Electronics Conference, IFEEC 2023

作者： Chen, Yun-Yen Fang, Yi-Hung Chen, Chen Chen, Kai-De Liu, Yu-Chen Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Cips LITE-ON Technology Taipei Taiwan Center of Core Competence LITE-ON Technology Taipei Taiwan National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350339888

This paper presents a significant contribution in the development of a high-efficiency, high-power-density wireless charger for E-bikes. By utilizing a 6.78 MHz switching frequency and employing wide band gap devices (GaN) and Schottky diodes, the system achieves remarkable advancements in power component selection, leading to reduced switching losses and improved performance. Moreover, the integration of high-frequency magnetic materials in the coils enhances coupling and minimizes the impact on the surrounding magnetic field. The use of Maxwell analysis software enables accurate simulation and optimization of the circuit, coil, and core design. By addressing phase-related challenges through the implementation of an auxiliary circuit, the issue of voltage switching is effectively mitigated. Ultimately, the achieved wireless charger for E-bikes stands out with its 110V input/output voltage, 240W output power, 6.78 MHz switching frequency, and an impressive maximum efficiency of 92.45%. This research contributes significantly to the advancement of wireless charging technology for E-bikes, offering valuable insights for further development and practical applications in the field. © 2023 IEEE.

关键词： Energy gap

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Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 597-605页

作者： Bo Huang MengChu Zhou Cong Wang Abdullah Abusorrah Yusuf Al-Turki School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net ***,a Petri net for the deadlock control of such systems is *** admissible markings and first-met inadmissible markings(FIMs)are ***,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable *** also ensures that the obtained deadlock-free supervisor does not observe any unobservable *** addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added *** condition under which the supervisor is maximally permissive in behavior is ***,experimental results with the proposed method and existing ones are given to show its effectiveness.

关键词： Deadlock prevention Petri nets robotic manufacturing cells structure-minimized supervisor supervisory control uncontrollability unobservability

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Digital engineering Framework for Promoting Health and Human Performance with Immersive technology

Digital Engineering Framework for Promoting Health and Human...

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Annual IEEE Systems Conference

作者： Gayathri Boopathy Emi Aoki Flore Norcéide Charles Thompson Erika Lewis Kavitha Chandra Department of Electrical and Computer Engineering Center for Advanced Computation and Telecommunications Department of Physical Therapy & Kinesiology UMass Movement Research Center University of Massachusetts Lowell Lowell USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

The need for point-of-care health solutions and digital health interventions continues to increase, driven by an aging population and those displaced from the systemic impacts of environmental, infrastructural, and economic factors. These demographic and social shifts are placing unprecedented strain on the healthcare system. In response to these challenges, digital health solutions are emerging, although data indicates low confidence among both consumers and healthcare professionals due to a lack of regulatory oversight compared to traditional medical systems. This research explores the use of augmented reality (AR) technology in physical therapy and rehabilitation as a potential digital health intervention. The focus is on improving upper limb and hand mobility. This study employs a digital engineering design process in collaboration with physical therapists and clinical researchers. The goal is to utilize data from the AR sensors to capture quantitative information about a patient's therapeutic progress. The Digital engineering framework presented can be applied for improving the validation and verification of technology-based digital health interventions, with the potential to enhance access to rehabilitation resources. Use-case and requirement diagrams are developed through close collaboration with physical therapy clinicians, to assess user engagement and therapeutic outcomes. Activity diagrams capture successive stages of improvement in the proposed system. The performance of the AR application in tracking joint angles and measuring mobility during prescribed gestures is evaluated against published normative values, highlighting the potential of this technology to improve upon manual measurement methods.

关键词： Hands Point of care Medical treatment Collaboration Manuals Electronic healthcare Sensors Modeling Augmented reality Strain

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Characterization and Modeling of Non-Conducting RF Hot Carrier Stress in FinFETs 17

Characterization and Modeling of Non-Conducting RF Hot Carri...

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17th IEEE International Conference on Solid-State and Integrated Circuit technology, ICSICT 2024

作者： Niu, G. Ding, X. Zhang, H. Wang, W. Imura, K. Dai, F. Alabama Micro/Nano Electronics Science and Technology Center Auburn University Electrical and Computer Engineering Department AuburnAL United States Maxlinear Inc. CarlsbadCA United States

ISBN: (纸本)9798350361834

A significant reliability concern in RF power amplifiers is non-conducting RF hot carrier stress. We present experimental characterization and lifetime modeling of non-conducting RF hot carrier stress in modern FinFETs and demonstrate the importance of near-threshold stress. The relationship between RF and DC stresses is examined, and the quasi-static approximation is discussed. Die-to-die threshold voltage variation makes the measurement and modeling of V ds dependence difficult. A solution using the source current is proposed and demonstrated. © 2024 IEEE.

关键词： FinFET

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