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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Chen, Caijuan Zhang, Chaofeng Li, Gaolei Liu, Yuchen National Institute of Informatics The Information Systems Architecture Science Research Division Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Nc State University Department of Computer Science NC United States

ISBN: (纸本)9798350368369

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints. © 2025 IEEE.

关键词： Multiobjective optimization

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System 2

Leveraging Artificial Intelligence for Enhanced Data Generat...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Bandopadhyay, Srijita Dutta, Srimonti Haider, Imran Anuraag, Bhavaraju Zhu, Jerry Bazaz, Saad Ahmed University of Engg. & Management Dept. of Electronics and Communication Engineering Kolkata India Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India Vellore Institute of Technology Dept. of Electronics and Communication Engineering Vellore India University of Waterloo Faculty of Mathematics ON Canada National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350360523

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the ß- VAE filtered approach employed in previous literature. © 2024 IEEE.

关键词： Generative adversarial networks

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Safe Gradient Flow for Bilevel Optimization

arXiv

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arXiv 2025年

作者： Sharifi, Sina Abolfazli, Nazanin Hamedani, Erfan Yazdandoost Fazlyab, Mahyar Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Systems and Industrial Engineering The University of Arizona TucsonAZ85721 United States

Bilevel optimization is a key framework in hierarchical decision-making, where one problem is embedded within the constraints of another. In this work, we propose a control-theoretic approach to solving bilevel optimization problems. Our method consists of two components: a gradient flow mechanism to minimize the upper-level objective and a safety filter to enforce the constraints imposed by the lower-level problem. Together, these components form a safe gradient flow that solves the bilevel problem in a single loop. To improve scalability with respect to the lower-level problem’s dimensions, we introduce a relaxed formulation and design a compact variant of the safe gradient flow. This variant minimizes the upper-level objective while ensuring the lower-level decision variable remains within a user-defined suboptimality. Using Lyapunov analysis, we establish convergence guarantees for the dynamics, proving that they converge to a neighborhood of the optimal solution. Numerical experiments further validate the effectiveness of the proposed approaches. Our contributions provide both theoretical insights and practical tools for efficiently solving bilevel optimization problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Control theory

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Performance Evaluation of Intrusion Detection System with SVM

Performance Evaluation of Intrusion Detection System with SV...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ricky Aurelius Nurtanto Diaz I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Bali Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Various machine learning techniques have been proposed to improve the effectiveness of Intrusion Detection systems (IDS), where IDS is one of the important parts of the network that functions to maintain network security. With its various capabilities and reputation, Support Vector Machine (SVM) is often applied in various classification cases. However, it has constraints with accuracy and computing time when it comes to data that has large dimensions. Utilizing metaheuristic techniques is one possibility that researchers can use to solve this problem. To overcome this, in this study, we will evaluate the performance of SVM for the IDS case by utilizing the metaheuristic Grey Wolf Optimization (GWO) algorithm. From the experiments with three different iteration models, the best results of the combination of SVM and GWO were found in a model with 10 GWO iterations and 10 populations with an accuracy of 97.47% using 15 features out of 48 original features of the UKM-IDS20 dataset.

关键词： Support vector machines Performance evaluation Accuracy Metaheuristics Intrusion detection Machine learning Network security Intelligent systems Cybernetics

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On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods 38

On Partial Optimal Transport: Revising the Infeasibility of ...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

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computers, Materials & Continua 2021年第10期69卷 1323-1341页

作者： Walid El-Shafai Noha A.El-Hag Ghada M.El-Banby Ashraf A.M.Khalaf Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of EngineeringMinia UniversityEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the *** has dramatically affected daily lives,public health,and the world *** paper presents a segmentation and classification framework of COVID-19 images based on deep ***,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT ***,the resulting segmented images are used to identify the infected region,whether COVID-19 or *** proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and *** maxpooling layers are employed with a stride of 2 and a 2×2 *** classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT *** acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

关键词： Classification segmentation COVID-19 CNN deep learning diagnosis applications

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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

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Energy and AI 2022年第2期8卷 177-212页

作者： Daniel Mitchell Jamie Blanche Sam Harper Theodore Lim Ranjeetkumar Gupta Osama Zaki Wenshuo Tang Valentin Robu Simon Watson David Flynn Smart Systems Group Institute of SensorsSignals and SystemsSchool of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghEH144ASUnited Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems GroupCWIAmsterdam1098 XG Netherlands Algorithms Group Faculty of Electrical EngineeringMathematics and Computer Science(EEMCS)Delft University of Technology(TU Delft)Delft2628 XE Netherlands The University of Manchester Department of Electrical and Electronic EngineeringOxford RoadManchesterM139PL United Kingdom James Watt School of Engineering University of GlasgowG128QQUnited Kingdom

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

关键词： Artificial intelligence Autonomous systems Digitalization Offshore renewable energy Offshore wind farms Robotics

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Diagnosis of Dynamic Eccentricity Fault in Inverter-Fed Induction Machine Using Three Phase Stator Currents Intersection Signal

Diagnosis of Dynamic Eccentricity Fault in Inverter-Fed Indu...

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Electronics, Energy and Measurement (IC2EM), International Conference on

作者： Hamid Khelfi Salim Hamouda Kamel Kassa Baghdouche Samir Hamdani Department of Electrical Engineering (DGE) Electrical and Industrial Systems Laboratory (EISL) Faculty of Electrical Engineering (FGE) University of Science and Technology Houari Boumediene (USTHB) Algiers Algeria

The current research presents a dependable and practical indirect approach for detecting and diagnosing Dynamic Eccentricity in inverter-feed induction machines operating at varied load circumstances. The spectral analysis of the Three Phase Currents Intersection Signal (TPCIS) and its positive envelope is the main focus of the suggested technique. The sinusoidal TPCIS signal is extracted from finding time and amplitude of each positive and negative intersection point in three-phase stator current signals rather than using the classical Zero Crossing Time Approach (ZCTA), and the TPCIS signal is formed through searching and interpolating all the crossing points. The positive envelope of TPCIS can be obtained through spline interpolation of all positive crossing points. The theoretical foundation of the suggested technique is presented, and it is then experimentally confirmed by testing two induction machines: one healthy for comparison, and the other with Dynamic Eccentricity fault (DEF). Each machine is exposed to two loads (low, and high load). The acquired experimental findings illustrate the resilience and usefulness of the suggested approach, as well as its capacity to distinguish defective cases from healthy ones under varied load conditions, even at low load.

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Ideation of engineering Solutions for Sustainable Development Goals: A Collaborative Course Between Thai and Japanese Students 38

Ideation of Engineering Solutions for Sustainable Developmen...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Krutphong, Kodchakorn Leelawat, Natt Tang, Jing Ota, Eri Suzuki, Rie Murakami Taoka, Yuki Punyabukkana, Proadpran Environmental Engineering and Disaster Management Program Mahidol University Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Thailand International School of Engineering Chulalongkorn University Thailand Center for International Education Tokyo Institute of Technology Japan School of Environmet and Society Tokyo Institute of Technology Japan Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand The Learning Innovation Center Chulalongkorn University Thailand

ISBN: (纸本)9798350302196

A collaborative course called Global Awareness for Technology Implementation was conducted by the faculty of engineering, Chulalongkorn University, Thailand, and the Tokyo Institute of Technology, Japan. The objective of this course was to facilitate the exchange of knowledge, perspectives, and experiences between Thai and Japanese students through project work, using an engineering design approach and the design thinking process to create innovation relevant to the Sustainable Development Goals (SDGs). During the class, experts shared their knowledge and experiences with the students. The students then worked on their respective projects to develop solution concepts using the design thinking process. The proposed ideas include the application for English class, smart water quality checker, hydrogen-based energy system for rural area, reduced wasted fashion solution, and auto waste sorting machine. Finally, this study concluded with a summary of the results and findings on the empathy process in the chosen SDGs, and of the commonalities and differences between Thailand and Japan in terms of ideation. © 2023 IEEE.

关键词： Students

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SPD Miscoordination in PV systems Due to Lightning Strikes

SPD Miscoordination in PV Systems Due to Lightning Strikes

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International Conference on Applied and Theoretical Electricity (ICATE)

作者： Osmel Pérez Baluja Bernardo Yaser León Avila Carlos A. García Vázquez Daniel Tudor Cotfas Petru Adrian Cotfas Orlys E. Torres Breffe Department of Electronics and Computers Faculty of Electrical Engineering and Computer Science Transilvania University of Braşov Brasov Romania Department of Electric Power Systems Faculty of Electrical Engineering Technological University of Havana Havana Cuba

ISBN: (数字)9798350388107

ISBN: (纸本)9798350388114

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes.

关键词： Grounding Software packages Lightning Surge protection Stars Switches Inverters Power transformers Solar panels Fault current limiters

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