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Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms

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Swarm and Evolutionary Computation 2025年 95卷

作者： Silva, Carlos Leandro Borges da Pires, Thyago Gumeratto Silva Filho, Antonio Marcelino Bulhões, Junio Santos Belo, Orlando M. Oliveira Rodrigues, Clóves Gonçalves Coimbra, Antonio Paulo Calixto, Wesley Pacheco Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Goias PC 74.605-220 Brazil Institute of Systems and Robotics Coimbra University CoimbraPC 3030-290 Portugal Department of Informatics School of Engineering University of Minho BragaPC 4710-057 Portugal Electrical Power Systems Study Group Federal Institute of Tocantins Palmas TocantinsPC 77.021-090 Brazil Graduate Program in Production and Systems Engineering Pontifical Catholic University of Goias Goiania Goias PC 74605-010 Brazil Mato Grosso Brazil Technology Research and Development Center Federal Institute of Goias Goiania Goias PC 74.055-110 Brazil

This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50Hz and 60Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities. © 2025 The Authors

关键词： Electric grounding

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

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Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms

arXiv

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arXiv 2025年

作者： Ma, Nachuan Song, Zhengfei Hu, Qiang Liu, Chuang-Wei Han, Yu Zhang, Yanting Fan, Rui Xie, Lihua College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Donghua University Shanghai201620 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, there is a lack of systematic reviews on state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. Also, we create a dataset called UDTIRI-Crack, comprising 2, 500 high-quality images from seven public annotated sources, as the first extensive online benchmark in this field. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. In addition, the feasibility of foundation models and large language models (LLMs) for road crack detection is explored. Afterwards, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The released benchmark UDTIRI-Crack is available at https://***/submission/. © 2025, CC BY.

关键词： Self-supervised learning

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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IEEE Conference on Wireless Communications and Networking

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

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

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.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Integrated Sensing and Communication System Based on Radio Frequency Resonance Beam

arXiv

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arXiv 2025年

作者： Guo, Yixuan Xia, Shuaifan Xiong, Mingliang Liu, Qingwen Zhou, Shengli Fang, Wen Jiang, Qingwei Yan, Gang Mu, Jiangchuan The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The College of Electronics and Information Engineering Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China The Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States The School of Physics Science and Engineering Tongji University Shanghai200092 China

To address the challenge of complex beam control in traditional multiple-input multiple-output (MIMO) systems, research has proposed establishing adaptive beam alignment by utilizing retro-directive antenna (RDA) arrays to create echo resonance between the base station (BS) and user equipment (UE), thereby reducing system computational load. However, in conventional resonant beam systems (RBS), the use of the same frequency for uplink and downlink inevitably leads to echo interference issues. Therefore, this paper proposes an innovative design for an resonance beam-based integrated sensing and communication (RB-ISAC) system to achieve efficient passive sensing and bidirectional communication. In this system, the UE does not actively transmit signals but instead relies on a passive phase conjugation and frequency conversion structure to separate the uplink and downlink carrier frequencies. Additionally, through effective compensation for signal propagation loss, resonance is achieved after multiple iterations, at which point the beam’s field distribution becomes a low-diffraction-loss, high-focusing pattern, automatically aligning the transmitter with the receiver. This enables high-precision passive positioning while facilitating uplink and downlink communication. Simulation results demonstrate that the proposed system can achieve resonance after multiple iterations, and can support uplink and downlink communication within a range of 5 meters while achieving passive direction of arrival (DOA) estimation with an error of less than 2 degrees. Copyright © 2025, The Authors. All rights reserved.

关键词： Directive antennas

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C2AV-TSE: Context and Confidence-aware Audio Visual Target Speaker Extraction

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Wu, Wenxuan Chen, Xueyuan Wang, Shuai Wang, Jiadong Meng, Lingwei Wu, Xixin Meng, Helen Li, Haizhou The Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong Nanjing University School of Intelligence Science and Technology Suzhou215163 China The Chinese University of Hon g Kong Shenzhen Research Institute of Big Data Shenzhen518172 China Technical University of Munich Germany The Chinese University of Hon g Kong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore

Audio-Visual Target Speaker Extraction (AV-TSE) aims to mimic the human ability to enhance auditory perception using visual cues. Although numerous models have been proposed recently, most of them estimate target signals by primarily relying on local dependencies within acoustic features, underutilizing the human-like capacity to infer unclear parts of speech through contextual information. This limitation results in not only suboptimal performance but also inconsistent extraction quality across the utterance, with some segments exhibiting poor quality or inadequate suppression of interfering speakers. To close this gap, we propose a model-agnostic strategy called the Mask-And-Recover (MAR). It integrates both inter- and intra-modality contextual correlations to enable global inference within extraction modules. Additionally, to better target challenging parts within each sample, we introduce a Fine-grained Confidence Score (FCS) model to assess extraction quality and guide extraction modules to emphasize improvement on low-quality segments. To validate the effectiveness of our proposed model-agnostic training paradigm, six popular AV-TSE backbones were adopted for evaluation on the VoxCeleb2 dataset, demonstrating consistent performance improvements across various metrics. © 2025 IEEE.

关键词： Self-supervised learning

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Social network group decision making with minimum cost and maximum satisfaction consensus based on bargaining game

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Information Fusion 2025年 123卷

作者： Wang, Feng Yu, Xiaobing Mao, Yaqi Pedrycz, Witold School of Management Science and Engineering Nanjing University of Information Science & Technology Nanjing210044 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Institute of Systems Engineering Macau University of Science and Technology Macau SAR Taipa999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

In group decision making (GDM), different decision makers (DMs) will provide different evaluation opinions for alternatives. Consensus-reaching process on these opinions is a critical issue. To improve consensus efficiency, a dynamic social network GDM method based on a bargaining game is developed. First, we build a minimum total cost consensus model for the moderator and then a maximum individual satisfaction consensus model for inconsistent DMs. For the difference in the modified opinions and unit compensation derived from these two types of models, we devise offer-counteroffer strategies for the moderator and DMs under various cases. At the same time, we establish a complete management system for the DM weights based on different behaviors in consensus promotion. In addition, we formulate the trust evolution process of all types of DMs to further update the weights of DMs. Based on this, a consensus feedback iterative mechanism driven by the trust network is constructed. Finally, we use the example of location of an international R&D center to illustrate the entire GDM process. The comparative analysis demonstrates the effectiveness of the proposed method. © 2025 Elsevier B.V.

关键词： Risk perception

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The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review

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PeerJ computer Science 2025年 11卷 e2400-e2400页

作者： Gadekallu, Thippa Reddy Yenduri, Gokul Kaluri, Rajesh Rajput, Dharmendra Singh Lakshmanna, Kuruva Fang, Kai Chen, Junxin Wang, Wei Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Computer Science Engineering and Information Systems Vellore Institute of Technology University Tamil Nadu India Zhongda Group Jiaxing China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India School of Mathematics and Computer Science Zhejiang Agricultural and Forestry University Hangzhou China School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities. This motivated us to conduct an extensive review to assess the effectiveness of GPT in enhancing accessibility and inclusivity in higher education for students with learning disabilities. This review offers a comprehensive analysis of the GPT and its significance for enhancing inclusivity in the field of higher education. In this research, we also examined the possible challenges and constraints associated with the integration of GPT into inclusive higher education, along with potential solutions. Overall, this review is intended for educators, students with and without learning disabilities, policymakers, higher education institutes, researchers, and educational technology developers. This review aims to provide a comprehensive understanding of GPT in promoting inclusive higher education for people with various learning disabilities, its impacts on inclusive higher education, emerging challenges, and potential solutions. Copyright 2025 Gadekallu et al.

关键词： Contrastive Learning

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