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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Monolithic 3-D Integration of Diverse Memories: Resistive Switching (RRAM) and Gain Cell (GC) Memory Integrated on Si CMOS

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IEEE Transactions on Electron Devices 2025年第5期72卷 2685-2690页

作者： Liu, Shuhan Radway, Robert M. Wang, Xinxin Moro, Filippo Nodin, Jean-Francois Jana, Koustav Yan, Lixian Du, Shuting Upton, Luke R. Chen, Wei-Chen Kang, Jimin Chen, Jian Li, Haitong Andrieu, Francois Vianello, Elisa Raina, Priyanka Mitra, Subhasish Wong, H.-S. Philip Stanford University Department of Electrical Engineering StanfordCA94305 United States University of Pennsylvania School of Engineering and Applied Science Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States Université Grenoble Alpes CEA-Leti Grenoble38058 France Purdue University Department of Electrical and Computer Engineering West LafayetteIN47907 United States

The future memory is massive, diverse, and tightly integrated with computing. This research presents tight integration, both physically and architecturally, of two on-chip memory technologies, resistive switching random access memory (RRAM) and gain cell (GC) memory. HfO2 RRAM and indium tin oxide (ITO) GC memory are monolithically integrated on 130-nm Si CMOS technology to form a joint memory that enables low-energy training and low-standby-power inference for edge devices. High-bandwidth on-chip data transfer can have a bandwidth that is 90x state-of-the-art (SoTA) HBM3E and 211x PCIe 7.0, enabled by high-density monolithic 3-D interconnections between memory arrays and high-speed transfer circuit within the integrated joint memory macro. Fabricated atomic layer deposition (ALD) ITO FET exhibits positive VTHof 0.67 V, excellent subthreshold slope (SS) of 65 mV/dec, high ON-current of 20 μA/μm, and low OFF-current of 5 x 10-18 A/μm, as extracted from >5000 s retention. The joint memory macro consumes 78% less standby power and 95% less training energy for MobileBERT compared to SRAM with iso-capacity. This RRAM-GC joint memory facilitates efficient continual learning in edge devices, addressing the challenges of a resource-constrained environment while supporting adaptive artificial intelligence (AI) model updates. © 1963-2012 IEEE.

关键词： RRAM

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Comments on a Key Agreement Scheme for the V2G System 14th

Comments on a Key Agreement Scheme for the V2G System

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Ya-Fen Chang, Wen-Ting Tai, Wei-Liang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Vehicle-to-grid (V2G) technology supporting bidirectional power transfer allows electric vehicles (EVs) to contribute and consume energy bidirectionally. Because the specific properties and requirements of V2G, Khan et al. proposed a key agreement scheme for the V2G system to ensure the security of communication between a vehicle user and the grid server. However, after analyzing Khan et al.’s scheme, we find that it suffers from some flaws such that it cannot ensure anonymity, resist impersonation attack, man-in-the-middle attack and denial-of-service attack, and work as expected. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Research on De-identification Applications of LLMs in Medical Records

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International Journal of Network Security 2025年第1期27卷 213-222页

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yang-Shu Wu, Nan-I Lin, Iuon-Chang Department of Computer Science & Information Engineering Asia University 500 Lioufeng Rd. Wufeng Taichung41354 Taiwan Department of Management Information Systems National Chung Hsing University Taiwan Department of Information Management Lunghwa University of Science and Technology Taoyuan33306 Taiwan

The primary objective of this research is to develop and promote efficient and secure de-identification technology to address the application of sensitive personal information in healthcare big data. Considering the outstanding performance of large language models (such as GPT) in understanding text, we aim to utilize these advanced models to process medical records. Compared to traditional methods such as manual processing, rule-based, or machine learning methods, large language models can more effectively de-identify unstructured medical record data. We can enhance the recognition and de-identification accuracy of personal identification information in medical records through deep learning techniques. Additionally, the contextual understanding capabilities of large language models can comprehensively process medical record data, ensuring that the de-identification process does not affect the integrity and availability of medical information. Therefore, the research methodology will include model training, optimization, and validation to ensure that the developed de-identification technology achieves efficient and reliable results in practical applications. We explore different large language models, considering the diversity of personal medical data to address various de-identification needs in different medical record data. In this article, we propose three research issues for de-identification: 1) Research on the application of deep language models and de-identification technology in medical cases;2) optimization of prompt engineering to enhance de-identification of medical cases by large language models;3) Optimization and promotion of de-identification technology for practical applications in the medical. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Electronic health record

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Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques

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Artificial Intelligence Review 2025年第8期58卷 1-41页

作者： Islam, Saidul Rjoub, Gaith Elmekki, Hanae Bentahar, Jamal Pedrycz, Witold Cohen, Robin Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Department of Computer Science 6 G Research Center Khalifa University Abu Dhabi United Arab Emirates Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Research Center of Performance and Productivity Analysis Istinye University Sariyer/Istanbul Turkey David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR), marking a paradigm shift from conventional, manually driven resuscitation practices to intelligent, data-driven interventions. It examines the evolution of CPR through the lens of predictive modeling, AI-enhanced devices, and real-time decision-making tools that collectively aim to improve resuscitation outcomes and survival rates. Unlike prior surveys that either focus solely on traditional CPR methods or offer general insights into ML applications in healthcare, this work provides a novel interdisciplinary synthesis tailored specifically to the domain of CPR. It presents a comprehensive taxonomy that classifies ML techniques into four key CPR-related tasks: rhythm analysis, outcome prediction, non-invasive blood pressure and chest compression modeling, and real-time detection of pulse and Return of Spontaneous Circulation (ROSC). The paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings. Furthermore, it highlights the role of eXplainable AI (XAI) in fostering clinical trust and adoption. By bridging the gap between resuscitation science and advanced ML techniques, this survey establishes a structured foundation for future research and practical innovation in ML-enhanced CPR. It offers clear insights, identifies unexplored opportunities, and sets a forward-looking research agenda identifying emerging trends and practical implementation challenges aiming to improve both the reliability and effectiveness of CPR in real-world emergencies. © The Author(s) 2025.

关键词： Reinforcement learning

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Physically Aware Synthesis Revisited: Guiding Technology Mapping with Primitive Logic Gate Placement 24

Physically Aware Synthesis Revisited: Guiding Technology Map...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Pan, Hongyang Lan, Cunqing Liu, Yiting Wang, Zhiang Shang, Li Zeng, Xuan Yang, Fan Zhu, Keren State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University China School of Computer Science Fudan University China Department of Electrical and Computer Engineering University of California San Diego United States

ISBN: (纸本)9798400710773

A typical VLSI design flow is divided into separated front-end logic synthesis and back-end physical design (PD) stages, which often require costly iterations between these stages to achieve design closure. Existing approaches face significant challenges, notably in utilizing feedback from physical metrics to better adapt and refine synthesis operations, and in establishing a unified and comprehensive metric. This paper introduces a new Primitive logic gate placement guided technology MAPping (PigMAP) framework to address these challenges. With approximating technology-independent spatial information, we develop a novel wirelength (WL) driven mapping algorithm to produce PD-friendly netlists. PigMAP is equipped with two schemes: a performance mode that focuses on optimizing the critical path WL to achieve high performance, and a power mode that aims to minimize the total WL, resulting in balanced power and performance outcomes. We evaluate our framework using the EPFL benchmark suites with ASAP7 technology, using the OpenROAD tool for place-and-route. Compared with OpenROAD flow scripts, performance mode reduces delay by 14% while increasing power consumption by only 6%. Meanwhile, power mode achieves a 3% improvement in delay and a 9% reduction in power consumption. © 2024 Copyright is held by the owner/author(s).

关键词： Logic design

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Development and Evaluation of an Interactive Web Interface for BPMN Elements Identification from Natural Language Text 16

Development and Evaluation of an Interactive Web Interface f...

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16th International Conference on ENTERprise Information systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： De Moura, Vitor Camargo Rockembach, Guilherme Rego Pimenta, Marcelo Soares Gasparini, Isabela Sosa-Sánchez, Encarna Thom, Lucineia Heloisa Porto Alegre91501-970 Brazil Departament of Computer Science University of Santa Catarina State Joinville 89219-710 Brazil Department of Computer Systems and Telematics Engineering-University of Extremadura Cáceres10003 Spain

Process discovery represents a high percentage of the time demanded along the business process management lifecycle. Texts in natural language, or process descriptions, can be explored as a critical information source. Extracting information automatically from these documents is challenging due to their ambiguity and complexity. While various (semi-)automated solutions are proposed in the literature, functional code access is rare, and tool deployment is almost nonexistent. This research introduces an accessible web interface that allows users to upload process descriptions and interact with the text, automatically identifying the main process elements from the Business Process Model and Notation. Interactions include highlighting elements associated with specific participants. An evaluation experiment with ten potential real-world users was conducted to assess the tool's benefits. The experiment compared performance in answering questions related to process descriptions across three scenarios: using plain text, using the proposed tool, and using ChatGPT. Results indicated an advantage in using the tool, particularly regarding time consumption. Data collected on ChatGPT usage also provided insights into how users leverage this technology for problem-solving. © 2025 The Author(s).

关键词： BPMN development evaluation natural language process description Web interface

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Blockchain-Enabled Secure Offloading for VEC: A Multi-Agent Reinforcement Learning Approach

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2978-2995页

作者： Lu, Xiaozhen Xiao, Liang Xiao, Yilin Xiong, Zehui Liu, Zhe Zhang, Yanyong Zhuang, Weihua Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Xiamen University Department of Information and Communication Engineering Xiamen361005 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Zhejiang Lab Hangzhou310027 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Vehicular edge computing (VEC) helps improve the task computational performance of vehicles on roads but has difficulty in defending against eavesdropping and selfish attacks simultaneously. In this paper, we design a reputation-based smart contract with blockchain and propose a multi-agent reinforcement learning (RL) based secure offloading scheme for VEC against both eavesdropping and selfish attacks. This scheme has a three-level hierarchical structure for each vehicle and uses the reputations obtained from the blockchain as the basis to optimize the edge node selection, offloading ratio, and power allocation, which aims to reduce the task computational latency, the vehicle energy consumption and eavesdropping rate. By using a punishment function based on the constraints, this scheme avoids exploring dangerous policies that can cause task failure or severe data leakage. A multi-agent deep RL-based secure offloading scheme is proposed for vehicles with sufficient resources, which evaluates the long-term risk rather than the punishment function to further improve the secure offloading performance. The regret bound is analyzedand the cumulative reward upper bound is provided. Simulation results verify the effectiveness of our schemes as compared with the benchmark. © 2004-2012 IEEE.

关键词： Computation offloading

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