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IEEE International Conference on Consumer Electronics (ICCE)

作者： Gilang Titah Ramadhan Yori Pusparani Ardha Ardea Prisilla Wei-Cheng Shen Hsu-Tang Cheng Ben-Yi Liau Yih-Kuen Jan Wen-Thong Chang Chih-Yang Lin Chi-Wen Lung Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Digital Media Design Asia University Taichung Taiwan Department of Fashion Design LaSalle College Jakarta Jakarta Indonesia Department of Creative Product Design Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana USA Department of Mechanical Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Changes in epidermal thickness are linked to various skin diseases, such as diabetic foot. Optical Coherence Tomography (OCT), a noninvasive imaging technology, enables detailed visualization of skin layers. This study employed a deep learning approach using the U-Net architecture to analyze OCT images, specifically focusing on the stratum corneum (SC) and epidermis (ED) layers. Utilizing a dataset of 10,000 images, the U-Net model achieved 92 % accuracy in predicting SC and 95% accuracy for ED. These results highlight the potential of this method for accurate 3D visualization of SC and ED layers, paving the way for precise assessment of skin layer thickness and its changes in medical applications.

关键词： Deep learning Visualization Solid modeling Image segmentation Accuracy Three-dimensional displays Optical coherence tomography Medical services Predictive models Epidermis

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Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

arXiv

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arXiv 2025年

作者： Sandrini, Samuele Faroni, Marco Pedrocchi, Nicola Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council of Italy Milan20133 Italy Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy

Collaborative robotics cells leverage heterogeneous agents to provide agile production solutions. Effective coordination is essential to prevent inefficiencies and risks for human operators working alongside robots. This paper proposes a human-aware task allocation and scheduling model based on Mixed Integer Nonlinear Programming to optimize efficiency and safety starting from task planning stages. The approach exploits synergies that encode the coupling effects between pairs of tasks executed in parallel by the agents, arising from the safety constraints imposed on robot agents. These terms are learned from previous executions using a Bayesian estimation;the inference of the posterior probability distribution of the synergy coefficients is performed using the Markov Chain Monte Carlo method. The synergy enhances task planning by adapting the nominal duration of the plan according to the effect of the operator’s presence. Simulations and experimental results demonstrate that the proposed method produces improved human-aware task plans, reducing unuseful interference between agents, increasing human-robot distance, and achieving up to an 18% reduction in process execution time. © 2025, CC BY.

关键词： Markov chains

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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A multiple criteria analysis approach for assessing regional and territorial progress toward achieving the Sustainable Development Goals in Italy

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Decision Analytics Journal 2025年 15卷

作者： Gastaldi, Massimo Uricchio, Antonio Felice Department of Computer Control and Management Engineering Sapienza University of Rome Rome 00185 Italy Department of Industrial and Information Engineering and Economics University of L'Aquila L'Aquila 67100 Italy President of the Governmental Agency for the Evaluation of Universities and Research System (ANVUR) Rome 00153 Italy University of Bari Aldo Moro Bari 70121 Italy

Sustainability is a pressing global challenge demanding an integrated approach balancing economic, environmental, and social perspectives. Numerous indicators have been proposed in the literature to assess progress toward the Sustainable Development Goals (SDGs), including equitable and sustainable well-being (BES). To effectively manage and monitor these indicators, robust analytical models are essential. The present study proposed an integrated analytical framework combining the 0–1 (min–max) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The analysis examined 61 indicators from the 2024 Equitable and Sustainable Well-being of the Territories, a dataset developed by the Italian National Institute of Statistics (ISTAT) to assess regional social, economic, and environmental sustainability across Italy. The results confirmed a persistent north–south divide, with average scores of 3.9 in the north and 1.4 in the south on a 1–5 scale, and central Italy demonstrating an intermediate performance of 3.1. At the regional level, Trentino-Alto Adige, Lombardia, and Valle d'Aosta emerged as top performers, while at the territorial level, Milano, Bologna, and Trieste stood out. These insights highlight the need for stronger synergies between territories to enhance competitiveness and elevate “Made in Italy” on the global stage. Effective regional collaboration may optimize resource allocation, harmonize territorial disparities, and accelerate progress toward the SDGs by leveraging local strengths and fostering sustainable development. © 2025 The Author(s)

关键词： Min-Max method Multiple criteria decision analysis Performance indicators Regional and territorial analysis Sustainable Development Goals TOPSIS

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Simultaneous Placement of Tie-lines and Distributed Generations to Optimize Distribution System Post-outage Operations and Minimize Energy Losses

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 318-328页

作者： Farzaneh Shojaei Mohammad Rastegar Morteza Dabbaghjamanesh Department of Power and Control Engineering School of Electrical and Computer EngineeringShiraz UniversityShirazIran Electrical and Computer Engineering Department The University of Texas at DallasUSA.

This study proposes a new method which aims to optimally install tie-lines and distributed generations *** is done to optimize the post-outage reconfiguration and minimize energy losses and energy not supplied of distribution *** number and location of tie-lines,as well as the number,size,and location of DGs,are pinpointed through teaching the learning-based optimization(TLBO)*** objective function in the current research is to minimize the costs pertaining to the investment,operation,energy losses,and energies not *** addition to the normal operational condition,fault operational condition is also ***,the optimal post-fault reconfigurations for fault occurrences in all lines are ***,the operational constraints such as the voltage and line current limits are taken into account in both normal and post-fault operational ***,the modified IEEE 33-bus and 69-bus distribution test systems are selected and tested to demonstrate the effectiveness of the simultaneous placement of DGs and tie-line technique proposed in this paper.

关键词： Distribution system reliability distributed generation energy loss post-outage reconfiguration tie-lines

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CS-Net: Contribution-based Sampling Network for Point Cloud Simplification

arXiv

引用

arXiv 2025年

作者： Guo, Tian Chen, Chen Yuan, Hui Mao, Xiaolong Hamzaoui, Raouf Hou, Junhui School of Control Science and Engineering Shandong University Ji’nan250061 China School of Software Shandong University Ji’nan250061 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Point cloud sampling plays a crucial role in reducing computation costs and storage requirements for various vision tasks. Traditional sampling methods, such as farthest point sampling, lack task-specific information and, as a result, cannot guarantee optimal performance in specific applications. Learning-based methods train a network to sample the point cloud for the targeted downstream task. However, they do not guarantee that the sampled points are the most relevant ones. Moreover, they may result in duplicate sampled points, which requires completion of the sampled point cloud through post-processing techniques. To address these limitations, we propose a contribution-based sampling network (CS-Net), where the sampling operation is formulated as a Top-k operation. To ensure that the network can be trained in an end-to-end way using gradient descent algorithms, we use a differentiable approximation to the Top-k operation via entropy regularization of an optimal transport problem. Our network consists of a feature embedding module, a cascade attention module, and a contribution scoring module. The feature embedding module includes a specifically designed spatial pooling layer to reduce parameters while preserving important features. The cascade attention module combines the outputs of three skip connected offset attention layers to emphasize the attractive features and suppress less important ones. The contribution scoring module generates a contribution score for each point and guides the sampling process to prioritize the most important ones. Experiments on the ModelNet40 and PU147 showed that CS-Net achieved state-of-the-art performance in two semantic-based downstream tasks (classification and registration) and two reconstruction-based tasks (compression and surface reconstruction). CS-Net also achieved high average precision for objection detection on the KITTI LiDAR point cloud dataset, demonstrating its effectiveness in three-dimensional object detection. Co

关键词： Importance sampling

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Research on Selective Harmonic Elimination Pulse Width Modulation of Three-Level NPC Inverter Based on Adaptive Restart Genetic Algorithm

Research on Selective Harmonic Elimination Pulse Width Modul...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Tao Jing Yuehua Liang A. S. Maklakov A. A. Nikolaev School of Computer and Information Engineering Henan University Kaifeng Henan China Zhejiang Entive Smart Kitchen Appliance Co. Ltd. Shengzhou Zhejiang China Department of Automation and Control Moscow Polytechnic University Moscow Russia Department of Automated Electric Drive and Mechatronics Nosov Magnitogorsk State Technical University Magnitogorsk Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The harmonic elimination task of three-level neutral point clamped (3L-NPC) inverters is related to a complicated engineering optimization problem, which mainly involves constrained nonlinear transcendental equations with multiple solutions. In order to solve the problems of difficult initial value determination, slow convergence rates and susceptibility to local optima trapping in the process of using traditional numerical algorithms, This paper introduces a novel selective harmonic elimination pulse width modulation (SHEPWM) framework, enhanced by an adaptive restart genetic algorithm, to address the limitations of conventional optimization methods. By introducing the adaptive restart mechanism, the performance and robustness of the solution algorithm can be improved, so as to circumvent entrapment in local optima and secure the global optimum across the entire solution space. The simulation results show that there are divergent trends in the switching angles and the total harmonic distortion (THD) values of the output voltage obtained by the proposed method, but all of them can effectively eliminate the specified low-order harmonics. These findings verifies the practical applicability and solution diversity of the proposed method, as well as provides new ideas and methods for solving optimization problems in other engineering fields.

关键词： Total harmonic distortion Voltage Switches Pulse width modulation Harmonic analysis Inverters Robustness Systems simulation Genetic algorithms Convergence

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A Sensorless Method based on Zero-sequence Back-EMF for Five-phase Open-end Winding PMSM with Deadtime Compensation

引用

IEEE Transactions on Energy Conversion 2025年

作者： Jiang, Chunyang Wang, Yingjie Liu, Hongchen Wheeler, Pat Bao, Danyang Northeast Forestry University College of Computer and Control Engineering Harbin150001 China Harbin Institute of Technology School of Electrical Engineering and Automation Harbin150001 China University of Nottingham Department of Electrical and Electronics Engineering NottinghamNG7 2RD United Kingdom Shenzhen Polytechnic University School of Mechanical and Electrical Engineering Shenzhen518000 China

This paper proposes a sensorless method using the zero-sequence back electromotive force (ZSB-EMF) for five-phase open-end winding permanent magnet synchronous machine (OEW-PMSM) with common dc bus. The output of the zero-sequence controller, which is applied to suppress the zero-sequence current (ZSC), is used to reconstruct the ZSB-EMF. Then the position of the rotor is extracted from the output signal of the zero-sequence controller by single phase locked loop. A compensation of the deadtime is proposed to eliminate the negative effect for the position estimation and increase the effective modulation index. The proposed method needs no parameters of the motor, like the resistance, d-q inductance, which are vulnerable and might affect the accuracy of the estimation for the conventional sensorless methods based on the ZSB-EMF. It is easy to implement and the resistance network and complex calculation of conventional methods are also avoided. Experimental results on the five-phase open-end winding PMSM drive system are shown to validate the proposed sensorless method. © 1986-2012 IEEE.

关键词： Permanent magnets

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

引用

IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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