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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology the Center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the Department of Electronic and Information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Cumulative histogram as a feature selection technique for anomaly detection

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Multimedia Tools and Applications 2025年第12期84卷 10095-10107页

作者： Nassar, Mostafa Salama, Rania A. Saleeb, Adel A. El-bahnasawy, Nirmeen A. Ahmed, Hossam Eldin H. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology Faculty of Computers and Information Suez University Suez Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques. © The Author(s) 2024.

关键词： Anomaly detection

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HDEL: a hierarchical deep ensemble approach for text-based emotion detection

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Multimedia Tools and Applications 2025年第4期84卷 1799-1820页

作者： Vora, Shivani Mehta, Rupa G. Information Technology Department C.K.Pithawala College of Engineering and Technology Gujarat Surat India Department of Computer Science and Engineering SVNIT Gujarat Surat India

Emotion detection from social media data plays a crucial role in studying societal emotions concerning different events, aiding in predicting the reactions of specific social groups. However, it is complex to automatically extract implicit emotional information from noisy social media text data. This study introduces the Hierarchical Deep Ensemble Learning (HDEL) system to identify emotions in text data. The proposed HDEL model utilizes BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), BiGRU (Bidirectional Gated Recurrent Unit), and RCNN (Recurrent Convolutional Neural Network) in the first level of its hierarchy. The predicted probabilities of the four models are embedded with input data to prepare the intermediate hybrid data. This hybrid data is fed to the next layer of the proposed system, which utilizes a Random Forest (RF) algorithm to predict the emotion. The proposed approach is tested using three emotion datasets: the WASSA-2017 Emotion Intensity (EmoInt) dataset, the International Survey on Emotion Antecedents and Reactions (ISEAR) dataset, and the CrowdFlower (CF) dataset. EmoInt and ISEAR are clean and balanced, while CF is noisy and imbalanced. The results are compared with various state-of- the-art Machine Learning models. The outperforming results depict the superiority of the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

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Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Employing a Diversity Control Approach to Optimize Self-Organizing Particle Swarm Optimization Algorithms

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Computers, Materials & Continua 2025年第3期82卷 3891-3905页

作者： Sung-Jung Hsiao Wen-Tsai Sung Department of Information Technology Takming University of Science and TechnologyTaipei City11451Taiwan Department of Electrical Engineering National Chin-Yi University of TechnologyTaichung City411030Taiwan

For optimization algorithms,the most important consideration is their global optimization *** research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster *** stochastic optimization algorithms based on population search methods,the search speed and solution quality are always *** that the random range of the group search is larger;in that case,the probability of the algorithm converging to the global optimal solution is also greater,but the search speed will inevitably *** smaller the random range of the group search is,the faster the search speed will be,but the algorithm will easily fall into local ***,our method is intended to utilize heuristic strategies to guide the search direction and extract as much effective information as possible from the search process to guide an optimized *** method is not only conducive to global search,but also avoids excessive randomness,thereby improving search *** effectively avoid premature convergence problems,the diversity of the group must be monitored and *** fact,in natural bird flocking systems,the distribution density and diversity of groups are often key factors affecting individual *** example,flying birds can adjust their speed in time to avoid collisions based on the crowding level of the group,while foraging birds will judge the possibility of sharing food based on the density of the group and choose to speed up or *** aim of this work was to verify that the proposed optimization method is *** compared and analyzed the performances of five algorithms,namely,self-organized particle swarm optimization(PSO)-diversity controlled inertia weight(SOPSO-DCIW),self-organized PSO-diversity controlled acceleration coefficient(SOPSO-DCAC),standard PSO(SPSO),the PSO algorithm with a linear decreasing inertia weight(SPSO-LDIW),and th

关键词： Diversity control optimize self-organizing PSO

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Relayer Aggregation Using Chainless Multi-Layer Consensus

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IEEE Access 2025年 13卷 70928-70946页

作者： Yanagihara, Takaaki Fujihara, Akihiro Suwa University of Science Department of Applied Information Engineering Chino392-0292 Japan Chiba Institute of Technology Department of Information and Communication Systems Engineering Narashino275-0016 Japan

The increasing adoption of blockchain technology has underscored the need for cross-chain systems that enable seamless communication among multiple BC networks. Achieving cross-chain interoperability, which ensures secure and efficient data storage and transfer across BCs, remains a critical technical challenge. Among existing solutions, the Inter-Blockchain Communication (IBC) protocol within the Cosmos ecosystem is a prominent framework that facilitates cross-chain communication using light clients and continuous monitoring. However, IBC faces limitations due to its reliance on the consensus algorithms and block generation intervals of participating blockchains. Cross-chain transactions are processed sequentially, requiring approval at each stage, which reduces efficiency. Furthermore, the increasing number of relayers introduces scalability and operational challenges. To address these issues, this study proposes a novel framework called Relayer Aggregation (RA), which aims to enhance cross-chain communication by employing a chainless multi-layer consensus mechanism. RA enables parallel transaction processing to improve performance and scalability. Experimental nodes were developed, and comparative performance evaluations of RA and IBC were conducted to validate the proposed approach. The results demonstrate that RA significantly reduces the number of required relayer nodes and enhances processing efficiency through parallelization. By overcoming the sequential processing limitations of IBC, RA offers a scalable and efficient solution for cross-chain interoperability. This study contributes to advancing blockchain ecosystems by addressing key bottlenecks in cross-chain systems and providing a foundation for future optimization in distributed environments. © 2013 IEEE.

关键词： Blockchain

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A Method for Practical Stability Region Evaluation and A Preventive Control Strategy for MicroGrids

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IEEE Transactions on Industry Applications 2025年第3期61卷 4890-4897页

作者： Amato, Giulia De Tuglie, Enrico Elio Martella, Angelo Montegiglio, Pasquale Rasolomampionona, Désiré Pai, Hung-Yu Department of Electrical and Information Engineering Polythecnic University of Bari Bari Italy Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Institute of Electrical Power Engineering Warsaw University of Technology Warsaw Poland Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

This paper presents an innovative method for practical Static Security Assessment (SSA) of a Smart MicroGrid (MG). The proposed methodology defines a feasibility region where the MG exhibits stable behavior according to voltage-related relay settings. This offline approach facilitates the proactive feasibility assessment of any assumed operating condition expressed in terms of active and reactive injected powers. The feasibility region is defined through an ellipsoid that, with a minimum volume, encloses feasible points randomly generated and tested by load flow analyses. If an infeasible operating point is detected, the method is able to reschedule generators, storages and loads by proposing a new condition having a minimal Euclidean distance from the infeasible one. The method's simplicity, its ability to rapidly assess the feasibility of any assumed operating point and the possibility to implement fast preventive control strategies, give rise to a practical and useful tool in small enterprises owning MGs where sophisticated algorithms are not usually available, even if it can be adopeted also in more large and complex systems. The proposed strategy was subsequently applied to the static model of a subset of the experimental MG in the PrInCE laboratory, located at the Polytechnic University of Bari. © 1972-2012 IEEE.

关键词： Microgrids

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An Approach to Identify the Complete Reduplicated Multiword Expressions in Digital Bengali Text

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 521-537页

作者： Pan, Subrata Department of Information Technology Bankura Unnayani Institute of Engineering West Bengal Bankura722146 India

This work presents an approach recognizing the complete reduplication of bi-word multiword expressions in a robust Bengali dataset. Reduplication, denoting the repetition of any language unit in linguistic studies, is a crucial aspect of identifying multiword expressions. The proposed method performs in two stages: the first stage includes pre-processing activities, and the second involves identifying bi-gram word pairs using two different methods and a comprehensive validation to find the accuracy of the proposed system. The proposed approach, employing the Levenshtein distance method, achieves a significant accuracy of 99% for three categories of bi-gram combinations of complete reduplicated multiword expressions. It exhibits a notable improvement of 1%, surpassing the result of the related work. © The Institution of Engineers (India) 2024.

关键词： Natural language processing systems

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