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Progress in Applied Electrical Engineering (PAEE)

作者： Piotr Schneider Dariusz Mikołajewski Anna Bryniarska Magdalena Igras-Cybulska Artur Cybulski Walery Marcinowicz Maciej Janiszewski Aleksandra Kawala-Sterniuk Department of Neuroinformatics and Biomedical Engineering Institute of Computer Science Maria Curie-Sklodowska University Lublin Poland Hishoo Sp. z o.o Gdynia Poland Faculty of Computer Science Kazimierz Wielki University Bydgoszcz Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

Automatic or semi-automatic recognition of selected emotions using Machine Learning (ML) algorithms requires practical, fast and accurate implementation of the steps involved in acquiring the actual signals (e.g. speech, facial expression, posture and body movements), recording them in the form of an optimal feature vector/matrix, correctly interpreting the above data and reproducing and generalising this process within the ML system. Depending on the accuracy and speed required and the degree of its ‘invasiveness’ (e.g. by attaching sensors to the user’s body), single or multi-modal interpretations of biometric signals associated with different groups of emotions are possible. This group of studies, despite their relative non-invasiveness, requires the caution prescribed by the Artificial Intelligence (AI) Act and RODO Act and the possibility of being replaced (if necessary) by alternatives. This article aims to analyse the opportunities and risks associated with this group of technologies, with a particular focus on directions for further development.

关键词： Emotion recognition Machine learning algorithms Accuracy Video on demand Speech recognition Machine learning Feature extraction Recording Convolutional neural networks Web sites

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Influence of Pulse Discharging on Lithium-Ion Battery

Influence of Pulse Discharging on Lithium-Ion Battery

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International Electrical Engineering Congress (iEECON)

作者： Hsu Myat Naing Chinda Charoenphonphanich Masaki Yamakita Pera Tanateerapong Burin Kerdsup Manop Masomtob Department of Mechanical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan National Astronomical Research Institute of Thailand (NARIT) Chiangmai Thailand National Electronics and Computer Technology Center(NECTEC) National Science and Technology Development Agency Pathum Thani Thailand National Energy and Technology Center(ENTEC) National Science and Technology Development Agency Pathum Thani Thailand

This paper aims to investigate the impact of switching frequencies in pulse discharging of batteries by testing with Lithium-ion cells. Applying lithium-ion batteries in high power applications is needed to be managed according to the demand of load power and current profile. The pulse current discharging technique with different frequencies is expected to improve the charging/ discharging capacity and energy of lithium-ion batteries. In this paper, lithium-ion cells were tested with pulse current at various switching frequencies with 75% duty cycle during discharging. The results of pulse discharging with different switching frequencies were compared with constant current discharging method by evaluating capacity and energy. From this research, the results indicated that pulse discharging at low frequencies generated high discharging capacity than constant current discharging.

关键词： Lithium-ion batteries Electrical engineering Switching frequency Testing

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PVWA: Privacy-preserving and Verifiable Weighted Aggregation for Federated Learning

PVWA: Privacy-preserving and Verifiable Weighted Aggregation...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zihan Li Xiaodong Wang Shuai Yuan Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Finance Operations and Information Systems (FOIS) Brock University Canada Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients’ local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency.

关键词： Training Privacy Data privacy Protocols Federated learning Training data Servers Security Global communication Homomorphic encryption

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Fusion Learning of Multimodal Neuroimaging with Weighted Graph AutoEncoder

Fusion Learning of Multimodal Neuroimaging with Weighted Gra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Gen Shi Yifan Zhu Fuquan Zhang Wenjin Liu Yuxiang Yao Xuesong Li School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer and Control Engineering Minjiang University Fuzhou China Department of Nephrology Northern Jiangsu People’s Hospital Yangzhou University Yangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Neuroimaging plays an significant role in diagnosing and pathological study of brain diseases. Considering that both functional and structural abnormalities may lead to brain dis-eases and disorders, single modal neuroimaging approach may not fully characterize brain activities and working modes. Fusion of multimodal neuroimaging data is expected to provide more comprehensive characterization of brain diseases, given that the different modalities contain more complementary information. Recently, Graph Convolutional Networks (GCNs) is shown to have powerful capacity in representation learning for graph-structure data, which is considered to integrate both graph se-mantic structure and node information. Therefore, in this paper, we propose the Weighted Graph AutoEncoder (WGAE), a GCN- driven multimodal fusion model, to learn the combinational latent node representation of fMRI and DTI neuroimaging data, which are used as node features and graph structure respectively in the graph in unsupervised manner. Experimental results on two real-world datasets show the superiority of the proposed model over other existing single-modal or multi-modal methods in learning representations for disease prediction as the downstream task. Furthermore, ablation experiments also show the collaborative contribution of multimodal neuroimaging fusion in the proposed model, and also show the feasibility of assessing the respective importance of the two modalities during the disease prediction.

关键词： Neuroimaging Representation learning Pathology Predictive models Functional magnetic resonance imaging Brain modeling Data models

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Application of various CNNs for the Identification of Hypertrophic Cardiomyopathy

Application of various CNNs for the Identification of Hypert...

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Artificial Intelligence, Computational Electronics and Communication System (AICECS), International Conference on

作者： Jimcymol James Anjan Gudigar Raghavendra U Jyothi Samanth Simran Kumari Rathore Aashna Baheti Rhea Tiwari Mukund A Prabhu Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Cardiovascular Technology Manipal College of Health Professions Manipal Academy of Higher Education Manipal India Department of Cardiology Kasturba Medical College Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350391244

ISBN: (纸本)9798350391251

Hypertrophic Cardiomyopathy (HCM) is an abnormal condition of the heart due to the thickening of heart muscles. Early diagnosis and prognosis of HCM is appreciable as it can be a threaten to life if left untreated. The structural definitions of the heart are easily captured by Echocardiography. An automated system utilizing Echocardiography for the identification of HCM is thus beneficial to the well-being of the society. This study performs a comparative analysis of various Convolutional Neural Networks (CNNs), that are used for extracting the features which are relevant for the classification of HCM. Heart ultrasound images from 35 subjects each of HCM and normal are utilized for the study. Variants of Support Vector Machine (SVM) are used to perform the classification. Further, significant features are analyzed by feature ranking methods. Finally, it is observed from the result that ResNet50 and AlexNet are the best CNN models towards the identification of HCM.

关键词： Support vector machines Heart Deep learning Training Ultrasonic imaging Echocardiography Feature extraction Convolutional neural networks Testing Residual neural networks

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Coordinated control design for steering and torque-vectoring in Model-Free control structure*

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IFAC-PapersOnLine 2022年第6期55卷 496-501页

作者： Tamás Hegedűs Dániel Fényes Balázs Németh Zoltán Szabó Péter Gáspár Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 H-1111 Budapest Hungary

Model-Free control (MFC) approach is a novel technique to handle nonlinearities and uncertainties in control systems in order to provide enhanced performance level. The design of MFC is based on an ultra-local model, which is an approximation of the dynamics of the controlled system for a short period of time. This structure also involves a model-based robust control, which guarantees the accurate tracking performance of the closed-loop system. In this paper, a novel technique is proposed for designing the robust control for taking into account the varying characteristics of the ultra-local model. A novel modeling method is presented, which combines the original plant of the controlled system and the ultra-local model. Then, a robust control design is proposed, by which the tracking performance and the robustness of the closed-loop system can be guaranteed. The design steps and effectiveness of the proposed control strategy is demonstrated through a vehicle-oriented control problem using the high-fidelity simulation software, CarMaker.

关键词： model-free control torque vectoring automated vehicles robust control

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Architecture Descriptions Analysis Based on Text Mining and Crawling technology 3

Architecture Descriptions Analysis Based on Text Mining and ...

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3rd IEEE International Conference on Frontiers technology of Information and computer, ICFTIC 2021

作者： Zheng, Tong Zhao, Dongmiao Wang, Xingtian Gao, Weijun Miao, Sheng Qingdao University College of Computer Science and Technology Qingdao China Qingdao University of Technology Innovation Institute for Sustainable Maritime Architecture Research and Technology Qingdao China Faculty of Environmental Engineering University of Kitakyushu Kitakyushu Japan Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9781665406055

Nowadays, text mining technology is developed rapidly and employed by various contexts, such as healthcare, public opinion analysis, and document retrieval. Architectural design descriptions usually include large amount of massive text, which means information and knowledge are hidden. Since more and more attention are paid to the sustainable development, this paper research the main building design factors on nature aspect by using text mining. Collecting and processing 41,062 descriptions of architecture projects from website, the frequency of occurrence words with nature aspect are analyzed in 11 architecture categories respectively. The top 100 words of frequency are taken to constitute the word cloud, and the design factors on natural environment aspect could be summarized as area, light, landscape, structure, material, façade, site, water, wind, and view. This will provide architects guidance on dealing with the contradiction between building and nature. It will help us to further explore the relationship between architectural design and natural environment and realize a green and sustainable future. © 2021 IEEE.

关键词： Architectural design

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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Progress in Applied Electrical Engineering (PAEE)

作者： Aleksandra Kawala-Sterniuk Dariusz Mikołajewski Luis A. Leiva Tuukka Ruotsalo Adam Lysiak Barbara Grochowicz Edward Jacek Gorzeńariczyk Adrian Luckiewicz Anna Wieczorek Mariusz Pelc Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared.

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A Resilient Decentralized Learning Approach against Byzantine Attack via Reputation Evaluation

A Resilient Decentralized Learning Approach against Byzantin...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Heng Zhang Wenting Wang Xuguo Jiao Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan P. R. China College of Control Science and Engineering Zhejiang University Hangzhou China State Grid Shandong Electric Power Research Institute Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Machine learning is a key component of many applications, among which decentralized learning has attracted wide attention because of its cost-effectiveness and high efficiency. However, decentralized learning is vulnerable to various Byzantine attacks. Existing defense models face challenges in defending against complex attacks and are often constrained by strict network topologies. To address this issue, a parameter aggregation rule based on reputation evaluation (RPV) is proposed in this paper. This rule establishes a reputation model for neighboring nodes and continuously updates it based on their distance performance during each iteration. By applying the reputation model of neighboring nodes to the Beta distribution, their reliability is obtained. Based on the reliability of neighboring nodes, the set of reliable nodes for this iteration and select parameter values from them for aggregation are determined. Finally, we gradually eliminate the influence of Byzantine nodes. Our experimental results on the MNIST dataset demonstrate that the proposed algorithm is resilient to attacks from any number of Byzantine nodes and outperforms previous defense models in terms of network topology constraints, training accuracy, and computational costs.

关键词： Training Learning systems Accuracy Network topology Computational modeling Machine learning Classification algorithms

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