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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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Distributed Nash Equilibrium Seeking in Coalition Games for Uncertain Euler-Lagrange Systems With Application to USV Swarm Confrontation

arXiv

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arXiv 2025年

作者： Yuwen, Cheng Wen, Guanghui Zhou, Jialing Luan, Meng Huang, Tingwen School of Automation Southeast University Nanjing210096 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing519088 China Department of System Science School of Mathematics Southeast University Nanjing211189 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen218055 China

In this paper, a coalition game with local and coupling constraints is studied for uncertain Euler-Lagrange (EL) systems subject to disturbances with unknown bounds. In the coalition game, each agent collaborates with other agents within the same coalition to optimize its coalition’s cost function while simultaneously competing against agents in other coalitions. Under a distributed framework where each agent has access only to its own action, cost function, and constraint parameters, a distributed strategy is proposed to seek the Nash equilibrium (NE). By combining adaptive methods and sign functions, model uncertainties and disturbances with unknown bounds in the EL system are compensated and suppressed, respectively. Furthermore, an integration of adaptive methods and consensus protocols is employed to update the Lagrange multipliers of both local and coupling constraints. A dynamic average consensus is employed to estimate the gradient of coalition function, while the leader-following protocol is adopted to estimate the actions of other agents. By leveraging Lyapunov theory, the NE is proven to be asymptotically stable. Moreover, an unmanned surface vehicle swarm confrontation is meticulously modeled and analyzed in the coalition game framework. A diverse array of tasks, including formation, encirclement, and interception, are systematically formulated. A numerical example demonstrates the effectiveness of the proposed algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Lagrange multipliers

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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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Risk-Sensitive Reinforcement Learning with Exponential Criteria

arXiv

引用

arXiv 2022年

作者： Noorani, Erfaun Mavridis, Christos N. Baras, John S. Department of Electrical and Computer Engineering the Institute for Systems Research University of Maryland College Park United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variability in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive reinforcement learning methods are being thoroughly studied. In this work, we provide a definition of robust reinforcement learning policies and formulate a risk-sensitive reinforcement learning problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely-used Monte Carlo Policy Gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments. © 2022, CC BY.

关键词： Contrastive Learning

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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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Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

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SICE Journal of control, Measurement, and System Integration 2021年第1期14卷 116-127页

作者： Omainska, Marco Yamauchi, Junya Beckers, Thomas Hatanaka, Takeshi Hirche, Sandra Fujita, Masayuki Department of Information Physics and Computing The University of Tokyo Tokyo Japan Department of Electrical and Systems Engineering University of Pennsylvania PennsylvaniaPA United States Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we propose an observer-based visual pursuit control integrating three-dimensional target motion learning by Gaussian Process Regression (GPR). We consider a situation where a visual sensor equipped rigid body pursuits a target rigid body whose velocity is unknown but dependent on the target's pose. We estimate the pose from visual information and propose a Gaussian Process (GP) model to predict the target velocity from the pose estimate. We analyse stability of the proposed control by showing that estimation and control errors are ultimately bounded with high probability. Finally, simulations illustrate the performance of the proposed control schemes even if the visual measurement is corrupted by noise. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Gaussian distribution

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Two-Player Incomplete Games of Resilient Multiagent Systems

arXiv

引用

arXiv 2022年

作者： Nugraha, Yurid E. Hayakawa, Tomohisa Ishii, Hideaki Cetinkaya, Ahmet Zhu, Quanyan Department of Electrical Engineering Sepuluh Nopember Institute of Technology Surabaya60111 Indonesia Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan Department of Functional Control Systems Shibaura Institute of Technology Tokyo135-8548 Japan Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

Evolution of agents’ dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this paper we focus on a game-theoretical approach of multiagent systems where the players have incomplete information on their opponents’ strength. We consider repeated games with both simultaneous and sequential player actions where players update their beliefs of each other over time. The effect of the players’ optimal strategies according to Bayesian Nash Equilibrium and Perfect Bayesian Equilibrium on agents’ consensus is examined. It is shown that an attacker with incomplete knowledge may fail to prevent consensus despite having sufficient resources to do so. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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