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2023 China Automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Frank Kulwa Oluwarotimi Williams Samuel Mojisola Grace Asogbon Tolulope Tofunmi Oyemakinde Olumide Olayinka Obe Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Computer Science Federal University of Technology Akure Nigeria

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

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The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

arXiv

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arXiv 2024年

作者： Ma, Jiating Zhou, Yipeng Li, Qi Sheng, Quan Z. Cui, Laizhong Liu, Jiangchuan The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Shenzhen518060 China The School of Computing Faculty of Science and Engineering Macquarie University Macquarie ParkNSW2119 Australia The Institute for Network Sciences and Cyberspace Tsinghua University Beijing100190 China The Beijing National Research Center for Information Science and Technology Beijing100190 China The School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an essential but largely overlooked problem in DPFL is the heterogeneity of clients’ privacy requirement, which can vary significantly between clients and extremely complicates the client selection problem in DPFL. In other words, both the data quality and the influence of DP noises should be taken into account when selecting clients. To address this problem, we conduct convergence analysis of DPFL under heterogeneous privacy, a generic client selection strategy, popular DP mechanisms and convex loss. Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently. Accordingly, we propose the DPFL-BCS (biased client selection) algorithm. The extensive experiment results with real datasets under both convex and non-convex loss functions indicate that DPFL-BCS can remarkably improve model utility compared with the SOTA baselines. © 2024, CC BY.

关键词： Federated learning

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A Prediction Approach Based on Long Short-Term Memory Networks for Dynamic Multiobjective Optimization

SSRN

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SSRN 2024年

作者： Xu, Biao Rang, Gejie Li, Wenji Gong, Dunwei Fan, Zhun Yang, Shengxiang He, Jie College of Engineering Shantou University Shantou515063 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China Shenzhen institute for Advanced Study UESTC Shenzhen518110 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom Minjiang University China

Dynamic multiobjective optimization problems (DMOPs) present significant challenges to conventional evolutionary optimization methods because of the continuous changes in their Pareto-optimal sets (PSs) and fronts (PFs). Prediction-driven approaches have demonstrated potential in rapidly adapting to these changes. However, many existing methods depend on linear models to forecast the evolving PSs, which may be restrictive. To counteract this limitation, this research presents a novel dynamic multiobjective evolutionary optimization algorithm that incorporates predictions from long short-term memory (LSTM) networks. Initially, in our methodology, the PS for each problem is segmented into multiple clusters, and the centroid of each cluster is identified. These cluster centroids, representing the PSs across various environmental conditions, are then transformed into a series of time series data. The LSTM network models are subsequently trained on this time series data as input samples. Utilizing these refined models, the centroids of the evolving PSs are predicted with improved precision. Moreover, to enhance the performance of the algorithm, an innovative population-generation strategy is also introduced that guarantees a well-converged and diverse starting population. Our proposed algorithm undergoes rigorous testing using benchmark functions, and the outcomes validate its proficiency in tackling DMOPs, showing superior performance compared to existing state-of-the-art algorithms. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Sensing With Random Communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Shabaz, Mohammad Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Algamdi, Shabbab Ali Li, Yang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Model Institute of Engineering and Technology J and K Jammu India Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams. © 1975-2011 IEEE.

关键词： Sensor data fusion

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Nanowires for UV–vis–IR Optoelectronic Synaptic Devices

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advanced Functional Materials 2023年第1期33卷 2208807-2208807页

作者： Chen, Xue Chen, Bingkun Jiang, Bei Gao, Tengfei Shang, Gang Han, Su-Ting Kuo, Chi-Ching Roy, Vellaisamy A. L. Zhou, Ye Institute of Microscale Optoelectronics Shenzhen University Shenzhen518060 China College of Physics and Optoelectronic Engineering Harbin Engineering University Harbin150001 China Faculty of Physics and Electronic Science Hubei University Wuhan430062 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Institute of Organic and Polymeric Materials Research and Development Center of Smart Textile Technology National Taipei University of Technology Taipei10608 Taiwan James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Institute for Advanced Study Shenzhen University Shenzhen518060 China

Simulating biological synaptic functionalities through artificial synaptic devices opens up an innovative way to overcome the von Neumann bottleneck at the device level. Artificial optoelectronic synapses provide a non-contact method to operate the devices and overcome the shortcomings of electrical synaptic devices. With the advantages of high photoelectric conversion efficiency, adjustable light absorption coefficient, and broad spectral range, nanowires (NWs)-based optoelectronic synapses have attracted wide attention. Herein, to better promote the applications of nanowires-based optoelectronic synapses for future neuromorphic systems, the functionalities of optoelectronic synaptic devices and the current progress of NWs optoelectronic synaptic devices in UV–vis–IR spectral range are introduced. Furthermore, a bridge between NWs-based optoelectronic synaptic device and the neuromorphic system is established. Challenges for the forthcoming development of NWs optoelectronic synapses are also discussed. This review may offer a vision into the design and neuromorphic applications of NWs-based optoelectronic synaptic devices. © 2022 Wiley-VCH GmbH.

关键词： Nanowires

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Efficiently Removing Dissolved Organic Pollutants in Landfill Leachate Concentrate Using Dual-Anode Fe2+/Hclo System: The Significance of Insolubilization Based On Oxidative Coupling of Organics

SSRN

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SSRN 2023年

作者： Liang, Sheng Wang, Jinling Shen, Zhizhi Yan, Wei Nengzi, Lichao Feng, Chunhua Yu, Li Hu, Junqing College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen518118 China Faculty of Chemical Engineering Kunming University of Science and Technology Kunming650500 China State Environmental Protection Key Laboratory of Managing Technology of Drinking Water Source Shenzhen Academy of Environmental Science Shenzhen518001 China Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters Ministry of Education School of Environment and Energy South China University of Technology Guangzhou510006 China

The strategy to mineralize dissolved organics for the decontamination of landfill leachate concentrate (LLC) is expensive. Because mineralization requires high stoichiometric ratio of oxidants (e.g., •OH) or electron transfer (i.e., energy) to the pollutants molecular. Whereas, transferring the dissolved organic pollutants into separable insoluble matters is an energy-saving strategy. Herein, we developed a dual-anode Fe2+/HClO system for removing dissolved organic pollutants in LLC through insolubilization based on oxidative coupling. In this system, the majority of dissolved organic pollutants were transferred into insoluble substances, which could be removed easily. 89.8% of the dissolved organic carbon (DOC) in LLC was fast removed in 3 hours with a low specific energy consumption (SEC, 4.0 kWh kg-1 COD and 17.6 kWh kg-1 DOC), and the formation of toxic by-products can be well controlled. Interestingly, the formation of insoluble substances was predominantly attributed to the oxidative coupling of organic small molecules into the macromolecular ones. This was evidenced by the generation of insoluble compounds with the larger molecular mass (evidenced by mass spectrums), and the formation of coupling or polymerization products of the model pollutant (guaiacol). Specifically, the macromolecular matters were derived from the organic radicals (evidenced by EPR tests) coupling with each other. It was reported that Fe2+/HClO reaction produced reactive Fe(IV) under acid condition. According to the investigations in this work, we propose it is possibly because Fe(IV) was generated in the dual-anode Fe2+/HClO system, and Fe(IV) oxidized pollutants to produce the organic radicals. This work offers a novel electrochemical method for efficient removal of organic pollutants in LLC with minimal toxic by-products generated, and revealed the significance of oxidative coupling process in the electrochemical Fe2+/HClO system. © 2023, The Authors. All rights reserved.

关键词： Organic pollutants

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Design Method for Series-Fed Omnidirectional Filtenna Based on Full-Metal 3-D Printing

Design Method for Series-Fed Omnidirectional Filtenna Based ...

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Cross Strait Quad-Regional Radio science and Wireless technology Conference (CSRWTC)

作者： Shu-Qing Zhang Shu-Ting Wu Sai-Wai Wong Yejun He Zhonghe Zhang Xuzhou Yu State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen Key Laboratory of Antennas and Propagation Guangdong Engineering Research Center of Base Station Antennas and Propagation College of Electronics and Information Engineering Shenzhen University China The Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau SAR China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

This paper proposed a design approach for a series-fed omnidirectional antenna based on 3D full-metal printing. A major issue when designing series-fed antennas is the radiation pattern lobing caused by the unique feeding mechanism. In this paper, necessary adjustments are made based on the phase relationships between the currents and electric fields of each radiating element, resulting in improved radiation characteristics for the series-fed antenna. To validate this idea, the paper designs a rod-shaped full-metal omnidirectional antenna, achieving a wide operating bandwidth and high gain. Additionally, a filtering antenna with omnidirectional radiation is designed, which achieves a high level of out-of-band suppression as well as good omnidirectional radiation performance.

关键词： Wireless communication Phased arrays Filtering Design methodology Omnidirectional antennas Bandwidth Three-dimensional printing Antenna feeds Electric fields Antenna radiation patterns

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Fedmed-Gan: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis

SSRN

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SSRN 2023年

作者： Xie, Guoyang Wang, Jinbao Huang, Yawen Lyu, Jiayi Zheng, Feng Zheng, Yefeng Jin, Yaochu Department of Computer Science and Engineering Southern University of Science and Technology 518055 China Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom Tencent Jarvis Lab Shenzhen518040 China Faculty of Technology Bielefeld University Bielefeld33619 Germany Shenzhen-Shanwei Central Hospital China

Utilizing multi-modal neuroimaging data has been proved to be effective in investigating human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, \textit{e.g.}, high examination cost, long acquisition time, and image corruption. In addition, these data are dispersed into different medical institutions and thus cannot be aggregated for centralized training considering the privacy issues. There is a clear need to launch federated learning and facilitate the integration of dispersed data from different institutions. In this paper, we propose a new benchmark for federated domain translation on unsupervised brain image synthesis (FedMed-GAN) to bridge the gap between federated learning and medical GAN. FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators, and is widely applied to different proportions of unpaired and paired data with variation adaptation properties. We treat the gradient penalties using the federated averaging algorithm and then leverage the differential privacy gradient descent to regularize the training dynamics. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods, demonstrating that the proposed algorithm outperforms the state-of-the-art. © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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