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Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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Path Planning Algorithm for Unmanned Surface Vessel Based on Multiobjective Reinforcement Learning

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Computational intelligence and neuroscience 2023年第1期2023卷 2146314页

作者： Caipei Yang Yingqi Zhao Xuan Cai Wei Wei Xingxing Feng Kaibo Zhou MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China. Wuhan Second Ship Design and Research Institute Wuhan 430205 China.

It is challenging to perform path planning tasks in complex marine environments as the unmanned surface vessel approaches the goal while avoiding obstacles. However, the conflict between the two subtarget tasks of obstacle avoidance and goal approaching makes the path planning difficult. Thus, a path planning method for unmanned surface vessel based on multiobjective reinforcement learning is proposed under the complex environment with high randomness and multiple dynamic obstacles. Firstly, the path planning scene is set as the main scene, and the two subtarget scenes including obstacle avoidance and goal approaching are divided from it. The action selection strategy in each subtarget scene is trained through the double deep -network with prioritized experience replay. A multiobjective reinforcement learning framework based on ensemble learning is further designed for policy integration in the main scene. Finally, by selecting the strategy from subtarget scenes in the designed framework, an optimized action selection strategy is trained and used for the action decision of the agent in the main scene. Compared with traditional value-based reinforcement learning methods, the proposed method achieves a 93% success rate in path planning in simulation scenes. Furthermore, the average length of the paths planned by the proposed method is 3.28% and 1.97% shorter than that of PER-DDQN and dueling DQN, respectively.

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The estimation of the laser point temperature based on CNN (Convolutional Neural Network)

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IOP Conference Series: Materials Science and Engineering 2020年第1期740卷

作者： Yingmin Yi Ruidong Xie Haichuan Yang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Key Lab of Manufacturing Equipment of Shaanxi Province Xi'an University of Technology Xi'an 710048 China

For the requirements of the laser temperature in additive manufacturing, there is a high precision and high heat need in the industry. The method is proposed to estimate the temperature of laser point, which is based on CNN. In this method, a model of CNN is carried out. The collected laser thermal radiation images are used to train the model. image recognition and isotherm estimation can be obtained by the trained model. The conclusion can be verified by the experiment. The isotherm and temperature of the laser can be measured efficiently in this method.

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A Power Optimization control Method Based on IMC for 3D Printer

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Journal of Physics: Conference Series 2020年第1期1654卷

作者： Ruidong Xie Haichuan Yang Yingmin Yi Key Lab of Manufacturing Equipment of Shaanxi Province Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China

Aiming at the effect of selective laser sintering technology used in 3D printing, the recursive least squares method was applied to fitting a control system model for quantification the relationship between laser power and corresponding temperature in this paper. The internal model control method is considered based on the establishing model. The controller is put into the actual 3D printing power control system for controlling the laser power. In the experiment, both internal model controller and the PID controller for the 3D printer are researched and deployed for comparing the their effects in the paper. The experimental result illustrates that the IMC based on the recursive least squares method is of effectiveness.

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A Detection and Identification Method Based on CNN for Laser Point Thermal Radiation

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Journal of Physics: Conference Series 2020年第1期1654卷

作者： Haichuan Yang Ruidong Xie Yingmin Yi Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Key Lab of Manufacturing Equipment of Shaanxi Province Xi'an University of Technology Xi'an 710048 China

Aiming at the high demands of temperature and precision in the aspect of additive manufacturing, a method based on CNN was proposed for estimating measurement. The network was trained through the collected laser thermal radiation images for image recognition and isotherm estimation after modeling a CNN. The experimental conclusion verifies that the isotherm detection and temperature estimation of the laser point can be efficiently implemented in proposed method.

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Unsupervised Hyperspectral Band Selection With Multigraph Integrated Embedding and Robust Self-Contained Regression

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Sui, Chenhong Zhou, Jun Li, Chang Zheng, Qiang Feng, Jie Mei, Xiaoguang Wang, Jing Yantai Univ Sch Optoelect Informat Sci & Technol Yantai 264005 Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia Hefei Univ Technol Dept Biomed Engn Hefei 230009 Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Commonwealth Sci & Ind Res Org Canberra ACT 2601 Australia

Band selection is an effective means to alleviate the curse of dimensionality in hyperspectral data. Many methods select a compact and low redundant band subset, which is inadequate as it may degrade the classification performance. Instead, more emphasis shall be put on selecting representative bands. In this article, we propose a robust unsupervised band selection method to address this issue. Our method reveals bandwise representativeness based on the comprehensive interband neighborhood structure. It incorporates an interband neighborhood graph into a sparse self-contained regression model in order to provide a reasonable measure for bandwise representativeness. The derived coefficient matrix not only uncovers bandwise importance values but also is coherent to the generalized interband local neighborhood structure. For constructing the interband neighboring structural graph, an integrated multigraph model is employed to achieve better generalization performance. It combines the benefit of multiple graphs but is insusceptible to the defects of a single one. To enhance the reliability of this model, a joint trace minimum and nonnegative constraint is imposed on the coefficient matrix. Accordingly, a multigraph integrated embedding and robust self-contained regression model (MGRSR) is formulated. In addition, an iterative update algorithm is developed to solve the problem. Comparative experiments on three hyperspectral data sets illustrate that MGRSR is robust to various data and has superior performance compared with several state-of-the-art methods.

关键词： Sparse matrices Hyperspectral imaging Dictionaries Reliability Redundancy Dimensionality reduction Correlation Hyperspectral band selection multigraph representativeness self-contained regression unsupervised

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Dynamic Ca²⁺ Sensitivity of BK Channels

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BIOPHYSICAL JOURNAL 2014年第2期106卷 437A-437A页

作者： Hou, Panpan Xiao, Feng Ming Yuchi Zhang, Guohui Wu, Ying Wang, Wei Zeng, Wenping Ding, Mingyue Wu, Zhengxing Cui, Jianming Ding, Jiuping Huazhong Univ Sci & Technol Key Lab Mol Biophys Minist Educ Coll Life Sci & Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Dept Biomed EngnColl Life Sci & Technol Wuhan 430074 Hubei Peoples R China Washington Univ Dept Biomed Engn Ctr Invest Membrane Excitabil Disorders Cardiac Bioelect & Arrhythmia Ctr St Louis MO 63130 USA Soochow Univ Dept Pharmacol Coll Pharmaceut Sci Suzhou 215123 Peoples R China

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Capacity optimization and energy dispatch strategy of hybrid energy storage system based on proton exchange membrane electrolyzer cell

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ENERGY CONVERSION AND MANAGEMENT 2022年 272卷

作者： Zhao, Dongqi Xia, Zhiping Guo, Meiting He, Qijiao Xu, Qidong Li, Xi Ni, Meng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Intelligent Control Educ Minist Wuhan Hubei Peoples R China Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Kowloon Hong Kong Peoples R China Shenzhen Huazhong Univ Sci Technol Res Inst Shenzhen Guangdong Peoples R China

The introduction of proton exchange membrane electrolyzer cells into microgrids allows renewable energy to be stored in a more stable form of hydrogen energy, which can reduce the redundancy of battery energy storage system and the abandonment of wind and photovoltaic energy. However, most studies of energy dispatch strategies for microgrids only focus on the costs without considering the long-life operation. Therefore, in this study, the proposed capacity optimization method first ensures that the optimized distributed energy capacity can meet the user demand even in the most impoverished meteorological conditions. Moreover, the optimal efficiency and operating conditions of the electrolyzer corresponding to the reference power are determined through the artificial neural network, thereby realizing efficient hydrogen production. Subsequently, the multi -objective energy dispatch strategy is analyzed and designed, considering both low-cost and long-life operations. Compared with the economical energy dispatching strategy, the multi-objective energy dispatching strategy only increases the average daily dispatching cost by 0.055 $, however, reduces the volatility indicator of the elec-trolyzer by 49 %, which is beneficial to the sustainable operation of the electrolyzer. Furthermore, the required electrolyzer capacity is also reduced by 17.5 % by suppressing the power fluctuation of the electrolyzer. This study can provide useful information for understanding the energy dispatch strategy in hydrogen-electric coupling microgrids.

关键词： High temperature proton exchange membrane electrolyzer cell Capacity optimization Economic dispatch strategy Multi -objective dispatch strategy Hydrogen energy Long -life operation

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Dynamic Ca2+ sensitivity of BK channels

Dynamic Ca2+ sensitivity of BK channels

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The 4th International Ion Channel Conference(第四届国际离子通道会议)

作者： Panpan Hou Jianming Cui Ding Jiuping Feng Xiao Ming Yuchi Guohui Zhang Ying Wu Wei Wang Wenping Zeng Mingyue Ding Zhengxing Wu Department of pharmacology Soochow University college of pharmaceutical Sciences SuzhouChina Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Department of Biomedical Engineering College of Life Science and Technology Wuhan China Department of Biomedical Engineering Center for the Investigation of Membrane Excitability Disorders Cardiac Bioelectricity and Arrhythmia Center Washington University St Louis MOUSA Key Laboratory of Molecular Biophysics Huazhong University of Science and Technology Ministry of Education College of Life Science and Technology Wuhan China Department of Biomedical Engineering Center for the Investigation of Membrane Excitability Disorders Cardiac Bioelectricity and Arrhythmia Center Washington University St Louis MOUSA

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Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

arXiv

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arXiv 2024年

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The Department of Informatics School of Physics Astronomy and Informatics Neurocognitive Laboratory Center for Modern Interdisciplinary Technologies Nicolaus Copernicus University Torun Poland The Ministry of Education Key Laboratory for Biomedical Engineering Zhejiang University Hangzhou China The Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The NetsUnion Clearing Corporation Beijing100045 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100049 China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs. © 2024, CC BY.

关键词： Differential privacy

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