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第34届中国控制与决策会议

作者： Xiao-long Wu Hongli Liu Hong Zhang Jinting Fu Bin Xie Han Hu Xi Li School of information engineering Nanchang University School of Artificial and Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & Technology

An adaptive optimal control strategy for solid oxide fuel cell power generation systems(SOFCs) is proposed in this paper.A new estimator is proposed in a simple form to solve the modeling uncertainty,and express the control of SOFCs as an optimal control ***,a new system thermoelectric parameter tracking control strategy is *** strategy uses data-driven to build the recursive neural network model of the system thermoelectric *** order to ensure convergence,an adaptive law based on parameter estimation error is proposed to update the weights of neural networks *** simulation results show that the data-driven model can effectively identify the power generation process of solid oxide fuel cell *** the same time,the control method can realize the optimal tracking control of power,voltage,stack temperature and afterburner temperature with limited input.

关键词： Solid oxide fuel cell adaptive optimal control adaptive dynamic programming system thermoelectric performance

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Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors

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BMC NEUROLOGY 2022年第1期22卷 229-229页

作者： Ren, Kang Chen, Zhonglue Ling, Yun Zhao, Jin Kobe Univ Syst Informat Kobe Hyogo Japan GYENNO Sci CO LTD Shenzhen Guangdong Peoples R China Huazhong Univ Sci & Technol Key Lab Image Informat Proc & Intelligent Control Minist Educ Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Hubei Peoples R China

Freezing of gait is a common gait disorder among patients with advanced Parkinson's disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.

关键词： Parkinson's disease Freezing of gait Sensor configuration Feature selection

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A Robust image Dehazing Model Using Cycle Generative Adversarial Network with an Improved Atmospheric Scatter Model 33rd

A Robust Image Dehazing Model Using Cycle Generative Adversa...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Guo, Xinlai Tao, Yanyun Zhang, Yuzhen Xu, Biao Zheng, Jianyin Ji, Guang Soochow Univ Sch Rail Transportat Suzhou 215005 Peoples R China Minist Educ Key Lab Informat Proc & Intelligent Control Shanghai 200240 Peoples R China Key Lab Digital Signal & Image Proc Guangdong Pro Shantou Peoples R China Soochow Univ Affiliated Hosp 1 Suzhou 215006 Peoples R China Suzhou City Univ Suzhou 215104 Peoples R China Beijing TeleSound Elect Co Ltd Beijing 100089 Peoples R China Suzhou Transportat Big Data Innovat & Applicat La Suzhou Jiangsu Peoples R China

ISBN: (纸本)9783031723377;9783031723384

Learning-based dehazing methods have attained numerous achievements in dehazing images. However, during the dehazing, the image depth and the atmospheric scattering factor are completely ignored. Current models result in the poor quality in dehazing real-world haze images. In this study, we propose a robust image dehazing model (RIDehaze) based on cycle generative adversarial network (CycleGAN). In RIDehaze, a dehazing GAN leverages a transmission map estimator with an improved atmospheric scattering model to restore clear images, which contain more real-world physical characteristics. CycleGAN utilizes depth estimator to generate more realistic haze images to train the dehazing GAN, thereby improving its generalization. Meanwhile, considering that the discriminator in CycleGAN struggles to support network training in the later stages, we designed a multi-level discriminator. This design ensures that even in the later stages of training, the discriminator can still effectively guide the network in dehazing images. On the real-world haze images of the RESIDE and Haze4k datasets, RIDehaze obtains clearer and more natural haze-free images and can specially produce better visual effects of distant scenery. On the synthetic hazy images, RIDehaze obtains similar PSNR and SSIM to supervised learning methods and outperforms the other unsupervised learning methods. The code for the method in this article can be downloaded at "GitHub address", where the GitHub address is a hyperlink to: https://***/Paris0703/icann_RIDehaze/tree/main.

关键词： Single image Dehaze CycleGAN Unsupervised learning image Depth Multiple Discriminator

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Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency

Learning from Untrimmed Videos: Self-Supervised Video Repres...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Xu, Yi Wang, Xiang Tang, Mingqian Gao, Changxin Jin, Rong Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China Natl Univ Singapore ARC Singapore Singapore Dalian Univ Technol Dalian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Natural videos provide rich visual contents for selfsupervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos, leading to limited diversity in visual patterns and limited performance gain. In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos. To this end, we propose to learn a hierarchy of consistencies in videos, i.e., visual consistency and topical consistency, corresponding respectively to clip pairs that tend to be visually similar when separated by a short time span and share similar topics when separated by a long time span. Specifically, a hierarchical consistency learning framework HiCo is presented, where the visually consistent pairs are encouraged to have the same representation through contrastive learning, while the topically consistent pairs are coupled through a topical classifier that distinguishes whether they are topicrelated. Further, we impose a gradual sampling algorithm for proposed hierarchical consistency learning, and demonstrate its theoretical superiority. Empirically, we show that not only HiCo can generate stronger representations on untrimmed videos, it also improves the representation quality when applied to trimmed videos. This is in contrast to standard contrastive learning that fails to learn appropriate representations from untrimmed videos.

关键词： Representation learning Visualization Computer vision Supervised learning Performance gain Pattern recognition Classification algorithms

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An Assist-as-Needed Variable Admittance controller for Adaptive Support of Mobile Walking Assistive Robots

An Assist-as-Needed Variable Admittance Controller for Adapt...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Xinxing Chen Yue Zheng Zongming Su Yuquan Leng Jian Huang Chenglong Fu Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Technology Planning Department Huawei Technologies Shenzhen China Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Mobile walking assistive robots have been widely used for monitoring patients’ gait patterns and providing support to prevent falls. Traditional approaches often separate the operational modes of these robots into human-following and supporting modes, which can lead to discontinuities in assistance and potentially compromise safety. This paper proposes a novel assist-as-needed control strategy that incorporates a Balance Index (BI) to dynamically adjust the robot’s support based on the user’s balance capability. The BI is calculated using the interaction force between the user and the robot along the z-axis and the gait symmetry index (GSI). The proposed variable admittance controller modifies its parameters based on the BI, ensuring seamless transitions between compliance and support modes. Experimental validation was conducted with a healthy subject walking both with and without a knee fixation brace. The results demonstrate that the robot provides minimal interference during natural gait and increased support during impaired gait, validating the effectiveness of the proposed control strategy in enhancing user safety and promoting natural walking patterns.

关键词： Legged locomotion Force Stability criteria Interference Assistive robots User experience Safety Admittance Indexes Robots

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Causal Invariant Hierarchical Molecular Representation for Out-of-distribution Molecular Property Prediction

Causal Invariant Hierarchical Molecular Representation for O...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wen, Xinlong Guo, Yifei Wei, Shuoying Long, Wenhan Zhu, Lida Zhu, Rongbo College of Informatics Huazhong Agricultural University Wuhan430070 China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen518000 China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518000 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Hubei Wuhan430074 China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9798350386226

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model's capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://***/Chertuion/CHiMoGNN. © 2024 IEEE.

关键词： Graph neural networks

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Manifold Learning-Inspired Mating Restriction for Evolutionary Multiobjective Optimization With Complicated Pareto Sets

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第6期51卷 3325-3337页

作者： Pan, Linqiang Li, Lianghao Cheng, Ran He, Cheng Tan, Kay Chen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Educ Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Southern Univ Sci & Technol Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Under certain smoothness assumptions, the Pareto set of a continuous multiobjective optimization problem is a piecewise continuous manifold in the decision space, which can be derived from the Karush-Kuhn-Tucker condition. Despite that a number of multiobjective evolutionary algorithms (MOEAs) have been proposed, their performance on multiobjective optimization problems with complicated Pareto sets (MOP-cPS) is still unsatisfying. In this article, we adopt the concept of manifold and propose a manifold learning-inspired mating strategy to enhance the diversity maintenance in MOEAs for solving MOP-cPS efficiently. In the proposed strategy, all of the individuals are first clustered into different manifolds according to their distribution in the objective space, and then the mating reproduction is restricted among individuals in the same manifold. Moreover, we embed the proposed mating strategy in three representative MOEAs and compare the embedded MOEAs with their original versions using the assortative genetic operators on a variety of MOP-cPS. The experimental results demonstrate the significant performance improvements benefitting from the proposed mating restriction strategy.

关键词： Manifolds Optimization Maintenance engineering Sociology Principal component analysis Self-organizing feature maps Assortative crossover complicated Pareto set diversity maintenance manifold learning mating selection multiobjective optimization

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Formation-containment control for multi-agent systems with sampled data and time delays

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NEUROCOMPUTING 2021年 424卷 125-131页

作者： Zhang, Jinxin Su, Housheng Hunan Univ Technol Sch Sci Zhuzhou 412008 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China

This paper investigates the formation containment control under sampling and with time delays for multi-agent systems (MASs). First, a novel sampling control protocol for the MASs to achieve formation containment is proposed, where the communication among agents is only occurred at the sampling in stance. Then the communication load and the waste of energy can be decreased significantly. In addition, time delays are considered in this paper. Second, by establishing an appropriate Lyapunov function, sufficient conditions for MASs with sampled data and time delays reaching formation containment are obtained by handling the established Lyapunov function. Finally, the obtained results are demonstrated by a simulation example. (c) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-agent systems (MASs) Formation containment Under sampling Time delays

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Global stabilization of fractional-order memristor-based neural networks with incommensurate orders and multiple time-varying delays: a positive-system-based approach

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NONLINEAR DYNAMICS 2021年第3期104卷 2303-2329页

作者： Jia, Jia Wang, Fei Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Qufu Normal Univ Sch Math Sci Qufu 273165 Shandong Peoples R China

This paper addresses global stabilization of fractional-order memristor-based neural networks (FMNNs) with incommensurate orders and multiple time-varying delays (MTDs), where the time delay functions are not necessarily bounded. First, without assuming that time delay functions are bounded, the asymptotical stability condition is given for fractional-order linear positive system with incommensurate orders and MTDs. Then, comparison principle for such a system is established. By virtue of two kinds of vector Lyapunov functions (absolute-value-function-based and square-function-based vector Lyapunov functions), stability condition of fractional-order linear positive system and comparison principle, two stabilization criteria are derived and the equivalence between them is illustrated. In comparison with the reported criterion, the criteria derived in this paper are less conservative, since they allow controller parameters to satisfy weaker algebraic conditions. Lastly, numerical examples are displayed to validate the availability of the controller and correctness of the stabilization criteria.

关键词： Stabilization Fractional-order Memristor-based neural networks Incommensurate orders Positive system

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Optimizing Binary Sequence Excitation Signals for Rapid Impedance Spectrum Measurement in Proton Exchange Membrane Fuel Cells

Optimizing Binary Sequence Excitation Signals for Rapid Impe...

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第43届中国控制会议

作者： Jingzhi Lei Chuang Sheng Jingjing Wang Liansong Yu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology State Grid Electric Power Research Institute Wuhan Nanrui Limited Liability Company Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

With the increasing demand for efficient and reliable renewable energy sources,Proton Exchange Membrane Fuel Cells(PEMFCs) have emerged as a promising *** experiments demonstrate that Electrochemical Impedance Spectroscopy(EIS) effectively differentiates various states of *** broadband signals as testing excitations significantly reduces EIS measurement time,facilitating real-time monitoring of PEMFCs' health under dynamic *** signal design requires maintaining adequate amplitude across frequency components for noise resistance while minimizing signal peak value to reduce operational *** paper introduces an EIS simulation environment,leveraging PEMFCs' impedance and electrochemical noise *** focuses on optimizing the excitation signal's amplitude-frequency distribution using discrete interval binary *** results indicate that our method achieves minimal perturbation amplitude while preserving measurement accuracy in amplitude-frequency signal design.

关键词： Proton exchange membrane fuel cell Electrochemical impedance spectroscopy Electrochemical noise Excitation signal optimization

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