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4th International Conference on intelligent Autonomous Systems (ICoIAS)

作者： Deng, Shiheng Sun, Zhigang Zhang, Kai Xiao, Li Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9781665441957

Robots are often equipped with cameras and Laser Rangefinders (LRFs), and it is important to obtain their relative pose since they are separated. In this paper, an algorithm for calibrating the extrinsic parameters between a camera and a 2D LRF is proposed, and a system consisting of 2D LRF and a rotating unit is designed to obtain a 3D scanning map of the surroundings. Since the LRF beam is invisible, the proposed calibration method uses a common calibration object and establish geometric constraints through observations from both sensors. The calibration setup geometry is described by the normal vector of the plane. Besides, we add weights to the formula to represent the uncertainty of the image and LRF data. The simulation and real experiments show that our method is more robust in noise.

关键词： extrinsic calibration LRF point cloud

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Improved Proxy-based Sliding Mode control Integrated Adaptive Dynamic Programming For Pneumatic Muscle Actuators 6

Improved Proxy-based Sliding Mode Control Integrated Adaptiv...

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6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Peng, Zengqi Huang, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9780738133645

It is an urgent social problem that the rehabilitation needs of the elderly and the disabled can hardly be satisfied. The human-centered rehabilitation robot is a potential solution. In this paper, an auxiliary control scheme based on adaptive dynamic programming is proposed for a class of pneumatic artificial muscles. The improved proxy-based sliding mode controller is used as the basic controller, and an adaptive dynamic programming controller is used to provide auxiliary control signals. To ensure the reliability of the control strategy, the Lyapunov theorem is used to analyze the stability of the control scheme. The simulation results of the proposed method are compared with those of PID, proxy-based sliding mode control, and adaptive proxy-based sliding mode control without the auxiliary controller. Numerical results verify the effectiveness of the control scheme.

关键词： adaptive dynamic programming proxy-based sliding mode control pneumatic muscle actuator

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Dynamic Event-Triggered Disturbance Rejection control for Speed Regulation of Networked PMSM

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 6436-6445页

作者： Dai, Bin Sun, Jiankun Yang, Jun Li, Shihua Nanjing Inst Technol Sch Automat Nanjing 211167 Peoples R China Jiangsu Element One Energy Technol Co Ltd Huaian 211799 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU England Southeast Univ Sch Automat & Control Complex Syst Engn Minist Educ Nanjing 210096 Peoples R China

This article investigates the robust control problem for speed regulation of networked permanent magnet synchronous motor subject to the limited communication bandwidth. To handle this, a new sampled-data disturbance rejection control method is developed via a well-designed discrete-time dynamic event-triggered mechanism (DETM). First, a predictor-based generalized proportional integral observer is introduced to estimate the lumped disturbances, when only the sampled-data output is available. Then, a composite proportional feedback controller is formed by fully utilizing disturbance estimation. The composite controller updates only when the designed discrete-time DETM is violated, resulting in remarkable communication and computation resource savings while maintaining the desirable disturbance rejection ability. The designed DETM can be applied to digital computers easily due to the discrete-time detection. Simulations and experiments are carried out to validate the feasibility and effectiveness of the proposed control scheme.

关键词： Disturbance rejection control dynamic event-triggered control permanent magnet synchronous motor (PMSM) predictor-based generalized proportional integral observer (GPIO)

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Graph-Regularized Non-Negative Matrix Factorization for Single-Cell Clustering in scRNA-Seq Data

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第8期28卷 4986-4994页

作者： Jiang, Hanjing Wang, Mei-Neng Huang, Yu-An Huang, Yabing Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist China Wuhan Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Yichun Univ Sch Math & Comp Sci Yichun 336000 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710129 Peoples R China Wuhan Univ Renmin Hosp Dept Pathol Wuhan 430060 Peoples R China

The advent of single-cell RNA sequencing (scRNA-seq) has brought forth fresh perspectives on intricate biological processes, revealing the nuances and divergences present among distinct cells. Accurate single-cell analysis is a crucial prerequisite for in-depth investigation into the underlying mechanisms of heterogeneity. Due to various technical noises, like the impact of dropout values, scRNA-seq data remains challenging to interpret. In this work, we propose an unsupervised learning framework for scRNA-seq data analysis (aka Sc-GNNMF). Based on the non-negativity and sparsity of scRNA-seq data, we propose employing graph-regularized non-negative matrix factorization (GNNMF) algorithm for the analysis of scRNA-seq data, which involves estimating cell-cell sparse similarity and gene-gene sparse similarity through Laplacian kernels and p-nearest neighbor graphs (p-NNG). By assuming intrinsic geometric local invariance, we use a weighted p-nearest known neighbors (p-NKN) to optimize the scRNA-seq data. The optimized scRNA-seq data then participates in the matrix decomposition process, promoting the closeness of cells with similar types in cell-gene data space and determining a more suitable embedding space for clustering. Sc-GNNMF demonstrates superior performance compared to other methods and maintains satisfactory compatibility and robustness, as evidenced by experiments on 11 real scRNA-seq datasets. Furthermore, Sc-GNNMF yields excellent results in clustering tasks, extracting useful gene markers, and pseudo-temporal analysis.

关键词： Kernel Laplace equations Bioinformatics Clustering algorithms Sparse matrices Data mining Unsupervised learning scRNA-seq non-negative matrix factorization clustering gene marker

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Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment

arXiv

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arXiv 2024年

作者： Wang, Ziwei Li, Siyang 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 Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nature of seizures. Here we propose a multi-space alignment approach based on cross-species and cross-modality electroencephalogram (EEG) data to enhance the detection capabilities and understanding of epileptic seizures. By employing deep learning techniques, including domain adaptation and knowledge distillation, our framework aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and within-modality models. Experiments on multiple surfaces and intracranial EEG datasets of humans and canines demonstrated substantial improvements in detection accuracy, achieving over 90% AUC scores for cross-species and cross-modality seizure detection with extremely limited labeled data from the target species/modality. To our knowledge, this is the first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance. This is a pilot study that provides insights into the challenges and potential of multi-species and multi-modality data integration, offering an effective solution for future work to collect huge EEG data to train large brain models. © 2024, CC BY.

关键词： Electroencephalography

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Data-driven approaches for predicting performance degradation of solid oxide fuel cells system considering prolonged operation and shutdown accumulation effect

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JOURNAL OF POWER SOURCES 2024年 598卷

作者： Wu, Xiao-long Li, Yu Cai, Shiyun Xu, Yuanwu Hu, Lingyan Chi, Bo Peng, Jingxuan Li, Xi Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Ctr Fuel Cell Innovat Sch Mat Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized

关键词： Solid oxide fuel cells (SOFC) Degradation prediction Shutdowns factors Neural network Data -driven

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GPC-LIO-SAM:Lidar inertial odometry and mapping method for mobile robot with ground plane constraint

GPC-LIO-SAM:Lidar inertial odometry and mapping method for m...

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第43届中国控制会议

作者： Chong Cao Gang Peng Dingxin He Yihao Cao Yue Xu Zhang Deng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan Lisai Technology Co. Ltd.

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Due to the limitation of the vertical angle resolution of the lidar sensor,the observation information in the vertical direction is reduced,resulting in the elevation cumulative drift phenomenon of many lidar SLAM algorithms in large-scale environment,which reduces the accuracy of pose estimation and the quality of *** order to solve this problem,based on LIO-SAM,this paper proposes a lidar SLAM algorithm with ground plane *** method can effectively extract the ground point cloud and obtain the plane normal vector,and construct the constraint relationship with the prior plane normal *** optimal state estimation is solved by nonlinear optimization method,which effectively reduces the elevation drift caused by the long-term operation of the system in large-scale *** paper compares the improved algorithm with A-LOAM,LeGO-LOAM and original LIO-SAM algorithms in challenging datasets such as KITTI,M2 DGR and *** results show that this method effectively improves the accuracy of pose estimation and the quality of mapping,and limit the elevation drift of mobile robot in ground operation,which has high application value.

关键词： Ground plane constraint elevation drift LIDAR simultaneous localization and mapping(SLAM)

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A meta-path graph-based graph homogenization framework for machine fault diagnosis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Yang, Chaoying Liu, Jie Zhou, Kaibo Yuan, Xiaohui Jiang, Xingxing Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automati MOE Key Lab Image Informat Proc & Intelligent Cont Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Belt & Rd Joint Lab Measurement & Control Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital River Basin Sci & Technol Wuhan 430074 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China

Graph data-driven methods have swept the field of machine fault diagnosis by merits of modeling relationships between samples. Their performance is highly affected by the constructed graphs quality. Compared to the single-sensor data, multi-sensor data can provide more information, so as to construct higher-quality graphs. However, existing graph data-driven diagnosis methods using multiple sensors still have two limitations. Firstly, heterogeneous multi-sensor data are mainly processed as homogeneous data, ignoring the heterogeneity of heterogeneous multi-sensor data. Secondly, the heterogeneous graph is often with a complex graph structure, and consumes much computational cost to learn. To overcome these limitations, A meta-path graph-based graph homogenization framework for machine fault diagnosis is proposed. Heterogeneous multi-sensor data are converted into the heterogeneous graph, modeling the heterogeneity of heterogeneous multi-sensor data. Further, instead of directly inputting the heterogeneous graph into graph deep learning model, a heterogeneous graph homogenization framework is designed to generate a meta-path graph, reducing the complexity of graph structure and improving the graph quality. Finally, a graph convolutional network is used for graph feature learning, obtaining the diagnosis results. Verification experiments show that the proposed method performs better than machine learning-based and graph deep learning-based methods. In addition, discussive experiments show that the meta-path graph is with lower complexity in graph structure and a higher clustering accuracy than single-sensor data-based K-nearest neighborhood graph.

关键词： Machine fault diagnosis Heterogeneous multi-sensor data K-nearest neighborhood graph Meta-path graph Graph convolutional network

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MTPA-based high-frequency square wave voltage signal injection strategy for IPMSM control

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JOURNAL OF POWER ELECTRONICS 2021年第10期21卷 1461-1472页

作者： Zhang, Zeyu Shen, Anwen Li, Peihe Luo, Xin Tang, Qipeng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

This paper proposes a high-frequency (HF) square wave voltage signal injection strategy for interior permanent magnet synchronous motor (IPMSM) maximum torque per ampere (MTPA) drives. Unlike previous methods, this strategy injects a square wave HF signal into the voltage directly regardless of the current loop bandwidth limitations. In addition, the injected frequency can surpass the cut-off frequency of the current loop. Therefore, the disturbance caused by the injected signal can be reduced. The process of MTPA operating point adjustment only needs to sample and analyze the current amplitude without additional digital filters. Thus, the dynamic response promotes, and avoids the extra hardware and calculation burden. To decrease the convergence time when the load changes rapidly, an equivalent mathematical model of an IPMSM is employed to provide prior current references. Both simulation and experimental results confirm the validity and feasibility of the proposed strategy.

关键词： Interior permanent magnet synchronous motor (IPMSM) Maximum torque per ampere (MTPA) High-frequency (HF) square wave voltage signal injection Equivalent model

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Imputation of missing data from offshore wind farms using spatio-temporal correlation and feature correlation

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ENERGY 2021年 229卷 120777-120777页

作者： Sun, Chuan Chen, Yueyi Cheng, Cheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Intelligent Control & Image Proc MOE Wuhan 430074 Peoples R China

Many industrial applications, such as fault diagnosis and remaining useful life prediction, require high dimensional inputs to predict a reliable output. For offshore wind farm supervisory control and data acquisition (SCADA) systems, unfortunately, signal inputs are often missing due to harsh weather, resulting in the failure of network/sensors. It limits the accuracy of subsequent diagnostic or prognostic tasks. Although many methods have been proposed for imputing missing data, their applicability in offshore wind farms is still problematic because wind turbines (WTs) are time-varying systems, and conventional learning methods require high computational cost. To address this problem, we propose a learning framework containing two learning models, corresponding to two missing-data conditions. The framework imputes missing data by designing a spatio-temporal correlation method for entire feature missing conditions and a feature-correlation method for partial feature-missing conditions, respectively. A real-world offshore wind farm dataset of a SCADA system with 33 WTs and 68 features, which was recorded over a one-month period, is used for experimental validation. We demonstrate that the proposed framework imputes the missing data with much smaller mean absolute error (MAE) and mean squared error (MSE) and requires less computational time, compared to the existing machine-learning methods for both imputation conditions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Offshore wind farm Missing data imputation Spatio-temporal correlation Feature correlation

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