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arXiv 2024年

作者： Jia, Tianwang Meng, Lubin Li, Siyang Liu, Jiajing Wu, Dongrui The Key Laboratory The Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University Science and Technology Research Institute Shenzhen518063 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy. © 2024, CC BY.

关键词： Federated learning

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A Visual Kinematics Calibration Method for Manipulator Based on Nonlinear Optimization

A Visual Kinematics Calibration Method for Manipulator Based...

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Chinese Automation Congress (CAC)

作者： Gang Peng Zhihao Wang Shangbin Guan Zhang Deng Wentao Guo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing China Wuhan Lisai Technology Co. Ltd. Wuhan China

The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera’s parameters affects the calibration accuracy of the kinematics of the robot arm. In order to solve this problem, this paper reduces the dimensionality of the experimental data and redesigns the objective function to be optimized. That is, the three-dimensional positions of the feature points of the calibration plate under each manipulator attitude corresponding to the actual kinematic model and the classic D-H kinematic model are mapped into the pixel coordinate system, and the sum of Euclidean distance errors of the pixel coordinates of the two is used as the objective function to be optimized. And then, nonlinear optimization and parameter compensation are performed. The experimental results show that the pixel deviation of the end pose corresponding to the optimized D-H kinematic model proposed in this paper and the end pose corresponding to the actual kinematic model in the pixel coordinate system is 0.99 pixels. Compared with the 7.9 deviation pixels between the pixel coordinates calculated by the classic D-H kinematic model and the actual pixel coordinates, the deviation is reduced by nearly 7 pixels for an 87% reduction in error. Therefore, the proposed method can effectively avoid system errors caused by camera parameters in visual calibration, can improve the absolute positioning accuracy of the end of the robotic arm, and has good economy and universality.

关键词： Visualization Robot kinematics Instruments Robot vision systems Kinematics Manipulators Cameras

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition

A Generalized Subspace Distribution Adaptation Framework for...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shaokai Li Peng Song Liang Ji Yun Jin Wenming Zheng School of Computer and Control Engineering Yantai University China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province School of Physics and Electronic Engineering Jiangsu Normal University China Key Laboratory of Child Development and Learning Science (Southeast University) Ministry of Education Research Center for Learning Science Southeast University China

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches.

关键词： Measurement Learning systems Emotion recognition Databases Transfer learning Signal processing algorithms Speech recognition

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Temperature measurement based on electron spin resonance of magnetic nanoparticles

arXiv

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arXiv 2021年

作者： Wang, Shuai Zhong, Jing Liu, Wenzhong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China

Magnetic nanoparticles (MNPs) have excellent magnetic-temperature characteristic. However, current temperature measurement based on MNPs is interfered by concentration. Utilizing the electron spin resonance (ESR), we propose a highly sensitive temperature measurement method without concentration coupling. The anisotropic field is affected by temperature, thus affecting the g-value. The influence of the MNP concentration, size, and the data analysis method on temperature estimation are studied. The optimal temperature sensitivity is achieved with 15-nm MNPs while Gaussian smoothing method allows an optimal accuracy at Fe concentration of 5 mg/ml with a standard deviation of 0.07 K. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic moments

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controller Synthesis of Signal Temporal Logical Tasks for Cyber-Physical Production Systems via Acyclic Decomposition

Controller Synthesis of Signal Temporal Logical Tasks for Cy...

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IEEE Conference on Decision and control

作者： Shuling Wang Shanying Zhu Cailian Chen Liang Xu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Institute of Artificial Intelligence Shanghai University Shanghai China

Modularization facilitates the adaptability of cyber-physical production systems (CPPSs) with a variety of collaborative tasks. Various production rules can be captured by signal temporal logic (STL) specifications imposed on interconnected multi-agent systems (MASs). In this paper, we focus on the controller synthesis of STL tasks for interconnected MASs to accomplish collaborative tasks in modular CPPSs. Firstly, a class of STL specifications characterizing tasks by the combination of fixed-time reachability and finite-time persistence tasks is proposed, which encompasses a large class of production specifications for the MAS. Secondly, the acyclic decomposition of the global STL formula is constructed to enable conflict-free collaborative tasks and unidirectional couplings between subsystems. By establishing the equivalence between the proposition and the state set of the MAS, necessary and sufficient conditions are respectively proposed for the satisfaction of reachability and persistence tasks. In addition, an algorithm is presented to synthesize controllers for the MAS with the global STL specification based on local controllers of subsystems. An illustrative example is given to show the effectiveness of the proposed method.

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Water Supply Prediction Based on Initialized Attention Residual Network

Water Supply Prediction Based on Initialized Attention Resid...

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第三十九届中国控制会议

作者： Yuhao Long Yeming Zhang Jingcheng Wang Miaoshun Bai Dept of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of China Shanghai Municipal Engineering Design Institute(Group)Co. Ltd Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

Real-time and accurate water supply forecast is crucial for water supply control. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply *** this paper, we address a generic artificial neural network, Initialized Attention Residual Network(IARN), to tackle the time series prediction problem in water supply domain. Specifically, instead of continuing to use the recurrent neural network(RNN)in time-series tasks, we try to build a convolution neural network(CNN) to recede the disturb from other factors and get a more credible results. Experiments show that our method achieves state-of-the-art performance on several datasets in terms of accuracy,robustness and generalization ability. The source code of our method will available at https://***/SJTU-L-dd/IARN.

关键词： Neural networks Prediction methods Water supply Time-series analysis Attention module Residual network

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A hybrid model based on stacking and multi-correction mechanisms for urban water demand prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 16685-16690页

作者： Yang Lan Jingcheng Wang Miaoshun Bai Ibrahim Brahmia Haotian Xu Piao Hu Yuhao Long Yeming Zhang Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of education of China Shanghai 200240 China. Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi710021 China. Shanghai Municipal engineering Design Institute (Group) Co. Ltd.

Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water demand prediction models to guarantee high accuracy for a long period. Different holiday types and even tiny changes in temperatures can affect urban water demand seriously. This paper proposes a Stacking-based hybrid model which integrates multi-correction mechanisms to address these problems. A better stacking model is proposed to minimize the generalization error. The stability and reliability of predictions are improved through the design of multi-correction mechanisms such as high temperature weather compensation feature, holiday-type correction model and water quantity fluctuation correction model. Comparing different models before and after stacking also before and after correcting, the prediction accuracy of the proposed hybrid model is much higher and the predictions are more stable and reliable.

关键词： Water supply system Prediction methods Hybrid models Stacking methods Multi-correction mechanisms Neural-network models Time-series analysis

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Simulation Research on Generalized Proportional Integral Observer Based Active Disturbance Rejection control for Coarse Pointing Assembly

Simulation Research on Generalized Proportional Integral Obs...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Jijun Ma Yulong Guo YuNing Wang Yongkang Shi Beijing Research Institute of Telemetry Beijing China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Research Institute of Telemetry Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

The coarse pointing assembly (CPA), as the outer loop of the laser terminal, its tracking stability is the basis for ensuring laser communication. This paper presents the model of the CPA. Aiming at the disturbance factors in the low-speed operation process of the CPA, an active disturbance rejection control (ADRC) method for the position loop of the CPA is designed based on the generalized proportional integral observer (GPIO) to achieve disturbance compensation control. Finally, simulations are carried out on the system model. The simulation results show that compared with the PID control strategy adopted, the proposed ADRC strategy based on GPIO of the CPA can effectively improve the tracking accuracy.

关键词： Robust control Tracking loops Accuracy Simulation Computational modeling Laser stability Observers Laser modes Stability analysis Assembly

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A New Method for Deriving Weights in Group Fuzzy Analytic Hierarchy Process and Evaluation Measures

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IFAC-PapersOnLine 2020年第2期53卷 7941-7946页

作者： Lin Che Yeming Zhang Jingcheng Wang Miaoshun Bai Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Municipal Engineering Design Institute (Group) Co. Ltd. China Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi 710021 China

The paper discusses fuzzy comparison matrices, consistency check, weight prioritization methods and weight evaluation methods in fuzzy group analytic hierarchy process. There are various methods of weight prioritization, however, they are not critically evaluated. In the paper, two measures are introduced for the evaluation of the group weights. Then, a new method is proposed to improve the process of deriving weights and use it in an application compared to another common three methods. Our results show that the new method is a good method for deriving weights of indexes.

关键词： Group Fuzzy AHP Ari-Geo Method Assessment Measure Fuzzy Consistency Check Triangular Fuzzy Number

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Integration of nonlinear disturbance observer within proxy-based sliding mode control for pneumatic muscle actuators

arXiv

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arXiv 2019年

作者： Cao, Yu Huang, Jian Wu, Dongrui Zhang, Mengshi Xiong, Caihua Li, Zhijun Key Laboratory for Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical Science Engineering State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper presents an integration of nonlinear disturbance observer within proxy-based sliding mode control (IDO-PSMC) approach for Pneumatic Muscle Actuators (PMAs). Due to the nonlinearities, uncertainties, hysteresis, and timevarying characteristics of the PMA, the model parameters are difficult to be identified accurately, which results in unmeasurable uncertainties and disturbances of the system. To solve this problem, a novel design of proxy-based sliding mode controller (PSMC) combined with a nonlinear disturbance observer (DO) is used for the tracking control of the PMA. Our approach combines both the merits of the PSMC and the DO so that it is effective in both reducing the "chattering" phenomeno n and improving the system robustness. A constrained Firefly Algorithm is used to search for the optimal control parameters. Based on the Lyapunov theorem, the states of the PMA are shown to be globally uniformly ultimately bounded. Extensive experiments were conducted to verify the superior performance of our approach, in multiple tracking scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Sliding mode control

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