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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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Application of Hybrid Variable Admittance Force Tracking and Fixed-time Position Control for Robot Interaction Tasks

Application of Hybrid Variable Admittance Force Tracking and...

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International Conference on Control, Mechatronics and automation (ICCMA)

作者： Hong Zhan Chao Zeng Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.

关键词： Mechatronics automation Trajectory tracking Force Dynamics Position control Trajectory Robots Admittance control Convergence

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PCART: Automated Repair of Python API Parameter Compatibility Issues

arXiv

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arXiv 2024年

作者： Zhang, Shuai Xiao, Guanping Wang, Jun Lei, Huashan He, Gangqiang Liu, Yepang Zheng, Zheng The College of Computer Science and Technology The Key Laboratory for Safety-critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The School of Automation Science and Electrical Engineering Beihang University Beijing China

In modern software development, Python third-party libraries play a critical role, especially in fields like deep learning and scientific computing. However, API parameters in these libraries often change during evolution, leading to compatibility issues for client applications reliant on specific versions. Python’s flexible parameter-passing mechanism further complicates this, as different passing methods can result in different API compatibility. Currently, no tool can automatically detect and repair Python API parameter compatibility issues. To fill this gap, we introduce PCART, the first solution to fully automate the process of API extraction, code instrumentation, API mapping establishment, compatibility assessment, repair, and validation. PCART handles various types of Python API parameter compatibility issues, including parameter addition, removal, renaming, reordering, and the conversion of positional to keyword parameters. To evaluate PCART, we construct PCBENCH, a large-scale benchmark comprising 47,478 test cases mutated from 844 parameter-changed APIs across 33 popular Python libraries. Evaluation results demonstrate that PCART is both effective and efficient, significantly outperforming existing tools (MLCatchUp and Relancer) and the large language model ChatGPT (GPT-4o), achieving an F1-score of 96.49% in detecting API parameter compatibility issues and a repair precision of 92.26%. Further evaluation on 30 real-world Python projects from GitHub confirms PCART’s practicality. We believe PCART can significantly reduce the time programmers spend maintaining Python API updates and advance the automation of Python API compatibility issue repair. Copyright © 2024, The Authors. All rights reserved.

关键词： Python

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Adaptive Stiffness Control of Series Elastic Actuator Manipulators Based on Dynamic System

Adaptive Stiffness Control of Series Elastic Actuator Manipu...

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IEEE International Conference on automation science and engineering (CASE)

作者： Kunlin Guo Chao Zeng Weiyong Si Ning Wang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK School of Computer Science and Electronic Engineering University of Essex Colchester Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In traditional control methods, Series Elastic Actuator (SEA) joint manipulators are limited by their hardware and can only perform simple tasks with low stiffness. To address this issue, we propose a stiffness adjustment control strategy for SEA manipulators based on Dynamic Systems (DS) with good generalization performance. By enhancing the generalization capability of DS in complex tasks and combining posture control, the SEA manipulators can autonomously adjust control gains and postures according to task requirements to achieve higher stiffness at the end-effector. Experimental results involving the activation of air switches with different stiffness levels and installation angles have demonstrated the effectiveness of our proposed method. The results indicate that compared to traditional methods of adjusting the control gain and retraining DS for similar tasks, our approach exhibits superior generalization performance while maintaining end-effector stiffness during interactions.

关键词： Actuators computer aided software engineering automation Adaptive systems Control systems End effectors Hardware Dynamical systems Manipulator dynamics

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A Novel Computational Nanobiosensing Approach to Improve the Exploitation of In Vivo Computation

A Novel Computational Nanobiosensing Approach to Improve the...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Shi, Shaolong Chen, Yifan Jiang, Zhaoyang Liu, Qiang Ding, Jurong Zhang, Qingfu School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China University of Electronic Science and Technology of China Quzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province Zigong China School of Automation and Information Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798350314588

A novel nanobiosensing framework named 'in vivo computation' has been proposed recently, where the challenge of early tumor detection is overcome from an optimization perspective. The biological gradient field (BGF) triggered by the tumor lesion is viewed as the optimizable objective function with the tumor site being the global optimum. The externally manipulable nanorobots playing the role of agents are manipulated in the search space (i.e., the vascular network of high-risk tissue). Several computational strategies have been proposed to realize tumor targeting by overcoming the in vivo constraints which focused on the tumor detection process without any emphasis on the nanorobots aggregation at the tumor. In this paper, we focus on the utilization rate of agents, which means to improve the percentage of nanorobots that detect the tumor site after it has been found by the first arrival agent (i.e., the nanorobot that detects the tumor at the earliest), and the solution set search, which means to find the tumor region as whole as possible. This process is interpreted as the exploitation process of in vivo computation. An exploitation approach named center-aided weak priority evolution strategy (CWP-ES) is developed for the setting of nanorobot moving direction in this paper. In the approach, a direction generated by the center of agents that have found the tumor mixed with the direction generated by the weak priority evolution strategy (WP-ES) proposed in the previous work is used to steer the motion of nanorobots that have not detected the tumor. Several numerical experiments are performed in a 3D search space to show the effectiveness of this novel computational nanobiosensing approach in three BGF landscapes with different degrees of optimization complexity. © 2023 IEEE.

关键词： Tumors

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A Digital Twin-Driven Hybrid Estimate Method for Health Status of Train Braking System 25

A Digital Twin-Driven Hybrid Estimate Method for Health Stat...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Peng, Jun Zhao, Qin Gao, Dianzhu Yang, Yingze Zhou, Feng Rong, Jieqi Fan, Yunsheng Liu, Bowen Jiang, Fu Zhang, Xiaoyong Central South University School of Computer Science and Engineering Changsha China Central South University School of Automation Changsha China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China Hunan Engineering Laboratory of Rail Vehicles Braking Technology Changsha China

ISBN: (纸本)9781665405270

The braking system is the key part of trains, and its full life-cycle of health status is essential to ensure the safety of trains. How to accurately assess real-time health status throughout the full life-cycle of the train braking system is a challenge. In this paper, a digital twin-driven hybrid estimate method for health status of the braking system is proposed. Firstly, an equivalent model of the braking system is built in the digital twin platform. Then, a hybrid method of fusing model and data is proposed to assess the health status. Finally, a cloud digital twin experimental platform for health status assessment of the braking system is built, and the health status is shown by visualization framework. The experiments verify the effectiveness and practicality of the proposed scheme. © 2022 IEEE.

关键词： Life cycle

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Temperature Characteristics of Oil-Immersed Transformer Based on Fiber Bragg Grating Temperature Sensing Technology

SSRN

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SSRN 2024年

作者： Luo, Chuan Li, Zizhou Yang, Bo Zhao, Zhengang Li, Chuan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

Oil-immersed transformer hot-spot temperature and location directly affects the load capacity and service life of the transformer. The traditional hot-spot temperature measurement is indirect and large deviation. Through the fiber Bragg grating technology, the temperature of the internal components of the transformer under variable operating conditions can be measured directly, reflecting the temperature characteristics of the transformer hot-spot location. Based on this, two full dielectric stress-insensitive FBG temperature sensors with different package forms are designed, with sensitivities of 10.22∼10.65 pm/◦C, linearity of 0.99%∼1.65%, and repeatability of 1.07%∼1.45%. Multiple FBG temperature sensors are installed on the iron-core, windings, and oil channel to build a monitoring system. In the temperature rise test, the temperature inside the transformer variation with space and time under variable operating conditions are obtained. The hot-spot winding temperature rise is 62.9◦C, and the top-oil temperature rise is 39.6◦C. The hot-spot location changes with the operating conditions. © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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Supercapacitor Digital Twin Management System Based on Cloud Environment 23

Supercapacitor Digital Twin Management System Based on Cloud...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Yingze Zhao, Qinxian Gao, Dianzhu Rong, Jieqi Fan, Yunsheng Li, Heng Peng, Jun School of Computer Science and Engineering Central South University Changsha410083 China School of Automation Central South University Changsha410083 China Hunan Engineering Laboratory of Rail Vehicles Braking Technology Changsha410083 China

ISBN: (纸本)9781665494571

With the continuous promotion of the green transportation concept, supercapacitors have gained popularity for their excellent charging and discharging characteristics. However, the unreasonable management of supercapacitor will lead to poor safety and reliability of the supercapacitor system. Aiming at this problem, a supercapacitor cloud management system based on the digital twin is proposed in this paper, which improves the computing power and data storage capacity through cloud computing for the whole life cycle state monitoring of supercapacitors. Firstly, the mathematical modeling of the supercapacitor system is carried out, and the state parameters during the charging and discharging of the supercapacitor are obtained in real-time at the physical layer and uploaded to the data interaction network. Secondly, in the network layer, all data related to the supercapacitor are measured and transmitted to the cloud through IoT, and a digital twin cloud model of the supercapacitor management system is established to evaluate the usage status of the supercapacitor by using the real-time data in the data interaction network. Finally, the supercapacitor charging and discharging control strategy is adjusted in real-time based on the state evaluation results at the control layer and given a visualization window visible to users. The application of the supercapacitor system in the digital twin is explored by developing a parameter estimation algorithm suitable for cloud computing. The experimental results verify the feasibility and practicality of the proposed supercapacitor cloud management system. © 2021 IEEE.

关键词： Cloud computing Smart cities Supercapacitors Real-time systems Data models Digital twins Topology

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A Deep Neural Network-Assisted Diagnosis Method for Cardiovascular Disease Based on the Optimization of the TAdam Algorithm

A Deep Neural Network-Assisted Diagnosis Method for Cardiova...

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2023 China automation Congress, CAC 2023

作者： Hu, Yumei Li, Xing Zhang, Yang Zhu, Yuxuan Sun, Yuechao College of Computer Science and Technology Dongguan University of Technology Dongguan China School of Electrical Engineering & Intelligentization Dongguan University of Technology Dongguan China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China School of Mechanical Engineering of Dgut Dongguan University of Technology Dongguan China Northeastern University College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350303759

Cardiovascular disease is a sudden and extremely disabling and deadly disease that hinders the development of public health care in China and poses a serious threat to the health of users. Therefore, this paper proposes a deep neural network-assisted diagnosis of cardiovascular disease based on TAdam algorithm optimization under a distributed medical monitoring network to enable rapid identification of cardiovascular patients, timely detection and reporting of the condition. This paper addresses the problems that deep neural network models are susceptible to the influence of their structure and hyperparameters and poor generalization convergence, firstly, the mainstream optimization algorithms based on deep learning are sorted out, and finally the T Adam algorithm is used to optimize the model. By comparing the common gradient optimization algorithms SGD (Stochastic Gradient Descent), Adagrad (Adaptive Gradient Algorithm), RMSprop (Root Mean Square propagation), Adam (Adaptive Moment Estimation) and the improved TAd am optimization algorithm trained and tested on the same dataset, the experimental results show that the TAdam algorithm exhibits the fastest convergence speed and the best generalization performance on the cardiovascular disease dataset. The method proposed in this paper enables rapid diagnosis of the user's disease risk and feedback to the relevant personnel to maximise user safety. © 2023 IEEE.

关键词： Deep neural networks

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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