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第34届中国控制与决策会议

作者： Tian Gao Qinglin Wang Yaping Dai School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

In order to solve the problem that the clustering number in Fuzzy C-Means(FCM) needs to be set manually in advance,a two-phase hybrid fuzzy clustering approach using membership fusion(TPHFC) is *** the first phase,conventional FCM is used for *** the second phase,the results obtained by pre-clustering are fused according to the relationship between the membership of samples to different clusters and the membership threshold.A density-based clustering validity measurement is established for this *** proposed method obtains better clustering effect with setting fewer *** on synthetic datasets conforming to Gaussian distribution and UCI datasets demonstrate the effectiveness of the proposed clustering *** clustering number and clustering centers can be obtained adaptively.

关键词： Fuzzy Clustering Fuzzy C-Means Two-Stage Clustering Adaptive Parameter Adjustment

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Joint Moment Estimation for Hip Exoskeleton control: A Generalized Moment Feature Generation Method

arXiv

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arXiv 2024年

作者： Zhang, Yuanwen Xiong, Jingfeng Xian, Haolan Chen, Chuheng Chen, Xinxing Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China The Key Laboratory of Image Processing and Intelligent Control The Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (p Copyright © 2024, The Authors. All rights reserved.

关键词： Exoskeleton (Robotics)

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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Congress on Evolutionary Computation

作者： Yaqi Ti Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space.

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GAT-Net:A Network using Grid Attention and Transformer for Dynamic Facial Expression Recognition

GAT-Net:A Network using Grid Attention and Transformer for D...

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第34届中国控制与决策会议

作者： Wenxin Dai Yaping Dai Kaoru Hirota Shuai Shao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Dynamic facial expression recognition(DFER) in the wild has received widespread attention *** are complex factors such as face occlusion and pose variation in the *** expression recognition has a subtle competition between capturing local features of a human face and obtaining a global feature *** paper proposes an end-to-end DFER network GAT-Net based on the grid attention module and Transformer,which improves the robustness and accuracy of DFER in the ***,GAT-Net is divided into two components:spatial feature extraction and temporal feature *** grid attention module of the spatial feature extraction component guides the network to pay attention to the local salient features of the face,which reduces the interference of field occlusion and non-frontal *** Transformer in the temporal feature processing component guides the network to learn the temporal relationship of high-level semantic features and the global representation of facial expression *** two components balance the subtle competition between local features and global feature representations of facial *** ablation experiment has proved the effectiveness of the grid attention module and *** demonstrate that our GAT-Net outperforms state-of-the-art methods on DFEW and AFEW benchmarks with accuracies of 67.53%,and 50.14% respectively.

关键词： Facial Expression Recognition Deep Learning Attention Mechanism Transformer

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Power System Events Classification Technology Based on Deep-Learning

Power System Events Classification Technology Based on Deep-...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Xin Lei Hongwei Ma Bin Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing China College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

As the complexity of the power system continues to increase, the frequency of the power system anomalies is on the rise. These anomalies have significant and widespread impacts on the stability of the power grid. Therefore, the rapid and accurate classification of these anomalies is crucial in preventing their further propagation and mitigating potential economic losses. This study presents an algorithm based on Phasor Measurement Unit (PMU) data for monitoring the state of power systems and identifying the types of anomalies. First, a dataset for anomaly event classification is created based on PMU data, which is used to train and validate the anomaly event classification model. Subsequently, a robust anomaly event classification model is constructed, consisting of a residual module with one-dimensional Convolutional Neural Networks (CNN) and a cascaded fully connected neural network classifier. This algorithm has undergone rigorous testing in the IEEE New England 39 bus test system, demonstrating exceptional event recognition accuracy.

关键词： Accuracy Power system stability Propagation losses Phasor measurement units Data models Stability analysis Classification algorithms

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Temperature control System of Thermoelectric Cooler Based on Improved ADRC

Temperature Control System of Thermoelectric Cooler Based on...

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第34届中国控制与决策会议

作者： Xiyuan Shan Tao Cai Zhicheng Zong Hongbo Mu School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Aiming at the problems containing complex working conditions,modeling difficulties and long-time delay of thermoelectric cooler temperature control system,an improved ADRC control method combining ADRC and Smith predictor is proposed in this *** deals with the disturbances and uncertain dynamics in the system,SP compensates the time delay to improve the control *** with the traditional PID controller,the proposed control method has faster response speed and stronger anti-disturbance ability,meanwhile,overcomes the dependence of Smith predictor on object parameters to a certain *** simulation and experimental verification,a good control effect is obtained,which provides a positive guidance of the related application of thermoelectric cooler.

关键词： Thermoelectric cooler(TEC) Active Disturbance Rejection control(ADRC) Smith Predictor(SP) Temperature control System

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Finite-time sliding-mode control for semi-Markov systems with delayed impulses

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Ren, Fangmin Wang, Xiaoping Li, Yangmin Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Braininspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

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Reconstruction of spectral reflectance based on fusion convolution neural network 9

Reconstruction of spectral reflectance based on fusion convo...

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Zhang, Lijun Wang, Xiangguo Zhang, Jingjing Qiao, Jinlong Li, Penghui School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China

ISBN: (纸本)9781450387835

An improved method for spectral reflectance reconstruction from the RGB response of the digital camera is proposed by deep convolution neural network. The proposed method learns a fusion mapping theory that represents the basic characteristics of spectral reflectance, including spectral nonlinear mapping and spatial similarity. The deep residual network greatly deepens the network structure and improves the performance of the model, enabling the recovery network to make full use of the sample structure information to accurately recover the spectral reflectance. The performance of the method is compared with several existing methods in the cases of the same experimental data set. Experimental results show that the proposed method has the best accuracy in estimating spectral reflectance and colorimetric values in most cases in comparison with existing methods. © 2020 ACM.

关键词： Deep neural networks

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Design of intelligent monitoring and dynamic early warning system for geological hazards

Design of intelligent monitoring and dynamic early warning s...

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Chinese automation Congress (CAC)

作者： Ziliang Yu Weihua Cao Chao Gan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

intelligent monitoring system is crucial to the process of geological disaster prevention and control, and plays a vital role in improving prevention and control efficiency and early warning accuracy. An intelligent monitoring and dynamic early warning system for geological hazards is designed. First, four main functional requirements of data fusion and visualization, single landslide dynamic warning, regional landslide susceptibility evaluation and risk level inquiry are analyzed in detail. Then, the system network architecture is analyzed and a six-layer software framework is designed. Finally, a new online data architecture is proposed. The designed system provides a feasible solution for geological disaster prevention and control.

关键词： Geology Scalability Software algorithms Computer architecture Network architecture Alarm systems Terrain factors

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Surface Defect Detection of Sanitary Ceramic Product Utilizing High-resolution Image on Two Different Scales

Surface Defect Detection of Sanitary Ceramic Product Utilizi...

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Chinese control and Decision Conference, CCDC

作者： Xing-Ao Wang Jianqi An Yisheng Mei Xin Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665478977

Surface defect detection of sanitary ceramic products is an important part of the production process. The deep learning method is the mainstream research direction in the field of defect detection. Since there are many small defects on the surface of sanitary ceramic products, it is an effective means to use a high-resolution camera to obtain images. The detection speed of high-resolution pictures and the interference of background are the difficult problems in detection. Therefore, this paper designs a two-scale detection framework to detect and locate defects of sanitary ceramics utilizing high-resolution images. The two-scale detection framework speeds up the detection of high-resolution images and weakens the interference of the background by dividing the defect detection task into two parts, a workpiece region recognition task on large scale and a defect detection task on small scale. The experiments show that the two-scale detection method has a better detection speed and accuracy rate compared with the single-scale method, which proves the superiority of the proposed method.

关键词： Deep learning Image recognition Ceramic products Interference Production Cameras Ceramics

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