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第40届中国控制会议

作者： Rennong Wang Juan Zhao Zhentao Liu Feng Wang Jinhua She Graduate School of Engineering Tokyo University of Technology School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Surface electromyography（sEMG） can capture muscle activity and motor function,which is considered an effective tool for assessing local muscle *** to the complex environment and individual differences,the collected sEMG signals contain lots of noising information.A novel method is devised to effectively solve the problem of sEMG denoising based on complementary ensemble empirical mode decomposition（CEEMD） and multi-scale entropy（MSEn）.The method uses the concept of MSEn and analyzes the feature trend of MSEn of intrinsic mode functions（IMFs） obtained by *** method adaptively determines the main components of IMFs according to the complexity of sEMG on the time scale,avoiding insufficient selection based on *** experiments verified the effectiveness of the method.

关键词： Surface electromyography complementary ensemble empirical mode decomposition multi-scale entropy data processing

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Multi-objective Evolutionary Algorithm Based Graph Neural Network Architecture Search 11th

Multi-objective Evolutionary Algorithm Based Graph Neural Ne...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： He, Lianyi Liu, Xiaobo Xiang, Hongbo Wang, Guangjun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819647521

Graph Neural Networks (GNN) has become a powerful graph data processing method, which has been widely used in node classification, link prediction, and other graph analysis tasks. Due to the diversity and complexity of graph structures and information propagation, as well as the handling of heterogeneous graphs, etc., the design of GNNs presents many challenges. The existing methods have high classification accuracy, but their structures are very complex. In this paper, a graph neural network architecture search framework named MO-GNN, which is based on multi-objective evolutionary algorithm is proposed. Furthermore, a weight sharing strategy MO-GNN-WS is proposed to reduce the resource consumption caused by weight training of different architectures. To verify the performance of the proposed algorithm, experiments on four popular graph datasets are used for transduction and induction tasks. The experimental results show that the MO-GNN-WS algorithm outperforms the most advanced neural network architecture search methods in terms of classification accuracy and resource consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data Sharing

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Spatial-nonlocality-induced non-Markovian electromagnetically induced transparency in a single giant atom

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Physical Review A 2022年第4期106卷 043710-043710页

作者： Y. T. Zhu S. Xue R. B. Wu W. L. Li Z. H. Peng M. Jiang Department of Automation Shanghai Jiao Tong University Shanghai 200240 People's Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 People's Republic of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 People's Republic of China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing 100084 People's Republic of China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 People's Republic of China School of Electronics and Information Engineering Soochow University Suzhou 215006 People's Republic of China

The nonlocal waveguide coupling of giant atoms can bring up novel physics and applications. In this paper, we find unconventional electromagnetically induced transparency in giant atoms that has not been reported so far. We present a consistent theory for describing the unconventional EIT which includes a real-space scattering method and a time-delayed master equation. Our analysis shows this phenomenon is a full quantum effect led by a quantized interaction between the atom and the waveguide. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom. This phenomenon results from the inherent nonlocality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time.

关键词： Quantum optics with artificial atoms Scattering theory Quantum master equation

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NccFlow: Unsupervised learning of optical flow with non-occlusion from geometry

arXiv

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arXiv 2021年

作者： Wang, Guangming Ren, Shuaiqi Wang, Hesheng The Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer vision

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Towards Collision Detection, Localization and Force Estimation for a Soft Cable-driven Robot Manipulator

Towards Collision Detection, Localization and Force Estimati...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yuxin Wang Hesheng Wang Fan Xu Junzhi Yu Weidong Chen Yun-Hui Liu Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Institute of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai China College of Engineering Peking University Beijing China The Chinese University of Hong Kong Hong Kong

Soft robots have been applied widely to various constrained scenarios due to the advantages over traditional rigid manipulators such as softness, deformability and adaptability to constrained surroundings. To make full use of this merit, this paper proposes a method that integrates collision detection, localization and force estimation for a cable-driven soft manipulator without any prior geometrical knowledge of its surroundings. First of all, a collision detection algorithm is presented based upon Cosserat-rod statics by a threshold method through using the cable tension and the shape information, which are obtained by the load cells and the Vicon system, respectively. Secondly, a collision localization and force estimation method is proposed through optimizing the discrepancy between the actual and the theoretical shapes. Finally, experiments are carried out to validate these algorithms. The experimental results demonstrate that the site, the magnitude as well as the direction can be estimated.

关键词： Location awareness Shape Conferences Force Estimation Soft robotics Manipulators

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Research on fault feature extraction of rotating machinery based on adaptive VMD and optimized CYCBD

Research on fault feature extraction of rotating machinery b...

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Chinese control and Decision Conference, CCDC

作者： Ran Xie Ling Xiong Binbin Dan Zeyu Ren Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control of Ministry of Education Wuhan China School of Physics and Electronic Science Hunan Institute of Science and Technology Yueyang China

ISBN: (纸本)9781665478977

To solve the problem that early fault features of rotating machinery are difficult to extract, an adaptive k-value hierarchical variational mode decomposition (H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) fault feature extraction method is proposed in this paper. Mode decomposition of the vibration signal is performed with H-VMD. and then the noise dominant component is denoised by wavelet threshold denoising (WTD). Furthermore, the improved autocorrelation-weighted correlated kurtosis (ACK) when CYCBD enhances the periodic shock component of the denoised signal is the fitness function of ChOA, and the envelope demodulation analysis of the feature-enhanced signal is performed using the teager energy operator (TEO). Simulation analysis and experimental results show that the interference of background noise can be effectively removed and the periodic shock component of the vibration signal be enhanced by the proposed method, which is a new feature extraction method for the fault diagnosis of rotating machinery.

关键词： Vibrations Fault diagnosis Electric shock Noise reduction Interference Feature extraction Linear programming

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Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal Information-Based Broad and Deep Learning Model f...

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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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Low-Friction Soft Robots for Targeted Bacterial Infection Treatment in Gastrointestinal Tract

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类生命系统（英文） 2024年第5期2024卷 299-313页

作者： Ben Wang Yunrui Chen Zhicheng Ye Haidong Yu Kai Fung Chan Tiantian Xu Zhiguang Guo Weimin Liu Li Zhang College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060China Guangxi Key Laboratory of Processing for Non-Ferrous Metals and Featured Materials School of ResourceEnvironments and MaterialsGuangxi UniversityNanning 530004China Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong KongShatinNew TerritoriesHong Kong SARChina Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Key Laboratory of Biomedical Imaging Science and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Hubei Collaborative Innovation Centre for Advanced Organic Chemical Materials and Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials Hubei UniversityWuhan 430062China State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical PhysicsChinese Academy of ScienceLanzhou 730000China State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical PhysicsChinese Academy of ScienceLanzhou 730000China Department of Mechanical and Automation Engineering The Chinese University of Hong KongShatinNew TerritoriesHong Kong SARChina Multi-Scale Medical Robotics Center Hong Kong Science ParkShatinNew TerritoriesHong Kong SARChina

Untethered and self-transformable miniature robots are capable of performing reconfigurable deformation and on-demand locomotion,which aid the traversal toward various lumens,and bring revolutionary changes for targeted delivery in gastrointestinal(GI)***,the viscous non-Newtonian liquid environment and plicae gastricae obstacles severely hamper high-precision actuation and payload ***,we developed a low-friction soft robot by assembly of densely arranged cone structures and grafting of hydrophobic *** magnetic orientation encoded robot can move in multiple modes,with a substantially reduced drag,terrain adaptability,and improved motion velocity across the non-Newtonian ***,the robot stiffness can be reversibly controlled with magnetically induced hardening,enabling on-site scratching and destruction of antibiotic-ineradicable polymeric matrix in biofilms with a low-frequency magnetic ***,the magnetocaloric effect can be utilized to eradicate the bacteria by magnetocaloric effect under high-frequency alternating *** verify the potential applications inside the body,the clinical imaging-guided actuation platforms were developed for vision-based control and delivery of the *** developed low-friction robots and clinical imaging-guided actuation platforms show their high potential to perform bacterial infection therapy in various lumens inside the body.

关键词： astro gast bacterial infection intestinal low-friction robots soft targeted tract

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Effective Throughput Optimization of SAG Milling Process Based on BPNN and Genetic Algorithm

Effective Throughput Optimization of SAG Milling Process Bas...

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Industrial Cyber-Physical systems (ICPS)

作者： Zhenhong Liao Ce Xu Wen Chen Qifu Chen Feng Wang Jinhua She 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 Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Chagnsha China Hunan Valin Lianyuan Iron And Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

Grinding is an energy-consuming process in mineral processing industry. Improving grinding processing capacity per unit power consumption is an effective means to reduce grinding production cost. In this paper, a new index for evaluating the effective processing throughput of SAG milling is proposed. The production process model is established by BP neural network (BPNN). Through combining the process mechanism and production constraints, the genetic algorithm is adopted to optimize the operating parameters of the SAG milling process to maximize the effective throughput, thus improving the grinding efficiency. The experimental results showed that through optimization of effective throughput function proposed in this paper, the SAG mill processing capacity has been increased by 4% and the operating power drawn reduced by 1.12%. It has important guiding significance for the actual production process.

关键词： Industries Power demand Neural networks Milling Throughput Minerals Materials processing

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