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Fault diagnosis of control moment gyroscope based on a new CNN scheme using attention-enhanced convolutional block

Science China(Technological Sciences) 2022年第11期65卷 2605-2616页

作者： ZHAO HaoTian LIU Ming SUN YiYong CHEN Zhang DUAN GuangRen CAO XiBin Research Center of Satellite Technology School of AstronauticsHarbin Institute of TechnologyHarbin 150001China School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Department of Automation TsinghuaUniversityBeijing 100084China Control Theory and Guidance Technology School of AstronauticsHarbin Institute of TechnologyHarbin150001China

control moment gyroscope(CMG)is a typical attitude control system component for satellites and mobile robots,and the online fault diagnosis of CMG is crucial because it determines the stability and accuracy of the attitude control *** paper develops a data-driven CMG fault diagnosis scheme based on a new CNN *** this design,seven types of fault signals are converted into spectrum datasets through short-time Fourier transformation(STFT),and a new CNN network scheme called AECB-CNN is proposed based on attention-enhanced convolutional blocks(AECB).AECB-CNN can achieve high training accuracy for the CMG fault diagnosis datasets under different sliding window ***,simulation results indicate that the proposed fault diagnosis method can achieve an accuracy of nearly 95%in 1.28 s and 100%in 2.56 s,respectively.

关键词： fault diagnosis control moment gyroscope CNN scheme attention-enhanced convolutional block

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Design of Generalized Inverse Covariance Intersection with Multiple Estimates

Design of Generalized Inverse Covariance Intersection with M...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

作者： Park, Woo Jung Park, Chan Gook Department of Aerospace Engineering Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of Department of Aerospace Engineering Institute of Advanced Aerospace Technology Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9781624106316

In this paper, generalized inverse covariance intersection (GICI) with multiple estimates was proposed. Distributed fusion is widely used due to its easy structure to manage tracks and lower computational load than centralized fusion. Since it is hard to know the correlation of the nodes in distributed fusion, covariance intersection (CI) and inverse covariance intersection (ICI) which guarantee consistency regardless of the common information are developed. In recent research, the GICI for two nodes and the ICI for multiple sensors are proposed, respectively. By combining the two previous results, GICI with multiple estimates was driven in this paper. The simulation results on the fusion of multiple pdfs showed better results than naïve fusion that ignores common information of the nodes. © 2022, American institute of Aeronautics and Astronautics Inc.. All rights reserved.

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Modeling and Calculation of Temperature Field in Slotless Motor Based on a New Lumped Parameter Thermal Network Method 26

Modeling and Calculation of Temperature Field in Slotless Mo...

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26th International Conference on Electrical Machines and systems, ICEMS 2023

作者： Zhao, Jing Guo, Penglei Zheng, Yun Wang, You Yang, Lei Beijing Institute of Technology School of Automation Beijing China Beijing Aerospace Control Instrument Research Institute Department of Motor Power Supply Beijing China Beijing Institute of Control Engineering Beijing Key Laboratory of Long-life Technology of Precise Rotation and Transmission Mechanisms Beijing China

ISBN: (纸本)9798350317589

In order to achieve accurate analysis of the motor temperature field, the temperature field of the slotless motor is modeled and calculated based on the centralized parametric thermal network method. The distribution of the motor thermal network nodes is given, and the axial model of the motor is divided. By deriving the thermal resistance model of each component of the slotless motor, a full-domain thermal resistance network model of the motor is constructed, and the accuracy of the model is verified by comparing the results with the finite element simulation. © 2023 IEEE.

关键词： Finite element method

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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Computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Data-Driven Robust Optimization for Energy Chemical Processes under Uncertainties: A Review and Tutorial 4

Data-Driven Robust Optimization for Energy Chemical Processe...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Ning, Chao Li, Longyan Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

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Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

Event-Triggered Online Learning Distributionally Robust Ener...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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An Obstacle Avoidance control Strategy for DELTA Parallel Manipulator via Improved Genetic Algorithm 37

An Obstacle Avoidance Control Strategy for DELTA Parallel Ma...

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37th Youth Academic Annual Conference of Chinese Association of automation, YAC 2022

作者： Zheng, Hao Xu, Yihang Li, Yuantao Ran, Guangtao Liu, Jian Southeast University School of Automation Nanjing210018 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China

ISBN: (纸本)9781665465366

Aiming at the requirement of the DELTA parallel manipulator for performing grasping operations, a method for obstacle avoidance based on a genetic algorithm is proposed. Firstly, the classical DELTA parallel manipulator is analyzed mathematically, and its model is simplified. Then, the motion space and inverse kinematics solutions of the DELTA parallel manipulator are solved by using the spatial geometry method. Secondly, a cylindrical envelope surface is added to the manipulator's end for distance calculation. This envelope surface ensures that obstacles do not collide with the manipulator during obstacle avoidance. Thirdly, an improved genetic algorithm is proposed for obstacle avoidance. Finally, the simulation results are given to verify the effectiveness and reliability of the proposed algorithm. © 2022 IEEE.

关键词： Genetic algorithms

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A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves

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船舶力学 2024年第12期28卷 1803-1819页

作者： CHEN Zhan-yang ZHAN Zheng-yong CHANG Shao-ping XU Shao-feng LIU Xing-yun Department of Ocean Engineering Harbin Institute of Technology at WeihaiWeihai 264209China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China Flight Control System Department AVIC Xi'an Flight Automatic Control Research InstituteXi'an 710065China

Ship motions induced by waves have a significant impact on the efficiency and safety of offshore ***-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive ***,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate ***,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed *** numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship *** related experimental data were employed to verify the numerical simulation *** results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.

关键词： deep learning LSTM ship motion real-time prediction irregular waves

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Proposal of Serious Games for a Socially Assistive Robot Based on Eye Contact and Visual Attention of Children with Autism Spectrum Disorder 9th

Proposal of Serious Games for a Socially Assistive Robot ...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Negri, Yuri Panceri, João Freitas, Éberte Schreider, Sheila Caldeira, Eliete Bastos-Filho, Teodiano Electrical Engineering Department Federal University of Espirito Santo Av. Fernando Ferrari 514—Goiabeiras ES Vitória29075-910 Brazil Postgraduate Program in Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Automation and Control Engineering Department Federal Institute of Espirito Santo Linhares Brazil Postgraduate Program in Biotechnology Federal University of Espirito Santo Vitoria Brazil

ISBN: (纸本)9783031494062

Eye contact is one of the main human skills, and a prerequisite of verbal language. However, children with Autism Spectrum Disorder (ASD) often have an important deficit in this skill, compromising their entire cognitive and social development. This work shows the development of two Serious Games (SGs), based on the concept of Child-Robot Interaction (CRI), for the exercise and improvement of eye contact and visual attention, as well as concepts such as imitation and emotion recognition. For face detection and eye movement monitoring, an open source development framework of machine learning named MediaPipe was used. Tests with children with ASD will be conducted, and it is expected these SGs have a positive impact regarding the improvement of both eye contact and visual attention for these children. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Serious games

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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