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Manufacturing Service Network of Digital Twin Systems Under Cloud Computing Environment

Procedia CIRP 2022年 109卷 460-465页

作者： Feng Xiang Ping Zhou Ying Zuo Fei Tao Dashun Zhang Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan 430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan 430081 China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan 430081 China Research Institute for Frontier Science Beihang University Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China No.55 Research Institute China North Industries Group Corporation Changchun 130012 China

With the promotion and application of the digital twin system in the industrial Internet platform, and around the "intelligent, integrated, and service-oriented" manufacturing characteristics, a digital twin system manufacturing service network with full-process, full-object, and full-factor coverage of virtual and real resources has gradually formed. In a typical cloud computing environment, this paper defines the manufacturing service network of the digital twin system based on the virtual and real mapping, simulation calculation, feedback control and other characteristic requirements of the digital twin system, and constructs the network model from the network nodes and network connections. Based on this, the optimization steps for resource allocation of manufacturing service network based on the digital twin system, and the key technologies for the construction of the manufacturing service network of the digital twin system are analyzed.

关键词： Digital Twin Cloud Environment Service Network Resource Allocation

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Fault-tolerant control based on Back-stepping and Fixed-time Disturbance Estimation for Hypersonic Reentry Vehicles

Fault-tolerant Control based on Back-stepping and Fixed-time...

引用

IEEE International Conference on Unmanned Systems (ICUS)

作者： Tiancai Wu Honglun Wang Yue Yu Na Li Yiheng Liu Shenyuan Honors College School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Aerospace Automatic Control Institute Beijing China Unmanned Systems Research Institute Beihang University Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

In this paper, a novel robust back-stepping fault- tolerant control scheme is proposed, which enables hypersonic reentry vehicles with model uncertainties to accurately track the attitude commands under the condition of both unknown actuator faults and external disturbances. Firstly, a control-oriented hypersonic reentry vehicle model is established on the basis of regarding three factors mentioned above as lumped disturbance. Then, a fixed-time observer is used to achieve the fixed-time lumped disturbance estimation. Based on the disturbance observer and back-stepping technology, the proposed fault-tolerant control laws are designed to ensure control performance. Meanwhile, the “differential explosion” problem is solved by the sigmoid function-based tracking differentiator. Finally, the stability and effectiveness of the proposed controller are verified from theoretical analysis and extensive numerical simulations respectively.

关键词： automation Field-flow fractionation Electrical engineering Attitude control Heart rate variability Fault tolerant systems Fault tolerance

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Muscle Synergy Analysis Based on NMF for Lower Limb Motor Function Assessment

Muscle Synergy Analysis Based on NMF for Lower Limb Motor Fu...

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IEEE International Conference on Robotics and Biomimetics

作者： Kexin Xiang Weiqun Wang Zeng-Guang Hou Chutian Zhang Jiaxing Wang Weiguo Shi Yuze Jiao Tianyu Lin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artifificial Intelligence University of Chinese Academy of Sciences Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Macao Macau Institute of Systems Engineering Macau University of Science and Technology Macau China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

ISBN: (纸本)9781665481106

Accurate rehabilitation assessments are essential for designing effective rehabilitation methods and helping patients recover better. It's well known that commonly used scale assessment methods for neurorehabilitation suffer from the issue of subjectivity, thus investigation of objective assessment methods is very necessary. Muscle synergy analysis can be uesd to assess limb motor functions from the perspective of neuromuscular control. In this paper, a method for evaluation of human lower limb motor functions based on muscle synergy analysis is presented. Muscle synergy modules are designed using surface electromyography (sEMG) signals of the subjects' lower limbs by non-negative matrix factorizations (NMF). By comparing the cosine similarities of these synergy modules, it can be seen that muscle synergies of healthy subjects and patients are significantly different, while they are similar among healthy subjects. Therefore, a reference synergy module (RSM) is designed by averaging the muscle synergy modules for healthy subjects, and the similarities can be calculated by comparing the synergy modules for healthy subjects or patients with the RSM. In the experiment carried out in this study, average similarities of the three synergy modules for healthy subjects are respectively 0.97166, 0.87368 and 0.84932, and on the other hand, the average similarities for the three synergy modules for patients are respectively 0.59979, 0.56426 and 0.69042. Therefore, the similarities for healthy subjects are much higher than those for SCI patients, which denotes that the similarity between an individual synergy module and the RSM can be used as an objective assessing index for evaluating patients' motor function.

关键词： Neuromuscular Biomimetics Stability criteria Muscles Electromyography Indexes Robots

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Distributed synchronous and asynchronous algorithms for semi-definite programming with diagonal constraints

arXiv

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arXiv 2021年

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Ministry of Education Beijing100081 China School of Electronic and Information Engineering Tongji University Shanghai200082 China

This paper develops distributed synchronous and asynchronous algorithms for the large-scale semi-definite programming with diagonal constraints, which has wide applications in combination optimization, image processing and community detection. The information of the semi-definite programming is allocated to multiple interconnected agents such that each agent aims to find a solution by communicating to its neighbors. Based on low-rank property of solutions and the Burer-Monteiro factorization, we transform the original problem into a distributed optimization problem over unit spheres to reduce variable dimensions and ensure positive semi-definiteness without involving semi-definite projections, which are computationally expensive. For the distributed optimization problem, we propose distributed synchronous and asynchronous algorithms, both of which reduce computational burden and storage space compared with existing centralized algorithms. Specifically, the distributed synchronous algorithm almost surely escapes strict saddle points and converges to the set of optimal solutions to the optimization problem. In addition, the proposed distributed asynchronous algorithm allows communication delays and converges to the set of critical points to the optimization problem under mild conditions. By applying proposed algorithms to image segmentation applications, we illustrate the efficiency and convergence performance of the two proposed algorithms. © 2021, CC BY.

关键词： Optimization

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Category-oriented self-learning graph embedding for efficient image compact representation

TechRxiv

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TechRxiv 2021年

作者： Hu, Liangchen Dai, Zhenlei Tian, Lei Zhang, Wensheng The School of Computer and Information Anhui Normal University Wuhu241003 China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China

As one of the ways to acquire efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are intended to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within the class but excluding the affinity of different classes, which results in the inability to fully capture the marginal structure between distributions of different classes. To overcome these issues, we propose a learning framework that implements Category-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can easily be extended to the semi-supervised situation. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method comparing with some state-of-the-art approaches. © 2021, CC BY.

关键词： Embeddings

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Hybrid compensation topology for constant current/voltage charging mode of EV wireless chargers

Hybrid compensation topology for constant current/voltage ch...

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Sustainable Power and Energy Conference (iSPEC)

作者： Shiliang Hou Bo Yu Zhenghao Wu Skate Key Laboratory of Start Grid Protection and Control NARI Group Corporation School of Electrical and Automation Engineering Nanjing Normal University Nanjing China Skate Key Laboratory of Start Grid Protection and Control NARI Group Corporation Nanjing China School of Electrical and Automation Engineering Nanjing Normal University Nanjing China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

This paper proposes a hybrid compensation topology aiming to realize constant current (CC) and constant voltage (CV) charging mode for magnetic coupling resonance wireless power transmission (WPT) technology. This charging mode is designed to charge a load (battery used for electric vehicles) intelligent, stably and efficiently. This study develops WPT systems with LCC-S and LCC-LCC compensations. According to the voltage and current state of the battery, this hybrid compensation topology realizes the switching of the receiver topology, so as to realize the intelligent charging with CC and CV.

关键词： Inductive charging RLC circuits Switches DC-DC power converters control systems Hybrid power systems Topology

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Estimating Lithium-Ion Battery Health Status: A Temporal Deep Learning Approach With Uncertainty Representation

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IEEE Sensors Journal 2025年

作者： Chen, Chuang Chen, Hongtian Shi, Jiantao Yue, Dongdong Shi, Ge Lyu, Dongzhen Shanghai Jiao Tong University Department of Automation Shanghai200240 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Nanjing Tech University School of Geomatics Science and Technology Nanjing211816 China Wenzhou University College of Mechanical and Electrical Engineering Wenzhou325035 China

Precise estimation of the state-of-health (SOH) of lithium-ion batteries is essential for the reliable and safe functioning of energy storage systems. However, existing data-driven methods seldom account for the uncertainties involved in the battery degradation process, which diminishes the reliability of SOH estimation models. To address this issue, this paper proposes a novel temporal deep learning approach with uncertainty representation. In this methodology, temporal convolutional networks are employed for front-end feature extraction, effectively capturing temporal patterns within battery data. The Transformer architecture is utilized for SOH prediction, leveraging its capability to model complex dependencies in sequential data. To enhance predictive performance, the Transformer decoder is replaced by bidirectional long short-term memory quantile regression, allowing for the estimation of SOH values at various quantiles, providing a more detailed understanding of the battery’s health status. Finally, kernel density estimation is applied to transform these quantile estimates into probability density functions, offering a probabilistic interpretation of the SOH values. Experimental findings indicate that the proposed framework significantly improves the precision and reliability of SOH estimation when compared to other intelligent methods, making it a promising solution for real-time monitoring and management of lithium-ion batteries. © 2001-2012 IEEE.

关键词： Lithium-ion batteries

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A Simulation-based Online Evolutionary Algorithm for Combat in StarCraft II

A Simulation-based Online Evolutionary Algorithm for Combat ...

引用

International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Yongfeng Liu Changhe Li Sanyou Zeng School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

ISBN: (数字)9781728165172

ISBN: (纸本)9781728165189

Real-time strategy (RTS) games have become one of the hotspots in the field of artificial intelligence research due to the large search space, long-term planning, and real-time constraint. In view of the fact that most of the current research on solving the RTS combat problem uses offline training methods, and these methods may need a lot of training time, this work uses an evolutionary algorithm (EA) to directly optimize the units' action sequences online. In order to solve the problems of repetitive actions caused by the neutral area and the size correlation caused by cooperation between units, three search operators are proposed. It is shown that this method can effectively solve the combat problem in RTS games.

关键词： Games Genomics Bioinformatics Sociology Statistics Data models Springs

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3D Correspondence Grouping with Compatibility Features

arXiv

引用

arXiv 2020年

作者： Yang, Jiaqi Chen, Jiahao Huang, Zhiqiang Quan, Siwen Zhang, Yanning Cao, Zhiguo School of Computer Science Northwestern Polytechnical University China School of Electronic and Control Engineering Chang’an University China School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

We present a simple yet effective method for 3D correspondence grouping. The objective is to accurately classify initial correspondences obtained by matching local geometric descriptors into inliers and outliers. Although the spatial distribution of correspondences is irregular, inliers are expected to be geometrically compatible with each other. Based on such observation, we propose a novel representation for 3D correspondences, dubbed compatibility feature (CF), to describe the consistencies within inliers and inconsistencies within outliers. CF consists of top-ranked compatibility scores of a candidate to other correspondences, which purely relies on robust and rotation-invariant geometric constraints. We then formulate the grouping problem as a classification problem for CF features, which is accomplished via a simple multilayer perceptron (MLP) network. Comparisons with nine state-of-the-art methods on four benchmarks demonstrate that: 1) CF is distinctive, robust, and rotation-invariant;2) our CF-based method achieves the best overall performance and holds good generalization ability. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

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Black-box domain adaptation for cross-domain on-device machinery fault diagnosis via hierarchical debiased self-supervised learning

引用

Knowledge-Based Systems 2025年 324卷

作者： Mengliang Zhu Jie Liu Yanglong Lu Zhongxu Hu Li Yuan Kaibo Zhou MOE Key Laboratory of Image Information Processing and Intelligent Control Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National Center of Technology Innovation for Digital Construction School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

This paper explores black-box domain adaptation (BBDA) for cross-domain on-device machinery fault diagnosis. Specifically, a pre-trained black-box source model is deployed on a cloud platform with only its input-output API accessible, and a randomly initialized target model is trained locally utilizing unlabeled target data on an edge device. In BBDA, neither raw data nor model parameters are transmitted across domains, and the only available supervised information for adaptation is API-queried predictions on unlabeled target data. However, API-queried predictions are inevitably noisy and class-imbalanced due to cross-domain distribution discrepancy and inherent category bias. Directly utilizing them for on-device training easily leads to confirmation bias and exacerbates category bias, thereby severely degrading performance. This paper proposes a hierarchical debiased self-supervised learning framework for BBDA from a novel research perspective of concurrently alleviating both confirmation and category biases. Specifically, debiased knowledge distillation is proposed to gradually enhance discriminability of the randomly initialized target model by providing debiased training predictions. An adaptive category-unbiased sample division strategy is devised to divide unlabeled target data into certain-aware and uncertain-aware sets, enabling fully exploiting intrinsic data structures and utilizing underlying sample relationships within the target domain. On this basis, debiased prototypical self-training is proposed for the certain-aware set to obtain reliable pseudo-labels for self-training, leveraging global semantic structure. Hierarchical neighborhood adaptation is customized for the uncertain-aware set to enforce the cluster assumption for noise refinement, exploiting local clustering structure and utilizing underlying sample relationships. Extensive experiments and analyses on public and practical datasets validate the proposed framework’s superiority in cross-dom

关键词： Black-box domain adaptation Category bias Confirmation bias Cross-domain on-device machinery fault diagnosis Self-supervised learning

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