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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

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Generative manifold learning thermography for non-destructive evaluation of defects in composite materials 10

Generative manifold learning thermography for non-destructiv...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Liu, Kaixin Fan, Yu Zhang, Kuan Yang, Jianguo Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Suzhou Big Vision Medical Imaging Technology Company Suzhou215000 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan Institute of Innovation Research Zhejiang University of Technology Shengzhou Shengzhou312400 China

ISBN: (纸本)9781665424233

In the non-destructive evaluation of infrared thermography, the thermographic data modeling and analysis steps play an important role in improving the inspection results. However, thermal image analysis still faces challenges such as a small number of informative images and difficulty in extracting defect features. In this work, a novel generative manifold learning thermography (GMLT) method for defect detection of composite materials is proposed. In detail, the spectral normalization generative adversarial network is used as a data augmentation strategy to generate more informative thermal images. Sequentially, the MLT-based thermographic data analysis method is adopted to extract and visualize defects in the thermal images. Experiments on carbon fiber reinforced polymers verify the effectiveness and advantages of the proposed method. Key Words: non-destructive evaluation, generative adversarial network, manifold learning, thermographic data analysis, carbon fiber reinforced polymer © 2021 ieee.

关键词： Defects

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Machine learning-driven Traffic Flow Prediction Using Cloud control Big data Analysis 10

Machine Learning-Driven Traffic Flow Prediction Using Cloud ...

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10th International conference on Virtual Reality, ICVR 2024

作者： Jalil, Khurrum Chen, Qian Zahid, Muhammad Noaman Jalil, Fazila University of Shanghai for Science and Technology Department of Traffic Engineering Shanghai200093 China Hunan University of Humanities Science and Technology School of Information Loudi417000 China University of Science and Technology Beijing School of Economics and Management Beijing100083 China

ISBN: (纸本)9798350364231

Due to the increasing number of intelligent vehicles (IVs) on existing roads, IVs present several challenges, such as congestion, CO2 emissions, idling, and travel delays. Transportation departments rely on forecasting methods using traffic flow data to address issues before implementing new plans for smooth traffic flow. To achieve this, managing the vast amounts of intelligent transportation system (ITS) data necessitates the adoption of cloud computing for data storage capabilities and machine learning methods to facilitate efficient traffic prediction analysis. In addition, real-time data analysis is crucial for evaluating the effectiveness of the on-going implementation of the ITS plans and identifying gaps for improvement. In this paper, the proposed method integrates a machine learning-based traffic flow prediction model that uses cloud computing for traffic data analytics. then, the efficiency ratio of the proposed method is evaluated. through experimentation, we demonstrate the effectiveness of our method, achieving a prediction accuracy of up to 90%. this result suggests that the proposed approach is highly competitive compared to existing solutions. © 2024 ieee.

关键词： Adversarial machine learning

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An Automatic Train Operation Strategy Based on Prescribed Performance control 11

An Automatic Train Operation Strategy Based on Prescribed Pe...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Li, Zikang Wu, Yanzhi Huang, Deqing Cai, Hanlin School of Electrical Engineering Southwest Jiaotong University Chengdu610031 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

With the fast development of 5G communications, big data and intelligent control technologies, automatic train operation (ATO) is expected to supersede traditional manual driving for high-speed railway systems. To study the precise automatic control algorithm for high-speed trains (HST), a novel prescribed performance tracking control method based on the barrier Lyapunov function (BLF) is proposed in this paper. First, the dynamic model of HST with basic and additional resistances is established considering actual operating condition and actuator saturation phenomenon. Second, an auxiliary system is constructed to eliminate the negative effect caused by input saturation, and a prescribed performance tracking controller with adaptive law is designed for ATO system. Finally, the stability of the close-loop system and the boundedness of the tracking error signals are proved based on Lyapunov stability theorem. Simulation results demonstrate the effectiveness of the proposed controller. © 2022 ieee.

关键词： Lyapunov functions

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Operation State Assessment and Prediction of Distribution Transformer Based on data driven 10

Operation State Assessment and Prediction of Distribution Tr...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Fan, Min Peng, Gang Zhang, Bo Zhou, Meng Jia, Shitao College of Automation Chongqing University Chongqing400044 China China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781665424233

With the rapid development of Power Internet of things, power grid monitoring data and analysis methods are increasing, so real-time dynamic monitoring of power equipment becomes possible. this paper presents a data driven method for evaluation and trend prediction of distribution transformer operation state. the key features reflecting dynamic change of operation state are extracted from voltage and current data of distribution transformer, and characteristic data flow is input into dynamic evaluation model to make real-time portrait description of distribution transformer operation state. According to time order and change trend of characteristic data flow, Long Short-Term Memory network (LSTM) is used to analysis regulation of characteristic data, and Support Vector Regression model (SVR) for its prediction. the future characteristic data flow is obtained, which is input into the dynamic evaluation model to realize the future operation trend prediction of the distribution transformer. Finally, examples are given to illustrate the feasibility, advanced nature and applicability of the method. © 2021 ieee.

关键词： Long short-term memory

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Omnidirectional Drift control of an Underwater Biomimetic Vehicle-Manipulator System via Reinforcement learning 10

Omnidirectional Drift Control of an Underwater Biomimetic Ve...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Ma, Ruichen Wang, Yu Wang, Rui Wang, Shuo University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665424233

this paper proposes an omnidirectional drift control on an underwater biomimetic vehicle-manipulator system (UB-VMS). the UBVMS has two biomimetic propellers, they obtain propulsive force by actuating their undulating fins. A configuration of the system, the dynamics of the UBVMS and the kinematics of the fins are given respectively. then the control problem is designed as a Markov decision process (MDP). A reinforcement learning method based on the twin delayed deep deterministic policy gradient (TD3) is proposed for this MDP. A control policy is well trained by the reinforcement learning method and tested in eight different simulations. An analysis of the simulation results is also given. © 2021 ieee.

关键词： Reinforcement learning

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On gradation and classification of faults for permanent magnet synchronous motor systems based on v-gap metric 10

On gradation and classification of faults for permanent magn...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Jiang, Yan Yu, Wei Yu, Wen Wen, Chenglin School of Mechatronic Engineering and Automation Foshan University Foshan China Guangzhou Huali Science and Technology Vocational College Pipe China Guangdong Pipeline Network Co Ltd China Automation College Guangdong University of Petrochemical Technology Maoming China

ISBN: (纸本)9781665424233

Permanent magnet synchronous motor has become the mainstream motor of electric vehicle, elevator and other drive systems due to its high torque density, high efficiency and high reliability. Most of the existing gradation and classification of faults in permanent magnet synchronous motor systems use the Euclidean metric method, which is extremely dependent on the physical magnitude of each component and difficult to portray the intrinsic changes of the system. In this paper, normal and fault models for permanent magnet synchronous motor systems are established under normal and different degrees of abnormal operating conditions, and classify different degrees and types of faults in a hierarchical manner based on the inter-system v-gap metric values. the simulation experiments apply the v-gap metric to the permanent magnet synchronous motor system to achieve a hierarchical classification for different degrees and different types of faults. © 2021 ieee.

关键词： Permanent magnets

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AI for Smart Battery State Estimation: A Perspective 10

AI for Smart Battery State Estimation: A Perspective

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10th ieee International Power Electronics and Motion control conference, IPEMC 2024 ECCE Asia

作者： Sui, X. Che, Y. Zheng, Y. Weinreich, N. Andre He, S. Teodorescu, R. Aalborg University Department of Energy Denmark

ISBN: (纸本)9798350351330

In battery management systems (BMSs), state estimation stands as a pivotal element yet encounters significant challenges. these include the poor observability inherent in fixed configuration battery packs, limited generalizability of pre-trained machine learning models, and the deficiency of higher-level management strategies. To address these obstacles, we propose a forward-looking perspective on the future BMS state estimation, introducing the concept of a "Smart Battery". Battery digital twin enables synthetic data generation and physics-informed AI development. this approach integrates battery digital twin to generate synthetic data, which is then used for data augmentation and physics-informed AI development. Additionally, it incorporates advanced data cleaning and selection techniques to preserve essential information and augment data management efficiency. Leveraging cutting-edge AI algorithms, such as transfer learning and meta-learning, aims to mitigate issues of model generalization and feature invalidation under various operating conditions. Furthermore, this paper emphasizes the importance of multi-task learning for batteries, enabling comprehensive health assessments. By fully utilizing both short-term estimations and long-term predictions, the proposed framework contributes to the advancement of higher-level health and thermal management designs. We aim to furnish pioneering insights for state estimation in future intelligent BMSs. © 2024 ieee.

关键词： Battery management systems

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MKE Scheme for the control of Dynamic Constrained Redundant Robots Based on Discrete-time Neural Network 10

MKE Scheme for the Control of Dynamic Constrained Redundant ...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Liu, Baiyang Su, Dan Liu, Mei Shi, Yang Li, Shuai School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Information Engineering Yangzhou University Yangzhou225127 China

ISBN: (纸本)9781665424233

It is necessary to make physical constraints on the joints for the redundant robot motion control in order to avoid damage. In this paper, a discrete-time neural network model with minimum kinetic energy as the performance index is proposed, which has predominant convergence performance. then, a solution in robot motion control is studied and further transformed into a dynamic quadratic programming (QP) with equality and inequality constraints. In addition, for solving the formulated QP problem, a continuous-time neural network model is designed by introducing the Lagrange multiplier method, and a discrete-time neural network model is obtained by the Euler forward difference formula. Moreover, the simulations on robot motion control are carried out, and the simulative results further substantiate the superiority, thus extending a solution for motion control of redundant robots with double-bound constraints. © 2021 ieee.

关键词： Neural network models

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Randomly Occurring Cluster Synchronization of Complex Networks via Adaptive Pinning control 10

Randomly Occurring Cluster Synchronization of Complex Networ...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Jiang, Chenhui Ding, Dong Zhang, Jiancheng Tang, Ze School of Internet of Things Engineering Jiangnan University Wuxi214122 China School of Science Jiangnan University Wuxi214122 China

ISBN: (纸本)9781665424233

the article studies the cluster synchronization for a kind of nonlinear coupled complex network with time-varying delay. Considering the networks may subject to certain uncertainties, the model of complex networks consisting of nonidentical systems with randomly occurring disturbance which described by Bernoulli stochastic variable is established. Secondly, a kind of pinning feedback controllers under randomly occurring disturbance is proposed in order to not only synchronize the systems in the same clusters but also weaken the mutual influence among clusters, which will be imposed on the systems in current cluster which have directed connections with the systems in the other clusters. then, sufficient conditions for the realization of the cluster synchronization are derived in terms of the QUAD function class, the NCF function class and the Lyapunov stability theorem. Furthermore, the optimal feedback control gain is obtained by designing the adaptive updating laws. Finally, a numerical experiment is presented to illustrate the effectiveness of theoretical analysis. © 2021 ieee.

关键词： Complex networks

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An Optimal Sliding Mode controller Against False data Injection Attacks 11

An Optimal Sliding Mode Controller Against False Data Inject...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Wu, Chengwei Dong, Bo Han, Shuo Yao, Weiran Harbin Institute of Technology Harbin150001 China Second Academy of CASIC Department X-Lab Institution Beijing China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper investigates the optimal discrete sliding mode control problem of cyber-physical systems, actuators of which are intercepted by false data injection attacks. Although false data injection attacks can be regarded as matched input uncertainties, some existing sliding mode control schemes cannot be directly applied since attacks may not satisfy some properties of uncertainties, for example, the smoothness condition. In the design process, the linear quadratic optimal control is combined to design an optimal sliding mode surface, based on which an optimal discrete-time sliding mode controller is derived by using a modified Gao's reaching law. To implement such a control scheme, an estimator is introduced to estimate false data injection attacks. Finally, a numerical example is given to validate the effectiveness of the proposed secure controller. © 2022 ieee.

关键词： Cyber Physical System

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