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SSRN 2024年

作者： Huang, Mengqi Du, Zhengyu Lu, Ruibo Wang, Xiaoji Peng, Changhong School of Nuclear Science and Technology University of Science and Technology of China Anhui Hefei230027 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Sichuan Chengdu610213 China

Lithium-ion batteries are extensively used in medical, transportation, industrial, and other applications due to rapid charging and discharging, constant power output, and light weight. Accurately predicting their remaining useful life (RUL) is essential for safe, reliable operation and timely maintenance. This paper presents a two-stage model based on symbolic regression and particle filter, aims to address the issues present in the current research on lithium-ion battery lifetime prediction. These issues include difficulties in early prediction, using a single degradation model, and the failure to consider the uncertainty of prediction. The proposed model constructs a fuzzy transition point between the two stages based on the degradation process similarity of history and operational batteries. Historical battery degradation data were employed to train the two-stage degradation model using symbolic regression. Parameter updating and state prediction of the degradation model were carried out using an improved mixed-state particle filter. This method can calculate the batteries RUL and their distribution. The performance was evaluated by analysing the CALCE and MIT datasets. The prediction error was less than 22 cycles, over 50% lower than the commonly used FCNN and LSTM timing prediction algorithms, demonstrating the feasibility and effectiveness of the method. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Preliminary research on the thermal-mechanical coupling behavior simulation method of m3 fuel 28

Preliminary research on the thermal-mechanical coupling beha...

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2021 28th International Conference on Nuclear Engineering, ICONE 2021

作者： Changbing, Tang Yongjun, Jiao Yuanming, Li Kun, Zhang Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institution of China Chengdu China

ISBN: (纸本)9780791885253

M3 fuel which microencapsulated TRISO particles in zirconium metal matrix is an integrated fuel designed to replace the traditional pellet-cladding fuel rod. The fuel design can effectively solve the problems of LWR fuel, such as water side corrosion, abrasion, pellet-cladding interaction, stress corrosion cracking and internal pressure bracing. Because of the multiple coating structure of TRISO particles and complex contacting behavior and deformation between TRISO particle and metal matrix, M3 fuel exhibits complex in-pile behavior. In order to establish the simulation method for the M3 fuel, the three-dimensional model was established by using ABAQUS software. The complex contacting behavior and deformation of coated layers and matrix for M3 fuel was considered. The results indicated that bonded strength between TRISO particle and Zr matrix has great effect on the stress distribution of SiC layer and matrix. The vulnerable area of Zr matrix located in the nearest position of TRISO particle to TRISO particle. The maximum tensile hoop stress of SiC layer was up to 150 MPa under strong bonding state, while SiC layer suffered compressive stress during all operation time for the weak bonding state. The failure probability of SiC layer was about 7 10-3 under strong bonding state which much higher than the weak ones. Thus, the integrity of SiC layer can be maintained by controlling the bonding strength between TRISO particle and Zr matrix during manufacturing process. © 2021 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Silicon carbide

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th International Conference on Computing, Control and Industrial Engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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design of Emulation system for Safety DCS 5th

Design of Emulation System for Safety DCS

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5th International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, ISNPP 2020

作者： Peng, Hao Zhang, Xu Deng, Zhi-Guang Chen, Qi Zhang, Yu Jiang, Wei Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9789811634550

DCS (Digital Control system) is a very important part in safety protection in NPP (Nuclear Power Plant). Virtual DCS realizes the algorithm configuration of physical DCS running on general computers and gets rid of the limitation of hardware. In this paper, an emulation system (virtual DCS) is designed using virtual DPU technology. The virtual DCS can be used to verify the algorithm logic with the closed-loop system, which is composed by virtual DCS and digital reactor processing model together. The virtual DCS and controlled object are closed to test the control effects and in this way to verify the design of the NPP DCS. For the purpose of scientific research, training and design verification, virtual DCS should have malfunction simulation and other functions. These functions reflect the superiority of emulation technology. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital control systems

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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High-Precision Positioning with Continuous Delay and Doppler Shift using AFT-MC Waveforms

High-Precision Positioning with Continuous Delay and Doppler...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yi, Cong Yin, Haoran Lu, Xianjie Tang, Yanqun Liu, Fan Sun Yat-sen University School of Electronics and Communication Engineering China Southern University of Science and Technology School of System Design and Intelligent Manufacturing China

ISBN: (纸本)9798350351255

This paper explores a novel integrated localization and communication (ILAC) system using the affine Fourier transform multicarrier (AFT-MC) waveform. Specifically, we consider a multiple-input multiple-output (MIMO) AFT-MC system with ILAC and derive a continuous delay and Doppler shift channel matrix model. Based on the derived signal model, we develop a two-step algorithm with low complexity for estimating channel parameters. Furthermore, we derive the Cramér-Rao lower bound (CRLB) of location estimation as the fundamental limit of localization. Finally, we provide some insights about the AFT-MC parameters by explaining the impact of the parameters on localization performance. Simulation results demonstrate that the AFT-MC waveform is able to provide significant localization performance improvement compared to orthogonal frequency division multiplexing (OFDM) while achieving the CRLB of location estimation. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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A Survey on Distributed Network Localization from a Graph Laplacian Perspective

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Journal of systems science & Complexity 2024年第1期37卷 273-293页

作者： HAN Zhimin LIN Zhiyun FU Minyue Artificial Intelligence Institute School of AutomationHangzhou Dianzi UniversityHangzhou 310018China Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent ManufacturingSouthern University of Science and TechnologyShenzhen 518055China Peng Cheng Laboratory Shenzhen 518000China

Network localization serves as a fundamental component for enabling various position based operations in multi-agent systems,facilitating tasks like target searching and formation control by providing accurate position information for all nodes in the *** localization focuses on the challenge of determining the positions of nodes within a network,relying on the known positions of anchor nodes and internode relative *** the past few decades,distributed network localization has garnered significant attention from *** paper aims to provide a review of main results and advancements in the field of distributed network localization,with a particular focus on the perspective of graph *** to its favorable characteristics,graph Laplacian unifies various network localization,even when dealing with diverse types of internode relative measurements,into a unified protocol framework,which can be constructed by a linear method and ensure the global convergence.

关键词： Distributed network localization graph Laplaian sensor network

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Uncertainty-based Continual Learning for Neural Networks with Low-rank Variance Matrices

Uncertainty-based Continual Learning for Neural Networks wit...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Rao, Xuan Zhao, Bo Liu, Derong School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufac-turing Southern University of Science and Technology Shenzhen518000 China

ISBN: (纸本)9781665410205

Bayesian inference has provided the continual learning (CL) with an elegant framework where past experiences and new knowledge are consolidated into the posterior constantly. Typical approaches rely on Bayesian neural networks whose parameters are updated by variational inference, namely, maximizing the evidence lower bound of log-likelihood. In this paper, we discuss the effects of local reparameterization on the optimization of such networks in the context of CL. The empirical results show that it does not only increase the inference speed of neural networks, but also enhance the CL performance in some scenarios. Additionally, motivated by the observation that variance matrices have low-rank structures, we propose the d-tied variational continual learning (d-tied-VCL) to improve the parameter efficiency of variational continual learning (VCL). Experiments on random classification, per-muted MNIST, and split CIFAR100 show that even VCL with rank-1 variance matrices achieves competitive performance. © 2024 IEEE.

关键词： Contrastive Learning

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Advancements in graphene-based composites: A review of the emerging applications in healthcare

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Smart Materials in Medicine 2025年第1期6卷 120-138页

作者： Batool, Farwa Muhammad, Saz Muazzam, Rabia Waqas, Muhammad Ullah, Zia Roy, Shubham Zhang, Yinghe Wang, Ke Guo, Bing Department of Chemistry Lahore Garrison University DHA Phase VI Lahore Pakistan School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China School of Science Shenzhen Key Laboratory of Flexible Printed Electronics Technology Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application Harbin Institute of Technology Shenzhen518055 China Government Queen Mary Graduate College Lahore Pakistan

Graphene and related materials are emerging revolutionary materials due to fascinating stimuli-responsive physiochemical properties having widespread applications in developing advanced nano-systems for detection, diagnostics, and therapeutics in biomedical fields. Various materials like metal nanoparticles, polymers, dyes, drugs, and proteins could be combined yielding graphene-based composite materials (GBCMs) to synergistically fulfill different biomedical requirements. The 2D conjugated structure, easy surface functionalization via covalent or van der Waal interactions, and combination with different components for versatile functionalities altogether contribute to the development of GBCM for healthcare applications. This review specifically focuses on GBCMs and discusses specific material properties relevant to their material applications in healthcare. Furthermore, the synthesis, toxicity, and biocompatibility of these GBCMs, and their interactions with biological materials are thoroughly discussed. Importantly, the recent cutting-edge applications in the field of healthcare, including its role in the development of antibacterial, anti-viral, and photothermal therapies, wound healing, drug delivery, neurodegenerative disease therapy, and biosensing based on GBCMs are well-updated. Additionally, the applications of GBCMs in point-of-care devices, wearable electronics, and tissue engineering are well summarized. We hope this review opens up more possibilities to design more innovative healthcare materials derived from graphene. © 2025 The Authors

关键词： Biocompatibility

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Multi-Level Robotic Pouring Using Offline Reinforcement Learning

Multi-Level Robotic Pouring Using Offline Reinforcement Lear...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Xiaoxiong Liu, Junwei Zhang, Wei School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China

ISBN: (纸本)9781665481090

This paper presents a novel learning-based method for a robotic manipulator to achieve liquid pouring across different liquid levels using only visual sensors. Previous works have relied on either online reinforcement learning or imitation learning, which are limited by sim-to-real gaps or data efficiency. In this paper, we propose to combine supervised learning and offline reinforcement learning, utilizing a human demonstration dataset containing only successful pouring at the highest level. Specifically, our approach employs supervised learning for a visual classifier, transforming the visual input into a categorical distribution over liquid levels. The offline reinforcement learning method is applied to a binary-conditioned control policy, which takes a binary signal and the robot's proprioception as inputs to determine the action. The binary signal indicates whether the desired liquid level has been reached based on the target liquid level. Through experiments, our method demonstrates superior effectiveness compared to a state-of-the-art imitation learning algorithm. Moreover, tests in unseen scenarios and multiple liquid level commands verify the generalization and transferability of our approach. © 2024 IEEE.

关键词： Self-supervised learning

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