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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Youcheng Chen Zhitao Liu Xuchi Xue Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798331529277

ISBN: (纸本)9798331529307

In wireless power transmission system, due to the changes of coupling coefficient, load impedance and other factors, it destroys the system constant voltage, constant power output and transmission efficiency optimal working condition. Therefore, this paper proposes an optimization seeking method for the maximum transmission efficiency of the DWPT system of an orbital inspection robot with constant voltage and constant power output. Firstly, the LCC-S topology mathematical model with additional PI control circuit and the convex optimization model of transmission efficiency are established, and the gradient descent algorithm is used to iteratively search for the maximum value of transmission efficiency when the orbital coupling coefficient changes. Then, based on the mutual inductance model between coils, the coupling coefficients are converted into coil inductance parameters L 1 and L 2 , and the coil self-inductance is utilized for matching regulation to keep the transmission efficiency at the optimal value. Finally, in order to further optimize and predict the transmission efficiency, the fitting effects of the four fitting curves of the transmission efficiency are observed by the coefficient of determination and the residual paradigm, and the optimal fitting curves are used to calculate the maximum and average transmission efficiency, which reduces the system transmission loss and improves the charging efficiency.

关键词： Coils Couplings Inductance Fitting Inspection Orbits Mathematical models Integrated circuit modeling Optimization Robots

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Real-Time Intrusion Detection Based on Decision Fusion in industrial control systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on industrial cyber-Physical systems 2024年 2卷 143-153页

作者： Xue, Yawen Pan, Jie Geng, Yangyang Yang, Zeyu Liu, Mengxiang Deng, Ruilong Information Engineering University Zhengzhou450001 China Singapore University of Technology and Design ITrust 487372 Singapore The University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS10 2TN United Kingdom Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

industrial control systems (ICSs) are becoming increasingly interconnected as the rapid convergence of information technology (IT) and operation technology (OT) networks, and meanwhile massive attack surfaces have been exposed. However, traditional intrusion detection systems (IDSs) are difficult to be directly deployed in ICSs due to the hard real-time requirement and rare patching chance. Besides, the design of effective and practical IDSs is hampered by the lack of benchmarking ICS cybersecurity datasets. To bridge the gaps, this paper makes the first attempt by open-sourcing the developed ICS cybersecurity datasets and proposing a decision fusion based real-time IDS. Firstly, we design a customized cybersecurity dataset in a full-hardware and high-fidelity platform, including 7 types of cyber threats tailored for ICSs. The collected dataset includes network traffic, sensor readings, actuator status, and system parameters, providing the state-of-the-art benchmark dataset for ICSs consisting of cross-layer characteristics. Furthermore, we design an online decision fusion-based IDS by strategically integrating 4 widely-used machine learning models. The proposed IDS is deployed on a real-time running ethanol distillation, surpassing the performance of single detection models in terms of precision and F1-score, which substantially enhances intrusion detection accuracy and cybersecurity of ICS. © 2023 IEEE.

关键词： Intrusion detection

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On Klein tunneling of low-frequency elastic waves in hexagonal topological plates

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Applied Mathematics and Mechanics(English Edition) 2024年第7期45卷 1139-1154页

作者： Yuxin YAO Yuansheng MA Fang HONG Kai ZHANG Tingting WANG Haijun PENG Zichen DENG School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an710072China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of TechnologyDalian116024Liaoning ProvinceChina

Incident particles in the Klein tunnel phenomenon in quantum mechanics can pass a very high potential *** the concept of tunneling into the analysis of phononic crystals can broaden the application *** this study,the structure of the unit cell is designed,and the low frequency(<1 k Hz)valley locked waveguide is realized through the creation of a phononic crystal plate with a topological phase transition *** defect immunity of the topological waveguide is verified,that is,the wave can propagate along the original path in the cases of impurities and ***,the tunneling phenomenon is introduced into the topological valley-locked waveguide to analyze the wave propagation,and its potential applications(such as signal separators and logic gates)are further explored by designing phononic crystal *** research has broad application prospects in information processing and vibration control,and potential applications in other directions are also worth exploring.

关键词： topological metamaterial elastic wave Klein tunneling valley-locked waveguide

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Global optimization of ORC process parameters based on SSL artificial neural network

Global optimization of ORC process parameters based on SSL a...

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2023 China Automation Congress, CAC 2023

作者： Qin, Jiabin Zhou, Junjie Wu, Xialai Lin, Ling Yi, Jian Lin, Runze Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9798350303759

The Organic Rankine Cycle (ORC) is a commendable technology for waste heat recovery. However, the optimization problem of ORC operating parameters based on its mechanism model is not easy to be solved for obtaining the global optimal solution, and it is time-consuming. To deal with this problem, a global optimization method of ORC process parameters is proposed to maximize the net output power of the system. Utilizing the SSL Artificial Neural Network (ANN) is utilized as a surrogate model in the optimization, based on this, the parametric optimization problem can be converted into a Mixed-Integer Linear Optimization Problem (MILP). The optimization results show that, compared with the process parameter optimization method based on the ORC mechanism model, the solution time of the optimization is greatly reduced, and the obtained solution is globally optimal. © 2023 IEEE.

关键词： Neural networks

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FedAWS: A Federated Tuning Approach with Adaptive Weight Shrinking for Pre-trained Foundation Models 13

FedAWS: A Federated Tuning Approach with Adaptive Weight Shr...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Cheng, Yubin Song, Zhihuan Jiang, Xiaoyu Yao, Le Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Hangzhou310027 China School of Mathematics Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350361674

In recent years, pre-trained foundation models have developed rapidly and have been applied to various downstream tasks through fine-tuning methods. However, due to the existence of data silos, the lack of data for fine-tuning has become a vital issue preventing the application of the foundation model to industry. In addition, the high complexity of the network makes the foundation model prone to overfitting when used for data-constrained industrial tasks. Therefore, a federated tuning approach with adaptive weight shrinking, named FedAWS, is proposed for collaborative fine-tuning between factories. FedAWS adaptively selects the weight-shrinking factor to reduce overfitting and improve the generalization of the global model without relying on additional data sets or human experience. The case study on an industrial dataset demonstrates the effectiveness of FedAWS. This successful validation provides a new way for inter-factory collaboration to apply the foundation model to industry, thus facilitating the efficient utilization of industrial data. © 2024 IEEE.

关键词： Federated Learning Fine-tuning Foundation Model Weight Shrinking

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Attacks on Data-Driven Process Monitoring systems: Subspace Transfer Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第3期3卷 470-484页

作者： Jiang, Xiaoyu Ge, Zhiqiang Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Hangzhou310027 China Peng Cheng Laboratory Shenzhen518000 China

With the rapid development of information technology, intelligent upgrading of the manufacturing industry has broken the closed environment of traditional industrial control systems (ICS);thus, the information security of ICS has been seriously threatened. As part of ICS, the process monitoring system (PMS) is heavily subject to external risks. Data-driven PMSs have been widely used as initial lines of defense to ensure ICS safety. Once the PMS is under attack, the consequences on the whole ICS will be unimaginable. Unfortunately, the safety issues of the PMS have received inadequate attention. This article reveals PMS's vulnerabilities through effective attacks. A novel method called subspace transfer network (STN) is proposed to conduct adversarial and poisoning attacks on the PMS simultaneously. Then the attack task flow is defined and explained to make online adversarial attacks and data poisoning on PMS. Meanwhile, aiming at two poisoning goals, targeted and untargeted attacks of STN are designed, respectively. Finally, the PMS's fragility is verified in two industrial benchmarks. © 2020 IEEE.

关键词： Security of data

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Obstacle Based Fast Marching Tree for Global Motion Planning 48

Obstacle Based Fast Marching Tree for Global Motion Planning

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48th Annual Conference of the IEEE industrial Electronics Society, IECON 2022

作者： Hou, Jiale Liu, Zhitao Su, Hongye Zhejiang University Polytechnic Institute Hangzhou China Zhejiang University Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper we introduce a novel method to improve the general performance of fast marching tree for collision-free path planning called obstacle-based fast marching tree. Our proposed OB-FMT∗ samples the map in discussion initially to obtain the vertexes of all obstacles and connect those vertexes to build a collision-free roadmap, an A*algorithm is adopted to find an optimal path in the built roadmap. Then multiple prolate hyper spheroids with focuses on vertexes of optimal path are established to limit the sampling range. Within the limited sampling point set, we utilize FMT∗ to find optimal solution with high efficiency. Theoretical analyses are given to prove the probabilistic completeness and computational complexity of our proposed method. Experiments in multiple scenarios show that our algorithm has a better performance comparing with other existing algorithms including RRT*, FMT*, Informed RRT*. © 2022 IEEE.

关键词： Motion planning

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CDQKL: Consensus-Based Distributed Quantum Kernel Learning 1

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Ma, Wenxuan Liu, Mengxiang Deng, Ruilong State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350366778

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL. © 2024 IEEE.

关键词： Quantum computers

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MATD3-Based End-Edge Collaborative Resource Optimization for NOMA-Assisted industrial Wireless Networks

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Computers, Materials & Continua 2025年第2期82卷 3203-3222页

作者： Ru Hao Chi Xu Jing Liu College of Information Engineering Shenyang University of Chemical TechnologyShenyang110142China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China

Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely MATD3-CO, for iterative optimization. Specifically, we first decouple SOM into two sub-problems, i.e., joint sub-channel allocation and task offloading sub-problem, and computation resource allocation sub-problem. Then, we propose MATD3 to optimize the sub-channel allocation and task offloading ratio, and employ the convex optimization to allocate the computation resource with a closed-form expression derived by the Karush-Kuhn-Tucker (KKT) conditions. The solution is obtained by iteratively solving these two sub-problems. The experimental results indicate that the MATD3-CO scheme, when compared to the benchmark schemes, significantly decreases system overhead with respect to both delay and energy consumption.

关键词： industrial wireless networks(IWNs) multi-access edge computing(MEC) non-orthogonal multiple access(NOMA) task offloading resource allocation

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ZSEE: A Dataset based on Zeolite Synthesis Event Extraction for Automated Synthesis Platform

ZSEE: A Dataset based on Zeolite Synthesis Event Extraction ...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： He, Song Peng, Xin Cai, Yihan Li, Xin Yuan, Zhiqing Du, Wenli Yang, Weimin State Key Laboratory of Industrial Control Technology East China University of Science and Technology China State Key Laboratory of Green Chemical Engineering and Industrial Catalysis Sinopec Shanghai Research Institute of Petrochemical Technology China

ISBN: (纸本)9798891761193

Automated synthesis of zeolite, one of the most important catalysts in chemical industries, holds great significance for attaining economic and environmental benefits. Structural synthesis data extracted through NLP technologies from zeolite experimental procedures can significantly expedite automated synthesis owing to its machine readability. However, the utilization of NLP technologies in information extraction of zeolite synthesis remains restricted due to the lack of annotated datasets. In this paper, we formulate an event extraction task to mine structural synthesis actions from experimental narratives for modular automated synthesis. Furthermore, we introduce ZSEE, a novel dataset containing fine-grained event annotations of zeolite synthesis actions. Our dataset features 16 event types and 13 argument roles which cover all the experimental operational steps of zeolite synthesis. We explore current state-of-the-art event extraction methods on ZSEE, perform error analysis based on the experimental results, and summarize the challenges and corresponding research directions to further facilitate the automated synthesis of zeolites. The code is publicly available at https://***/Hi-0317/ZSEE. © 2024 Association for Computational Linguistics.

关键词： Zeolites

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