检索结果-内蒙古大学图书馆

第41届中国控制会议

作者： Mengzhi Wang Bing Liu Jichao Bi Kaixiang Yang Jingpei Wang Nanwei Yao State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Institute of Industry and Information Technology China Academy of Information and Communications Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This work develops a disturbance rejection model predictive control(MPC) algorithm for discrete-time linear systems with bounded disturbances.A disturbance observer is designed to estimate the unknown matched disturbances with bounded incremental constraint.A tube-based MPC algorithm is presented to handle the system constraints.A composite controller is presented to stabilize the systems,including a MPC controller to drive the system state into the neighborhood of the origin,and a disturbance estimation-dependent controller to compensate for the effect of external *** feasibility of the disturbance rejection MPC is analyzed,and the stability of the system can be *** results show the effectiveness of the proposed methods.

关键词： Model predictive control Disturbance observer Bounded disturbances

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning 42

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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42nd Chinese control Conference, CCC 2023

作者： Khan, Malak Abid Ali Ma, Hongbin Jin, Ying Ma, Jingxiang Rehman, Zia Ur Rahman, Mizanur School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9789887581543

Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its power. Various technologies provide connectivity and a fast-updating system to eliminate the paper-based approach. LoRa is one of the contenders to design the system for electronic shelf labels (ESLs). In this paper, LoRa has been used to minify data losses and guarantee the successful decoding of the carrier signals. The data parallelism at the network server (NS) is used to distribute the data packets among the gateways (GWs) for concurrent transmissions to the end devices (EDs). The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and collision. The data rate (DR) and spreading factors (SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated tags. The orthogonality principles follow industrial, scientific, and medical regulations (ISM) to avoid data traffic congestion. GWs under different duty cycles (DC) and bandwidth (BW) are examined to minify the network saturation and reduce the energy harvesting of the tags. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deforestation

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An Enhanced Distributed Optimization Subject to Multiple Constraints: A Case Study on Entire Ethylene Separation Process

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IEEE Transactions on control of Network systems 2025年

作者： Liu, Bing Du, Wenli Li, Zhongmei East China University of Science and Technology State Key Laboratory of Industrial Control Technology Ministry of Education Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China

This paper studies an enhanced distributed optimization algorithm in an undirected topology based on local communication and computation to optimize the sum of local objective functions under multiple constraints. In particular, the adapt-then-combine distributed inexact gradient tracking algorithm (ATC-DIGing-MC) is developed for smooth and strongly convex local functions with multiple constraints. By implementing the adapt-then-combine scheme and using the gradient tracking technique, rapidity and flexibility can be obtained by the ATC-DIGing-MC with uncoordinated step sizes. Subsequently, the parameter projection strategy is implemented to deal with realistic production limitations in the presence of multiple constraints. Meanwhile, rigorous theoretical proofs and convergence theorem are provided to verify the geometrical convergence rate of the ATC-DIGing-MC. Furthermore, the numerical experiments based on the entire ethylene separation process validate the superior performance of the ATC-DIGing-MC algorithm in terms of time and energy savings compared to established algorithms. © 2025 IEEE.

关键词： Optimization algorithms

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A Reinforcement Learning-Based Detection Method for False Data Injection Attack in Distributed Smart Grid

A Reinforcement Learning-Based Detection Method for False Da...

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2021 International Conference on Information, cybernetics, and Computational Social systems, ICCSS 2021

作者： Zhang, Kuo Wu, Zheng-Guang Zhejiang University Institute of Cyber-Systems and Control Hangzhou China Zhejiang University The State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9781665402453

False data injection attack(FDIA) is a traditional attack for the smart grid. There are many methods for the detection of the FDIA, but few of them can send the attack alarm successfully without an attack model. In this paper, we propose a reinforcement learning-based FDIA detection method for the distributed smart grid. The detection problem is formulated as a partially observable Markov decision process(POMDP) problem, and the observation of the POMDP can be obtained from the estimation of state and attack which come from the Kalman filter. By using the Sarsa algorithm, we can get a Q-table through online training. Finally, we use the IEEE-118 bus power system to evaluate the performance of our detector, and numerical results show the accurate response for the FDIA. © 2021 IEEE.

关键词： Reinforcement learning

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Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

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Unsupervised learning on particle image velocimetry with embedded cross‐correlation and divergence‐free constraint

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IET cyber-systems and Robotics 2022年第3期4卷 200-211页

作者： Yiwei Chong Jiaming Liang Tehuan Chen Chao Xu Changchun Pan College of Control Science and Engineering Zhejiang UniversityHangzhouZhejiangChina School of Mechanical Engineering and Mechanics Ningbo UniversityNingboZhejiangChina Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingboZhejiangChina The State Key Laboratory of Industrial Control Technology Institute of Cyber‐Systems and ControlZhejiang UniversityHangzhouZhejiangChina School of Electronic Information and Electrical Engineering Key Laboratory of Shanghai Navigation and Location Based ServicesShanghai Jiao Tong UniversityShanghaiZhejiangChina

Particle image velocimetry(PIV)is an essential method in experimental fluid *** recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of ***,the supervised learning of PIV is driven by large volumes of data with ground truth ***,the authors consider unsupervised PIV *** has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning *** authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical *** this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV *** authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic ***,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle *** a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.

关键词： neural network particle image velocimetry unsupervised learning

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Primitive-Planner: An Ultra Lightweight Quadrotor Planner with Time-optimal Primitives

arXiv

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arXiv 2025年

作者： Hou, Jialiang Pan, Neng Wang, Zhepei Ji, Jialin Guan, Yuxiang Gan, Zhongxue Gao, Fei Academy for Engineering and Technology Fudan University Shanghai200433 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China

It is a significant requirement for a quadrotor trajectory planner to simultaneously guarantee trajectory quality and system lightweight. Many researchers focus on this problem, but there’s still a gap between their performance and our common wish. In this paper, we propose an ultra lightweight quadrotor planner with time-optimal primitives. Firstly, a novel motion primitive library is proposed to generate time-optimal and dynamical feasible trajectories offline. Secondly, we propose a fast collision checking method with a deterministic time consumption, independent of the sampling resolution of the primitives. Finally, we select the minimum cost trajectory to execute among the safe primitives based on user-defined requirements. The propsed transformation relation between the local trajectories ensures the smoothness of the global trajectory. The planner reduces unnecessary online computing power consumption as much as possible, while ensuring a high-quality trajectory. Benchmark comparisons show that our method can generate the shortest flight time and distance of trajectory with the lowest computation overload. Challenging real-world experiments validate the robustness of our method. Copyright © 2025, The Authors. All rights reserved.

关键词： Flight paths

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CHANNEL-INTEGRATED DIGITAL MICROFLUIDIC CHIP FOR NUCLEIC ACID EXTRACTION AND DIGITAL PCR DETECTION 26

CHANNEL-INTEGRATED DIGITAL MICROFLUIDIC CHIP FOR NUCLEIC ACI...

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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： He, Yu Lu, Zefan Liu, Ke Fan, Hongliang Zhang, Tao Research Center for Analytical Instrumentation Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Public Health Hangzhou Medical College Hangzhou China Huzhou Institute of Zhejiang University Huzhou China

ISBN: (纸本)9781733419048

Digital PCR (dPCR) is a powerful technique capable of absolute quantification of nucleic acids. However, the indispensable process, i.e., nucleic acid extraction (NAE), remains left off-chip. To address this issue, we have developed a channel-integrated digital microfluidic chip (CDMF) by merging channel-based microfluidics (CMF) with digital microfluidics (DMF). Through a composite elastomer-enabled photofabrication method, CDMF can be made into the desirable two-plate configuration, thereby, allowing for all basic functions of DMF. Meanwhile, the DMF-CMF integration enables both automatic NAE and subsequent droplet dPCR in a single device. As a proof of concept, sample-to-answer digital assay of Salmonella has been demonstrated. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Digital microfluidics

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Additive manufacturing of high-quality tritium breeding pebbles using ceramic precursors

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Journal of the European Ceramic Society 2024年第7期44卷 4719-4730页

作者： Su, Fang Li, Yuzeng Xing, Huanhuan Liu, Yu Liu, Zhiyuan Liu, Changyong Jiang, Cho-Pei Chen, Zhangwei Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics Shenzhen University Shenzhen518060 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an710049 China

In this study, the fabrication of advanced lithium orthosilicate (Li4SiO4) ceramic tritium breeding pebbles through photopolymerization using ceramic precursors (i.e., SiO2 and Li2CO3) is reported for the first time. The pebbles exhibit excellent mechanical and physical properties. Through optimization of manufacturing parameters and a geometrical correction model to compensate printing deviations, the printing accuracy of the pebbles was enhanced. The resulting pebbles had a diameter of approximately 1 mm, a sphericity of 1.01, a uniformly distributed pore structure, a phase purity > 95%, and a satisfying relative density reaching 91.39%. The average crush load 25.7 ± 0.3 N, surpassed that achieved by previously reported methods. Furthermore, the thermal conductivity was enhanced compared to that of solid-phase synthesized Li4SiO4 powder, facilitating the tritium release process for fusion applications. This study may serve as a guidance for designing appropriate ceramic breeders, providing a cost-effective approach applicable to produce other high-precision ceramic components with complex compositions. © 2024 Elsevier Ltd

关键词： Lithium compounds

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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