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Passivity-Based Robust control Against Quantified False Data Injection Attacks in Cyber-Physical Systems

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1440-1450页

作者： Yue Zhao Ze Chen Chunjie Zhou Yu-Chu Tian Yuanqing Qin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Computer Science Queensland University of TechnologyBrisbane QLD 4001Australia

Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control *** quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion ***,we design an energy controller to dynamically adjust the system energy changes caused by unknown *** designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping ***,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack *** addition,this secure control method is simple to implement because it avoids complicated mathematical *** effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.

关键词： Cyber-physical systems energy controller energy conversion false data injection attacks L2 disturbance attenuation technology

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An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem

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Journal of Systems Engineering and Electronics 2021年第2期32卷 365-379页

作者： SHEN Yindong PENG Liwen LI Jingpeng Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computing Science and Mathematics University of StirlingStirling FK94LAUK

The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service *** solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local *** results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA *** addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale *** experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.

关键词： multi-compartment vehicle routing problem electric vehicle routing problem(EVRP) soft time window multiple charging type estimation of distribution algorithm(EDA) Lévy flight

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Data-Driven Modeling for Pneumatic Muscle Using Koopman-Based Kalman Filter

Data-Driven Modeling for Pneumatic Muscle Using Koopman-Base...

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IEEE International Conference on Development and Learning, ICDL

作者： Yu Caol Mengshi Zhang Bo Yang Jian Huang Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

This paper presents a data-driven modeling approach for enhancing the understanding of pneumatic muscles (PMs) through Koopman kalman filter (KKF). The proposed method involves the construction of a comprehensive pneumatic muscle motion data-set, obtained by capturing extensive input-state-output data through the application of random inputs. Subsequently, the states of pneumatic muscle are lifted through a kernel-based technique. The nonlinear pneumatic muscle model is then transformed into a linear counterpart using the extended dynamic mode decomposition (EDMD). Then, a kalman filter is further used to improve the model accuracy. The effectiveness of this approach is demonstrated through simulation analysis, highlighting its capacity to yield superior pneumatic muscle modeling and state estimation results when compared to conventional Koopman operator methodologies.

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3-D temperature field reconstruction of lithium-ion battery pack using real-time distributed moving horizon estimation

3-D temperature field reconstruction of lithium-ion battery ...

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第35届中国控制与决策会议

作者： Jiayu Yan Yiming Wan School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand Key Laboratory of Image Processing and Intelligent ControlMinistry of Education

As lithium-ion batteries are widely used in different fields,the thermal effect is of serious *** to achieve accurate temperature estimation in real time is the main challenge of current *** address this problem,we propose a realtime distributed moving horizon estimation(RT-DMHE) based on partial differential equations describing thermal dynamics of a lithium-ion battery *** decomposes the real-time centralized moving horizon estimation(RT-CMHE) into multiple local estimators that run in parallel with information exchange from adjacent *** validation shows that the root mean square error of temperature estimates from the proposed RT-DMHE is smaller than that of an existing distributed Kalman filter in ***,compared to the RT-CMHE,the proposed RT-DMHE achieves comparable estimation accuracy while vastly reducing the average computation time per sample.

关键词： lithium-ion battery pack distributed moving horizon estimation real-time computation state-of-temperature estimation

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Thermal Safety Analysis and Dynamic control Strategy for Mode Switching Process of Reversible Solid Oxide Cell System

Thermal Safety Analysis and Dynamic Control Strategy for Mod...

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第42届中国控制会议

作者： Wei Zhao Guoqiang Liu Zexin Li Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology

Reversible solid oxide cells(rSOC) can operate in both electrolysis mode and in fuel cell mode with high efficiency and reduced cost using the same device for both *** used in dynamic operation with intermittent electrical power sources,rSOC system switches from the fuel cell(SOFC) to electrolyzer(SOEC) and vice versa depending on load and grid *** can lead to temperature profiles within the stack that can potentially lead to the failure of the stack and eventually the *** the present work,a system-level dynamic model of r SOC is established and validated against experimental ***,detailed dynamic thermal behavior of stack during the switching between the two modes is analyzed,and a temperature management controller based on Model Predict control method(MPC) is *** flow and temperature at the stack inlet are controlled by air flow and bypass valves,avoiding problems associated with temperature overshoot during transient *** simulation results show that the temperature controller has the ability to follow fast thermal changes while maintaining thermal safety,which indicates the competitiveness of the controller.

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MoLo: Motion-Augmented Long-Short Contrastive Learning for Few-Shot Action Recognition

MoLo: Motion-Augmented Long-Short Contrastive Learning for F...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiang Wang Shiwei Zhang Zhiwu Qing Changxin Gao Yingya Zhang Deli Zhao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception; ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo.

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Remaining Useful Life Prediction for a Multi-stack Solid Oxide Fuel Cell System with Degradation Interactions

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1207-1214页

作者： Xiaojuan Wu Liangfei Xu Yang Huang Danan Yang Junhao Wang Houjun Wang Xi Li School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 610054China State Key Lab of Automotive Safety and Energy School of Vehicle and MobilityTsinghua UniversityBeijing 100084China School of Artificial Intelligencei and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and TechnologyWuhan 430074China

Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cell stacks.A short lifetime is one of the main challenges for the fuel cell before largescale commercial applications,and prognostic is an important method to improve the reliability of fuel *** from the traditional prognostic approaches applied to single-stack fuel cell systems,the key problem in multi-stack prediction is how to solve the correlation of multi-stack degradation,which can directly affect the accuracy of *** response to this difficulty,a standard Brownian motion is added to the traditional Wiener process to model the degradation of each stack,and then the probability density function of the remaining useful life(RUL)of each stack is ***,a Copula function is adopted to reflect the dependence between life distributions,so as to obtain the remaining useful life for the whole multi-stack system.1 The simulation results show that compared with the traditional prediction model,the proposed approach has a higher prediction accuracy for multi-stack fuel cell systems.

关键词： Degradation interaction multi-stack remaining useful life(RUL) solid oxide fuel cell(SOFC)

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HyRSM++: Hybrid relation guided temporal set matching for few-shot action recognition

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Pattern Recognition 2024年 147卷

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Meta AI United States

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. However, existing attempts still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmarks demonstrate that our method consistently outperforms existing methods and achieves state-of-the-art performance under various few-shot settings. The source code is available at https://***/alibaba-mmai-research/HyRSMPlusPlus. © 2023 Elsevier Ltd

关键词： Benchmarking

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A Recipe for Scaling up Text-to-Video Generation with Text-free Videos

A Recipe for Scaling up Text-to-Video Generation with Text-f...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiang Wang Shiwei Zhang Hangjie Yuan Zhiwu Qing Biao Gong Yingya Zhang Yujun Shen Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group Zhejiang University Ant Group

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10m vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube. Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared. Following such a pipeline, we study the effect of doubling the scale of training set (i.e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9.67 to 8.19 and FVD from 484 to 441), demonstrating the scalability of our approach. We also find that our model could enjoy sustainable performance gain (FID from 8.19 to 7.64 and FVD from 441 to 366) after reintroducing some text labels for training. Finally, we validate the effectiveness and generalizability of our ideology on both native text-to-video generation and compositional video synthesis paradigms. Code and models will be publicly available at here.

关键词： Training Video on demand Scalability Pipelines Text to image Performance gain Market research

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Fusion-CAM: Fine-Grained and High-Faithfulness Explanations for Deep Convolutional Network via Hierarchical Fusion

Fusion-CAM: Fine-Grained and High-Faithfulness Explanations ...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Chen Zheng Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevant to neural network decisions. However, many CAM methods use the feature maps produced by the final convolution layer to generate the class activation maps, which usually have a low spatial resolution and can only generate coarse-grained visual explanations that provide a rough spatial location of the target object. In this paper, we propose a novel CAM method named Fusion-CAM. It improves traditional CAM methods by combining final class activation map containing semantic information with intermediate layer class activation maps containing fine-grained details, to generate fine-grained visual explanations with high faithfulness. In order to obtain high-quality intermediate layer class activation maps, we utilize Layer-wise Relevance Propagation (LRP) to obtain the weighting components of each channel of the intermediate layer feature maps, and the intermediate layer class activation maps generated by weighted summation are less noisy and have clear fine-grained details, which help to improve the quality of the final class activation map. Qualitative and quantitative experiments show that Fusion-CAM can be easily attached to different CAM methods to improve their performance.

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