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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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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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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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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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Adaptive Learning-Based Distributed control of Cooperative Robot Arm Manipulation for Unknown Objects

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第2期53卷 1298-1307页

作者： Zhang, Hai-Tao Xu, Hao Xu, Bowen Wu, Yue Huang, Jian Han, Qing-Long Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc Wuhan 430074 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia

This article proposes a distributed cooperative manipulation control scheme for multirobot systems to track reference trajectories with unknown payload dynamics, grasp positions, and external disturbances. An online learning module is established to estimate the payload dynamics. Then a wrench-synthetic trajectory tracking control protocol is thereby developed to manipulate an object under unknown external disturbances no matter where the grasping points are. Moreover, sufficient conditions are derived to guarantee the uniform boundedness of the tracking errors of the closed-loop cooperative manipulation system. Finally, numerical simulations are conducted to substantiate the effectiveness of the proposed cooperative manipulation control scheme.

关键词： Robots Robot kinematics Manipulator dynamics Payloads Dynamics Trajectory tracking Uncertainty control systems cooperative systems manipulators neural network applications robot dynamics

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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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Fault diagnosis of key components in the rotating machinery based on Fourier transform multi-filter decomposition and optimized LightGBM

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第1期32卷 015004-015004页

作者： Zhang, Changhe Kong, Li Xu, Qi Zhou, Kaibo Pan, Hao Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Rotating machinery is a primary element of mechanical equipment, and thus fault diagnosis of its key components is very important to improve the reliability and safety of modern industrial systems. The key point to diagnose the faults of these components is to extract effectively the hidden fault information. However, the actual vibration signals of rotating machinery have nonlinear and non-stationary characteristics, so traditional signal decomposition methods are unable to extract the frequency components accurately, leading to spectrum overlap of the decomposed sub-signals. Therefore, a rotating machinery fault diagnosis approach based on Fourier transform multi-filter decomposition (FTMFD), fuzzy entropy (FE), joint mutual information maximization (JMIM), and a light gradient boosting machine (LightGBM), is proposed in this paper. FTMFD is used to extract the frequency domain information of the raw vibration signals, whereas FE is used to calculate and extract the fault information of the decomposed sub-signals. Then feature selection is carried out by using JMIM to reduce the influence of redundant features on data analysis and classification accuracy. Furthermore, LightGBM is used to rank the candidate features and outputs the fault diagnosis result. Experimental results from two real datasets show that the proposed method achieves higher accuracy with fewer features than some existing methods for fault recognition. Various working conditions are also considered and verified.

关键词： Fourier transform multifilter decomposition fuzzy entropy joint mutual information maximization LightGBM classifier rotating machinery fault diagnosis

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Disentangling Spatial and Temporal Learning for Efficient image-to-Video Transfer Learning

Disentangling Spatial and Temporal Learning for Efficient Im...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Zhang, Yingya Gao, Changxin Zhao, Deli Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Natl Univ Singapore ARC Singapore Singapore

ISBN: (纸本)9798350307184

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://***/alibaba-mmai-research/DiST.

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Screen-printed advanced all-solid-state symmetric supercapacitor using activated carbon on flexible nickel foam

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JOURNAL OF ENERGY STORAGE 2022年 53卷

作者： Tu, Qian Li, Xianran Xiong, Zeyu Wang, Hongxia Fu, Jun Chen, Liangzhe Jingchu Univ Technol Sch Elect Informat Engn Jingmen 448000 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China

There is an urgent demand to develop an all-solid-state flexible supercapacitor for portable digital products, screen printing technology is deemed a valid way. However, it still faces challenges including the use of printed substrate and low specific capacitance caused by printing. In this paper, a new route for the fabrication of advanced all-solid-state symmetric supercapacitors (SSC) via screen-printing is demonstrated. The mesoporous activated carbon (AC) inks are prepared to fabricate electrodes, while their performances as well as the printing parameter i.e. stencil meshes are investigated carefully. Surprisingly, the optimal screen-printed electrode shows an enhanced specific capacitance (159.0 F/g at a current density of 1 A/g with a retention rate of 98.2 % after 5000 cycles at 10 A/g) than that of electrodes made by the smearing method (139.8 F/g with a retention rate of 76.1 %). This improvement can be ascribed to the orderly aligned activated carbon after the shearing interaction in the process of screen-printing. Furthermore, flexible and porous nickel foam is served as a printed substrate and current collector, and the SSC with an interdigital pattern is assembled coating with KOH/PVA gel elec-trolyte, which can deliver a remarkable areal energy density of 64.8 mu Wh/cm2 at a power density of 0.2 mW/ cm2. Notably, a red lamp (2.5 V) can be lightened up by SSC in series and sustained for more than 300 s. Therefore, this paper paves a new way for the facile fabrication of screen-printed energy storage devices.

关键词： Screen-printing Energy storage device Supercapacitor Nickel foam Activated carbon

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Robust Convex Model Predictive control for Quadruped Locomotion Under Uncertainties

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IEEE TRANSACTIONS ON ROBOTICS 2023年第6期39卷 4837-4854页

作者： Xu, Shaohang Zhu, Lijun Zhang, Hai-Tao Ho, Chin Pang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Autonomous Intelligent Unmanned Syst Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by similar to 11 x compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments.

关键词： Legged robots model predictive control (MPC) optimization and optimal control robust/adaptive control of robotic systems

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