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Hand Gesture Recognition from an Open-Set Perspective

IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Adaptive Critic Attitude Learning control for Hypersonic Morphing Vehicles without Backstepping

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Li, Shihong Shao, Xingling Wang, Hongyu Liu, Jun Zhang, Qingzhen School of Instrument and Electronics North University of China Taiyuan030051 China State Key Laboratory of Instrumentation Science & Dynamic Measurement Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

This paper proposes an adaptive critic attitude learning control for hypersonic morphing vehicles (HMVs) under large uncertainties and deformation. Firstly, to remove the recursive design complexity caused by backstepping, an attitude-morphing coupled model in Brunovsky form is created by introducing a coordinate transformation. Secondly, a disturbance compensation controller is designed without using backstepping, wherein a resource-saving yet efficient unknown system dynamics estimator (USDE) is introduced to estimate the lumped uncertainties by a simple filtering operation. Then, a near optimal regulator capable of online learning is developed under a critic-only adaptive © 2022 IEEE.

关键词： Backstepping

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Distributed MPC Compensation-Based LFC for Heterogeneous Multi-area Interconnected Power systems 8th

Distributed MPC Compensation-Based LFC for Heterogeneous Mul...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Chi, Xiaobo Li, Xinyi Liu, Xiaolin Jia, Xinchun School of Automation and Software Engineering Shanxi University Taiyuan030031 China School of Mathematical Sciences Shanxi University Taiyuan030006 China The Key Laboratory of Complex Systems and Data Science Ministry of Education Taiyuan030006 China

ISBN: (纸本)9789819602247

This paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) with thermal and wind power generations, considering both inter-area and intra-area transmission delays and random packet losses induced by communication networks. Firstly, a heterogeneous framework of MAIPSs is constructed, where a networked distributed model predictive control (DMPC) scheme is proposed to compensate for transmission delays and random packet losses. Based on this framework, we then developed a distributed closed-loop LFC system model for heterogeneous MAIPS. Subsequently, a Luenberger observer is constructed to estimate the system state of the local area, and a networked feedback controller is designed to ensure stability with a certain H∞ performance level for the networked MAIPSs. Moreover, stability conditions and controller design criteria are provided by using the Lyapunov-Krasovskii stability theory. Finally, the control performance of the proposed method is verified through an example of a two-area interconnected power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive control systems

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A target group tracking algorithm for wireless sensor networks using azimuthal angle of arrival information

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Chinese Physics B 2012年第12期21卷 1-10页

作者：张淳费树岷周杏鹏 School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival （AOA） information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.

关键词： wireless sensor network target group tracking azimuthal angle estimation error

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Data-Driven Learning and control with Event-Triggered measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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A Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Co. Ltd Jiangxi Nanchang330000 China

Sparse Mobile Crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers' mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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Electromagnetic stealth technology: A review of wave-absorbing structures

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Materials and Design 2025年 253卷

作者： Guan, Chenglong Su, Sihang Wang, Bing Zhong, Jianfeng Chen, Junhao Sun, Fuwei Zhan, Lihua Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and Automation Fuzhou University Fuzhou350108 China Institute of Precision Instrument and Intelligent Measurement & Control Fuzhou University Fuzhou350108 China School of Mechanical and Electrical Engineering Central South University Changsha410083 China

As the core combat force for gaining air superiority, fighter aircraft directly influence the course and outcome of modern warfare. To address the integrated operational mode of reconnaissance, command, control and strike under the development of optoelectronics and information technology, and improve the battlefield survivability and combat effectiveness, electromagnetic stealth performance has become a key feature for next-generation combat aircraft. On the basis of rational aerodynamic shaping to minimize the radar cross-section, the application of electromagnetic absorbing technology plays a pivotal role in further enhancing the stealth performance. In this paper, the research progress of electromagnetic absorbing technology is summarized in light of the demand for the development of high-performance collaborative combat capabilities for military fighters. Focusing on the most revolutionary direction-the electromagnetic absorbing structure, the challenges associated with its maneuverability, stealth, and operational applicability are analyzed. Furthermore, from the perspectives of material selection, structural design, and process optimization, the paper systematically discusses ways to enhance the overall performance of electromagnetic absorbing structures and offers recommendations for forthcoming research directions. © 2025 The Author(s)

关键词： Radar cross section

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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Finite-time Bipartite Synchronization of Homogeneous and Heterogeneous Multiple Agents with Input Saturation: A TVRE-based Gain Approach

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IEEE Transactions on Automatic control 2025年

作者： Zhou, Yuan Liu, Yongfang Zhao, Yu Li, Zhongkui Hong Kong Polytechnic University Department of Applied Mathematics Hung Hom Hong Kong Northwestern Polytechnical University School of Automation Xi'an China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China

Pursuing faster convergence rates and smaller input magnitudes seem to be two conflicting goals in studying multi-agent systems. To give a trade-off between the two, this paper focuses on the bipartite synchronization problems over signed topologies and aims to achieve finite-time control for general linear agents subject to input saturation constraints. Firstly, this paper considers homogeneous agents and presents a class of bipartite synchronization protocols with saturation constraint, which exploits the solution of the time-varying Riccati equation (TVRE) to design the control gain. Then, a time-varying parameter scheduler is tactically designed for TVRE and achieves finite-time bipartite synchronization. Note that the design uses the solution computed online and brings a bit of conservatism in determining the settling time. So, for heterogeneous agents, this paper constructs a modified parameter scheduler computed off-line to reduce the conservatism. A class of finite-time bipartite synchronization generators and generator-based finite-time protocols are proposed. It shows that, in both designs, the control input subjects to the bound saturation during convergence even if the gain escapes to infinity towards the settling time. Moreover, the trade-off among the finite convergence time, the saturation bound of the input, and the initial domain are analyzed explicitly in theory. Finally, two simulations verify the validity of the theoretical results. © 1963-2012 IEEE.

关键词： Synchronization

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