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Policy iteration-based adaptive optimal control for Markov jump systems: a transition-probability-free asynchronous approach

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Science China(Information Sciences) 2024年第11期67卷 363-364页

作者： Weidi CHENG Chengcheng REN Shuping HE Changyin SUN Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education) School of Artificial Intelligence Anhui University

The controller and filter design problems of Markov jump systems(MJSs) have gained significant attention over the past few decades. These studies include various aspects,including stochastic stabilization [1], optimal tracking control [2], and dissipative filter design [3]. Although numerous publications address the optimal controller design for MJSs, the issue of hidden MJSs, particularly those with mismatched jumping modes between the system and the controller, has rarely been explored.

关键词： Markov processes

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Type Synthesis of Parallel Mechanisms with Configurable-Structural Platforms 1

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24th IFToMM China International Conference on Mechanism and machine Science and engineering, IFToMM CCMMS 2024

作者： Shi, Anyu Wei, Jun Liu, Chenglei Lv, Weilin Zhang, Jianjun School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Hebei Provincial Key Laboratory of Robot Perception and Human Machine Fusion Tianjin300401 China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education Tianjin300401 China

ISBN: (数字)9789819614677

ISBN: (纸本)9789819614660

In this paper, a concept of parallel mechanisms with configurable–structural platforms (CSPs) based on configurable platforms (CP) is proposed. When the self-deformable motion of the configurable platform is constrained by chains, the CP degenerates into the structural platform (SP). The self-deformation motion of CP changes the axis arrangement of the kinematic joints at the end of each chain, and the transformation between the CP and the SP can be realized by combining the constraint change of the chains. Based on the finite displacement screw matrix, virtual kinematic joint motion is used to describe the self-deforming axis motion of Bennett linkage CPs. Then the motion of virtual rigid body is described by Lie group theory. Finally, a type synthesis method of parallel mechanisms with CSPs is established based on Lie group theory. Parallel mechanisms with CSPs can use CPs to grasp, position and cooperate with other functions, and also can use SPs to work as traditional parallel mechanisms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lie groups

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Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal Recognition 4th

Mirror Contrastive Loss Based Sliding Window Transformer for...

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4th International Workshop on human Brain and Artificial Intelligence, HBAI 2024

作者： Luo, Jing Mao, Qi Shi, Weiwei Shi, Zhenghao Wang, Xiaofan Lu, Xiaofeng Hei, Xinhong Shaanxi Key Laboratory for Network Computing and Security Technology and Human–Machine Integration Intelligent Robot Shaanxi University Engineering Research Center School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9789819640003

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts—mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://***/roniusLuo/MCL_SWT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Mirror contrastive loss based sliding window transformer for subject-independent motor imagery based EEG signal recognition

arXiv

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arXiv 2024年

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts—mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://***/roniusLuo/MCL_SWT. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Kinematics Analysis of the Wearable Waist Rehabilitation robot 1

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Qi, Kaicheng Fu, Kefei Wei, Jun Lui, Chenglei Song, Jingke Zhang, Jianjun School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education Tianjin300130 China Key Laboratory of Robot Perception and Human–Machine Fusion Hebei Province Tianjin300130 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Patients with lumbar injuries are becoming more common and there is a shortage of intelligent rehabilitation equipment. A wearable lumbar rehabilitation robot for the treatment of low back pain disease is designed to help patients recover their lumbar motion. Firstly, the motion characteristics of the human lumbar spine and its range of motion is determined, and based on the size and range of motion of the human lumbar spine, the configuration design of the lumbar rehabilitation robot is proposed, and a 2-PUU/2-PUS parallel mechanism with three rotations and one translation is proposed;then the degree of freedom(DoF) analysis is carried out based on the screw theory;Finally, the kinematic inverse solution analysis is performed based on the Euler transformation and the geometrical characteristics of this parallel mechanism respectively, and validated based on the established virtual prototype model, and the results show that the mechanism motion characteristics meet the requirements of lumbar rehabilitation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Kinematics

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A Method for Absolute Pose Regression Based on Cascaded Attention Modules

SSRN

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SSRN 2024年

作者： Song, Xiaogang Tang, Junjie Guo, Weixuan Lu, Xiaofeng Hei, Xinhong Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Engineering Research Center of Human-Machine Integration Intelligent Robot Universities of Shaanxi Province Xi’an710048 China Xi’an Microelectronics Technology Institute China

The absolute camera pose regressor estimates the position and orientation of the camera solely based on captured RGB images. However, current single-image techniques often lack robustness, resulting in significant outliers. To address the issues of pose regressors in repetitive textures and dynamic blur scenarios, this paper proposes an absolute pose regression method based on cascaded attention modules. This network integrates global and local information through cascaded attention modules and then employs a dual-stream attention module to reduce the impact of dynamic objects and lighting changes on localization performance by constructing dual-channel dependencies. Specifically, the cascaded attention modules guide the model to focus on the relationships between global and local features and establish long-range channel dependencies, enabling the network to learn richer multi-scale feature representations. Additionally, a dual-stream attention module is introduced to further enhance feature representation by closely associating spatial and channel dimensions. This method is evaluated and analyzed on various indoor and outdoor datasets, and experimental results demonstrate that the proposed method can significantly improve localization performance. Ablation studies on multiple categories further verify the effectiveness of the proposed modules. © 2024, The Authors. All rights reserved.

关键词： Cameras

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Configuration Synthesis of 4 DOF Knee Rehabilitation Parallel Mechanism Based on Multiset Theory 16th

Configuration Synthesis of 4 DOF Knee Rehabilitation Paralle...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Zhang, Zhongxin Yu, Bin Wang, Jinbao Song, Jingke Wei, Jun School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education Tianjin300130 China Key Laboratory of Robot Perception and Human–Machine Fusion Hebei Province Tianjin300130 China

ISBN: (纸本)9789819965007

The occurrence of diseases and accidental damage cause many people to have knee motion dysfunction, the synthesis design of the knee joint rehabilitation mechanism is of great significance. This paper proposes a new 4 DOF (degree-of-freedom) motion equivalent model of knee joint, and the limb synthesis and configuration synthesis of 4 DOF parallel rehabilitation mechanism of knee joint are carried out. Firstly, according to the physiological characteristics of the knee joint and the three non-negligible motion characteristics of the knee joint, a 4 DOF motion equivalent model of the knee joint that is more in line with the knee joint motion is obtained, and the motion screw system and constraint screw system of this model are obtained, Configuration synthesis conditions are proposed. Second, the type of limb constraint is obtained according to the configuration synthesis conditions, and the limb synthesis is carried out by using screw correlation and reciprocity. Finally, the constraint types of the moving platform are classified by using the constraint screw multiple set theory, and the configuration synthesis of each type is carried out, a large number of knee joint 4 DOF parallel rehabilitation mechanisms that meet the requirements are obtained. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Screws

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Event-Guided Attention Network for Low Light Image Enhancement

Event-Guided Attention Network for Low Light Image Enhanceme...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiaobin Wang Haiyan Jin Haonan Su Zhaolin Xiao Department of Computer Science and Engineering Engineering Research Center Shaanxi Key Laboratory for Network Computing and Security Technology Human-Machine Integration Intelligent Robot Shaanxi University Xi'an University of Technology

In the low-light conditions, images are corrupted by low contrast and severe noise, but event cameras can capture event streams with clear edge structures. Therefore, we propose an Event-Guided Attention Network for Low-light Image Enhancement (EGAN) using a dual branch Network and recover clear structure with the guide of events. To overcome the lack of paired training datasets, we first synthesize the dataset containing low-light event streams, low-light images, and the ground truth (GT) normal-light images. Then, we develop an end-to-end dual branch network consisting of a Image Enhancement Branch (IEB) and a Gradient Reconstruction Branch (GRB). The GRB branch reconstructs image gradients using events, and the IEB enhances low-light images using reconstructed gradients. Moreover, we develops the Attention based Event-Image Feature Fusion Module (AEIFFM) which selectively fuses the event and low-light image features using the spatial and channel attention mechanism, and the fused features are concatenated into the IEB and GRB, which respectively generate the enhanced images with clear structure and more accurate gradient images. Extensive experiments on synthetic and real datasets demonstrate that the proposed EGAN produces visually more appealing enhancement images, and achieves a good performance in structure preservation and denoising over state-of-the-arts.

关键词：

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Multi-Group Fusion Network with Transformer for Camouflaged Object Detection

SSRN

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SSRN 2023年

作者： Song, Xiaogang Zhang, Pengfei Shi, Weiwei Lu, Xiaofeng Hei, Xinhong Xi'an University of Technology School of Computer Science and Engineering Xi'An710048 China Engineering Research Center of Human-machine Integration Intelligent Robot Universities of Shaanxi Province Xi'An710048 China

The accurate identification of highly similar foregrounds and backgrounds is a challenging task in the detection of camouflage objects. Although existing methods improve detection performance by using boundary and texture clues, they are still inadequate for identifying camouflaged objects from the background in complex scenarios. To address this issue, we introduce a novel multi-group fusion network with Transformer, called MGNet, for camouflaged object detection. Specifically, the Transformer architecture is adopted as a backbone network extraction feature to capture long-range dependencies and build global content-aware interactions. Subsequently, a group-fusion attention module is constructed to enhance the discrimination ability of the network to the foreground and background in complex scenes by mining the weight relationship between each channel. Finally, a multi-scale feature fusion module is designed, which adopts a coarse-to-fine fusion strategy to generate high-resolution prediction maps by integrating features step by step, while preserving the global awareness of the network to the maximum extent. Extensive experiments were conducted on four challenging benchmark datasets, which demonstrate that MGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Configuration Design and Kinematic Performance Analysis of a Novel 4-DOF Parallel Ankle Rehabilitation Mechanism with Two Virtual Motion centers

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Chinese Journal of Mechanical engineering 2023年第6期36卷 87-104页

作者： Jingke Song Jun Wei Bin Yu Chenglei Liu Cunjin Ai Jianjun Zhang School of Mechanical Engineering Hebei University of TechnologyTianjin 300401China Hebei Provincial Key Laboratory of Robot Perception and Human-Machine Fusion Tianjin 300130China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education Tianjin 300130China

Aiming at the problem that the existing ankle rehabilitation robot is difficult to fully fit the complex motion of human ankle joint and has poor human-machine motion compatibility,an equivalent series mechanism model that is highly matched with the actual bone structure of the human ankle joint is proposed and mapped into a parallel rehabilita-tion *** parallel rehabilitation mechanism has two virtual motion centers(VMCs),which can simulate the complex motion of the ankle joint,adapt to the individual differences of various patients,and can meet the reha-bilitation needs of both left and right feet of ***,based on the motion properties and physiological structure of the human ankle joint,the mapping relationship between the rehabilitation mechanism and ankle joint is determined,and the series equivalent model of the ankle joint is *** to the kinematic and con-straint properties of the ankle equivalent model,the configuration design of the parallel ankle rehabilitation robot is carried ***,according to the intersecting motion planes theory,the full-cycle mobility of the mechanism is proved,and the continuous axis of the mechanism is judged based on the constraint power and its ***,the kinematics of the parallel ankle rehabilitation robot is ***,based on the OpenSim biomechanical soft-ware,a human-machine coupling rehabilitation simulation model is established to evaluate the rehabilitation effect,which lays the foundation for the formulation of a rehabilitation strategy for the later prototype.

关键词： Ankle rehabilitation robot Double-VMCs mechanism Kinematic performance human-machine rehabilitation simulation

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