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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

作者： Zhan-Jiang, Ji Wei, Shi School of Information and Electronic Engineering Wuzhou University China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University China Party Committee Office Wuzhou Vocational College China

ISBN: (纸本)9781728170817

The expansion property and chain mixing property have an important significance in terms of theory and application. The concept of the expansion property and chain mixing property are introduced in this paper. We study their dynamical properties. The following result are obtained: (1)Letbe the hyperspace of. Ifis expansive, thenis expansive;(2)Letbe the hyperspace of. Ifis chain mixing, thenis chain mixing;(3)Letbe the hyperspace of. Ifis chain transitive, thenis chain transitive;These results enriched the theory of the expansion property and chain mixing property of topological group. © 2020 IEEE.

关键词： Topology

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The research of g-mixing and g-equicontinuity on the hyperspace of topological group action 12

The research of g-mixing and g-equicontinuity on the hypersp...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-mixing and G-equicontinuity have an important significance in terms of theory and application. According to the definition of mixing and equicontinuity, we give the concept of G-mixing and G-equicontinuity in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace ofandbe a pseudo equivariant map. All non-empty open set are invariable for. Ifis a G-mixing map, thenis a G-mixing map;(2)Letbe the hyperspace of andbe a equivariant map. Thenis a G-mild mixing map if and only ifis a G-mild mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. Thenis a G-equicontinuity map if and only ifis a G-equicontinuity map. These results enriched the theory of the G-mixing and G-equicontinuity on the hyperspace of topological group action. © 2020 IEEE.

关键词： Mixing

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The research of G-chain transitive and G-chain mixing on the hyperspace of topological group action 12

The research of G-chain transitive and G-chain mixing on the...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-chain transitive and G-chain mixing have an important significance in terms of theory and application. According to the definition of chain transitive and chain mixing, we give the concept of G-chain transitiveand G-chain mixing in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace of. Ifis a G-chain transitive map, thenis a G-chain transitive map;(2)Letbe the hyperspace of. Ifis a G-chain mixing map, thenis a G-chain mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. All non-empty open set are invariable for. Thenis a G-exact map if and only ifis a G-exact map. These results enriched the theory of the G-chain transitive and G-chain mixing on the hyperspace of topological group action. © 2020 IEEE.

关键词： Group theory

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城市耦合风险——以人为本的复杂系统视角（英文）

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Engineering 2025年第1期 44-50页

作者：欧阳敏程泽楷马佳欣王红卫 Stergios Aristoteles Mitoulis School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology School of Management Huazhong University of Science and Technology School of Engineering University of Birmingham

The complexity of coupled risks, which refer to the compounded effects of interacting uncertainties across multiple interdependent objectives, is inherent to cities functioning as dynamic, interdependent systems. A disruption in one domain ripples across various urban systems, often with unforeseen consequences. Central to this complexity are people, whose behaviors, needs, and vulnerabilities shape risk evolution and response effectiveness. Realizing cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban risks. This paper adopts a complex systems perspective to examine the intricacies of coupled urban risks, emphasizing the critical role of human decisions and behavior in shaping these dynamics. We focus on two key dimensions: cascading hazards in urban environments and cascading failures across interdependent exposed systems in cities. Existing risk assessment models often fail to capture the complexity of these processes, particularly when factoring in human decision-making. To tackle these challenges, we advocate for a standardized taxonomy of cascading hazards, urban components, and their interactions. At its core is a people-centric perspective, emphasizing the bidirectional interactions between people and the systems that serve them. Building on this foundation, we argue the need for an integrated, people-centric risk assessment framework that evaluates event impacts in relation to the hierarchical needs of people and incorporates their preparedness and response capacities. By leveraging real-time data, advanced simulations, and innovative validation methods, this framework aims to enhance the accuracy of coupled urban risk modeling. To effectively manage coupled urban risks, cities can draw from proven strategies in real complex systems. However, given the escalating uncertainties and complexities associated with climate change, prioritizing people-centric strategies is crucial. This ap

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Visual SLAM Based on Dynamic Object Detection

Visual SLAM Based on Dynamic Object Detection

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第33届中国控制与决策会议

作者： Bocheng Chen Gang Peng Dingxin He Cheng Zhou Bin Hu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Shantui Construction Machinery Co. Ltd.

On the one hand,traditional visual SLAM does not consider dynamic objects in the scene,on the other hand,deep learning technology has been widely used in computer *** paper combines the two organically,and proposes an algorithm that uses dynamic object detection to improve the robustness of visual SLAM in a dynamic ***,we use the object detection network integrated into the attention mechanism to detect the dynamic target in the key ***,we follow the optical flow detection to further determine the dynamic feature points in the scene and eliminate ***,we use the static feature points for camera tracking to achieve highly robust monocular visual *** method described in this paper can not only eliminate dynamic feature points,but also retain as many static feature points as *** method described in this paper is compared with the original ORB-SLAM2 algorithm and DS-SLAM algorithm,and tested with public data *** results show that the method described in this paper can effectively eliminate the influence of dynamic objects on the visual SLAM algorithm.

关键词： Visual SLAM dynamic object detection deep learning optical flow detection attention mechanism

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Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

arXiv

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arXiv 2024年

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The Department of Informatics School of Physics Astronomy and Informatics Neurocognitive Laboratory Center for Modern Interdisciplinary Technologies Nicolaus Copernicus University Torun Poland The Ministry of Education Key Laboratory for Biomedical Engineering Zhejiang University Hangzhou China The Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The NetsUnion Clearing Corporation Beijing100045 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100049 China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs. © 2024, CC BY.

关键词： Differential privacy

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A Model Reference Adaptive Controller with Feedforward Decoupling for Active Magnetic Bearing Rotor System 4

A Model Reference Adaptive Controller with Feedforward Decou...

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4th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019

作者： Yi, Jian Ren, Gui-Ping Zhang, Hai-Tao Wu, Yue Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9781728119076

Active magnetic bearing (AMB) rotor system is widely applied in the industry for its remarkable advantages. However, it is a typical open-loop unstable mechatronics system suffering from nonlinear and couple characteristic. In this paper, the feedforward decoupling method is used to deal with the couple relationship of different axes. And the model reference adaptive control (MRAC) method is applied to improve the control performance and robustness and overcome the uncertainties. This control method makes the system output reach to the reference model by the designed adaptive law. Simulation results illustrate the effectiveness of the proposed scheme. © 2019 IEEE.

关键词： Model reference adaptive control

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Optimizing Neural Network image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： image classification

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Model-data co-driven U-net segmentation network for multimodal lung tumor images

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Applied Soft Computing 2025年 180卷

作者： Tao Zhou Pei Dang Yunfeng Pan Caiyue Peng Qitao Liu Huiling Lu Fuyuan Hu School of Computer Science and Engineering North Minzu University Yinchuan 750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan 750021 China School of Medical information and Engineering Ningxia Medical University Yinchuan 750004 China School of Electronic& Information Engineering Suzhou University of Science and Technology Suzhou 215009 China

There are some challenges in multimodal medical image segmentation. Based on this, the Model-Data Co-driven U-Net Segmentation Network for Multimodal Lung Tumor images is proposed. About “How to extract edge features in CT image?”, Edge-Driven U-Net is designed, which includes CT Data Stream Edge-Driven Module. The model ability to perceive lesion edge features is improved. About “How to extract position features in PET image?”, Position-Driven U-Net is designed, which includes PET Data Stream Position-Driven Module. The model ability to perceive lesion position features is improved. About “How to extract content features in PET/CT image?”, Content-Driven U-Net is designed, which includes PET/CT Data Stream Content-Driven Module. Under the guidance of semantic information in deep features, the model ability to perceive lesion content features is improved. About “How to fuse image features of PET/CT, PET and CT images?”, Model-Data Co-driven Module is designed. The main work of this model are as following: Firstly, the Shallow Model-Data Co-driven Module is designed, which realizes the interactive learning of different model data streams. Secondly, the Deep Model-Data Co-driven Module is designed, which realizes the interactive guide to learn position, edge and content features. The effectiveness of Model-Data Co-driven U-Net Segmentation Network for Multimodal Lung Tumor images is validated on a clinical multimodal lung medical image dataset. The results for Miou, Dice, Voe, Rvd, Acc, and Recall are 91.60 %，95.94 %，95.92 %，95.73 %，97.92 and 95.28 % in lung lesion segmentation. The method proposed in this paper has positive significance for computer-aided diagnosis.

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A self-supervised learning-based 6-DOF grasp planning method for manipulator

arXiv

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arXiv 2021年

作者： Peng, Gang Ren, Zhenyu Wang, Hao Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China IEEE senior member School of Automation South East University Nanjing China

To realize a robust robotic grasping system for unknown objects in an unstructured environment, large amounts of grasp data and 3D model data for the object are required, the sizes of which directly affect the rate of successful grasps. To reduce the time cost of data acquisition and labeling and increase the rate of successful grasps, we developed a self-supervised learning mechanism to control grasp tasks performed by manipulators. First, a manipulator automatically collects the point cloud for the objects from multiple perspectives to increase the efficiency of data acquisition. The complete point cloud for the objects is obtained by utilizing the hand-eye vision of the manipulator, and the TSDF algorithm. Then, the point cloud data for the objects is used to generate a series of six-degrees-of-freedom grasp poses, and the force-closure decision algorithm is used to add the grasp quality label to each grasp pose to realize the automatic labeling of grasp data. Finally, the point cloud in the gripper closing area corresponding to each grasp pose is obtained;it is then used to train the grasp-quality classification model for the manipulator. The results of data acquisition experiments demonstrate that the proposed method allows high-quality data to be obtained. The simulated results prove the effectiveness of the proposed grasp-data acquisition method. The results of performing actual grasping experiments demonstrate that the proposed self-supervised learning method can increase the rate of successful grasps for the manipulator. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

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