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Abnormal EEG Microstates in Post-stroke Patients: A Pilot Study

IEEE Sensors Journal 2025年第11期25卷 19863-19873页

作者： Chen, Xiaoling Zhai, Jiashun Cai, Yuhao Zhang, Tengyu Wang, Juan Mao, Gengsheng Cheng, Shengcui Zhang, Yuanyuan Xie, Ping Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao Hebei China National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Beijing China PLA General Hospital Senior Department of Neurosurgery Beijing China Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Capital Medical University Xuanwu Hospital Beijing China

Exploring brain function is crucial for unraveling the pathological mechanism underlying stroke. while most studies focus on brain function emphasize dynamic connections and interactions within or between brain regions, they often ignore the global properties of large-scale network topology. In this study, we analyzed resting-state EEG microstates in stroke patients, calculating key parameters such as mean duration (MD), occurrence (OC), time coverage (TC), and transition probability (TP) across different microstate classes. As a result, we identified four microstate classes (A, B, C, and D) in both healthy subjects and stroke patients. Notably, stroke patients showed significant changes in Class B, C, and D, with increased MD, OC, and TC in Class B and C and decreased MD, OC, and TC in Class D. additionally, stroke patients displayed higher TP between Class B and C than healthy controls. Moreover, we observed a positive correlation between the OC of Class D and clinical Fugl-Meyer scores, suggestion a link between microstate dynamic and motor recovery. This study highlights the connection between specific microstates and brain regions, providing evidence that stroke patients demonstrate increased activity in visual and salience networks, but decreased activity in the dorsal attention network. We speculate these results from changes in the occipital lobe, frontal lobe, parietal lobe, and dorsal anterior cingulate cortex. These findings deepen our understanding of stroke pathophysiology and support further research. © 2001-2012 IEEE.

关键词： Electroencephalography

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Sensor Deception Attacks Against Initial-State Privacy in Supervisory control systems

arXiv

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arXiv 2022年

作者： Yao, Jingshi Yin, Xiang Li, Shaoyuan Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China

This paper investigates the problem of synthesizing sensor deception attackers against privacy in the context of supervisory control of discrete-event systems (DES). We consider a DES plant controlled by a supervisor, which is subject to sensor deception attacks. Specifically, we consider an active attacker that can tamper with the observations received by the supervisor by, e.g., hacking on the communication channel between the sensors and the supervisor. The privacy requirement of the supervisory control system is to maintain initial-state opacity, i.e., it does not want to reveal the fact that it was initiated from a secret state during its operation. On the other hand, the attacker aims to deceive the supervisor, by tampering with its observations, such that initial-state opacity is violated due to incorrect control actions. In this work, we investigate from the attacker’s point of view by presenting an effective approach for synthesizing sensor attack strategies threatening the privacy of the system. To this end, we propose the All Attack Structure (AAS) that records state estimates for both the supervisor and the attacker. This structure serves as a basis for synthesizing a sensor attack strategy. We also discuss how to simplify the synthesis complexity by leveraging the structural property of the initial-state privacy requirement. A running academic example is provided to illustrate the synthesis procedure. Copyright © 2022, The Authors. All rights reserved.

关键词： Personal computing

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Research on emotional space for movie and TV drama videos

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 73-82页

作者： Li Yujie Zhang Jingjing Jiang Wei Wang Chunxiao Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of ChinaBeijing 100024China Beijing Key Laboratory of Modern Entertainment Technology Communication University of ChinaBeijing 100024China School of Information and Communication Engineering Communication University of ChinaBeijing 100024China School of Computer and Cyber Sciences Communication University of ChinaBeijing 100024China

Emotional space refers to a multi-dimensional emotional model that describes a group of subjective feelings or emotions. Since the existing discrete emotional space is mainly aimed at human’s primary emotions, it cannot describe the complex emotions evoked when watching movies. In order to solve this problem, an emotional fusion space for videos was constructed by selecting movies and TV dramas with rich emotional semantics as the research objects. Firstly, emotional words based on movie and TV drama videos are acquired and analyzed by using subjective evaluation and semantic analysis methods. Then, the emotional word vectors obtained from the above analysis are fused, reduced dimension by t-distributed stochastic neighbor embedding(t-SNE) algorithm, and clustered by bisecting K-means clustering algorithm to get a discrete emotional space for movie and TV drama videos. This emotional fusion space can obtain different categories by changing the value of the emotion classification number without re-labeling and calculation.

关键词： emotional space movie and TV drama videos subjective evaluation words semantic analysis fusion space

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Precise LiDAR SLAM in Structured Scene Using Finite Plane and Prior Constraint

Precise LiDAR SLAM in Structured Scene Using Finite Plane an...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xie, Yuhui Zhao, Wentao Wang, Jiahao Wang, Jingchuan Chen, Weidong Institute of Medical Robotics and Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Ministry of Education Shanghai200240 China

ISBN: (纸本)9781665481090

In this paper, we propose a precise LiDAR SLAM in optimization framework using plane-like object as landmark. Compared to general methods, finite plane feature is used to represent landmark and a new residual model is designed, making constraint from edge of landmark can be used to limit the parallel position between LiDAR and landmark, leading to a more accurate result. Moreover, floor plan is used to provide global pose for reduction of drift, and additionally feature orientation prior is used to prevent map distortion when updating the inaccurate part of floor plan. Experiments are conducted using data collected in real environment. The result qualitatively shows that proposed method can build a corrected map from floor plan without distortion, and quantitatively verifies that proposed method can outperform other baseline methods in accuracy. © 2022 IEEE.

关键词： Optical radar

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Research on Semi-physical Simulation Cloud control system of Three-Tank Based on Industrial Internet Architecture 18th

Research on Semi-physical Simulation Cloud Control System o...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Lin, Wenshui Zheng, Song College of Electrical Engineering and Automation Fuzhou University Fuzhou350116 China Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9789811962028

The Industrial Internet is becoming the most important infrastructure for industrial manufacturing systems. With the emergence and development of cloud control technology, cloud control will become one of the most important functions in the Industrial Internet. This paper takes the three-tank as the research object, adopts the industrial Internet architecture, and proposes a cloud control system. By applying data engine technology, an isomorphic three-tank mathematical model is constructed on heterogeneous PLCs, thereby establishing a semi-physical simulation system, and realizing the real-time human-computer interaction. The three-tank semi-physical simulation cloud control system is built by the software tools of the industrial Internet platform, and experimentally studied to verify that the industrial Internet architecture and cloud control algorithm proposed in this paper is scientifically effective. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Manufacture

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A Uniform Framework for Diagnosis of Discrete-Event systems with Unreliable Sensors using Linear Temporal Logic

arXiv

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arXiv 2022年

作者： Dong, Weijie Yin, Xiang Li, Shaoyuan Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China

In this paper, we investigate the diagnosability verification problem of partially-observed discrete-event systems (DES) subject to unreliable sensors. In this setting, upon the occurrence of each event, the sensor reading may be nondeterministic due to measurement noises or possible sensor failures. Existing works on this topic mainly consider specific types of unreliable sensors such as the cases of intermittent sensors failures, permanent sensor failures or their combinations. In this work, we propose a novel uniform framework for diagnosability of DES subject to, not only sensor failures, but also a very general class of unreliable sensors. Our approach is to use linear temporal logic (LTL) with semantics on infinite traces to describe the possible behaviors of the sensors. A new notion of -diagnosability is proposed as the necessary and sufficient condition for the existence of a diagnoser when the behaviors of sensors satisfy the LTL formula . Effective approach is provided to verify this notion. We show that, our new notion of -diagnosability subsumes all existing notions of robust diagnosability of DES subject to sensor failures. Furthermore, the proposed framework is user-friendly and flexible since it supports an arbitrary user-defined unreliable sensor type based on the specific scenario of the application. As examples, we provide two new notions of diagnosability, which have never been investigated in the literature, using our uniform framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Learning-Based Motion Planning with Mixture Density Networks

arXiv

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arXiv 2022年

作者： Wang, Yinghan Duan, Xiaoming He, Jianping The Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China

The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by a point cloud, MNP first efficiently compresses the point cloud into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*. © 2022, CC BY-NC-SA.

关键词： Motion planning

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A Hybrid Time/Event-Triggered Interaction Framework for Multi-Agent Consensus with Relative Output Measurements

A Hybrid Time/Event-Triggered Interaction Framework for Mult...

引用

第33届中国过程控制会议

作者： Xianwei Li Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper is concerned with the output-feedback consensus problem of linear multi-agent systems on undirected *** focus is on reducing sensing and communication burdens between *** on relative output measurements,a novel dynamic output-feedback(DOF) protocol is proposed,which,by leveraging the closed-loop dynamic structure,integrates a hybrid time/event-triggered sampling mechanism such that only intermittent interaction between agents is *** conditions are established by the Lyapunov method and numerical simulations are provided for illustrating the effectiveness of the *** with the existing results,the proposed DOF protocol has the following merits:1) it relies on relative output measurements but does not need absolute information about agents;2) it only requires agents to intermittently interact with neighbors and thus can greatly reduce sensing and communication burdens between agents;and 3) the designed event-triggering conditions do not need to perform extra reconstruction operations that are computationally demanding.

关键词： Multi-agent system(MAS) Consensus Relative output Event-triggered control Intermittent interaction

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Entropy Rate Maximization of Markov Decision Processes under Linear Temporal Logic Tasks

arXiv

引用

arXiv 2022年

作者： Chen, Yu Li, Shaoyuan Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China

We investigate the problem of synthesizing optimal control policies for Markov decision processes (MDPs) with both qualitative and quantitative objectives. Specifically, our goal is to achieve a given linear temporal logic (LTL) task with probability one, while maximizing the entropy rate of the system. The notion of entropy rate characterizes the long-run average (un)predictability of a stochastic process. Such an optimal policy is of our interest, in particular, from the security point of view, as it not only ensures the completion of tasks, but also maximizes the unpredictability of the system. However, existing works only focus on maximizing the total entropy which may diverge to infinity for infinite horizon. In this paper, we provide a complete solution to the entropy rate maximization problem under LTL constraints. Specifically, we first present an algorithm for synthesizing entropy rate maximizing policies for communicating MDPs. Then based on a new state classification method, we show the entropy rate maximization problem under LTL task can be effectively solved in polynomial-time. We illustrate the proposed algorithm based on two case studies of robot task planning scenario. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Research on Wheat Impurity Image Recognition Based on Convolutional Neural Network 1

引用

12th International Conference on Broadband Communications, Networks, and systems, BROADNETS 2021

作者： Zhu, Chunhua Miao, Tiantian Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

The doping rate is one of the important indexes to evaluate the quality grade and price of wheat. In order to accurately and quickly recognize impurities (wheat husk) in wheat grains, images of doped wheat were collected and Convolutional Neural Network (CNN) was used to realize the classification and recognition of grains and impurities in wheat grains. In this study, image segmentation and image enhancement were used to preprocess the acquired images to establish the image database of wheat grains and impurities. According to the characteristics of image data, the classic CNN, VGGNet and ResNet network models for wheat impurity images recognition were established. Simulation analysis shows that, compared with the classical CNN and VGGNet network models, the ResNet network model has the best recognition performance. The recognition accuracy of the test set is 96.94%, the recognition time is 5.60 ms. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Convolutional neural networks

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