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检索条件"任意字段=2023 6th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2023"

共 292 条记录，以下是221-230 订阅

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DV-Hop Localization Strategy Based on Beacon Node Confidence Analysis

DV-Hop Localization Strategy Based on Beacon Node Confidence...

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international conference on Computer Network, Electronic and Automation (ICCNEA)

作者： Han Shen Zhongsheng Wang School of Computer Science and Engineering Xi'an Technological University Xi'an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

To address the problem that the DV-Hop localization model for wireless sensor networks can no longer meet the current localization accuracy requirements, this paper proposes the concept of beacon node trustworthiness to provide ideas and basis for improving the optimization model. Based on the principle of least squares in the localization model, weighted least squares is introduced to present the trustworthiness for beacon nodes with different characteristics. To analyze the characteristics of the trustworthiness of beacon nodes, 18 functions are designed in terms of function instrumentality and decrement able, hierarchical faultiness, etc. Finally, the conclusions obtained from the credibility test are tested with weighted matrix and average hop distance weighting, and the localization error is reduced from 32.68% to 31.78%, with a role rate of 100%. the credibility characteristics of beacon nodes discussed in this paper provide ideas for subsequent optimization of the localization model.

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Real Time Surgical Instrument Object Detection Using YOLOv7 1

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6th EAI international conference on Robotics and Networks, EAI ROSENET 2022

作者： Zheng, Laiwang Liu, Zhenzhong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9783031338267

ISBN: (纸本)9783031338250

With the rapid development of minimally invasive surgery (MIS) in the medical field, surgical robot technology has developed rapidly. Human-machine combined assisted surgical technology has begun to bring good news to the majority of doctors and patients who need surgery. It has gradually become a common medical treatment. Minimally invasive surgery can overcome the shortcomings of traditional surgery. MIS has the advantages of less trauma, less impact on patients, faster recovery, less intraoperative blood loss, and so on. For surgical robots, surgical instruments are important execution components that have been widely concerned and studied in recent years. One of the core technologies of surgical robots is the detection of surgical equipment based on deep learning, which can effectively assist doctors to complete the surgery. Current surgical instrument detection methods need to be improved in real-time performance and accuracy. therefore, this paper proposes a new real-time detection algorithm, which is based on the deep learning object detection system YOLOv7. the real dataset, which contained information on seven surgical instruments, was selected for training and validation. Experiments show that the method has good precision, recall rate, and mAP, which can be used to improve the ability of robot-assisted doctors to identify instruments during surgery. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

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Enhanced DDoS Detection using Machine Learning

Enhanced DDoS Detection using Machine Learning

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international conference on Information Systems and Computer Networks (ISCON)

作者： Rashmikiran Pandey Mrinal Pandey Alexey Nazarov Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

the rapid growth of internet population poses a serious challenge to the security of internet resources. the security is directly affected by the hits of Denial of Services (DoS) attack which is rampant nowadays. With this evolving threat, designing a cutting-edge method is difficult from a cyber-security perspective. In this study, we propose a deep learning-based system for detecting Distributed Denial of Service (DDoS) attacks, which utilizes Logistic Regression, K- Nearest Neighbor, and Random Forest algorithms. We assess proposed models using a recently updated NSL KDD dataset. Our research’s findings also demonstrate that proposed model is highly accurate in detecting Distributed Denial of Service (DDoS) attacks. Our results show that our proposed model significantly improves upon current state-of-the-art attack detection methods

关键词： Machine learning algorithms Computational modeling Sociology Forestry Denial-of-service attack Security Computer crime

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Federated Kalman Filter Based on KMEWMA control Chart for Integrated Navigation

Federated Kalman Filter Based on KMEWMA Control Chart for In...

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IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhiyuan Jiao Xiyuan Chen Ning Gao Yulu Zhong Junwei Wang Cundeng Wang School of Instrument Science and Engineering Southeast university Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education Nanjing China

Integrated navigation systems are prone to faults in complex environments, necessitating the isolation or mitigation of their effects on the system. this paper introduces a federated Kalman filter (FKF) structure based on the kernel multivariate exponentially weighted moving-average (KMEWMA) control chart, which incorporates statistical process control techniques to detect and mitigate faults effectively in integrated navigation systems. the proposed method demonstrates outstanding fault detection capability, successfully addressing both gradual and sudden-varying faults. Compared with chi-square test, the KMEWMA control chart could detect faults more precisely. Furthermore, instead of solely isolating faults, the system adaptively adjusts them to enhance positioning accuracy and stability. the simulation results validate the effectiveness and superiority of the proposed method.

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Research on Path Planning control of Maze Robot

Research on Path Planning Control of Maze Robot

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international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Minglu Chi Longchao Zhao Fuchao Yang Zhixiang Li Yahao Zhang Leichang Ma Xiaoyan Qian Qinchao Ren School of Intelligent Engineering Henan Institute of Technology Xinxiang China Xinxiang Aviation Industry (Group) Co. LTD Xinxiang China School of Economics Henan Institute of Technology Xinxiang China Jiangsu Huibo Robot Technology Co. LTD Suzhou China

To solve the problem of slow exploration speed of maze robot, a new kind of maze robot is designed in this paper. STM32FI03C8T6 with strong performance is used as the control chip to effectively improve the working efficiency of maze robot. the existing left-right exploration algorithms are compared and optimized, and the left-right cross algorithm is proposed, which effectively improves the maze exploration speed and accuracy of the maze robot. the experimental platform of the maze robot is built, and the path exploration and control program of the robot is verified. the results show that the designed maze robot can effectively explore the path, the hardware system can reduce the influence of external environmental factors such as light and temperature on the maze robot, and improve the motion control stability and exploration algorithm accuracy of the maze robot.

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Multi-view Clustering via Deep Graph Eembedding

Multi-view Clustering via Deep Graph Eembedding

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Zeyu Huang Jiayao Wang Zhibiao Xue Shan Lin School of Information Engineering Chang’an University Xi’an China School of Electronics and Control Engineering Chang’an University Xi’an China

Due to the ability to analyze multi-view data with complex structure and sparse value, multi-view clustering has attracted increasing attention in recent years, among which graph-based methods have achieved impressive success. However, most of the current advanced methods obtain the clustering assignment on the unified similarity graph but ignored the latent information contained in the original data, which makes the clustering performance rely heavily on the quality of unified similarity graph. To overcome above drawback, we propose a multi-view deep clustering method based on Graph Convolutional Networks (GCN) called DGEMvC, which can mine deep embedding of the original data and optimize iterative clustering objectives. Firstly, for general type multi-view data, the necessary graph structure required by GCN is completed by the proposed cross-view similarity graph construction strategy. Secondly, the constructed similarity graph assists in mining the deep embedding of original data as auxiliary information. Significantly, we further design a novel strategy to generate a pseudo-label for each data point to realize the unsupervised learning pattern. Dozens experiments on six benchmark multi-view datasets verify that our proposed method is superior to other advanced multi-view algorithms and improves by 7% and 4% in ACC and NMI.

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A Foreign Object Detection Method for Railway Overhead Lines Based on Few-Shot Learning 1

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6th international conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Yu, Hang Qin, Yong Cao, Zhiwei Lian, Lirong Gao, Yang Bai, Jie Ge, Xuanyu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Key Laboratory of Railway Industry of Proactive Safety and Risk Control Beijing100044 China Beijing Rolling Stock Depot Beijing Group Co. Ltd Beijing100039 China

ISBN: (数字)9789819993192

ISBN: (纸本)9789819993185

It often occurs that foreign objects hang on railway overhead lines especially in windy days, which may lead to the delay of train, and even cause loss of life and property. With the gradual maturity of computer vision, railway foreign object detection has made great progress. this paper focuses on foreign object in railway overhead lines, and proposes a foreign object detection method based on few-shot learning. the proposed method consists of two stages: general training and fine-tuning training. In the first stage, the data-abundant common classes dataset is used to train the entire detector to obtain the underlying model parameters. In the fine-tuning stage, we make a balanced dataset consisting of common classes and enhanced railway classes, and fine-tune the last layer of the detector to accomplish few-shot foreign object detection. Finally, this proposed method is tested on images of overhead lines intrusions taken in actual railway scenario, and achieves 79% mAP in railway classes. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Object detection

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引用

6th international conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Lian, Lirong Qin, Yong Cao, Zhiwei Gao, Yang Bai, Jie Ge, Xuanyu Yu, Hang State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Key Laboratory of Railway Industry of Proactive Safety and Risk Control Beijing100044 China

ISBN: (数字)9789819993192

ISBN: (纸本)9789819993185

Real-time awareness of the service status of facilities and equipment in metro tunnels is essential for the safe movement of trains. For low illumination underground tunnel environments where objects have high similarity characteristics and are difficult to distinguish, this paper proposes a framework for tracking high similarity objects along the rail line based on on-board visual perception devices. the framework mainly consists of two stages: object detection and object association. In the first stage, an object detection algorithm is used to detect potentially faulty objects in the video, and this paper takes the continuous occurrence of insulated steel supports on the line as the high similarity research object. In the second stage a matching strategy is used to associate the same object in the video sequence and assign the same ID (identity document). Finally, the proposed framework was tested on metro tunnel video and achieved an accuracy of 95.64 and a detection performance of 25 FPS (frames per second), 82.17 MOTA (multiple object tracking accuracy) and 88.46 MOTP (multiple object tracking precision) tracking performance. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Object detection

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A Model-Predictive-Enabled Equivalent-Input-Disturbance Approach for Disturbance Rejection

A Model-Predictive-Enabled Equivalent-Input-Disturbance Appr...

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Industrial Cyber-Physical Systems (ICPS)

作者： Yujian Zhou Jinhua She Feng Wang Makoto Iwasaki School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Tokyo Japan Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

this paper presents a model-predictive-enabled equivalent-input-disturbance (MPEID) method for disturbance rejection. An EID estimator with a state observer estimates the effect of disturbances in a system. the disturbance estimation will not be directly added to the input channel for disturbance rejection. A cost function that considers the disturbance estimation is designed. An MPC controller calculates an optimal control input by minimizing the cost function. the stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show that our method has better disturbance-rejection performance than an MPC method when the control input is similar.

关键词： Simulation Estimation Optimal control Predictive models Observers Cyber-physical systems Cost function

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Stabilization of Stochastic Linear Systems via Path-dependent Feedback control

Stabilization of Stochastic Linear Systems via Path-dependen...

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Industrial Cyber-Physical Systems (ICPS)

作者： Ruizhe Yu Xiaofeng Zong Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

this paper aims to investigate the stabilization problem of stochastic linear system via path-dependent state-feedback control. For the given stochastic linear system, a novel feedback control is designed with the path-dependent information of the system states, and the control gains are determined by the stochastic algebraic Riccati equation. To prove that path-dependent control can drive the stochastic linear system to be exponentially stable, a novel Lyapunov function is proposed. Combined with the general theory on stability of stochastic system, it is shown that stochastic system will be stabilized in mean-square via path-dependent control.

关键词： Linear systems Wireless sensor networks Stochastic systems Riccati equations Filling Feedback control Synchronization

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