检索结果-内蒙古大学图书馆

International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Yang, Yang Li, Yichen Yu, Wenbin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789811966125

In this article, noncooperative target localization and tracking using multiple autonomous underwater vehicles (AUVs) are investigated. The main goal is to improve the target localization accuracy and tracking performance with the range-based information and to solve the problem of unobservability. A pose-based region-optimal (PBRO) algorithm is designed using range-only measurements in the scenario where three AUVs track and locate a noncooperative target. PBRO improves the target localization accuracy by modifying the target pose estimation using range-only measurements and enables the AUVs to have the optimal tracking performance in the obtained uncertainty region by the target motion constraints. Moreover, the proposed tracking strategy can get the maximum observability of the target. The simulation results validate the advantages of PBRO as compared with the alternative algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Autonomous underwater vehicles

来源：评论

学校读者我要写书评

暂无评论

Vision-Based Adaptive Prescribed-Time control of UAV for Uncooperative Target Tracking with Performance Constraint

引用

Journal of systems Science & Complexity 2024年第5期37卷 1956-1977页

作者： SHE Xuehua MA Hui REN Hongru LI Hongyi School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of TechnologyGuangzhou 510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou 510006China College of Electronic and Information Engineering and Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing 400715China

This paper discusses the uncooperative target tracking control problem for the unmanned aerial vehicle(UAV)under the performance constraint and scaled relative velocity constraint,in which the states of the uncooperative target can only be estimated through a vision *** the limited detection range,a prescribed performance function is designed to ensure the transient and steady-state performances of the tracking ***,the scaled relative velocity constraint in the dynamic phase is taken into account,and a time-varying nonlinear transformation is used to solve the constraint problem,which not only overcomes the feasibility condition but also fails to violate the constraint ***,the practically prescribed-time stability technique is incorporated into the controller design procedure to guarantee that all signals within the closed-loop system are *** is proved that the UAV can follow the uncooperative target at the desired relative position within a prescribed time,thereby improving the applicability of the vision-based tracking *** results have been presented to prove the validity of the proposed control strategy.

关键词： Nonlinear transformation performance constraint prescribed-time tracking uncooperative target vision-based measurement

来源：评论

学校读者我要写书评

暂无评论

A Low-complexity Decision Variables Classification Brainstorm Optimization Algorithm

A Low-complexity Decision Variables Classification Brainstor...

引用

2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Li, Qiyue Qiu, Lipeng Zhou, Aoran Wu, Yali Wang, Ziren Department of Information and Control Engineering Xi'An University of Technolog Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd. Xi'an710016 China

In the complex optimization scenarios of real life, we often need to not only weigh the conflicts between multi-objectives but also face the challenges brought by large-scale decision variables. When the scale of decision variables increases sharply, the search space will expand exponentially, which makes it difficult for the algorithm to traverse the whole search space under limited resources, so that the number of local optimal solutions increases sharply. The so-called 'dimension disaster' appears. To effectively deal with this problem, this paper proposes a large-scale multi-objective brainstorm optimization algorithm (LMaOBSO) based on low complexity decision variable classification method and improved penalty boundary intersection strategy. The algorithm uses a low-complexity tree sorting variable classification method to divide the decision variables into converges and diversity variables quickly. At the same time, the convergent variables are optimized by the improved penalty boundary crossover strategy to solve the problems existing in large-scale optimization problems. © Published under licence by IOP Publishing Ltd.

关键词： Disasters

来源：评论

学校读者我要写书评

暂无评论

A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive control

A Real-time Local Planner Design for Smart Wheelchairs Based...

引用

2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Yu, Guanfeng Zhang, Han Wang, Zehao Wang, Jingchuan Shanghai Jiao Tong University Institute of Medical Robotics Department of Automation China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities. © 2024 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Learning-Based Defense Against Prediction-Based Displacement Attacks on Formation control

Learning-Based Defense Against Prediction-Based Displacement...

引用

2024 China Automation Congress, CAC 2024

作者： Chen, Bingtong Li, Yushan Wu, Shukun Fang, Chongrong He, Jianping Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Student Innovation Center Shanghai Jiao Tong University China

ISBN: (纸本)9798350368604

Formation control is a widely performed task in multi-agent systems. However, agents under formation control are susceptible to attacks, and how to defend against the attack is important for system security. This paper aims to design a new method to protect the system from prediction-based displacement attack, which predicts the movement of agents, diverts them from their original tracks and drives them to a preset trap. The defense is challenging due to the robustness of the attacker’s prediction mechanism. Also, undermining the attack can be costly and harm the system’s performance. To address this issue, we first build obstacle-avoidance and formation control models of the holonomic and non-holonomic agents and specify the prediction-based attacks to limit the cases where the defense strategy can be effective. Then, we propose a learning-based defense strategy, which learns the prediction mechanism of the attackers. It modifies the control input based on self-adaptive fuzzy methods to reduce prediction accuracy and guarantee that the agents can reach the goal. Simulation results demonstrate that our method is better than the previous noise-based one at reducing the accuracy of the attacker’s prediction. © 2024 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

引用

Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

来源：评论

学校读者我要写书评

暂无评论

Deep-learning-based method for concealed object detection in terahertz (THz) images

Deep-learning-based method for concealed object detection in...

引用

2023 Advanced Fiber Laser Conference, AFL 2023

作者： Ge, Zihao Zhang, Yuan Wu, Xuyang Jia, Zhiyuan Wang, Heng Jia, Keke Key Laboratory of Grain Information Processing and Control [Henan University of Technology Ministry of Education Zhengzhou450001 China Henan Provincial Key Laboratory of Grain Information Detection and Control Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9781510677661

Terahertz (THz) technology has become a new trend in various fields due to its high penetration and harmlessness towards human body and objects. The object detection of concealed and hidden objects based on THz images is of great significance for ensuring public safety. However, the poor quality of original THz images leads to insufficient accuracy in target detection. Therefore, it is necessary to preprocess the images before performing object detection. In this work, in order to investigate the impact of different pre-processing methods on object detection using images, we adopt two methods, namely non-local mean (NLM) filtering and histogram equalization (HE). After pre-processing, YOLOv7 algorithm is used to perform object detection based on the preprocessed THz images. The experimental results show that YOLOv7 achieves highest recognition accuracy on NLM filtered THz images. The experimental results presented in this work provide a reference to select image processing method for performing concealed object detection based on THz images. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Traffic Pricing Based on Network Congestion Games with Player and Resource Failures 43

Traffic Pricing Based on Network Congestion Games with Playe...

引用

43rd Chinese control Conference, CCC 2024

作者： Liu, Shuqi Wang, Jinhuan Wang, Lin Hebei University of Technology Institute of Mathematics School of Science Tianjin300401 China Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581581

This paper investigates the pricing problem of transportation based on a framework of network congestion game with both player and resource failures. Firstly, the traffic pricing problem is modeled as a network congestion game by redefining the player's cost function in the event of player and resource failures. Secondly, by using the method of semi-tensor product of matrices, the game with player and resource failures is algebraized and verified to be a weighted potential game. The weighted potential function is also obtained. Then, the impact of player and resource failures on Nash equilibria (NEs) is studied, and the condition on keeping the NE of the original game unchanged is discussed. Finally, the theoretical results are verified by numerical examples. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Instance Reweighting Adversarial Training Based on Confused Label

引用

Intelligent Automation & Soft Computing 2023年第8期37卷 1243-1256页

作者： Zhicong Qiu Xianmin Wang Huawei Ma Songcao Hou Jing Li Zuoyong Li Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou511442China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger *** probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision ***,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our *** tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during ***-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial *** of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth ***-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger *** demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.

关键词： Reweighting adversarial training adversarial example boundary closeness confused label

来源：评论

学校读者我要写书评

暂无评论

LI-DPS: A Long Sequence Dual Prediction Scheme Based on Informer for Efficient High-Frequency Data Transmission 50

LI-DPS: A Long Sequence Dual Prediction Scheme Based on Info...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Wu, Yu Yang, Bo Zhu, Dafeng Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665464543

High-frequency time-series data such as vibration signals will consume a lot of communication resources and require very high network bandwidth. Reducing the amount of data transmission while ensuring its availability is particularly important and challenging in high-frequency scenarios. Dual prediction scheme (DPS) can significantly reduce the amount of data transmission while ensuring data accuracy. However, traditional DPS cannot be applied to high-frequency scenarios due to the limitation of the mechanism design and model inference speed. We propose a novel long sequence DPS to efficiently reduce the volume of high-frequency data transmission in real time. To overcome the accuracy degradation due to gradient vanishing in long sequence prediction, we also propose an attention-based prediction model. Furthermore, we propose Informer to reduce the model’s computational complexity and enable fast inference and deployment on resource-constrained edge gateways. Experiments based on the real-world dataset show that the proposed scheme can efficiently cope with high-frequency data, reduce its transmission volume, and ensure data accuracy. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：