检索结果-内蒙古大学图书馆

第42届中国控制会议

作者： Qingzhao Yin Zheng Tian Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates the speed regulation control of switched reluctance motor(SRM) *** improve the antidisturbance performance of SRM,a composite non-smooth control strategy is ***,the structure of SRM is analyzed,and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance.A virtual control function is introduced to represent the nonlinear part of the torque equation,whose inverse function is cascaded to linearized the nonlinear ***,a generalized proportional integral observer(GPIO) is constructed to estimate the lumped disturbance of the system,which is used for feedforward compensation ***,a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO(FTP+GPIO).The speed error closed-loop system can be regarded as a first-order finite time control system with bounded *** analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop *** effectiveness of the proposed method is verified by simulation ***,it is compared with proportional feedback combining feed-forward compensation(P+GPIO) method and proportional integral(PI) control method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Implicit Scene Context-aware Interactive Trajectory Prediction for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-17页

作者： Lan, Wenxing Li, Dachuan Hao, Qi Zhao, Dezong Tian, Bin Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China James Watt School of Engineering University of Glasgow Glasgow U.K State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions;2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories;3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance. IEEE

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Pylon Reconstruction Based on NeRF

Pylon Reconstruction Based on NeRF

引用

International Conference on Electronics Information and Emergency Communication

作者： Zijian Hu Chenxing Wang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

In this paper, we propose a pylon reconstruction method based on Neural Radiance Fields (NeRF) technology. The advantage of this method lies in its ability to reconstruct pylons from images, with a multi-view model maintaining good performance even under complex lighting and occlusion conditions. We demonstrate the process of using NeRF to reconstruct electric power towers through experiments. During the experimental process, we collect images of electric power towers from different environments and angles, then apply NeRF to process these images, using deep learning to learn and generate a network model capable of expressing the scene. The experimental results show that our method can complete the reconstruction of scenes with only image inputs, and the reconstructed models have a certain accuracy in terms of geometric details and textures.

关键词： Surface reconstruction Image segmentation Image resolution Shape Poles and towers Lighting Reconstruction algorithms

来源：评论

学校读者我要写书评

暂无评论

Optimal Tracking Performance of Networked control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

引用

2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

来源：评论

学校读者我要写书评

暂无评论

Traffic Signal control for Large-Scale Road Networks Based on Deep Reinforcement with PSR

Traffic Signal Control for Large-Scale Road Networks Based o...

引用

Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Zhicheng Zhou Hui Zhang Ya Zhang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper investigates the problem of Traffic Signal control (TSC) in large-scale road networks. In extensive road networks, it is customary to define each intersection as an agent, however, the issue of partial observability is particularly prominent. In this paper, Predictive State Representation (PSR) is employed to address the challenge of partial observability in large-scale multi-agent systems. A Multi-agent Deep Reinforcement Learning (DRL) model based on PSR called PSR-XLight is proposed in Large-Scale TSC systems. Multi-agent PSR is conducted with centralized training and independent filtering which overcome the challenge of prohibitive computations when the number of agents is large. Parameters sharing is adopted between each agent’s PSR model to enhance learning efficiency and facilitate utilization in large-scale multi-agent environments. Each agent undergoes independent DRL training and execution while parameters sharing is adopted. Experiments are conducted on real-world road networks and a large-scale road network comprising 1000 intersections.

关键词： Training Filtering Roads Computational modeling Deep reinforcement learning control systems Observability

来源：评论

学校读者我要写书评

暂无评论

Anomaly-Aware Semantic Self-Alignment Framework for Video-Based Person Re-Identification

Anomaly-Aware Semantic Self-Alignment Framework for Video-Ba...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhidan Ran Xiaobo Lu Wei Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

Video-based person re-identification (Re-ID) aims at matching the video snippets of the same person across multiple cameras. The ubiquitous appearance misalignment is a critical challenge in video person re-identification. Existing alignment-based methods rely on off-the-shelf human parsing models and cannot handle anomalous appearance information (e.g., obstacles and pedestrian interference) in video sequences. In this paper, we propose Anomaly-Aware Semantic Self-Alignment (ASSA), a novel video-based person Re-ID framework that seeks out body parts without prior human topology information and learns part-based feature representations against anomalous information. The proposed ASSA performs part classifier training and part-aligned representation learning alternately. For the classifier training, we design a Salient Region Extraction module to segment the entire foreground from the background in each input frame. Furthermore, a novel Anomaly-Aware Refinement module is proposed to suppress the influence of anomalous interference. Extensive experiments on three prevalent benchmarks demonstrate the effectiveness and superiority of the proposed framework.

关键词：

来源：评论

学校读者我要写书评

暂无评论

YOLO-MSP: Effective Cigarette Detection YOLO Network for Surveillance Scenario

YOLO-MSP: Effective Cigarette Detection YOLO Network for Sur...

引用

International Conference on systems and Informatics (ICSAI)

作者： Zhiwei Zhang Xiaobo Lu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

This paper presents an new approach to enhance the detection of smoking behavior using object detection neural networks, focusing on the challenge of small object detection, namely cigarettes in video frames. We introduce a Multi-Spatial Pooling (MSP) bottleneck mechanism integrated into a YOLOv8 backbone, which significantly improves the network's ability to discern smaller objects without compromising real-time inference capabilities. This enhancement facilitates the extraction of relevant features across various scales and efficiently handles the computational overhead, thereby boosting the detection accuracy and operational efficiency. Our results demonstrate a substantial improvement in performance metrics over traditional and existing neural network approaches, making this method highly suitable for real-time applications in diverse scenarios.

关键词： YOLO measurement Computational modeling Surveillance Neural networks Focusing Streaming media Feature extraction Real-time systems Informatics

来源：评论

学校读者我要写书评

暂无评论

Automatic Programming System of Painting Robots for Unstructured Workpieces Spraying 36

Automatic Programming System of Painting Robots for Unstruct...

引用

36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Liu, Xinkai Xu, Yunhui Zhou, Bo Fang, Fang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781665437127

Spraying technology has been applied more and more widely in aeronautical manufacturing field. However, aeronautical test workpieces usually have unstructured and complex shapes, which makes the spraying inefficient and the quality of coating cannot be guaranteed. Therefore, this paper designs an automatic programming and simulation system for aeronautical workpiece spraying. And the kinematics and inverse kinematics modeling of NACHI robot, spraying parameters optimization and trajectory planning of the robot are studied in detail. This paper also designs the simulation experiments on typical aeronautical test workpieces, and the result shows that this system can improve the spraying efficiency and coating thickness effect, which has a certain universality and effectiveness. © 2021 IEEE.

关键词： Inverse kinematics

来源：评论

学校读者我要写书评

暂无评论

MFANet: Mixed Feature Attention Network for Port Lane Detection 42

MFANet: Mixed Feature Attention Network for Port Lane Detect...

引用

42nd Chinese control Conference, CCC 2023

作者： Zhang, Jinwei Su, Jinya Yi, Dewei Yang, Jun Ministry of Education The School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9789887581543

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Data-Based Optimal Tracking of Autonomous Nonlinear Switching systems

引用

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 227-238页

作者： Xiaofeng Li Lu Dong Changyin Sun the School of Automation Southeast UniversityNanjing 210096 the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China the College of Electronics and Information Engineering Tongji UniversityShanghai 201804China

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching *** system state is forced to track the reference signal by minimizing the performance ***,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled *** linear-in-parameter(LIP)neural network is taken as the value function *** the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable ***,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is ***,the effectiveness of the algorithm is demonstrated with three simulation examples.

关键词： Adaptive dynamic programming approximation error data-based control Q-learning switching system

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：