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作者： Xinshen Huang Hao Li Zhenkun Zhang Jianguo Zheng Hailong Pei Guangzhou Customs Technology Center Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology

In view of complex modeling and inefficient calculation at existing stage finite element analysis method in bus body skeleton analysis. This paper presents a method on simulating complex large section beams by equivalent stiffness element. Equivalent stiffness element model is analyzed theoretically. Experiments are carried out to verify that the equivalent stiffness element method can be used in rapid analysis and calculation for girder, body frame and body optimization. It not only reduces modeling complexity, but also improves calculation efficiency. To a certain extent, it can provide a better analysis method for large-scale vehicle modeling and simulation.

关键词： Equivalent stiffness Fast calculation Aluminium alloy bus Finite element analysis

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Simultaneous dynamic controller and anti-windup synthesis for systems with sector-bounded and slope restrictions

Simultaneous dynamic controller and anti-windup synthesis fo...

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第三十九届中国控制会议

作者： Naizhou Wang Hailong Pei Lianwen Jin Tiancai Liang School of Electronic and Information Engineering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology

This paper presents new techniques for construction of dynamic output feedback controllers with a static anti-windup compensator for a class of saturated systems with sector-bounded and slope-restricted nonlinearities. To handle the slope restriction, a so-called differential compensator is incorporated into the traditional nonlinear controller. Considering its sector-bounded property and convex-hull representation, respectively, two LMI-based synthesis methods are proposed to address the problems of the estimates of the region of attraction（ROA） and L2 performance analysis of the closed-loop system. A numerical example illustrates the effectiveness of the proposed approaches.

关键词： Anti-windup Lur’e system Region of attraction Slope restriction Linear matrix inequality（LMI）

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Cross-domain intelligent cooperative spectrum sensing algorithm based on Federated Learning and Swin-Transformer neural network

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Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Lingwei Xu Zhihe Gao Yufang Li T. Aaron Gulliver Department of Information Science and Technology Qingdao University of Science & Technology Qingdao 266061 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 Hubei China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi Minzu University) Nanning 530006 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 2Y2 Canada

The deployment of 6th Generation (6G) mobile networks will bring revolutionary changes to organizations and users through higher speeds, lower latency, and more intelligent network management. This will require more efficient spectrum sharing to improve network performance. Cognitive Radio (CR) can be employed to help achieve this goal. However, data collection at the fusion center for cooperative spectrum sensing (CSS) creates a risk of sensing user (SU) data leakage. In this paper, an intelligent CSS algorithm for mobile communication is proposed based on Federated Learning with a Swin-Transformer. First, to achieve the cross-domain process, a Continuous Wavelet Transform (CWT) is used with the normalized time series data to obtain a time-frequency spectrum map with joint time-frequency features. Second, Federated Learning is employed to realize distributed CSS to reduce sensing overhead, improve the security of sensing data, and eliminate the SU data isolation with traditional distributed CSS. Then, a new Swin-Transformer spectrum sensing is proposed for fusion learning of the time-frequency spectrum map. The fusion of Federated Learning and Swin-Transformer neural network provides cooperation with multiple SUs to achieve efficient spectrum sensing using the K -rank fusion criterion. Compared with the Convolutional Neural Network (CNN), ViT-Transformer, Graph Neural Network (GNN) and EfficientNet algorithms, the detection probability of the proposed algorithm has increased by 45 %, and the false alarm probability has decreased by 13 %.

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Air Quality controlling-Oriented Highly Efficient Method for Monitoring Particulate Matters

Air Quality Controlling-Oriented Highly Efficient Method for...

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第三十九届中国控制会议

作者： Yonghui Zhang Ke Gu Zhifang Xia Junfei Qiao Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System State Information Center

Since Particulate Matters（PMs） are closely related to people’s living and health, it has become one of the most important indicator of air quality monitoring around the world. But the existing sensor-based methods for PMs monitoring have remarkable disadvantages, such as low-density monitoring stations and high-requirement monitoring conditions. It is highly desired to devise a method that can obtain the PMs concentration at any location for the following air quality control in *** prior works indicate that the PMs concentration can be monitored by using ubiquitous photos. To further investigate such issue, we gathered 1,500 photos in big cities to establish a new AQPDCITY dataset. Experiments conducted to check nine stateof-the-art methods on this dataset show that the performance of those above methods perform poorly in the AQPDCITY *** address the above issue, we propose a new photo-based model for PMs monitoring. To be specific, we use the Support Vector Regression（SVR） to incorporate four types of 18 features to obtain a high-density PMs monitoring map. Experiments show that the newly proposed model has achieved superior performance than recently developed methods in the AQPDCITY dataset.

关键词： Particulate Matters（PMs） Monitoring AQPDCITY Dataset Feature Extrastion and Fusion

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Photo-Based Monitoring of Particulate Matter in the Campus: A New Strategy for Student Health

Photo-Based Monitoring of Particulate Matter in the Campus: ...

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It is imperative for the students' future health to ensure the students in good physical levels. Recent years have witnessed the increasingly serious harm to student health caused by the continually growing concentration of Particulate Matters(PMs). Consequently, the task of preventing and controlling PM concentrations in the campus is eagerly required. A welldesigned model for the monitoring of PM(as the basis for PM prevention and control) has posed a big challenge. Prior works have revealed that photo-based methods are available for the monitoring of PM. Towards validating the effectiveness of existing methods for PM monitoring in the campus, we construct a novel dataset that involves 1,500 photos collected in the Beijing University of Technology. Results confirm that stated-of-the-art methods are far from ideal for the monitoring of PM in the campus. To solve the aforesaid issue, this paper further proposes a novel photo-based PM monitoring model by using the weighted average method solved by LASSO regression to fuse the above methods' outputs tested to infer the PM values. Results demonstrate the superiority of our proposed model as compared to state-of-theart methods on the large-scale AQPDBJUT dataset.

关键词： LASSO Photo-Based Monitoring of Particulate Matter in the Campus A New Strategy for Student Health

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A map accessibility analysis algorithm for mobile robot navigation in outdoor environment

A map accessibility analysis algorithm for mobile robot navi...

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Yu, Xiangyu Wang, Jingchuan Chen, Weidong Wang, Hesheng Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Medical Robotics Shanghai Jiao Tong University Autonomous Robot Lab Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728137261

autonomous mobile robots have been widely used to perform specific tasks in recent decades. Map accessibility analysis is necessary for those robots to achieve safe and efficient path planning and navigation, especially in outdoor environment where the terrain can be uneven. Researchers have done sufficient works on the influences of different factors. However, integrating these factors is still a great challenge since the information of some constraints are only needed for specific robot tasks. In this paper, we firstly build a Three Dimensional (3D) Occupancy Grid Map (OctoMap) using a 3D Lidar and encoder-based odometer, and then extract different factors from the OctoMap and real-time lidar data. In order to analyze the relationship between map accessibility and variety of constraints, we design an open system and propose a map accessibility analysis algorithm to integrate different factors which have influence on mobile robot navigation. The system and algorithm are demonstrated to be efficient via experiments. © 2018 IEEE

关键词： Motion planning

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AQPDBJUT Dataset: Picture-Based PM Monitoring in the Campus of BJUT

arXiv

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arXiv 2020年

作者： Zhang, Yonghui Gu, Ke Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology China

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FD-SLAM: Real-time Tracking and Mapping in Dynamic Environments

FD-SLAM: Real-time Tracking and Mapping in Dynamic Environme...

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International Conference on Unmanned systems and Artificial Intelligence

作者： Hua Xu Chenguang Yang Ying Feng Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

A robot needs the ability of Simultaneous Localization and Mapping(SLAM) in an unknown environment. It can help the robot navigation and tracking. The traditional method only considers that the robot works in a static environment. But in most cases, the robot should work in a highly dynamic environment like people. This paper presents a novel SLAM system named FD-SLAM, building on ORB-SLAM2 [1], which adds the abilities of dynamic object detection and fusion of depth image and semantic image to better estimate the position of people in an image. To improve the real-time performance of FD-SLAM, it has five threads that run simultaneously in FD-SLAM: tracking, semantic segmentation, local mapping, loop closing, and point cloud mapping. FD-SLAM uses the deep neural network algorithm to detect the dynamic object and combine the depth image with the corresponding semantic image to obtain a more accurate position of a dynamic object thus reducing the impact of dynamic objects to more accurately estimate the pose of the camera. Finally, the point cloud thread uses each keyframe to create a point cloud map. We experiment with a public dataset. The results show that the absolute trajectory error of FD-SLAM is smaller than that of ORB-SLAM and DS-SLAM, and it has better real-time performance than other SLAM systems for a dynamic environment.

关键词： SLAM Image segmentation Semantics Feature extraction

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Inter-area coordination approach for metro train regulation under localized bi-directional power supply shortage during peak periods

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Computers and Industrial Engineering 2025年 207卷

作者： Wang, Xuekai D'Ariano, Andrea Su, Shuai Tang, Tao Su, Boyi Yan, Maode School of Electronics and Control Engineering Chang'an University Xi'an700054 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome00154 Italy

Faults in the traction power supply system of metro lines frequently cause Localized Bi-directional Power Supply Shortage (LBPSS) during peak periods, significantly restricting the traction power available to trains in the under-supplied area, and have knock-on impacts on train operations in other regions. To mitigate the impacts of LBPSS on the entire line, this paper proposes an inter-area coordination approach for the train regulation problem, dividing the global problem into three sequential sub-problems. In the first stage, a cooperative control approach optimizes the control strategies in the under-supplied area, restoring the balance between power supply and demand within the power supply capacity constraints. Building on these results, the second stage reschedules the train timetable in the well-supplied areas using multiple dispatching measures. Since synchronization of entry and exit times between different regions may not be fully achieved in the first two stages, the third stage fine tures hard-to-synchronize entry/exit times, and adjusts control strategies in the under-supplied area via feedback adjustment. To address the unique challenges of each sub-problem, this paper adopts the specific algorithms: Independent Deep Q-Networks (IDQN) for cooperative control, the GUROBI solver for timetable rescheduling, and Adaptive Large-scale Neighborhood Search (ALNS) for fine-tuning synchronization. Notably, for complex constraints associated with the feedback adjustment problem, an improved ALNS algorithm is introduced, which incorporating a constraint operator to enhance convergence speed and solution quality through heuristic rules. Finally, numerical experiments based on the Beijing Metro Yizhuang Line validate the effectiveness of the proposed approach. Results indicate that the solution quality improves by up to 20.0% and 2.8% compared to two previous train regulation approaches: the conservative approach based on maximum traction power and the two-stage ap

关键词： Subways

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