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Vision Based Deflection Angle Measurement of Flight control Surfaces in Aircraft Testing

Vision Based Deflection Angle Measurement of Flight Control ...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Jiaxin Xu Cheng Ren Cailian Chen Yehan Ma Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

During the flight control system testing (FCST), it is crucial to accurately measure the deflection angles of flight control surfaces to determine whether they respond precisely to commands. However, the traditional measurement method, which relies on the manual use of angle measuring rulers for inspections, is inefficient, prone to wear, and lacks precision. To address this issue, we introduce a vision-based angle measurement method for FCST that replaces manual measurements, thereby significantly enhancing testing efficiency and accuracy. Our proposed triangular mesh alignment based angle measurement algorithm (TMA-AM) is a non-contact measurement method that involves two key procedures, capturing 3D coordinates from images and calculating deflection angles. The TMA-AM algorithm converts deflected angles into angular differences between two coordinate systems, while accounting for the curved characteristics of control surfaces. We evaluate TMA-AM algorithm on a 3D-printed wing test platform and an aircraft wing test platform. Experimental results demonstrate that our method achieves high accuracy, with an average angular measurement error below 0.05 ○ .

关键词： Measurement errors system testing Three-dimensional displays Coordinate measuring machines Accuracy Heuristic algorithms Process control Aircraft Surface treatment Aerospace control

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Energy-Efficient Safety-Aware Scheduling of Real-Time control systems with Burst Tasks

Energy-Efficient Safety-Aware Scheduling of Real-Time Contro...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Ting Cheng Yonghui Liang Qimin Xu Shanying Zhu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In industrial sites, multiple real-time control systems often share computing resources. However, burst computing tasks may lead to the random dropping of control computing subtasks, resulting in control failures and potential hazards. To address this issue, we propose an energy-efficient safety-aware task scheduling scheme based on mixed-integer programming. When burst computing tasks are triggered, this scheduling scheme adaptively releases resources from low-criticality control computing sub tasks and adjusts the processor speed based on the dynamic voltage and frequency scaling technology (DVFS), ensuring the timely completion of burst computing tasks and keeping the control system state deviation within a safe threshold. To achieve the scheduling scheme, we propose an efficient algorithm called EESASA. Simulation results show that EESASA can minimize the overall cost of control and processor energy while ensuring the safety of the control system and the timely completion of burst computing tasks.

关键词： Time-frequency analysis Job shop scheduling Costs Processor scheduling Process control control systems Dynamic scheduling Real-time systems Energy efficiency Voltage control

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Vehicle Tracking Method Based on Attention-YOLOv5 and Optimized DeepSort Models 11

Vehicle Tracking Method Based on Attention-YOLOv5 and Optimi...

引用

11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Li, Zhuang Tian, Xincheng Liu, Yan Shi, Xiaorui Shandong University Center for Robotics School of Control Science and Engineering Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Sinotruk Jinan Power Co. Ltd. Sinotruk Industry Park Zhangqiu Jinan250220 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Vehicle tracking plays a vital role in traffic management and autonomous driving. In order to further improve the accuracy of vehicle tracking, reduce the number of ID-Switch and enhance the anti-interference ability to the outside world, we propose a vehicle detection and tracking model based on Attention-YOLOv5 and optimized DeepSort. Firstly, YOLOv5 with attention mechanism was used as the front-end target detector, that is, add CBAM and ECA attention mechanisms to the backbone network of the benchmark YOLOv5 separately to improve the feature extraction ability. Then use the transfer learning idea to train it on the BDD dataset, it is concluded that ECA-YOLOv5 has the highest detection accuracy. Further we optimize the DeepSort algorithm, in order to overcome the disadvantages of the pre-training model not targeting traffic vehicles, we improve the re-recognition network structure and optimize the VeRi re-identification dataset, next perform re-identification pre-training on the Improved-VeRi dataset based on transfer learning. In order to make the vehicle features evenly distributed around the center of the class and improve feature resolution, a loss function L/CL combining the Softmax loss function and center loss function is proposed. In the experimental part, collecting some highway traffic videos of different scenes. At first, conduct visual analysis, then perform ablation experiments, and evaluate by using MOTA, MOTP and ID-Switch. Compared with the benchmark algorithm, the tracking performance is significantly improved. © 2022 IEEE.

关键词： Benchmarking

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Digital Twin Enabled Flight control system Testing: A Physical-Virtual Mapping Experiment

Digital Twin Enabled Flight Control System Testing: A Physic...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Cheng Ren Cailian Chen Shanying Zhu Yehan Ma Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Flight control system testing (FCST) is one of the most important testing during aircraft final assembly, however, traditional testing method highly rely on manual labor, resulting in low testing quality and efficiency. Targeting at improving the testing quality and efficiency, in this paper, we apply digtial twin (DT) technology in the FCST process for the first time. A virtual experimental platform named DT-FCST is constructed which is identical to a real wing experimental platform, supporting testing elements management and physical-virtual mapping experiments. The development process of the DT-FCST platform is detailed. Several experiments are conducted to demonstrate the effectiveness of DT technology. Based on motion scripts integrating real-time data from the cockpit, the cockpit control sticks and flight control surfaces are driven to achieve consistency between physical and virtual motions. With the help of DT technology, the entire movement process of control sticks and flight control surfaces are mapped with high fidelity in the DT-FCST platform, greatly enhancing the testing efficiency and monitoring quality.

关键词： system testing Process control Manuals Real-time systems Aircraft Informatics Surface treatment Monitoring Testing Aerospace control

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Adaptive Artificial Potential Field Ship Motion Planning Based on DDPG

Adaptive Artificial Potential Field Ship Motion Planning Bas...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Huimin Liu, Chenguang He, Zhibo Zhang, Guoqing Han, Bing State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China Navigation College Dalian Maritime University Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China National Engineering Research Center of Ship & Shipping Control System Shanghai200134 China

ISBN: (纸本)9789819639601

The paper presents a ship motion planning method that adjusts the attractive and repulsive gain parameters of the artificial potential field (APF) algorithm using the deep deterministic policy gradient (DDPG) algorithm, to address the challenges of parameter design and limited adaptability of traditional APF algorithms. This approach leverages the speed advantage of the APF planning algorithm and the exploration capability of the DDPG reinforcement learning algorithm. By optimizing the parameters of APF with DDPG, the local optima problem can be alleviated in complex obstacle environments. Simulation results demonstrate that the proposed motion planning algorithm can generate reasonable trajectories and exhibit strong adaptability in complex obstacle scenarios. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Motion planning

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Skewed Pins Detection of Aviation Connectors Based on Multi-Modal Cooperative Perception

Skewed Pins Detection of Aviation Connectors Based on Multi-...

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Artificial Intelligence of Things and systems (AIoTSys), International Conference on

作者： Jiaming Li Cheng Ren Jiaxin Xu Yao Wang Hanlin Xu Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Aviation electrical connectors are critically important components in electrical wiring interconnection systems. Currently, connector defect detection relies entirely on manual inspection, which is error-prone and time-consuming. Due to the unique structural characteristics of aviation connectors, single-modality sensors often struggle to simultaneously detect both pins and holes. To address this issue, we propose a 3D skewed pins detection (3D-SPD) algorithm that integrates both 2D and 3D modals features. Due to the low quality of the raw connector data, we propose a connector point cloud enhancement (CPCE) algorithm that combines multi-view point clouds for point cloud fusion enhancement. Based on enhanced data, we perform skewed pin detection. Experimental results indicate that CPCE and 3D-SPD are effective in detecting skewed pins, achieving an accuracy rate exceeding 99%. Our algorithm can be deployed as an AI assistant on the edge device for the skewed pin detection.

关键词： Connectors Point cloud compression Wiring Three-dimensional displays Accuracy Manuals Inspection Pins Artificial intelligence Defect detection

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Modeling of All-Vanadium Redox Battery Based on Dual Stream Network with Attention Mechanism

Modeling of All-Vanadium Redox Battery Based on Dual Stream ...

引用

International Conference on Computer and Communications (ICCC)

作者： Jiajie Zhu Bo Yang Mingxuan Cai Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Accurate voltage prediction for all-vanadium redox batteries (VRBs) affects the efficiency and safety of battery. In this paper, we propose a self-attention-based dual-stream neural network (SA-DSNN) model for voltage prediction under various working conditions. This approach independently processes temporal and condition data streams to reduce the noise interference. Using neural networks tailored to each of stream can more effectively extract intrinsic features. In particular, self-attention mechanisms are employed to enhance critical information. The features from both streams are fully integrated in output layer to generate final predictions. Experimental results indicate that the proposed method enhances the generalization and accuracy of VRBs model across different time scale.

关键词： Accuracy Neural networks Noise Voltage Interference Predictive models Redox Feature extraction Batteries Streams

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Unilateral Payoff control is Infeasible in Network Attack-Defense Interactions

Unilateral Payoff Control is Infeasible in Network Attack-De...

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Chinese Automation Congress (CAC)

作者： Kaiwen Zhao Yu Xia Qi Su Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Network resilience generally refers to the ability of a network to maintain normal operation in the face of various failures and disturbances, which has many applications in social, financial, communication, and transportation systems. There is a large amount of research on designing strategies to disrupt the network from the perspective of attack and to protect the network from the defense standpoint. However, attacks and defenses in the real world often occur simultaneously and repeatedly. The study of network resilience should not be limited to the structural characteristics but should consider the functional characteristics of the network. We develop a general game-theoretic framework to study attack-defense repeated interactions on complex net-works and examine the possibility of achieving precise control over the payoffs of attackers and defenders. Our findings reveal that neither the attacker nor the defender can unilaterally control their own payoff, the opponent's payoff, or any linear combination thereof. This work highlights the limitations of unilateral payoff control in real-world interaction scenarios, a consideration often overlooked in numerous theoretical studies.

关键词： Presses Interactive systems Stability criteria Transportation Games Power system stability Protection Game theory Faces Resilience

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Road Damage Detection and Classification Based on Multi-Scale Contextual Features

Road Damage Detection and Classification Based on Multi-Scal...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Yang, Lu He, Hualin Liu, Tao Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin300384 China

ISBN: (纸本)9781665480451

Road damage detection is particularly essential for improving road traffic safety. Existing methods do not perform well due to complex road conditions and sparse features contained in road damage. Therefore, we propose a new road damage detection network - YPLNet, which could obtain more refined multi-scale contextual features. Specifically, the network proposed in this paper is based on YOLOv5, by adding the PSA module to the backbone network, the spatial information of the feature map at multi-scale is obtained to enrich the feature space, and the long-term information dependence between channels is further constructed. In addition, in the feature fusion stage, the LCFI module is utilized to obtain contextual features under the multi-scale receptive fields, and these contextual features include rich low-level and high-level information. Our proposed network achieves a Mean-F1 Score of 0.635 in the competition. The source code and model are publicly available at https://***/hehualin-tut/YPLNet © 2022 IEEE.

关键词： Damage detection

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A Hybrid Meta-Heuristic Algorithm of Load Balancing for Cloud-based Railway Interlocking system

A Hybrid Meta-Heuristic Algorithm of Load Balancing for Clou...

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International Conference on intelligent Transportation

作者： Huan Zheng Qihe Zhang Zhiguo Liang Jiacheng Kong Dongdong Wei Yong Yang Ming Chai Haifeng Wang National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China

Cloud-based railway interlocking system is a novel solution for interlocking systems with the development of cloud computing. The load balancing problem caused by imbalanced resources in computing platforms is an essential problem in realizing cloud-based railway interlocking system. Conventional load balancing algorithms focus on makespan, throughput and costs. However, a load balancing algorithm for a cloud-based railway interlocking system should focus on optimizing resource utilization and execution time. To this end, we propose a hybrid meta-heurisite algorithm called Improved Genetic Annealing Algorithm (IGAA). The proposed algorithm can guarantee global optimization ability by using the Metropolis criterion and can converge rapidly due to the parallel searching characteristic of the Genetic Algorithm. Finally, the feasibility and effectiveness of the proposed algorithm are verified by experiments.

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