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6th International Conference on Information Science, Electrical, and automation engineering, ISEAE 2024

作者： Meng, Xiangrui Sun, Yongkui Luo, Chenshuo Yang, Fengbin An, Yuntong School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510682924

Due to the heavy haul rail's large single train capacity and strong impact on the track, the rails of heavy haul rail are prone to injuries, and the accurate detection and identification of injuries is the basis of intelligent operation and maintenance of the rails of heavy haul rail. However, the existing rail surface injury recognition methods mainly based on machine vision have the disadvantages of strong noise and limited field of view. Therefore, this paper proposes a detection method based on the fusion of profile data and vibration signals. The profile data has high accuracy, but it has the disadvantages of low detection rate and low accuracy of a single profile. The vibration signal is easy to measure, but it is easy to be interfered, and the vibration signals of some injuries will reflect the same characteristics. The respective defects are overcome by fusion of the two. The experimental results show that the detection accuracy is effectively improved after the fusion of the two. The vibration signals are extracted and dimensionality reduced using stacked self-coding network, and then input into the classifier to obtain the classification results based on the vibration data. At the same time, the profile data and standard rails are matched, and then the wear sequence is generated by the DTW algorithm, and then the features are extracted by the PCA algorithm and classified to obtain the results based on the profile data. Finally, the two methods are fused at the decision level to obtain a new rail surface injury recognition and detection method based on the fusion of the two. © 2024 SPIE.

关键词： Data fusion

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Suppression of Epileptiform Activity in a Wilson-Cowan Network Model by Distributed Linear Delayed Feedback control 4

Suppression of Epileptiform Activity in a Wilson-Cowan Netwo...

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2023 4th International Conference on Computer engineering and Intelligent control, ICCEIC 2023

作者： Ren, Hui Han, Chunxiao Li, Shanshan Che, Yanqiu Wang, Ruofan Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education School of Information Technology Engineering Tianjin China

ISBN: (纸本)9798350308877

Electrical brain stimulation (EBS) has been established as a potent instrument for modulating brain activities. This paper presents a spatially distributed linear delayed feedback control (LDFC) approach for EBS, designed to suppress epileptiform activity within a Wilson-Cowan network model of the cortex. The EBS is administered distributively via a grid of stimulating electrodes positioned on the cortex, and we evaluate the efficacy of LDFC in suppressing epileptiform activity. In a large range of control parameter space (feedback gain and delay time), the overall pathological oscillations can be substantially reduced, although not entirely suppressed. © 2023 IEEE.

关键词： Feedback control

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Safety-Certified Distributed Formation control of Networked Autonomous Surface Vehicles by Unifying control Lyapunov and control Barrier Functions

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Journal of Systems Science & Complexity 2025年第2期38卷 837-856页

作者： CONG Siming GU Nan WANG Haoliang WANG Dan PENG Zhouhua School of Marine Electrical Engineering Dalian Maritime University State Key Laboratory of Maritime Technology and Safety Dalian Maritime University Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships School of Marine Engineering Dalian Maritime University Department of Automation Shanghai Jiaotong University Dalian KeyLaboratory of Swarm Control and Electrical Technology for Intelligent Ships

This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs) subject to model uncertainties and environmental disturbances. A safety-certified path-guided coordinated control method is proposed for multiple ASVs to achieve a distributed formation in obstacle environments. Specifically, a neural predictor with a high-order tuner is presented to approximate unknown nonlinearities with accelerated learning performance. Subsequently, control Lyapunov functions(CLFs) and control barrier functions(CBFs) are constructed for mapping stability constraints and safety constraints on states to control inputs. A quadratic optimization problem is constructed with the norm of control inputs as the objective function, CLFs and CBFs as constraints. Neurodynamic optimization is used to deal with the quadratic programming problem and generate the optimal kinetic control signals, thereby attaining the desired safe formation. Unlike the high-order CBF, a CBF backstepping method is proposed to establish safety constraints such that repeated time derivatives of system nonlinearities can be avoided. The multi-ASVs system is ensured to be input-to-state safe irrespective of high-order relative degree. Through the Lyapunov theory, the multi-ASVs system is proven to be input-to-state stable. Finally, simulation results are presented to validate the efficacy of the presented safety-certified distributed formation control for networked ASVs.

关键词： Autonomous surface vehicles control barrier functions neurodynamic optimization path-guided formation control safety-certified control

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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RANSACs for 3D Rigid Registration:A Comparative Evaluation

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1861-1878页

作者： Jiaqi Yang Zhiqiang Huang Siwen Quan Zhiguo Cao Yanning Zhang the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China the School of Electronic and Control Engineering Chang’an UniversityXi’an 710064China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China IEEE

Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid *** sample consensus(RANSAC)and its variants are popular solutions to this *** there have been a number of RANSAC-fashion estimators,two issues remain ***,it is unclear which estimator is more appropriate to a particular ***,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain *** work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive *** objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation *** have been carried out on four standard datasets with different application scenarios,data modalities,and *** provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and *** on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.

关键词： 3D rigid registration performance evaluation point cloud pose estimation

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Simulation Device of Subway Short-Circuit Current Based on Luenberger Observer 11th

Simulation Device of Subway Short-Circuit Current Based on L...

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11th Frontier Academic Forum of Electrical engineering, FAFEE 2024

作者： Zou, Jiajin Chen, Peng Wang, Chunjie School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300382 China Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology Tianjin300382 China

ISBN: (纸本)9789819788194

With the rapid development of urban subway system in recent years, the failure of subway traction network has become a research hotspot. In order to ensure the reliability of the subway power supply, better study the fault characteristics, provide close to the real subway short circuit current, study the action characteristics of the subway high voltage DC circuit breaker under the fault current, design a simulation device based on phase shift full bridge that can generate the subway short circuit current, by controlling the amplitude, frequency, initial phase angle of the input waveform, so that the corresponding waveform can be generated. And it is verified by experiments. The experimental results show that the output has the characteristics of good dynamic response and small error. Considering that the sensor is affected by the interference noise such as the environment, the Luenberger observer is added to optimize the device control algorithm reasonably. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Short circuit currents

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Modeling and Simulation of Mixed Traffic Flow Considering Autonomous Vehicles and the Impact of Trucks 24

Modeling and Simulation of Mixed Traffic Flow Considering Au...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Kuang, Xianyan Zhang, Zuliang Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China School of Electrical Engineering and Automation Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Heavy trucks play a crucial role in shaping highway traffic dynamics. Their slower speed and unique acceleration and deceleration patterns can greatly influence the efficiency and stability of mixed traffic streams. This study explores a traffic scenario involving both heavy trucks and Connected and Autonomous Vehicles (CAVs), presenting an innovative model for mixed traffic flow and establishing specific lane-changing protocols for autonomous vehicles. Utilizing numerical simulations and comparative analyses, the research explores how heavy trucks affect traffic speed and lane-changing dynamics across different levels of CAV integration and varying traffic conditions. The findings of the study indicate that with lower rates of CAV penetration, increased traffic flow heightens the frequency of vehicle interactions, resulting in more pronounced variations in traffic speed. Conversely, when CAV penetration rates are elevated, there is a marked improvement in average traffic speed, alongside a significant reduction in speed fluctuations, particularly in high-density traffic situations. © 2024 Copyright held by the owner/author(s).

关键词： Automobiles

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED 14

A High-Precision Edge Detection Algorithm Based on Improved ...

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14th IEEE International Conference on Cyber Technology in automation, control, and Intelligent Systems, CYBER 2024

作者： Yuan, Yeborui Chen, Wenyuan Tang, Jie Yang, Jia School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics 110016 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China

ISBN: (纸本)9798331506056

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology. © 2024 IEEE.

关键词： Contrastive Learning

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Coronary Artery Segmentation in X-ray Angiography Based on Deep Learning Approach

Coronary Artery Segmentation in X-ray Angiography Based on D...

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第43届中国控制会议

作者： Shanbin Li Yiting Fan School of Automation Science and Engineering South China University of Technology School of Energy and Control Engineering Changji University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Segmenting coronary arteries from X-ray coronary angiography(XCA) images allows observation of coronary artery morphology and stenosis, which is of great significance for computer-aided diagnosis and coronary artery disease ***, XCA images have low contrast and irregular lighting due to the limits of existing imaging techniques, making vascular segmentation challenging. Segmentation of coronary arteries using conventional U-shaped segmentation networks is challenging due to the problem of major differences in vascular morphology, size, and overlap. This paper proposes a network based on the U-shape structure for multiscale context information fusion with lightweight GhostNetV2 as the backbone feature extraction network to improve the model's feature extraction capabilities. Then a multi-scale context fusion module(MCF) is proposed to effectively capture the contextual information of blood vessels. Finally, we propose a feature re-extraction module(FRM)to achieve effective fine feature re-extraction in complex backgrounds. Experimental results show that the model we propose achieves more accurate coronary artery segmentation in complex backgrounds with fewer parameters, improves the segmentation of fine vessels at the end of coronary arteries, and performs well compared with other artery segmentation models. The F1 score,Intersection over Union(IOU), accuracy(ACC), and Sensitivity(Sen) of the model can reach 82.23%, 69.83%, 98.79%, 81.93%,respectively.

关键词： Coronary Arteries Segmentation Multi-scale Context Feature Fusion Encoder and Decoder Feature Fe-extraction Deep Learning

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2D Human Pose Estimation with Residual Upsampling and Spatial Feature Enhancement

2D Human Pose Estimation with Residual Upsampling and Spatia...

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第43届中国控制会议

作者： Shanbin Li Xishan Zheng School of Automation Science and Engineering South China University of Technology School of Energy and Control Engineering Changji University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Computer vision,which has been extensively used in intelligent monitoring,self-driving,medical assistance,sports analysis,and other fields,includes human pose estimation as a key *** pose estimation algorithms have been appearing nonstop in recent years,and as they get more complex and structured,it becomes harder to analyze and actually implement the *** network is surprisingly efficient for such a simple *** study makes several modifications based on SBL to further enhance its *** order to fuse rich features from the feature extraction stage to the upsampling phase,we first introduce the residual upsampling module(RUSM).This guarantees that more detailed,high-resolution information can be utilized to upsampling features,significantly increasing the accuracy of joint recognition in the human ***,we present the spatial feature enhancement module(SFEM),which utilizes the spatial attention mechanism with the residual *** is employed to boost spatial features of the upsampling phase and further increase the detection precision of the ***,we conduct numerous experiments on the LSP dataset,which amply illustrates the enhanced functionality of the extra modules and the better performance of our suggested network.

关键词： Human Pose Estimation Attention Mechanism Residual Upsampling Spatial Feature Enhancement

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