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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

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PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

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control Theory and Technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

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

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

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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.

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Pylon Reconstruction Based on NeRF

Pylon Reconstruction Based on NeRF

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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

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MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT images via a Deep Learning Architecture

MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT ...

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Tang, Xiaoyu Chai, Lin Song, Chengze School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China School of Sino-french Engineering Institute Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781837242689

Computer-aided diagnosis has emerged as a crucial trend in modern medicine. Recently, CT-based automatic detection algorithms for lung infectious diseases suffer from issues of low accuracy and complex architecture. In this paper, three MoB-NetV2 models (MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL) for the detection of COVID-19 in CTs are proposed. the MoB-NetV2 model is proposed by inserting batch normalization layers between bottlenecks in MobileNetV2 and substituting the fully connected layer with the Net-1 model (SNN, ELM, RVFL), i.e. MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL. After comparison, it is found that the proposed MoB-NetV2-RVFL achieves an average accuracy of 93.42% on a private dataset comprising of 2392 images, while the precision is reached at 97.33%. Furthermore, it is demonstrated that our MoB-NetV2-RVFL outperforms other existing algorithms in the literature. High classification accuracy and precision and low computing amount are achieved, thereby enhancing the potential application of the model in the field of medical image-aided diagnosis of COVID-19 virus. © The Institution of Engineering & Technology 2024.

关键词： COVID-19

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Linear active disturbance rejection control for load frequency control with wind power and energy storage 21

Linear active disturbance rejection control for load frequen...

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5th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2021

作者： Qin, Feng Chen, Yifan Chen, Gonggui Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450384322

In this paper, linear active disturbance rejection control (LADRC) is adopted and designed for load frequency control (LFC). The biogeography-based optimization (BBO) algorithm is used to optimize parameters of LADRC, and the performance of BBO is improved by introducing the strategy of guidance and update. A two-area multi-source system with wind power and energy storage are designed, and the proposed LADRC optimized by improved biogeography-based optimization (IBBO) algorithm is tested in the designed system, and those system responses show that under the control of IBBO LADRC, controlled system has good dynamic performance and robustness. Those studies in this article show that the proposed method has excellent control performance and it is effective in solving the LFC problem. © 2021 ACM.

关键词： Wind power

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Multiscale Feature Fusion Module for Prohibited Item Detection 9

Multiscale Feature Fusion Module for Prohibited Item Detecti...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Xu, Ruiqi Zhang, Chirui School of Automation China University of Geosciences Hubei Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China

ISBN: (纸本)9798350376548

X-ray security check is a prevalent placement measure that plays a vital role in ensuring public safety. However, detecting prohibited items poses a complex challenge due to their variable sizes within X-ray images. In this study, we tackle this issue by curating a novel dataset through the integration of open-source data and introducing a Multiscale Feature Fusion Module. This module facilitates robust detection by amalgamating features across different scales. Extensive experiments verified that the superiority of our method compared to existing methods on the prohibited items test set constructed in this paper demonstrates extremely well the practical effectiveness of our proposed method. © 2024 IEEE.

关键词： component Environmental Security Prohibited Items Security Inspection X-ray Images

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Automatic Programming System of Painting Robots for Unstructured Workpieces Spraying 36

Automatic Programming System of Painting Robots for Unstruct...

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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

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Distribution Characteristics and Health Risk Assessment of Antimony in Atmospheric Particulates in a Northern City of China

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Chemical Research in Chinese Universities 2023年第3期39卷 465-471页

作者： SHEN Yiwen ZHAO Hao ZHAO Changxian DONG Shuofei CAO Yuanming XIE Jiaojao LYU Meiling YUAN Chungang Hebei Key Lab of Power-plant Flue Gas Multi-pollutants Control Department of Environmental Science&EngineeringNorth China Electric Power UniversityBaoding071000P.R.China Key Laboratory of Resources and Environmental Systems Optimization Ministry of EducationCollege of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing102206P.R.China Agilent Technologies Co. Ltd.(China)Beijing100102P.R.China

Over the last several decades,scientists have established a wealth of evidence to demonstrate the risks posed to human health by toxic elements in atmospheric particulate matter(PM).Antimony(Sb),as one of ever ignored PM-bound heavy metals,attracts more and more attentions and has been regarded as one emerging air pollutant with the change of pollution sources of particulate *** study the distribution of Sb in PM with different particle sizes is of great practical significance for understanding its source and health *** this study,the size distributions of Sb in PM(PM_(2.5),PM_(10)and TSP)in different seasons were studied from July 2018 to May *** high concentration of PM-bound Sb was found and the health risk was *** was enriched in fine particles and showed higher values in winter,which was probably caused by coal combustion and meteorological *** was also found that traffic-related non-exhaust emissions might become another main contribution to fine particle *** risk assessment demonstrated that the hazard quotient(HQ)of Sb in PM_(2.5)and PM_(10)for children(PM_(2.5):1.54,PM_(10):1.32)exceeded the acceptable threshold and ingestion contributed the most to the HQ for both children and adults.

关键词： Particulate matter(PM) Antimony Size distribution Health risk

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

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

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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

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