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2023 7th International Symposium on computer Science and Intelligent Control, ISCSIC 2023

作者： Wu, Xinyang Zhu, Jing Zhai, Xiangping Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China Anhui Jianzhu University Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350342987

Fault detection of missing insulator is critical to maintain the reliability, safety, and electrical performance of transmission lines. However, current methods generally have low accuracy and poor robustness for the images with complex background. Additionally, these methods require some shallow features and are unable to detect multiple faults. In this paper, we propose a method for fault detection of missing insulator based on deep learning. First of all, aerial images captured by unmanned aerial vehicles are collected and turned into the detection dataset using data augmentation. Secondly, an optimized network model is proposed for obtaining the locations of missing insulator faults with high accuracy. Eventually, the dataset is entered into the ultimate optimal network model for training and validation. Experimental results on the dataset with data augmentation indicate that our proposed method is more accurate than current methods for detecting missing insulator faults. © 2023 IEEE.

关键词： Electric lines

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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2023 China automation Congress, CAC 2023

作者： Bai, Yanxing Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.

关键词： Emotion Recognition

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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Human Multi-dimensional Stiffness Skills Transfer for Robot Teleoperation System

Human Multi-dimensional Stiffness Skills Transfer for Robot ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Situ, Liwen Lu, Zhenyu Si, Weiyong Yang, Chenguang College of Automation Science and Engineering South China University of Technology Guangzhou510640 China University of the West of England Bristol Robotics Laboratory BristolBS161QY United Kingdom University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9781665410205

Neuroscience research has demonstrated the sig-nificance of modulating stiffness during human task performance. Similarly, endowing robots with such capability is expected. However, existing methods for robot teleoperation require operators to simultaneously control position and stiffness, resulting in high workload and task inefficiency. On the other hand, learning from demonstration (LfD) offers a feasible approach for autonomously generating stiffness. Therefore, this paper proposes a robot teleoperation system that combines the advantages of teleoperation and LfD. Teleoperation enables precise positioning guided by human operators, while LfD can transfer human stiffness skills to robots. A teleoperation-oriented stiffness-adaptive Gaussian Mixture Model/Gaussian Mixture Regression method is proposed to learn human multi-dimensional stiffness and reproduce robot stiffness on a Riemannian manifold. To enhance generalization and cooperate with teleoperation, reference points and position-driven output are introduced. Furthermore, a teleoperation strategy for both the single-leader-single-follower configuration and the single-leader-dual-follower configuration are designed, which allows operators to control either one or two robot arms with a single leader device. Finally, the effectiveness of our method is verified through a plugging-in task and a continuous flipping task, demonstrating that the proposed system is capable of performing tasks that demand high positioning accuracy and stiffness adjustment. A supplementary video for this paper is available in GitHub∗∗https://***/setowenGit/TOSA-GMM-GMR-Video. © 2024 IEEE.

关键词： Robotic arms

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Guaranteed Cost Control Strategy for Sensorless Under-Tendon-Driven Prosthetic Hands

Guaranteed Cost Control Strategy for Sensorless Under-Tendon...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Julio Fajardo Guillermo Maldonado Luis Ayapan Jabes Guerra Eric Rohmer Department of Computer Engineering and Industrial Automation FEEC UNICAMP Campinas SP Brazil Turing Research Laboratory FISICC Galileo University Guatemala City Guatemala

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Prosthetic hands with limited functionality have been abandoned due to individual finger movements and responsiveness issues. Conventional strategies for addressing this issue are expensive and difficult to maintain. Our proposed control strategy uses a hybrid approach with an on-off controller for closing fingers and a robust position controller for opening. We have improved the system’s robustness by using linear matrix inequalities machinery and a guaranteed cost full-order filter. We tested this hybrid strategy with three different-sized fingers and three different objects, achieving satisfactory results. This strategy has the potential for future development, showing satisfactory results.

关键词： Robust control Uncertainty Costs Accuracy Current measurement Noise Estimation

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Longitudinal velocity control of autonomous driving based on extended state observer

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 36-46页

作者： Hongbo Gao Hanqing Yang Xiaoyu Zhang Xiangyun Ren Fenghua Liang Ruidong Yan Qingchao Liu Mingmao Hu Fang Zhang Jiabing Gao Siyu Bao Keqiang Li Deyi Li Danwei Wang Department of Automation University of Science and Technology of ChinaHefeiChina State Key Laboratory of Intelligent Vehicle Safety Technology ChongqingChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Traffic and Transportation Beijing Jiaotong UniversityBeijingChina Automotive Engineering Research Institute Jiangsu UniversityZhenjiangChina School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore School of Automotive Engineering Hubei University of Automotive TechnologyShiyanChina Beijing Idriverplus Technology Co. Ltd.BeijingChina Chery Automobile Co. Ltd.WuhuChina State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijingChina Department of Computer Science and Technology Tsinghua UniversityBeijingChina

Active Disturbance Rejection Control(ADRC)possesses robust disturbance rejection capabilities,making it well-suited for longitudinal velocity ***,the conventional Extended State Observer(ESO)in ADRC fails to fully exploit feedback from first-order and higher-order estimation errors and tracking error simultaneously,thereby diminishing the control performance of *** address this limitation,an enhanced car-following algorithm utilising ADRC is proposed,which integrates the improved ESO with a feedback *** comparison to the conventional ESO,the enhanced version effectively utilises multi-order estimation and tracking ***,it enhances convergence rates by incorporating feedback from higher-order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error *** improved ESO significantly enhances the disturbance rejection performance of ***,the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

关键词： active disturbance rejection control(ADRC) autonomous vehicle extended state observer(ESO) intelligent control longitudinal velocity control

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Advances in cost-effective integrated spectrometers

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Light(Science & Applications) 2022年第7期11卷 1379-1396页

作者： Ang Li Chunhui Yao Junfei Xia Huijie Wang Qixiang Cheng Richard Penty Yeshaiahu Fainman Shilong Pan Key Laboratory of Radar Imaging and Microwave Photonics Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjing 210016China Litin Technology XuzhouJiangsuChina Department of Engineering University of Cambridge9 JJ Thomson AvenueCambridge CB30FAUK Colllege of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Electrical and Computer Engineering University of California at San DiegoLa JollaCAUSA

The proliferation of Internet-of-Things has promoted a wide variety of emerging applications that require compact,lightweight,and low-cost optical *** substantial progresses have been made in the miniaturization of spectrometers,most of them are with a major focus on the technical side but tend to feature a lower technology readiness level for *** importantly,in spite of the advancement in miniaturized spectrometers,their performance and the metrics of real-life applications have seldomly been connected but are highly *** review paper shows the market trend for chip-scale spectrometers and analyzes the key metrics that are required to adopt miniaturized spectrometers in real-life *** progress addressing the challenges of miniaturization of spectrometers is summarized,paying a special attention to the CMOS-compatible fabrication platform that shows a clear pathway to massive *** for ways forward are also presented.

关键词： cost summarized spite

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Motion Prediction Based on sEMG- Transformer for Lower Limb Exoskeleton Robot Control 8

Motion Prediction Based on sEMG- Transformer for Lower Limb ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zeng, Min Gu, Jason Feng, Ying Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center AHU-IAI AI Joint Laboratory Hefei230601 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350300178

While lower limb exoskeleton robots can realize assisted walking by extracting the user's motion intention, it is difficult to effectively obtain the motion intention of the human body and convert it into information for the robot. In this paper, a novel model based on sEMG-Transformer is proposed for continuous motion prediction of multiple joint angles of lower limbs and applied to the developed lower limb exoskeleton robot. The sEMG-Transformer model can be used to extract the time series features from the sEMG sequences and establish the mapping between input data and multi-joint angle. Then, the predicted multi-joint angles are inputted into the developed lower limb exoskeleton robot. Experimental studies are performed with able-bodied human wearers and compared to existing methods, such as convolutional neural network (CNN), back propagation (BP), and long short-term memory (LSTM) networks. The motion intention estimation based on the sEMG-Transformer network has better estimation performance, which can effectively enable users to walk synchronously with the lower limb exoskeleton robot. © 2023 IEEE.

关键词： Backpropagation

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