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Terahertz Spectroscopic Characterization and Thickness Evaluation of Internal Delamination Defects in GFRP Composites

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Chinese Journal of Mechanical engineering 2023年第1期36卷 190-210页

作者： Walter Nsengiyumva Shuncong Zhong Manting Luo Bing Wang Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and AutomationFuzhou UniversityFuzhou350108China Institute of Precision Instrument and Intelligent Measurement&Control Fuzhou UniversityFuzhou350108P.R.China School of Mechanical and Electrical Engineering Putian UniversityPutian351100China

The use of terahertz time-domain spectroscopy(THz-TDS)for the nondestructive testing and evaluation(NDT&E)of materials and structural systems has attracted significant attention over the past two decades due to its superior spatial resolution and capabilities of detecting and characterizing defects and structural damage in non-conducting *** this study,the THz-TDS system is used to detect,localize and evaluate hidden multi-delamination defects(i.e.,a three-level multi-delamination system)in multilayered GFRP composite *** obtain accurate results,a wavelet shrinkage de-noising algorithm is used to remove the noise from the measured time-of-flight(TOF)*** thickness and location of each delamination defect in the z-direction(i.e.,through-the-thickness direction)are calculated from the de-noised TOF signals considering the interaction between the pulsed THz waves and the different interfaces in the GFRP composite laminates.A comparison between the actual and the measured thickness values of the delamination defects before and after the wavelet shrinkage denoising process indicates that the latter provides better results with less than 3.712%relative error,while the relative error of the non-de-noised signals reaches 16.388%.Also,the power and absorbance levels of the THz waves at every interface with different refractive indices in the GFRP composite laminates are evaluated based on analytical and experimental *** present study provides an adequate theoretical analysis that could help NDT&E specialists to estimate the maximum thickness of GFRP composite materials and/or structures with different interfaces that can be evaluated by the ***,the accuracy of the obtained results highlights the capabilities of the THz-TDS for the NDT&E of multilayered GFRP composite laminates.

关键词： Glass-fiber-reinforced polymer-matrix(GFRP)composites Terahertz time-domain spectroscopy(THz-TDS) Nondestructive testing and evaluation(NDT&E) Stationary wavelet transform(SWT) Thickness evaluation Delamination defects

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Unmanned aerial vehicles towards future Industrial Internet:Roles and opportunities

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Digital Communications and Networks 2024年第4期10卷 873-883页

作者： Linpei Li Chunlei Sun Jiahao Huo Yu Su Lei Sun Yao Huang Ning Wang Haijun Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China China Mobile Chengdu Institute of Research and Development Chengdu 610200China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Chengdu University of Information Technology Chengdu 610225China Zhengzhou University Zhengzhou 450001China

Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site *** UAVs to assist communications is one of the promising applications and research *** future Industrial Internet places higher demands on communication *** easy deployment,dynamic mobility,and low cost of UAVs make them a viable tool for wireless communication in the Industrial ***,UAVs are considered as an integral part of Industry *** this article,three typical use cases of UAVs-assisted communications in Industrial Internet are first ***,the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are *** to the current research,it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain ***,the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.

关键词： Unmanned aerial vehicles(UAVs) UAV-assisted communications Industrial Internet

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A Method for Evaluating the Observation Effectiveness of Microwave Remote Sensing Satellite Constellation 7th

A Method for Evaluating the Observation Effectiveness of ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhang, Tianyao Zhang, Chi Xiao, Jin Wang, Hong China Academy of Space Technology Beijing100094 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Control Engineering Beijing100094 China

ISBN: (纸本)9789819733316

Satellites tend to form a constellation for better performance over a region of thousands of kilometers. However, how to set up the configuration of the constellation is still a challenge for an engineer. This article proposes several statistical-based metrics to evaluate the effectiveness of constellations from the perspective of observation applications. Then, this article proposes a software tool to analyze the metrics of constellations over a target area. Furthermore, this article illustrates the proposed method with the help of a 40-satellite case study. Finally, this article concludes the proposed method and discusses future work. This method of evaluation for remote sensing constellations would help the engineer determine the design parameters of the large constellation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellites

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Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 99-110页

作者： Yang Yu Zhenyu Lei Yirui Wang Tengfei Zhang Chen Peng Shangce Gao College of Automation&College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Department of Automation School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 200444China

Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning ***,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local ***,more and more research adopts non-BP learning algorithms to train *** this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local *** performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting *** meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.

关键词： Artificial neuron networks(ANNs) dendrite neuron network differential evolution(DE) scale-free network

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Positioning Method for Covered Vehicles Based on Neutrino Detection and Reinforcement Learning 3

Positioning Method for Covered Vehicles Based on Neutrino De...

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3rd International Conference on Electronic Information engineering and computer, EIECT 2023

作者： Han, Mingren Gao, Jiashan Zhou, Peng Zhang, Yixin Beijing Institute of Control Engineering China Academy of Space Technology Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350357707

In complex environments like underwater or underground, traditional navigation technologies face limitations due to the restricted penetration of optical and radio signals. Neutrino signals, which have extremely strong penetration capabilities, offer a new possibility. This study explores the challenges of navigation and positioning of covered vehicles and proposes a neutrino-based navigation and positioning technology to overcome the limitations of traditional navigation technologies in complex environments. This research combines reinforcement learning methods and Kalman filters, using the Proximal Policy Optimization (PPO) algorithm to tune the parameters of the Kalman filter to improve navigation and positioning accuracy. In the study, a Kalman filter estimation system model for vehicles in navigation signal denied environment environments was first established, then a navigation and positioning method based on neutrino detection was designed, and finally, a parameter adaptive PPO Extended Kalman Filter (PPOEKF) state estimation method was proposed. Through mathematical simulation and comparative analysis, the research results show that using reinforcement learning methods to tune the parameters of the Kalman filter can improve the accuracy of navigation and positioning, especially in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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Efficient Adversarially Guided Actor-Critic

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IEEE Transactions on Games 2024年第2期17卷 346-359页

作者： Xu, Mao Ge, Shuzhi Sam Zhao, Dongjie Zhao, Qian Qingdao University School of Automation and the Institute for Future Qingdao266071 China National University of Singapore Department of Electrical and Computer Engineering 117576 Singapore Qingdao University Institute for Future Qingdao266071 China Zhoukou Normal University College of Physics and Telecommunication Engineering Zhoukou466001 China

Exploring procedurally-generated environments presents a formidable challenge in model-free deep reinforcement learning (RL). One state-of-the-art exploration method, adversarially guided actor-critic (AGAC), employs adversarial learning to drive exploration by diversifying the actions of the deep RL agent. Specifically, in the actor-critic framework, which consists of a policy (the actor) and a value function (the critic), AGAC introduces an adversary that mimics the actor. AGAC then constructs an action-based adversarial advantage to update the actor. This action-based adversarial advantage guides the deep RL agent toward actions that diverge from the adversary's predictions while maximizing expected returns. Although the action-based adversarial advantage drives AGAC to explore procedurally-generated environments, it can affect the balance between exploration and exploitation during the training period, thereby impairing AGAC's performance. To mitigate this adverse effect and improve AGAC's performance, we propose efficient adversarially guided actor-critic (EAGAC). EAGAC introduces a state-based adversarial advantage that directs the deep RL agent toward actions leading to states with different action distributions from those of the adversary while maximizing expected returns. EAGAC combines this state-based adversarial advantage with the action-based adversarial advantage to form a joint adversarial advantage, and then employs this joint adversarial advantage to update the actor. To further reduce this adverse effect and enhance performance, EAGAC stores past positive episodes in the replay buffer and utilizes experiences sampled from this buffer to optimize the actor through self-imitation learning. The experimental results in procedurally-generated environments from MiniGrid and the 3D navigation environment from ViZDoom show our EAGAC method significantly outperforms AGAC and other state-of-the-art exploration methods in both sample efficiency and final perf

关键词： Adversarial machine learning

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Deep Learning Method for Leakage Location Detection of Pneumatic Systems Based on Infrared Thermal Image Evaluation 24

Deep Learning Method for Leakage Location Detection of Pneum...

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7th International Conference on Software engineering and Information Management, ICSIM 2024

作者： Chang, Jiaqi Shi, Yan Yang, Liman Niu, Yanxia Nie, Yulong Yang, Zhiguo Li, Lei Zhang, Wenchao School of Automation Science and Electrical Engineering Beihang University China Engineering Training Center Beihang University China Guangzhou Industrial Control Environmental Protection Technology Co. Ltd China

ISBN: (纸本)9798400709197

Pneumatic systems are an essential fluid transmission method in the industrial field, which can achieve the transmission and control of power or signals. Leakage in pneumatic systems is challenging to detect and is a highly harmful fault. Most existing leak detection methods use flow or pressure sensors to detect specific leakage amounts, while leak localization is still in the traditional manual detection, which significantly restricts localization efficiency. This article establishes a deep learning framework-based thermal image localization method for pneumatic system leak detection. Infrared images captured in the pneumatic system were collected, and then a deep-learning localization method based on the YOLO framework was established. Then, the recognition accuracy of this method was calculated. The results indicate that the accuracy has reached 99.5% of mAP_0.5 and 86.27% of mAP_0.5:0.95, indicating that this work is a meaningful study that can apply intelligent computing to engineering. © 2024 ACM.

关键词： Object detection

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Detection of Edge-blurred Small Targets of Steel Surface Defects Based on SAM-YOLO 2

Detection of Edge-blurred Small Targets of Steel Surface Def...

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2nd IEEE International Conference on electrical, automation and computer engineering, ICEACE 2024

作者： Wu, Yanxu Liu, Kai Zhou, Xianghao Zhang, Xiumei University of Science and Technology Liaoning School of Electronics and Information Engineering Anshan China South-Central Minzu University School of Computer Science Wuhan China Beihang University School of Automation Science and Electrical Engineering Beijing China University of Science and Technology Liaoning School of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350368208

To address the issue of challenging detection tasks for tiny and medium-sized objects because of backdrop confusion and inadequate feature representation of steel surface flaws, this paper proposes an efficient feature-enhanced, feature-fused SAM-YOLO detector based on the YOLOv8 model. It adds a new feature fusion module MSFF based on the original YOLOv8 model and replaces the bottleneck module in the C2f module with a feature enhancement module SACM. These two modules improve the enhancement and detection of defect edge features, local area sensing, and the network capability of multi-scale feature fusion, and the effect of SAM-YOLO is verified through several experiments on the NEU-DET steel surface defect dataset. The mAP50 of SAM-YOLO reaches 77.9%. mAP50:95 reaches 49.1%, exceeding the original YOLOv8 baseline model by 6.9% and surpassing several other benchmark models. To confirm the legitimacy of each SAM-YOLO model module, ablation tests were conducted on the model in the interim. © 2024 IEEE.

关键词： Target tracking

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Fractional Order Sliding Mode control with Fuzzy Parameter Tuning Applied to a SCARA Robot

Fractional Order Sliding Mode Control with Fuzzy Parameter T...

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2024 IEEE International Multi-Conference on Smart Systems and Green Process, IMC-SSGP 2024

作者： Znidi, Aicha Abdelhedi, Fatma Nouri, Ahmed Said Derbel, Nabil National Engineering School of Gabes University of Gabes Lab: Numerical Control of Industrial Processes Tunisia College of Engineering King Abdulaziz University Department of Electrical and Computer Engineering Jeddah22254 Saudi Arabia University of Sfax National Engineering School of Sfax Control & Energy Management Laboratory Tunisia

ISBN: (纸本)9798350378382

This paper presents the application of Fractional Order Sliding Mode control (FO-SMC) in order to achieve a robust motion trajectory regulation in dynamic robot systems. The proposed control strategy benefits of both advantages of fractional calculus and sliding mode control to upgrade the robustness and the performances of the controller. Moreover, the paper proposes to use a fuzzy tuning of sliding mode controller in order to satisfy the capabilities of the actuators. The stability of the closed loop system has been demonstrated using the Lyapunov theory. Simulation results demonstrate the efficiency of the proposed method achieving precise trajectory tracking and robust performances, it has been shown that fuzzy tuning of the sliding function parameters significantly reduces control variable peaks, making the control more suitable for actuator application. © 2024 IEEE.

关键词： Sliding mode control

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