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PCB Defect Detection Based on YOLOv5s

PCB Defect Detection Based on YOLOv5s

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Data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Yuze Sun Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

With the rapid development of the electronics manufacturing industry, the quality and reliability of printed circuit boards (PCBs) have become key factors in the performance of electronic products. Traditional PCB defect detection methods suffer from low efficiency and inaccurate results. this paper proposes a PCB defect detection method based on the YOLOv5s algorithm, aiming to improve the efficiency and accuracy of detection. YOLOv5s, as a lightweight real-time object detection model, is particularly suitable for automated detection tasks. this paper provides a detailed introduction to the basic structure and network design of the YOLOv5s model, including the Input, Backbone, Neck, and Head, and discusses its application in PCB defect detection. through experimental design, we used an open-source PCB image dataset provided by the Peking University Intelligent robotics Open Lab and trained and evaluated the model. the experimental results show that the model achieves high precision and recall rates in PCB defect detection, with a high mean Average Precision (mAP) value, demonstrating excellent performance. this study not only optimizes the quality control process of PCBs but also provides strong technical support for the automation and intelligent upgrading of the electronics manufacturing industry.

关键词： YOLO Manufacturing industries Adaptation models automation Computational modeling Printed circuits Quality control Production Data models Defect detection

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Predictive Metrology Data Reconstruction Using Support Vector Regression

Predictive Metrology Data Reconstruction Using Support Vecto...

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international conference on Control, Mechatronics and automation (ICCMA)

作者： Fuyou Li Emmanuel Izuwa Gilbert Tang Phil Webb Tom Mighall Centre for Robotics and Assembly Cranfield University Cranfield UK Wing of Tomorrow Airbus Broughton UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

In the context of Industry 4.0, manufacturing processes increasingly rely on quality data to achieve precision and efficiency, particularly in high-value sectors such as aerospace. Conventional metrology methods, though precise, often fail to meet the production demands of modern large-scale manufacturing, as they are time-consuming and labor-intensive. Predictive metrology offers a promising solution by reconstructing measurement data using smaller, yet highly representative datasets. In this study, we leverage machine learning techniques such as Support Vector Regression (SVR) on a dataset generated through Finite Element Analysis (FEA). the FEA-based dataset provides a controlled and comprehensive foundation for training and validating machine learning models, enabling accurate predictions of geometric deformations and enhancing the efficiency of metrology processes in aerospace manufacturing. Comparative analysis with Random Forest Regression (RFR) and Neural Networks (NN) shows that SVR outperforms RFR in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-square (R2) score. this study highlights the potential of SVR to improve efficiency and accuracy in metrology processes.

关键词： Support vector machines Deformable models Training Accuracy Deformation Training data Metrology Predictive models Finite element analysis Optimization

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Research on methods of Vehicle Image Retrieval in Expressway Scenarios Based on Deep Learning Neural Network

Research on Methods of Vehicle Image Retrieval in Expressway...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Ke Yang Ziyi Zhang Chihang Zhao Hao Li Xinyi Ma Xuan Li Wenhao Deng Yanxin Huang Shandong Hi-speed Company Limited Jinan P. R. China School of Transportation Southeast University Nanjing P. R. China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

the vehicle image retrieval in highway scenes is influenced by the complexity and diversity of vehicle types, as well as the impact of lighting and climate changes on vehicle image clarity. the paper aims to achieve high-precision vehicle retrieval in complex scenes on highways. Firstly, the paper constructs a vehicle image retrieval dataset of Southeast University (SEU) including whole vehicle image test set, vehicle face image test set, vehicle window test set, vehicle license plate test set. Secondly, three variants of CNN models are investigated for the vehicle image retrieval in expressway scenarios, including VGG16-VIR-ES, ResNet50-VIR-ES and DenseNet121-VIR-ES. Finally, based on vehicle image retrieval dataset of Southeast University (SEU), the research of compare experiment on VGG16-VIR-ES, ResNet50-VIR-ES and DenseNet121-VIR-ES are carried out. the experimental results show that DenseNet121-VIR-ES outperform the other models, and the accuracy of the vehicle image retrieval method based on DenseNet121 network is 92.12%, 91.55%, 92.86% and 84.98% for the whole vehicle image test set, vehicle image test set, window image test set and license plate image test set, respectively.

关键词： Road transportation Accuracy Image retrieval Neural networks Lighting License plate recognition Robots

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A New Approach for Spread Spectrum Steganography in Audio Containers

A New Approach for Spread Spectrum Steganography in Audio Co...

引用

international conference on Control Systems, Mathematical Modelling, automation and Energy Efficiency (SUMMA)

作者： Stanimir Zhelezov Krasimir Kordov Atanas Nachev Daniela Pavlova Panayot Gindev Nikolay Litchkov Konstantin Nesterov dept. Computer Science and Technologies Konstantin Preslavsky University of Shumen Shumen Bulgaria dept. Computer Informatics Konstantin Preslavsky University of Shumen Shumen Bulgaria University of Library Studies and Information Technologies Sofia Bulgaria Institute of Robotics at Bulgarian Academy of Sciences Sofia Bulgaria Institute of Metal Science Equipment and Technologies with Center for Hydro- and Aerodynamics Acad. A. Balevski Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

the most common approach among researchers is to implement cryptographic techniques and methods to provide a secure and reliable way to transmit sensitive information. this paper proposes a steganographic embedding algorithm based on Circle Map and Modified Rotation Equations. Audio files randomly selected from the Internet are used for the purpose of the study. Messages of different sizes were embedded in them using the proposed steganographic embedding algorithm. the accumulated database of more than 200 containers and stegofiles was analyzed using the most common steganalysis methods. From the analyses, it can be clearly seen that the proposed method of steganographic embedding in audio containers provides a very high level of security in the transmission of hidden information.

关键词： Steganography automation Databases Containers Control systems Mathematical models Generators Energy efficiency Internet Reliability

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Model-free control Algorithm for PMSM With Improved Adaptive Time-varying Convergence Law and New Observer

Model-free control Algorithm for PMSM With Improved Adaptive...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Jiqiang Wu Jixing Zhao Bingyou Liu Dongxiang Zhang Dept. School of Electrical Engineering Anhui Polytechn University Wuhu Dept. R&D Department Huaneng Chaohu Power Generation Co. Ltd Chaohu Dept. R&D Department Anhui Tianbing Electronic Technology Co. Ltd Wuhu

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

In order to improve the response speed and anti-interference ability of the permanent magnet synchronous motor control system, this paper proposes a model-free controller based on improved adaptive exponential convergence law and double power sliding mode observer. Firstly, the sigmoid function is used instead of the sign function to make the control signal smoother and reduce the jitter; secondly, the system state variables are introduced to form an improved adaptive exponential convergence law to enhance the response speed and anti-interference ability; finally, an improved double power sliding mode observer is designed to enhance the anti-interference ability of the system and make the disturbance observation more accurate. Simulation results show that the improved controller reduces the overshoot by 0.46% compared with the traditional method, the adjustment time of speed and torque is shortened by at least 0.01s, the three-phase current recovers the stability faster when the load is added suddenly, and the speed reaches and stabilises at the target value more rapidly when the speed increases suddenly. In summary, the control strategy proposed in this paper achieves fast response, small overshooting, shortened adjustment time, and strong anti-interference ability in static state, effectively weakening the vibration phenomenon.

关键词： Vibrations Adaptation models Adaptive systems Perturbation methods Simulation Velocity control Observers Permanent magnet motors Mathematical models Convergence

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Intelligent Optimization of Laser Marking Parameters Using Neural Networks and Genetic Algorithms

Intelligent Optimization of Laser Marking Parameters Using N...

引用

robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Teng Yan Wenxian Li Zhenxi Yu Yuxiang Sun Haoxue Zhuang Xiaohong Zhou College of Applied Technology Shenzhen University Shenzhen China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Faculty of Computer Science and Business Information Systems Technical University of Applied Sciences Würzburg-Schweinfurt Wtirzburg -Schweinfurt Germany

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

Laser marking technology is essential in precision manufacturing, but optimizing its numerous parameters is com-plex and time-consuming with traditional methods. this study developed a fully automated laser marking system using the Draco series laser from Han's Laser, incorporating automatic loading and unloading mechanisms. By utilizing neural networks and genetic algorithms, we optimized laser marking on ABS materials. the research included COMSOL simulations of laser-material interactions, analysis of parameter variations on col-oration, temperature field distribution under laser irradiation, and prediction of marking effects for practical applications. the aim is to automate processes through algorithmic integration, providing an intelligent laser marking solution. the developed methodologies and algorithms are applicable to other manufac-turing optimizations, offering valuable insights for broader in-dustrial applications. Primary research focuses include COMSOL simulation and parameter analysis, developing an AI-integrated laser production line with color prediction, and exploring au-tomated laser marking on ABS material using neural networks and genetic algorithms.

关键词： Analytical models Temperature distribution Radiation effects Lasers Neural networks Predictive models Prediction algorithms Optimization Robots Genetic algorithms

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Synchronous Control of Bilateral Teleoperation under Hybrid Attacks

Synchronous Control of Bilateral Teleoperation under Hybrid ...

引用

robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Xiuzhu Lu Yijun Zhang Jingyuan Li School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

the wide prospect of bilateral teleoperation system in various practical applications has attracted many researchers to study its control methods. this scholarly work addresses the control challenges inherent to bilateral teleoperation systems subjected to hybrid cyber-attacks. A model is developed to describe the working state of a bilateral remote operating system, taking into account the impact of denial of service (DoS) attacks and false data injection (FDI) attacks that exist with probability. An adaptive controller is designed to mitigate issues arising from data loss due to DoS attacks and packet tampering resulting from FDI attacks. When an adversary launches an attack, there will be DoS or FDI attacks in the channel of a communication network therefore, this paper uses Bernoulli component to model the attack situation of the system. By utilizing the Lyapunov functional methodology, a criterion for global asymptotic stability is established, thereby substantiating that the master-slave system is capable of attaining global asymptotic synchronization. Finally, by selecting a manipulator system for numerical simulation, the effectiveness of the control strategy is verified.

关键词： Asymptotic stability Adaptation models Operating systems Stability criteria Robustness Synchronization Communication networks Adaptive control Numerical stability Cyberattack

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Research on Path Tracking Control Method of Fully Driven AUV under Ocean Current Interference

Research on Path Tracking Control Method of Fully Driven AUV...

引用

IEEE international conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Wanxi Zheng Automation Research Institute Lianyungang China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

Autonomous underwater vehicles (AUVs) play an important role in human understanding and management of the ocean. Fully actuated AUVs can achieve 6 degrees of freedom motion control, with high control accuracy and anti-interference ability, and have high application value in the field of underwater precision operations. this article vectorizes the modeling of a multi thruster fully driven underwater vehicle, establishes a path tracking error model in a non-projective Serret Frenet coordinate system, designs disturbance resistant kinematic and dynamic controllers using inverse control methods, and analyzes the stability of the designed controllers. the simulation results show that the controller can asymptotically stabilize path tracking error, velocity tracking error, and attitude tracking error.

关键词： Analytical models Uncertain systems Uncertainty Attitude control Oceans Simulation Interference Kinematics Vehicle dynamics Immune system

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A Channel-Augmented Multi-Domain Graph Convolutional Network for Fatigue Driving Detection

A Channel-Augmented Multi-Domain Graph Convolutional Network...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Mingyi Sun Weigang Cui Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China School of Engineering Medicine Beihang University Beijing China Department of Automation Science and Electrical Engineering State Key Laboratory of Virtual Reality Technology and Systems The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, the inherent limitations of EEG acquisition equipment during real driving scenarios have contributed to the constrained robustness of existing models. Moreover, most of recent methods failed to extract robust multi-domain features, leading to a suboptimal performance. To address these challenges, we propose a novel channel-augmented multi-domain graph convolutional network (CA-MDGCNet). Specifically, the initial EEG signals are enriched by incorporating supplementary virtual EEG channels, distinguished as learnable parameters within the network architecture. then, differential entropy features are extracted from the augmented EEG signals. Following this, a multi-domain graph convolutional network is designed to encode high-level EEG features by means of convolutions in diverse paths, which is beneficial to integrating the characteristics extracted from multiple domains. Finally, the classification block derives the detection outcome from the refined feature maps. To substantiate the potency of the proposed method, the validation was conducted on the publicly accessible SEED-VIG. the proposed CA-MDGCNet not only demonstrates more promising performance compared to state-of-the-art approaches but also underscores the potential viability of our method for the realm of fatigue driving detection.

关键词： Graph convolutional networks Convolution Service robots Neural activity Feature extraction Fatigue Brain modeling Electroencephalography Entropy Robustness

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YOLOv8-GDI: A lightweight YOLOv8 for real-time Pedestrian Detection

YOLOv8-GDI: A lightweight YOLOv8 for real-time Pedestrian De...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Jianbin Sang Mengwen Zhang Huicheng Yang Ke Xu Yaocong Hu Haiting Wang Mengbo Jia Jun Yuan Anhui Polytechnic University Wuhu China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Aiming at the current challenges of low detection efficiency in real-time pedestrian detection algorithms, complex model structures, and difficulties in implementing them on mobile devices, we proposed YOLOv8-GDI, a lightweight pedestrian detection model designed based on the backbone of YOLOv8n. Firstly, the C2f-Ghost module is constructed by incorporating the Ghost Convolution in the model, in order to decrease the number of floating-point operations during feature channel fusion and enhance feature expression capability. In addition, Dynamic Task Align Detection Head(DTADH) was designed to facilitate the interaction between the model classification and localization to further enhance the model's detection performance. Finally, the Inner-CIoU loss function was introduced into the YOLOv8n to improve the ability to localize features of different sizes. the experimental results show that compared with YOLOv8n, YOLOv8-GDI reduced the number of parameters by 50%, decreased the computational complexity by 25%, and maintained a high detection accuracy on the Wider person dataset. this suggested that the improved model is more lightweight and has better application value in the direction of real-time pedestrian detection.

关键词： Location awareness Adaptation models Pedestrians Accuracy Convolution Computational modeling Heuristic algorithms Feature extraction Real-time systems Computational complexity

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