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SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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Drowsy Driver Detection system For Poor Light Conditions

Drowsy Driver Detection System For Poor Light Conditions

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2023 IEEE International Conference on Mechatronics (ICM)

作者： Ahmed Ibnouf Ayman Fadlallah Muaiz Ali Abdelmalek Zidouri Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Information & Computer Science Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department Interdisciplinary Research Center for Intelligent Secure System King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

In recent years, the number of car accidents has been gradually increasing. A major factor in this increase is the drowsiness of drivers. There are many image recognition research studies on drowsiness detection but tend to neglect the case of low-light conditions. This paper focuses on detecting drowsy drivers in low-light conditions. In this paper, a behavioralbased drowsy driver detection system (DDDS) was proposed. The proposed DDDS applies a preprocessing algorithm that improves illumination and then focuses on detecting the driver’s face and eyes to calculate eye blink duration. A deep neural network model was used for facial recognition, and Haar Cascade classifiers were used for eyes detection. Every single eye was based to a Convolutional Neural Network (CNN) model to predict its state as either ‘‘Open’’ or ‘‘Closed’’. The proposed CNN was developed using a dataset of 1452 samples and gave an accuracy of 97.92% on the testing dataset, 15% of the 1452 samples. Different case scenarios in poor light conditions were implemented to evaluate the proposed DDDS. The system was able to identify the state of the driver even in harsh and severe poor light conditions.

关键词： Deep learning Face recognition Transportation industry Lighting Safety Convolutional neural networks Standards

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Seismic Interpolation Transformer for Consecutively Missing Data: A Case Study in DAS-VSP Data

arXiv

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arXiv 2024年

作者： Cheng, Ming Lin, Jun Dong, Xintong Lu, Shaoping Zhong, Tie College of Instrumentation and Electrical Engineering Jilin University Jilin Changchun130026 China School of Earth Sciences and Engineering Sun Yat-Sen University Guangdong Guangzhou510275 China Guangdong Zhuhai519000 China Guangdong Provincial Key Lab of Geodynamics and Geohazards Sun Yat-sen University Guangdong Guangzhou510275 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Jilin132012 China Department of Communication Engineering College of Electric Engineering Northeast Electric Power University Jilin132012 China

Distributed optical fiber acoustic sensing (DAS) is a rapidly-developed seismic acquisition technology with advantages of low cost, high resolution, high sensitivity, and small interval, etc. Nonetheless, consecutively missing cases often appear in real seismic data acquired by DAS system due to some factors, including optical fiber damage and inferior coupling between cable and well. Recently, some deep-learning seismic interpolation methods based on convolutional neural network (CNN) have shown impressive performance in regular and random missing cases but still remain the consecutively missing case as a challenging task. The main reason is that the weight sharing makes it difficult for CNN to capture enough comprehensive features. In this paper, we propose a transformer-based interpolation method, called seismic interpolation transformer (SIT), to deal with the consecutively missing case. This proposed SIT is an encoder-decoder structure connected by some U-shaped swin-transformer blocks. In encoder and decoder part, the multi-head self-attention (MSA) mechanism is used to capture global features which is essential for the reconstruction of consecutively missing traces. The U-shaped swin-transformer blocks are utilized to perform feature extraction operations on feature maps with different resolutions. Moreover, we combine the loss based on structural similarity index (SSIM) and L1 norm to propose a novel loss function for SIT. In experiments, this proposed SIT outperforms U-Net and swin-transformer. Moreover, ablation studies also demonstrate the advantages of new network architecture and loss function. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical fibers

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Comparison of Magneto-Optical Faraday Effect Method and Magnetic Camera for Imaging the 2-D Static Leakage Magnetic Flux Density

e-Journal of Nondestructive Testing

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e-Journal of Nondestructive Testing 2022年第9期27卷

作者： Dharmawan, I Dewa Made Oka Lee, Jinyi Wang, Dabin Department of Control Instrumentation and Robot Engineering Chosun University Gwangju61452 Korea Republic of Interdisciplinary Program in IT-BIO Convergence System Chosun University Gwangju61452 Korea Republic of Department of Electronic Engineering Chosun University Gwangju61452 Korea Republic of IT-Based Real-Time NDT Center Chosun University Gwangju61452 Korea Republic of

Recent development of electromagnetic inspection proposed auspicious results for imaging 2-D static of leakage magnetic flux density (LMFD) on surface and sub-surface defects. This ability encourages an interest in implementing electromagnetic-based magnetic flux leakage (MFL) testing for nondestructive inspection (NDI). In recent NDI technologies, advanced magnetic inspection using magneto-optical Faraday effect (MOE) and magnetic camera dominated the pre-service and in-service inspection in critical engineering industries such as aerospace, steel manufacturing, marine, railroad, and nuclear facilities. Unfortunately, a compact comparison of these approaches has not been proposed yet. This study aimed to compare MOE and magnetic camera according to five primary analyses: the target object, magnetic source, configuration of the magnetic sensor, signal processing, and AI implementation. A literature study was conducted using 47 papers to analyze the performance of each device in capturing LMFD. This comparison study successfully obtained the advantages and limitations of each imaging technique, which is very beneficial as a compact reference for developing and selecting suitable nondestructive inspection devices for MFL testing in the future. In addition, artificial intelligence (AI) as an option to escalate the inspection confidence could increase the assessment efficiency and reliability considering fast and complex engineering systems. © 2022, NDT. net GmbH and Co. KG. All rights reserved.

关键词： Nondestructive examination

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MAE-GAN: A Novel Strategy for Simultaneous Super-resolution Reconstruction and Denoising of Post-stack Seismic Profile

arXiv

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arXiv 2024年

作者： Yu, Wenshuo Dong, Shiqi Lu, Shaoping Dong, Xintong The College of Instrumentation and Electrical Engineering Jilin University Jilin Changchun130026 China The Key Laboratory of Modern Power System Simulation and the Control and Renewable Energy Technology Ministry of Education Jilin Jilin City132012 China Department of Communication Engineering College of Electric Engineering Northeast Electric Power University Jilin Jilin City132012 China School of Earth Sciences and Engineering Sun Yat-Sen University Guangdong Guangzhou510275 China Guangdong Zhuhai519000 China Guangdong Provincial Key Lab of Geodynamics and Geohazards Sun Yat-Sen University Guangdong Guangzhou510275 China

Post-stack seismic profiles are images reflecting containing geological structures which provides a critical foundation for understanding the distribution of oil and gas resources. However, due to the limitations of seismic acquisition equipment and data collecting geometry, the post-stack profiles suffer from low resolution and strong noise issues, which severely affects subsequent seismic interpretation. To better enhance the spatial resolution and signal-to-noise ratio of post-seismic profiles, a multi-scale attention encoder-decoder network based on generative adversarial network (MAE-GAN) is proposed. This method improves the resolution of post-stack profiles, and effectively suppresses noises and recovers weak signals as well. A multi-scale residual module is proposed to extract geological features under different receptive fields. At the same time, an attention module is designed to further guide the network to focus on important feature information. Additionally, to better recover the global and local information of post-stack profiles, an adversarial network based on a Markov discriminator is proposed. Finally, by introducing an edge information preservation loss function, the conventional loss function of the Generative Adversarial Network is improved, which enables better recovery of the edge information of the original post-stack profiles. Experimental results on simulated and field post-stack profiles demonstrate that the proposed MAE-GAN method outperforms two advanced convolutional neural network-based methods in noise suppression and weak signal recovery. Furthermore, the profiles reconstructed by the MAE-GAN method preserve more geological structures. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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基于自解耦三明治结构的横向运动栅场效应晶体管MEMS微力传感器

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engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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Traffic Flow Estimation using Graph Neural Network with Aggregation of Traffic Features

Traffic Flow Estimation using Graph Neural Network with Aggr...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Adiyana Putri Ferio Brahmana Endra Joelianto Herman Y. Sutarto Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Artificial Intelligence Control and Automation Laboratory Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia

ISBN: (纸本)9781665476881

The increasing vehicle volume every year affects the prediction of the traffic system. The purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions due to connection disruptions or ages. In estimating traffic flow, obtaining and explaining the historical data from the nearest segment sensor is necessary. The graph model can describe the spatial relationship between segments by connecting nodes with edges. Graph Neural Network (GNN) has been used as a learning method to predict the traffic flow in the segment sensor. GNN gets input in the form of spatial data built through a graph model, and the features are traffic density, average vehicle speed, and delay time on the road network. This paper shows that GNN's capability performs better in extracting spatial features and predicting the traffic flow by aggregating the features through connectivity nodes and edges.

关键词： Learning systems Roads Estimation Feature extraction Robot sensing systems Graph neural networks Spatial databases

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Measurement of surface stress-strain distribution inside a grain of carbon steel via optical microscope

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Measurement 2025年 255卷

作者： I Dewa Made Oka Dharmawan Jinyi Lee Department of Control Instrumentation and Robot Engineering Graduate School of Chosun University Gwangju 61452 Republic of Korea Interdisciplinary Program in IT-BIO Convergence System Chosun University Gwangju 61452 Republic of Korea Department of Electronic Engineering Chosun University Gwangju 61452 Republic of Korea

The advancement of alloys and steel in engineering highlights the critical need for robust methods to ensure structural integrity. While traditional Non-Destructive Testing and Evaluation (NDT/E) techniques provide valuable insights, they often fail to detect pre-existing stress conditions that contribute to fatigue cracks. Recent approaches have addressed this limitation by analyzing stress and strain at the grain level using advanced measurement systems—often based on diffraction techniques—integrated with Finite Element Method (FEM) and Digital Image Correlation (DIC) platforms. However, the application of these techniques for in-service inspections remains limited by challenges in accessibility, cost, and scalability. To overcome these issues, an integrated 2D surface system combining optical microscopy with DIC and grain-level analysis within the FEM framework is proposed. This method enables efficient visualization of grain-level deformation and stress distribution, thereby providing deeper insights into the microstructural behavior of polycrystalline materials under external loads. The proposed methodology is theoretically outlined and validated through simulations and experimental analyses, demonstrating its potential as a practical solution for in-service inspections. Ultimately, this approach is expected to advance inspection techniques by offering an accessible and scalable tool for monitoring microstructural behavior and identifying potential failure mechanisms through surface analysis.

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High-Performance Nonlinear control for Three-Phase Two-Level Boost PFC Rectifier

High-Performance Nonlinear Control for Three-Phase Two-Level...

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Materials and Energy: Energy in Electrical engineering (ICOME-EE), International Conference on

作者： Songklod Sriprang Babak Nahid-Mobarakeh Noureddine Takorabet Serge Pierfederici Apinya Siangsanoh Kanapot Yodmanee Pongsiri Mungporn Phatiphat Thounthong Nitchamon Poonnoy Nicu Bizon Thong-In Suyata Sakrit Noosawang GREEN-ENSEM Laboratory Université de Lorraine France Rattanakosin Wang Klai Kangwon Campus Petchkasem RD Rajamangala University of Technology Prachuap Khiri Khan Thailand LEMTA Laboratory Université de Lorraine France Renewable Energy Research Centre (RERC) Thai-French Innovation Institute King Mongkut’s University of Technology North Bangkok Bangkok Thailand Dept. Teacher Training in Electrical Engineering Faculty of Technical Education Faculty of Electronics Communication and Computers University of Pitesti Pitesti Romania Department of Electrical Engineering Faculty of Rajamangala University of Technology Thailand Dept. of Control System and Instrumentation Engineering Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9798331531577

ISBN: (纸本)9798331531584

This paper presents a high-performance nonlinear control strategy for a three-phase, two-level boost Power Factor Correction (PFC) rectifier. The proposed control method aims to achieve superior performance in terms of power factor correction, output voltage regulation, and dynamic response under various operating conditions. Utilizing advanced nonlinear control techniques, the system ensures optimal tracking of the reference signals, minimizing total harmonic distortion (THD) and improving overall efficiency. The effectiveness of the control strategy is validated through extensive simulations and experimental results, demonstrating significant improvements in the transient and steady-state performance of the rectifier. This approach provides a robust and efficient solution for PFC applications in modern power electronics systems.

关键词： Electrical engineering Dynamic response Total harmonic distortion Rectifiers Power factor correction control systems Power electronics Steady-state Voltage control Transient analysis

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Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules

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Applied Energy 2024年 355卷

作者： Jiaqi Qu Qiang Sun Zheng Qian Lu Wei Hamidreza Zareipour School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability.

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