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Discharge image reconstruction and frequency domain analysis based on event data

High Voltage 2024年第6期9卷 1195-1201页

作者： Quan Yuan Lei Deng Hao Guo Qishen Lyu Xin Zhang Jibin Wu Yu Deng Hongyang Zhou Xilin Wang Zhidong Jia Engineering Laboratory of Power Equipment Reliability in Complicated Coastal Environments Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenGuangdongChina Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijingChina College of Information and Computer Taiyuan University of TechnologyTaiyuanChina Shenzhen Power Supply Corporation China Southern Power Grid CorporationShenzhenChina Department of Computing The Hong Kong Polytechnic UniversityHong KongChina China Electric Power Research Institute BeijingChina School of Electrical Engineering and Automation Xiamen University of TechnologyXiamenChina

Optical image method has been the earliest and most used direct method for observing gas ***,research on gas discharge monitoring based on visible light mainly relies on high-speed cameras,but the large size,significant data storage requirements,and susceptibility to interference from complex backgrounds and lighting conditions limit their further *** vision sensing(DVS)technology is a neuromorphic sensing technology that asynchronously measures the luminance changes at each *** offers advantages such as a large dynamic range(>120 dB),high temporal resolution(up to 1µs),and small data volume(MB level).In this study,dynamic vision sensing technology was employed to monitor both 30 mm short-gaps and 1080 mm long-gaps discharge processes *** study developed the CountImage encoding method for event data and conducted image reconstruction,time-domain analysis,and frequency-domain charac-teristic analysis based on the event *** results show that the event-reconstructed images are highly consistent with the high-speed camera images,and the arc develop-ment process and its path can also be clearly ***,this study discovered a correlation between the electrical characteristics and event information during the discharge *** the time domain,the duration of the maximum DVS event count closely matches the duration during which the voltage drops to zero during *** the frequency domain,the Pearson correlation coefficient between the event stream spectrum and the voltage signal spectrum is greater than *** the maximum number of brightening events(ONmax)and the maximum number of darkening events(OFFmax)are positively correlated with the voltage applied between the *** study demonstrates that,compared to the GB/s data rate of high-speed cameras,this approach can record the discharge process and accurately reconstruct the discharge process,arc morphology,and discharge path at M

关键词： image analysis holds

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Detection of Military Vehicles based on Edge Devices from Unmanned Aerial Vehicle 2

Detection of Military Vehicles based on Edge Devices from Un...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Umasankar, L. Anitha, D. Trisheela, S. Al-Jawahry, Hassan M. Borra, Subba Reddy S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Nitte Meenakshi Institute of Technology Dept of Cse Bengaluru India The Islamic University Najaf Iraq Computer Science and Engineering Maisammaguda Hyderabad India

ISBN: (纸本)9798350382952

The recent technology in surveillance for military vehicle parts forms the basis of intelligence warfare through information realized through monitoring and tracking operations in complex situations. Therefore, the process of identifying and classifying vehicles by an aerial vehicle installed with a resource-limited device and an intelligent object detection algorithm significantly assists security agencies. It is possible to make use of this vehicle either manually or autonomously. To overcome these issues, edge device based military vehicle detection from Unmanned Aerial Vehicle (UAV) is proposed. At present, no publicly available dataset exists that includes different military classes. Therefore, the proposed approach use a dataset consisting of 6772 images labeled as Tanks, Military Trucks, Helicopters, Aircraft, Civilian Cars and Civilian Aircraft. This data set is used to train deep learning models such as Quantized SSD Mobilenetv2 and Tiny Yolo v5 that are then compared with resource limited edge devices. The results reveal that Tiny Yolo v5 outperforms other models, showing high efficiency and suitability for edge-based devices due to its lightweight design. © 2024 IEEE.

关键词： Military vehicles

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Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions

Multiuser Resource Allocation for Semantic-Relay-Aided Text ...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Hu, Zeyang Liu, Tianyu You, Changsheng Yang, Zhaohui Chen, Mingzhe Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China College of Information Science and Electronic Engineering ZheJiang University Hangzhou China Institute for Data Science and Computing University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States

ISBN: (纸本)9798350370218

Semantic communication (SemCom) is an emerging technology that extracts useful meaning from data and sends only relevant semantic information. Thus, it has the great potential to improve the spectrum efficiency of conventional wireless systems with bit transmissions, especially in low signal-to-noise ratio (SNR) and small bandwidth regions. However, the existing works have mostly overlooked the constraints of mobile devices, which may not have sufficient capabilities to implement resource-demanding semantic encoder/decoder based on deep learning. To address this issue, we propose in this paper a new semantic relay (SemRelay), which is equipped with a semantic receiver to assist multiuser text transmissions. Specifically, the SemRelay decodes semantic information from a base station and forwards it to the users using conventional bit transmission, hence effectively improving text transmission efficiency. To study the multiuser resource allocation, we formulate an optimization problem to maximize the multiuser weighted sum-rate by jointly designing the SemRelay transmit power allocation and system bandwidth allocation. Although this problem is non-convex and hence challenging to solve, we propose an efficient algorithm to obtain its high-quality suboptimal solution by using the block coordinate descent method. Last, numerical results show the effectiveness of the proposed algorithm as well as superior performance of the proposed SemRelay over the conventional decode-and-forward (DF) relay, especially in small bandwidth region. © 2023 IEEE.

关键词： joint power and bandwidth allocation Semantic communication semantic relay

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An Explainable Deep Learning Approach for EEG- Induced Motor Imagery Classification 20

An Explainable Deep Learning Approach for EEG- Induced Motor...

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20th IEEE India Council International Conference, INDICON 2023

作者： Ganguly, Biswarup Ghosh, Ahona Dey, Sharmistha Sarkar, Indranil Saha, Sriparna Meghnad Saha Institute of Technology Electrical Engineering Department Kolkata India Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India University Institute of Computing Chandigarh University Punjab Gharuan India Guru Nanak Institute of Technology Department of Computer Application Kolkata India

ISBN: (纸本)9798350305593

Movement interpretation from the motor imagery (MI) signal of electroencephalogram (EEG) is an emerging field that can potentially upgrade the quality of life of individuals with motor impairments. In this manuscript, an explainable deep learning (x-DL) approach has been proposed to identify four class MI form EEG signals. The acquired EEG signals are preprocessed and then converted to time-frequency (TF) spectra for extracting the frequency content of the MI signals. The obtained TF images are then fed to a deep learning (DL) framework employing residual convolutional neural network for proper classification. The classification score, obtained from the network, has been propagated through the layers of the DL framework up to the input layer for obtaining layer wise relevance maps. These relevance maps signify the explainability of the proposed network, in terms of visual results. Comparative analysis reveals the effectiveness of the proposed x-DL module using a benchmark dataset. The obtained results and the utilization of signals from only two EEG channels demonstrate the possibility of employing the suggested strategy for developing a cost-effective brain-computer interfacing architecture applicable in real-time healthcare applications. © 2023 IEEE.

关键词： Convolutional neural network electroencephalogram explainable deep learning motor imagery wavelet transform

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge computing Networks

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IEEE Transactions on Mobile computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

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Automatic Detection and Measurement Method for Road Block on UGVs 23

Automatic Detection and Measurement Method for Road Block on...

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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Shin, Jiwoo Kim, Seoyeon Kim, Taesik Jung, Jinman Department of Electrical and Computer Engineering Inha University Incheon Korea Republic of Institute of Human Centered Computing Inha University Incheon Korea Republic of Department of Civil and Environmental Engineering Hongik University Seoul Korea Republic of Department of Computer Engineering Inha University Incheon Korea Republic of

ISBN: (纸本)9798400702280

Deep excavation in urban areas has the potential to cause significant economic and human losses by damaging adjacent structures. Prior to such damage, deformation may occur in the sidewalk or road nearest to the construction site. Accurate measurement of deformation levels is crucial for assessing stability in such scenarios, but existing techniques face challenges in achieving precise measurements. In this paper, we propose a novel approach for detecting and measuring block joints using laser sensor data. Our method consists of two key steps: detection and measurement based on laser sensor data. In the detection stage, the proposed method constructs dynamic frames from line data from laser sensor that contain depth information and utilizes these frames to detect block objects. Furthermore, in the measurement stage, it employs a clustering based measurement, called CPLF (Clustered Piecewise Linear Fitting). We built its prototype implementation on an Unmanned Ground Vehicle (UGV) equipped with a laser sensor and performed measurements to quantify its run-time performance. The implementation results show that our proposed approach yields more accurate results within a 1 mm of error.1 © 2023 Owner/Author.

关键词： Object detection

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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Enhanced Consumer Healthcare Data Protection through AI-Driven TinyML and Privacy-Preserving Techniques

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IEEE Access 2025年 13卷 97428-97440页

作者： Aanjankumar, S. Muchahari, Monoj Kumar Urooj, Shabana Kaur, Ishmeet Dhanaraj, Rajesh Kumar Mengash, Hanan Abdullah Poonkuntran, S. Kaveri, Parag Ravikant VIT Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Institute of Engineering and Management University of Engineering and Management Department of Computer Application and Science Kolkata700091 India Princess Nourah bint Abdulrahman University Department of Electrical Engineering College of Engineering P.O. Box 84428 Riyadh11671 Saudi Arabia VIT Bhopal University School of Computing Science and Artificial Intelligence Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

In the contemporary digital landscape, securing healthcare data stored on mobile devices has become imperative due to the increasing prevalence of cyberattacks. Healthcare data, often vulnerable to breaches, requires robust privacy-preserving mechanisms to ensure secure storage and sharing across various platforms. This study proposes a novel approach integrating TinyML (machine learning) with federated learning (FL) and differential privacy (DP) to enhance the security and privacy of healthcare data on resource-constrained mobile devices. The proposed TinyML model processes patient data, including ECG readings and cardiac arrhythmia diagnoses, directly on handheld devices, enabling real-time analysis with minimal resource consumption. Federated learning facilitates decentralized training of models on local devices, ensuring that sensitive data remains on user devices while model parameters are aggregated centrally. Differential privacy further strengthens security by introducing controlled noise into the data, safeguarding against malicious attacks without compromising data utility. The system demonstrates a high accuracy of 99%, significantly outperforming traditional models such as Decision Tree (76.0%), Random Forest (98.1%), and Federated Learning with Deep Q (95%). The proposed framework offers a scalable and efficient solution for real-time healthcare data processing, ensuring data privacy and security across distributed environments. Experimental results validate the model’s effectiveness in safeguarding healthcare data, with implications for broader applications in secure, privacy-preserving medical data analysis. © 2013 IEEE.

关键词： Electrocardiography

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Independent control of Spatial Wavefront of Beam Scanning Antenna with Transmissive Phase Gradient Metasurface

Independent control of Spatial Wavefront of Beam Scanning An...

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International Symposium on Antennas and Propagation (ISAP)

作者： Ravi Anand Amine Mezghani Anirban Sarkar School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

To control and shape the spatial wavefront of reconfigurable leaky-wave antenna (LWA) by integrating a single layered transmissive phase gradient metasurface (TPGMS) is demonstrated. The amplitude and phase of transmission is controlled with single layer sub-wavelength design of TPGMS, where phase modulation is achieved through the geometrical manipulation of the unitcell structure. The proposed unique design of metasurface has advantage of transmission phase tunability and stability for polarization insensitive waves at different angle of incidence. The numerical simulation of the proposed integrated model shows the full control of the scanning beam from 22° to 48° at 5GHz for sub-6GHz applications.

关键词： Wireless communication Shape Phase modulation Transmitting antennas Metasurfaces Leaky wave antennas Stability analysis Numerical models Numerical stability Periodic structures

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Faba Bean Disease Detection Using Deep Learning Techniques

Faba Bean Disease Detection Using Deep Learning Techniques

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2023 International Conference on Cyber Management and engineering, CyMaEn 2023

作者： Salau, Ayodeji Olalekan Abeje, Bekalu Tadele Faisal, Abeer Naser Asfaw, Tesfa Tegegne Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Haramaya University College of Computing and Informatics Department of Information Technology Ethiopia Institute of Technology Faculty of Computing Bahir Dar University Department of Computer Science Ethiopia University of Sumer College of Computer Science and Information Technology Al-Rifai Iraq

ISBN: (纸本)9781665493291

Faba bean (Vicia faba L.) is an important cash crop for animal and human consumption in many countries, especially Ethiopia due to its high protein content and high rate of production. It also improves soil fertility with the help of bacteria, which forms nodules on its roots. Ethiopia is the second largest faba bean producer;however, according to research, the yield is generally low. Among the constraints contributing to low productivity of the crop, disease is the most important. Even though there are countermeasures such as the use of chemical sprays, which is used to reduce the damage or eliminate the disease, an effective disease identification technique is still required. Chocolate spot, faba bean gal, and rust are the most common diseases of faba beans. To effectively detect the disease, in this paper, we propose an end-to-end CNN model for training before and after the dataset is preprocessed. The CNN end-to-end model is trained with raw images and achieves 92.1% training accuracy. Also, the model is trained with preprocessed data and it achieves a training accuracy of 98.14% for the end-to-end CNN classifier. The results show that the proposed approach is suitable for faba bean disease detection and can be employed to detect diseases of other crops. © 2023 IEEE.

关键词： Convolutional neural networks

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