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2022 International Conference on Power, Energy and Innovations, ICPEI 2022

作者： Dorji, Tashi Suwanasri, Thanapong Poonnoy, Nitchamon Suwanasri, Cattareeya EPE Program The Sirindhorn International Thai-German Graduate School of Engineering KMUTNB 1518 Pracharat 1 Rd. Bangsue Bangkok Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education KMUTNB 1518 Pracharat 1 Rd. Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering KMUTNB 1518 Pracharat 1 Rd.Bangsue Bangkok Thailand

ISBN: (数字)9781665460194

ISBN: (纸本)9781665460194

This paper presents a realistic power transformer condition assessment and Health Index (HI) determination technique. The real-time operating data of twenty-seven power transformers of Bhutan Power Corporation Limited in transmission substations are used for condition evaluation. The diagnostic test data are combined into nine diagnostic groups for condition assessment. The conventional method of scoring and weighting average and the fuzzy logic methods are adopted for the condition assessment of power transformers. The excel program was developed for the conventional method for seamless evaluation. All the parameters harmonies the estimation and classification of the transformer operating condition. Then, this paper proposed new procedures for validating the conventional system of HI determination by the fuzzy logic method. © 2022 IEEE.

关键词： Power transformers

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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IEEE International Conference on Big Data

作者： Zheyi Qin Randy Paffenroth Anura P. Jayasumana Electrical and Computer Engineering Colorado State University Fort Collins USA Department of Data Science Program Worcester Polytechnic Institute Worcester MA USA

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

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Partial Discharge Measurement of Multi-void Inside Solid Insulation under AC and VLF Voltage Sources

Partial Discharge Measurement of Multi-void Inside Solid Ins...

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2022 International Conference on Power, Energy and Innovations, ICPEI 2022

作者： Sangpaijit, Pisit Suwanasri, Thanapong Panmala, Nattapon Suwanasri, Cattareeya The Sirindhorn International Thai-German Graduate School of Engineering EPE Program KMUTNB 1518 Pracharat 1 Rd.Bangsue Bangkok Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education KMUTNB 1518 Pracharat 1 Rd.Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering KMUTNB 1518 Pracharat 1 Rd.Bangsue Bangkok Thailand

ISBN: (数字)9781665460194

ISBN: (纸本)9781665460194

The problems of high voltage equipment usually come from the imperfection of solid insulation, which may be caused by poor workmanship during cable installation, leading to multi-void inside solid insulation and subsequently partial discharge when that cable is put in service. Therefore, this paper aims to investigate the discharge phenomena of multi-void inside solid insulation by creating artificial defects in epoxy resin and testing under AC and very low frequency voltage sources. The test object consists of a single void with different void diameters and multi-void created by injecting the air voids at atmospheric pressure into epoxy resin. The partial discharge inside a single void and multi-void is detected by using a conventional and unconventional sensor. The results of PD measurement are represented in the phase-resolved partial discharge (PRPD) and several partial discharge parameters, such as PD charge magnitude and PD repetition rate. In addition, the results are analyzed to observe the impact of the multi-void with different conditions such as variable void diameter, the thickness of epoxy resin, type of voltage source, and PD sensor. The test can specify trends toward breakdown and the severity of multi-void inside solid insulation. © 2022 IEEE.

关键词： Partial discharges

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Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks

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computer Networks 2024年 253卷

作者： Tu, Yi-Hao Lin, En-Cheng Ke, Chih-Heng Ma, Yi-Wei Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Master Program of Information Technology and Applications National Quemoy University Kinmen892009 Taiwan Department of Computer Science of Information Engineering National Quemoy University Kinmen892009 Taiwan

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions. © 2024

关键词： Wireless local area networks (WLAN)

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Review on Determinant Parameters of Genetic Algorithm for the Optimized Closed Loop Control

Review on Determinant Parameters of Genetic Algorithm for th...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Ing Ming Chew W. K. Wong Filbert H. Juwono Teck Chai Tiong Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Computer Science Program University of Southampton Malaysia Nusajaya Malaysia Department of Electrical and Computer Engineering Curtin University Malaysia Miri Sarawak Malaysia

ISBN: (纸本)9781665486644

This paper aims to analyze the determinant parameters of Genetic Algorithm (GA) analysis for the optimized control performance of a closed control loop. The determinant parameters of GA optimization analysis cover population size (nPop), mutation rate (mu) and iteration (iter.) are analyzed and justified. The control terminology covers the Proportional-Integral-Derivative (PID) controller, a prestigious solution for industrial control applications. Besides, the research proposed stability analysis to determine the upper and lower limit settings for the optimization analysis. The research has begun with model identification, stability analysis and is followed by determining the controller tunings. The performance indexes are applied to compare the response performance of GA with deterministic controller tunings. Analysis results and discussion shows that GA with proper determinant parameters’ settings are performing better than other tuning methods in the closed loop control performance.

关键词： Analytical models Terminology Sociology Process control Stability analysis Performance analysis Statistics

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Periodic phase diagrams in micromagnetics with an eigenvalue solver

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IEEE Transactions on Magnetics 2025年

作者： Ai, Fangzhou Lin, Zhuonan Duan, Jiawei Lomakin, Vitaliy University of California Department of Electrical and Computer Engineering San DiegoCA92093 United States University of California Center for Memory and Recording Research San DiegoCA92093 United States University of California Program in Materials Science and Engineering San Diego92093 United States

This work introduces an approach to compute periodic phase diagram of micromagnetic systems by solving a periodic linearized Landau-Lifshitz-Gilbert (LLG) equation using an eigenvalue solver with the Finite Element Method formalism. The linear operator in the eigenvalue problem is defined as a function of the periodic phase-shift wave vector. The dispersion diagrams are obtained by solving the eigenvalue problem for complex eigenfrequencies and corresponding eigenstates for a range of prescribed wave vectors. The presented approach incorporates a calculation of the periodic effective field, including the exchange and magnetostatic field components. The approach is general in that it allows 3D problems to be handled with any 1D, 2D, and 3D periodicities. The ability to calculate periodic diagrams provides insights into the spin wave propagation and localized resonances in complex micromagnetic structures. © 1965-2012 IEEE.

关键词： Spin waves

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Non-Hermitian metasurface for asymmetric thermal emission

Non-Hermitian metasurface for asymmetric thermal emission

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Prasad, Ciril S. Everitt, Henry O. Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States U.S. Army DEVCOM Army Research Laboratory-South HoustonTX United States

ISBN: (纸本)9781957171258

Non-Hermitian optics provides a unique platform to take advantage of absorption losses in materials and control radiative properties. We demonstrate a non-Hermitian metasurface that exhibit directional suppression of thermal radiation while maintaining transmission in mid-infrared. © Optica Publishing Group 2023 © 2023 The Author(s)

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Improving Visual Prompt Tuning for Self-supervised Vision Transformers

arXiv

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arXiv 2023年

作者： Yoo, Seungryong Kim, Eunji Jung, Dahuin Lee, Jungbeom Yoon, Sungroh Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks. It leverages extra learnable tokens, known as prompts, which steer the frozen pretrained ViTs. Although VPT has demonstrated its applicability with supervised vision transformers, it often underperforms with self-supervised ones. Through empirical observations, we deduce that the effectiveness of VPT hinges largely on the ViT blocks with which the prompt tokens interact. Specifically, VPT shows improved performance on image classification tasks for MAE and MoCo v3 when the prompt tokens are inserted into later blocks rather than the first block. These observations suggest that there exists an optimal location of blocks for the insertion of prompt tokens. Unfortunately, identifying the optimal blocks for prompts within each self-supervised ViT for diverse future scenarios is a costly process. To mitigate this problem, we propose a simple yet effective method that learns a gate for each ViT block to adjust its intervention into the prompt tokens. With our method, prompt tokens are selectively influenced by blocks that require steering for task adaptation. Our method outperforms VPT variants in FGVC and VTAB image classification and ADE20K semantic segmentation. The code is available at https://***/ryongithub/GatedPromptTuning. © 2023, CC BY-NC-SA.

关键词： Image classification

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Low Power Consumption of Non-Volatile Memory Device by Tunneling Process engineering

SSRN

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SSRN 2024年

作者： Wang, Fucheng Chu, Mengmeng Chen, Jingwen Pan, Zhong Kim, Yongsang Song, Jang Kun Yi, Junsin Khokhar, Muhammad Quddamah Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon16419 Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Compared with Si3N4 and Al2O3, SiO2 grown using thermal oxidation process as tunneling layer has the advantages of high bandgap and well interface contact with the surface of silicon wafer, which can be a great solution to the leakage current problem of metal-insulator-semiconductor (MIS) devices. This study investigates the effect of improving the SiO2 tunnel layer on the operating voltage of MIS devices with a SiO2/HfAlOx/Al2O3 structure. The result shows the operating voltage changes as the tunneling layer thickness decreases, with a minimum of only 12 V for a 1.5 nm tunneling layer thickness. In addition, we found that pinholes are generated on the film surface when annealing a 1.5 nm SiO2 tunnel layer at 850 °C N2, in which case the operating voltage of the device is reduced to only 10 V, though it was also accompanied by the deterioration of the retention characteristics. © 2024, The Authors. All rights reserved.

关键词： Silicon wafers

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Automated classification of renal tumors on ultrasound images using deep neural network

Automated classification of renal tumors on ultrasound image...

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Wei-Chieh Wu Te-Mei Chiang Zhi-Ren Tsai Program in Artificial Intelligence College of Information and Electrical Engineering Asia University Taichung City Taiwan Department of Radiation Oncology Asia University Hospital Taichung City Taiwan Department of Computer Science & Information Engineering Asia University

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Kidney cancer is one of the most common cancers worldwide. The major types of malignant renal tumors are renal cell carcinoma and urothelial carcinoma. Although the majority of renal tumors are malignant, up to $20\%$ are benign, most commonly renal cyst and angiomyolipomas. Ultrasound is the most accessible imaging tool in medical practice, but it highly depends on operator skill, which may lead to high false-negative rate in diagnosis. The purpose of this study was to develop a predictive model for the automated classification of renal tumors on ultrasound images using deep neural network. A total of 880 kidney ultrasound images were used for training and testing. Transfer learning was used to the ten Convolution neural network models. The kidney ultrasound images were classified as benign or malignant tumors. The classification performance of the model was evaluated by sensitivity and specificity. The research results show that VGG18 yielded the best performance, with a sensitivity of $79 \%$, and a specificity of $86 \%$.

关键词： Training Ultrasonic imaging Sensitivity Convolution Transfer learning Artificial neural networks Sensitivity and specificity Kidney Cancer Testing

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