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Effects of Incident Angle on Single-Event Leakage Current Degradation in Heavy-Ion Irradiated 1800 v and 4500 v SiC Power MOSFETs

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IEEE Transactions on Nuclear Science 2025年

作者： Sengupta, A. Martinella, C. Ball, D.R. Niskanen, K. Senarath, A.S. Islam, S. Harris, P.M. Hutson, J.M. Osheroff, J.M. Sternberg, A.L. Kettunen, H. Kosier, S.L. Grossner, U. Javanainen, A. Galloway, K.F. Schrimpf, R.D. Witulski, A.F. Zürich8092 Switzerland Vanderbilt University Interdisciplinary Materials Science Program NashvilleTN37235 United States Lipscomb University Electrical and Computer Engineering Department NashvilleTN37204 United States NASA/GSFC Code 561.4 GreenbeltMD20771 United States Vanderbilt University Electrical Engineering and Computer Science Department B 351825 NashvilleTN37235 United States University of Jyväskylä Department of Physics Jyväskylä40014 Finland

Experimental heavy-ion responses of two variants of SiC power MOSFETs are evaluated. The devices have similar epitaxial thickness but different doping. The higher doping in the epitaxial region results in lower breakdown voltage, and typically leads to a lower threshold drain voltage at which single-event leakage current (SELC) occurs. Furthermore, the heavy-ion-induced SELC degradation in the drain and gate currents of the SiC MOSFETs exhibit a strong dependence on the heavy-ion's angle of incidence. The devices' behavior significantly alters when irradiated with heavy-ions at different incident angles under the same fixed bias and total fluence, as also confirmed by 3-D Technology computer Aided Design (TCAD) simulations. © 1963-2012 IEEE.

关键词： Power MOSFET

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A Transparent Ultrasound Array for Real-Time Optical,Ultrasound,and Photoacoustic Imaging

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Biomedical engineering Frontiers 2022年第1期3卷 284-297页

作者： Haoyang Chen Sumit Agrawal Mohamed Osman Josiah Minotto Shubham Mirg Jinyun Liu Ajay Dangi Quyen Tran Thomas Jackson Sri-Rajasekhar Kothapalli Department of Biomedical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA School of Electrical Engineering and Computer Science The Pennsylvania State UniversityUniversity ParkPA 16802USA Penn State Cancer Institute The Pennsylvania State UniversityHersheyPA 17033USA Graduate Program in Acoustics The Pennsylvania State UniversityUniversity ParkPA 16802USA

Objective and Impact *** imaging of ultrasound and optical contrasts can help map structural,functional,and molecular biomarkers inside living subjects with high spatial *** is a need to develop a platform to facilitate this multimodal imaging capability to improve diagnostic sensitivity and ***,combining ultrasound,photoacoustic,and optical imaging modalities is challenging because conventional ultrasound transducer arrays are optically *** a result,complex geometries are used to coalign both optical and ultrasound waves in the same field of *** elegant solution is to make the ultrasound transducer transparent to ***,we demonstrate a novel transparent ultrasound transducer(TUT)linear array fabricated using a transparent lithium niobate piezoelectric material for real-time multimodal *** TUT-array consists of 64 elements and centered at~6 MHz *** demonstrate a quad-mode ultrasound,Doppler ultrasound,photoacoustic,and fluorescence imaging in real-time using the TUT-array directly coupled to the tissue mimicking *** TUT-array successfully showed a multimodal imaging capability and has potential applications in diagnosing cancer,neurological,and vascular diseases,including image-guided endoscopy and wearable imaging.

关键词： Ultrasound specificity transparent

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia computer Science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Role of Consumption Pattern in Optimal Allocation of Distributed Generators in Electric Power and Energy Systems

Role of Consumption Pattern in Optimal Allocation of Distrib...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Meisam Mahdavi Francisco Jurado Konrad Schmitt Manohar Chamana Stephen Bayne Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical & Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock TX USA

The placement of distributed generation (DG) units in power systems is an efficient way for energy loss reduction, especially when the penetration of DG in modern systems is growing due to their impacts on environmental sustainability. On the other hand, load variations and methods of electricity consumption affect energy losses amount. Therefore, power demand variations have an essential role in the determination of energy loss amount and optimal generation of DG. However, considering the variability of load level in the DG allocation problem increases the burden and computational time, and neglecting it causes the energy losses to be calculated inaccurately. Therefore, this paper aims to evaluate the effect of load patterns on renewable DG allocation plans in order to find out the importance of considering load variations in energy loss minimization via DG placement. The analysis has been conducted on 7-, 12-, 16-, 28-, 30-, 33-, 59-, 69-, 70-, 84-, and 119-bus distribution systems by a classic optimization tool named AMPL.

关键词： Energy loss Renewable energy sources Power demand Power distribution Companies Load management Minimization

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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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Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection

arXiv

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arXiv 2024年

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT © 2024, CC BY.

关键词： Data assimilation

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Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models

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Scientific Reports 2025年第1期15卷 1-16页

作者： Ghoneim, Sherif S. M. Baz, Mohammed Alzaed, Ali Zewdie, Yohannes Tesfaye Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Technology Debre Markos University P. BOX 269 Debre Markos Ethiopia

Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955. © The Author(s) 2025.

关键词： Dissolved gas analysis Furnic compounds Insulating oil Insulating paper Power transformer XGBoost/LightGBM

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Reconfiguration of Power Distribution Systems in the Presence of Voltage-Dependent Loads

Reconfiguration of Power Distribution Systems in the Presenc...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Meisam Mahdavi Konrad Schmitt Stephen Bayne Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical and Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock TX USA

Load power can be changed by voltage fluctuation of network buses, in which reconfiguring the topology of distribution systems impacts the magnitude of bus voltages. Only a few papers have considered voltage-dependent loads in their reconfiguration models but introduced nonlinear formulations or highly approximated linear approaches. While nonlinear models can be solved by metaheuristic algorithms without guaranteeing the optimality of solutions or may be implemented by commercial nonlinear solvers, they require intensive and time-consuming computations. Moreover, linearized models are highly approximated by piecewise linear functions with many unknown parameters. Thus, this paper presents the effective design of the reconfiguration problems, including voltage dependency of loads, which can be solved using commercial linear solvers. The proposed models are precise enough to find accurate results for the reconfiguration problem and are adequately fast to converge to optimal solutions. The results show that the proposed solutions not only reduce active power losses significantly but increase the minimum voltage of the system effectively.

关键词： Voltage fluctuations Fluctuations Network topology Computational modeling Metaheuristics Power distribution Distribution networks

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Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization

arXiv

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arXiv 2024年

作者： Song, Sanghyeob Lew, Jaihyun Jang, Hyemi Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Samsung Electro-Mechanics Korea Republic of

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Most early methods, though, assume that the given image pairs are from the same camera or have minor lighting differences. Consequently, while these methods perform effectively under such conditions, they generally fail when input image pairs come from different domains, referred to as multimodal image pairs. To address these limitations, we propose AltO, an unsupervised learning framework for estimating homography in multimodal image pairs. Our method employs a two-phase alternating optimization framework, similar to Expectation-Maximization (EM), where one phase reduces the geometry gap and the other addresses the modality gap. To handle these gaps, we use Barlow Twins loss for the modality gap and propose an extended version, Geometry Barlow Twins, for the geometry gap. As a result, we demonstrate that our method, AltO, can be trained on multimodal datasets without any ground-truth data. It not only outperforms other unsupervised methods but is also compatible with various architectures of homography estimators. The source code can be found at: https://***/songsang7/AltO Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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An Efficient Model for Optimal Allocation of Renewable Energy Sources in Distribution Networks with Variable Loads

An Efficient Model for Optimal Allocation of Renewable Energ...

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IEEE Texas Power and Energy Conference (TPEC)

Operational costs are affected by energy losses of distribution systems, in which these losses are higher than those of the transmission system. The placement of distributed generation (DG) units in power distribution networks is an efficient way of energy loss reduction. Today, DG penetration in distribution systems is increasing because of the great effect of renewable energy resources on environmental sustainability. Power demand variations have an essential role in the determination of energy losses amount and optimal generation of DGs. Besides, considering the variability of load level in the DG allocation problem raises the burden and computational time substantially. Therefore, this paper proposes an effective DG allocation strategy for energy loss minimization in the presence of variable power demand. The evaluation of numerical results indicates the desired performance of the presented framework for the optimal placement of DG units in distribution systems with time-varying loads.

关键词： Energy loss Renewable energy sources Power demand Costs Propagation losses Power systems Numerical models

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