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Analysis of the mechanism of degradation of p-type silicon solar cell

Transactions of the Korean Institute of Electrical Engineers 2021年第8期70卷 1181-1187页

作者： Cha, Yewon Jeong, Sungjin Kim, Sungheon Kim, Youngkuk Yi, Junsin Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

Two main factors that limiting the efficiency of the p-type solar cells are LID(Light Induced Degradation) and LeTID(Light and elevated temperature induced degradation). The major cause of LID is the formation of boron-oxygen defects in boron doped silicon. The LeTID is similar kinds as LID, but also occurs in the dark condition and by the recombination of metals and impurities or the reaction between the hydrogen and arbitrary substance. An overall 7% of LeTID degradation rate occurs over long-term scales. Unexpectedly, these degradations have been recently found to also arises in high purity materials such as float-zone(Fz-Si) and Czochralski-grown(Cz-Si). The proposed method to reduce these two degradations is to use appropriate firing process and annealing conditions. © 2021 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Degradation

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Multi-class Segmentation of Anatomical Structures Using Deep Learning in CBCT Images Containing Metal Artifacts

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Transactions of the Korean Institute of Electrical Engineers 2022年第1期71卷 253-260页

作者： Yang, Su Chun, Soyoung Kim, Dael Jeoun, Bo Soung Yoo, Jiyong Kang, Se-Ryong Choi, Min-Hyuk Kim, Jo-Eun Huh, Kyung-Hoe Lee, Sam-Sun Heo, Min-Suk Yi, Won-Jin Dept. of Applied Bioengineering Graduate School of Convergence Science and Technology Seoul National University Seoul Korea Republic of Interdisciplinary Program of Bioengineering Seoul National University Seoul Korea Republic of Dept. of Biomedical Radiation Sciences Graduate School of Convergence Science and Technology Seoul National University Seoul Korea Republic of Dept. of Oral and Maxillofacial Radiology Seoul National University Dental Hospital Seoul Korea Republic of Dept. of Oral and Maxillofacial Radiology School of Dentistry and Dental Research Institute Seoul National University Seoul Korea Republic of

In order to perform preoperative surgical planning, accurate segmentation of anatomical structures in cone-beam computed tomography (CBCT) images is required. However, this image segmentation is often impeded by metal artifacts, and it takes a lot of time due to morphological variability in patients. In this paper, we proposed a deep learning based automatic multi-calss segmentation method for anatomical structures in CBCT images containing metal artifacts. Four U-Net based deep learning models were used for anatomical structure segmentation. Each deep learning model was constructed by changing the encoder of U-Net architecture to the backbones (DenseNet121, VGGNet16, ResNet101, and EfficienNetB4). For training and testing our method, we used 20744 CBCT images containing metal artifacts from 30 patient datasets. Experimental results show that the segmentation performances of the mandible, midfacial bone, mandibular canal, and maxillary sinus were achieved F1 scores of , , , and using DenseNet121 with Tversky loss, respectively. Furthermore, our method was able to perform robust and accurate segmentation of anatomical structures in CBCT images containing metal artifacts. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Image segmentation

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A Study on Data Augmentation Methods Optimized for Gastric Cancer Classification in Gastroscopy Images

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Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 2015-2021页

作者： Lee, Jeong-Nam Cho, Hyun Chin Cho, Hyun-Chong Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Internal Medicine Institute of Health Sciences Gyeongsang National University School of Medicine Gyeongsang National University Hospital Korea Republic of

Gastric cancer is the most common cancer in Korea and an effective way to treat gastric cancer is early treatment. Gastroscopy is being performed for early detection of gastric cancer, and this paper proposes Computer-aided Diagnostics(CADx) system that can help gastroscopy. As a model for classifying gastric cancer, we use Xception, which reduces computation and improves performance. Due to the nature of medical images, data augmentation was used to solve the lack of data and overfitting could occur. The data augmentation methods used were AutoAugment and Variational AutoEncoder(VAE). AutoAugment is a data augmentation method using color changes, shear, rotation, etc., and VAE learns to standardize the probability distribution of the data, helping to generate data similar to the original data to capture features. The data augmentation method with the best performance is the augmentation method through VAE, and the recall showed 94.84% of the performance. The data augmentation method optimized for the gastroscopy data set is the augmentation method through VAE. It can help the endoscopy specialist diagnose and increase the gastric cancer complete cure rate. © The Korean Institute of Electrical Engineers

关键词： Probability distributions

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The Impact of Greening Strategies on Temperature Reduction at Bus Stop Utilizing Computational Fluid Dynamics (Cfd) Analysis

SSRN

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SSRN 2024年

作者： Kim, Na Youn Kim, Eun Sub Yun, Seokhwan Lee, Dong Kun Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of

This study analyzed the effectiveness of a vegetation strategy for mitigating heat within a bus stop shelter by implementing vegetation wind permeability and transpiration cooling processes using a computational fluid dynamics (CFD) simulation. In scenario 1, a pot-type vertical greening system (VGS) without wind permeability had no significant effect on temperature reduction in the bus stop shelter. In contrast, scenario 2, with a mesh VGS allowing wind permeability, resulted in an average temperature reduction of 0.16°C within the interior area of the bus stop shelter, accompanied by shelter improved ventilation. Scenario 3, which entailed the removal of the shelter and the planting of trees, resulted in the least reduction in wind speed and an average temperature reduction of 0.14°C in the bus waiting area. Scenario 2 demonstrated the most effective temperature reduction for interior and surrounding area of the bus stop. Considering the normal effective temperature (NET), Scenario 3 provided the lowest sensation temperature. This indicates that the improved ventilation in Scenario 3 had a substantial impact on NET. Considering the effects of the vegetation strategies proposed in this study, it is essential to select suitable strategies for heat mitigation in bus-stop shelters based on regional and spatial considerations. © 2024, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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A Precision Public Health Study on the Divergence of Life Expectancies Over Time in United States Counties

A Precision Public Health Study on the Divergence of Life Ex...

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Conference on Technologies for Sustainability (Sustech)

作者： Suraj Sheth Luís Bettencourt Pritzker School of Medicine and Interdisciplinary Scientist Training Program Postdoctoral Fellow Biomedical Informatics Precision Medicine and Global Health Mansueto Institute for Urban Innovation University of Chicago Chicago IL USA Mansueto Institute for Urban Innovation Professor Ecology and Evolution Associate Faculty Dept. of Sociology University of Chicago Chicago IL USA External Complex Systems Santa Fe Institute

Ensuring healthy lifespans and promoting equity in health outcomes is a critical component of the United Nations Sustainable Development Goals, and progress has largely been measured using national life expectancy averages. While national averages reveal broad trends over time, they also largely obscure differences and inequalities at the subnational level. In studies of sustainability and public health, divergence refers to the increasing difference between two values over time. A major topic of interest is how subnational units, such as counties, deviate from the national average over time. In this work, we examined how three factors critical to evaluating the health of communities and quality of life, namely life expectancy, education, and income, have deviated in the United States at the county level in comparison to the national average. We found that while deviations in income and education have remained largely constant, the life expectancies of some counties have increased significantly over the national average, while others have stalled or even decreased in comparison. There has been a systematic divergence in life expectancies in US counties over a 34-year period leading to increasing disparities between counties. Gains in life expectancy were primarily concentrated in metropolitan areas while rural counties lagged, increasing urban-rural disparities over time. Identifying the upward or downward trends in life expectancy at the county level and understanding the root causes and risk factors driving those trends is critical to formulating customized Precision Public Health interventions and policies to improve health outcomes and create sustainable communities.

关键词： Systematics Education Urban areas Market research Sustainable development Public healthcare

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time

arXiv

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arXiv 2021年

作者： Zhu, Feng Sedler, Andrew R. Grier, Harrison A. Ahad, Nauman Davenport, Mark A. Kaufman, Matthew T. Giovannucci, Andrea Pandarinath, Chethan Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill NC State University UNC Chapel Hill Neuroscience Center Coulter Dept. of Biomedical Engineering Emory University Georgia Tech Dept. of Neurosurgery Emory University

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data. © 2021, CC BY-NC-SA.

关键词： Neurons

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Federated Reinforcement Learning for Controlling Multiple Rotary Inverted Pendulums in Edge Computing Environments 2

Federated Reinforcement Learning for Controlling Multiple Ro...

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2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

作者： Lim, Hyun-Kyo Kim, Ju-Bong Kim, Chan-Myung Hwang, Gyu-Young Choi, Ho-Bin Han, Youn-Hee Korea University of Technology and Education Dept. of Interdisciplinary Program in Creative Engineering Cheon-an Korea Republic of Advanced Technology Research Center Korea University of Technology and Education Cheon-an Korea Republic of Korea University of Technology and Education Dept. of Computer Science Engineering Cheon-an Korea Republic of

ISBN: (纸本)9781728149851

Reinforcement learning has recently been studied in various fields and also used to optimally control real devices (e.g., robotic arms). In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own devices of the same type but with slightly different dynamics. For such multiple devices, there is no guarantee that an agent who interacts only with one device and learns the optimal control policy will also control another device well. Therefore, we may need to apply independent reinforcement learning to each device individually, which requires time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent device shares their learning experience with each other, and transfers a mature policy model parameters into other agents. We incorporate the Actor-Critic PPO algorithm into each agent in the proposed collaborative architecture, and propose an efficient procedure for the gradient sharing and the model transfer. We also use edge computing to solve network problems that occur when training multiple real devices at the same time. Using multiple rotary inverted pendulum devices, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple devices, and that the learning speed can be faster if more agents are involved. © 2020 IEEE.

关键词： Reinforcement learning

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Ultrafast evanescent heat transfer across solid interfaces via hyperbolic phonon-polaritons in hexagonal boron nitride

arXiv

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arXiv 2024年

作者： Hutchins, William Tomko, John A. Hirt, Dan M. Zare, Saman Matson, Joseph R. Diaz-Granados, Katja He, Mingze Pfeifer, Thomas Li, Jiahan Edgar, James Maria, Jon-Paul Caldwell, Joshua D. Hopkins, Patrick E. Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Tim Taylor Dept. of Chemical Engineering Kansas State University ManhattanKS United States Department of Materials Science and Engineering Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States

The efficiency of phonon-mediated heat transport is limited by the intrinsic atomistic properties of materials, seemingly providing an upper limit to heat transfer in materials and across their interfaces. The typical speeds of conductive transport, which are inherently limited by the chemical bonds and atomic masses, dictate how quickly heat will move in solids. Given that phonon-polaritons (PhPs), or coupled phonon-photon modes, can propagate at speeds approaching 1% of the speed of light – orders of magnitude faster than transport within a pure diffusive phonon conductor – we demonstrate that volume-confined, hyperbolic phonon-polariton (HPhP) modes supported by many biaxial polar crystals can couple energy across solid-solid interfaces at an order of magnitude higher rates than phonon-phonon conduction alone. Using pump-probe thermoreflectance with a mid-infrared, tunable, probe pulse with sub-picosecond resolution, we demonstrate remote and spectrally selective excitation of the HPhP modes in hexagonal boron nitride (hBN) in response to radiative heating from a thermally emitting gold source. Our work demonstrates a new avenue for interfacial heat transfer based on broadband radiative coupling from a hot spot in a gold film to hBN HPhPs, independent of the broad spectral mismatch between the pump (visible) and probe (mid-IR) pulses employed. This methodology can be used to bypass the intrinsically limiting phonon-phonon conductive pathway, thus providing an alternative means of heat transfer across interfaces. Further, our time-resolved measurements of the temperature changes of the HPhP modes in hBN show that through polaritonic coupling, a material can transfer heat across and away from an interface at rates orders of magnitude faster than diffusive phonon speeds intrinsic to the material, thus demonstrating a pronounced thermal transport enhancement in hBN via phonon-polariton coupling. © 2024, CC BY.

关键词： III-V semiconductors

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First Demonstration of Defect Elimination for Cryogenic Ge FinFET CMOS Inverter Showing Steep Subthreshold Slope by Using Ge-on-Insulator Structure

First Demonstration of Defect Elimination for Cryogenic Ge F...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Yu, X.-R. Hsieh, C.-C. Chuang, M.-H. Chiu, M.-Y. Sun, T.-C. Geng, W.-Z. Chang, W.-H. Shih, Y.-J. Lu, W.-H. Chang, W.-C. Lin, Y.-C. Pai, Y.-C. Lai, C.-Y. Chuang, M.-H. Dei, Y. Yang, C.-Y. Lu, H.-Y. Lin, N.-C. Wu, C.-T. Kao, K.-H. Ma, W.C.-Y. Lu, D.D. Lee, Y.-J. Luo, G.-L. Chiang, M.-H. Maeda, T. Wu, W.-F. Li, Y.-M. Hou, T.-H. National Cheng Kung University Dept of Electrical Engineering Tainan Taiwan National Cheng Kung University Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan Taiwan National Yang Ming Chiao Tung University Inst. of Communications Engineering Hsinchu Taiwan National. Sun Yat-Sen University Dept. of Electrical Engineering Kaohsiung Taiwan National Institute of Advanced Industrial Science and Technology Ibaraki Japan National Yang Ming Chiao Tung University Inst. of Artificial Intelligence Innovation Hsinchu Taiwan Taiwan Semiconductor Research Institute Taiwan Intelligent Computing Industrial Program Ming Wu School of Computing National Cheng Kung University Taiwan Inst. of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Inst. of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798350327670

This work presents experimental electrical characteristics and circuit prediction at cryogenic temperatures (down to 10 K) for three different kinds of germanium (Ge)-based FETs with advanced Fin/GAA structures. Among them, the layer transferred Ge-on-Insulator (GeOI) FinFET significantly improves its I-V characteristic during cryogenic measurements, such as a steeper subthreshold swing at 10K and a better Ion. The developed GeOI fabrication method provides an effective way to eliminate the defects originating from misfit dislocations at the Ge/Si substrate during epitaxial growth, which would be treated as the key to device performance enhancement under 10 K. According to the measured IV at 10 K and circuit prediction, GeOI FinFETs with high Ge crystallinity are strong candidates for High-Performance-Computing (HPC) applications. © 2023 IEEE.

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time 21

Deep inference of latent dynamics with spatio-temporal super...

引用

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci Chethan Pandarinath Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago and Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill and NC State University and UNC Chapel Hill Neuroscience Center and NC State/UNC Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech and Dept. of Neurosurgery Emory University

ISBN: (纸本)9781713845393

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