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IEEE Symposium on Intelligent Vehicle

作者： Joonwoo Ahn Minsoo Kim Jaeheung Park Dynamic Robotics Systems (DYROS) Lab. Graduate School of Convergence Science and Technology Seoul National University Seoul Republic of Korea ASRI RICS Seoul National University Seoul South Korea Advanced Institutes of Convergence Technology (AICT Suwon South Korea

The target-tree * algorithm, which is a variant of the optimal rapidly-exploring random tree (RRT * ) has been proposed to reduce the parking path planning time. This algorithm pre-generates a set of backward paths (target-tree) around a parking spot and extends an RRT * from the initial pose until it is connected to a random sample of the target-tree. However, it is difficult to obtain the shortest (optimal) parking path within a short planning time because connected samples between the tree and the target-tree are randomly searched. To deal with this problem, this paper proposes a biased target-tree * algorithm with RRT * that searches connected random samples in a biased range near the target-tree. This range has a Gaussian distribution centered on the optimal connected sample where the shortest parking path can be obtained quickly and is obtained through supervised learning. In actual parking situations, the biased target-tree * algorithm obtained a shorter path with less length deviation than the original target-tree * algorithm within a shorter planning time.

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Effective Electricity Demand Prediction via Deep Learning

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IEIE Transactions on Smart Processing and Computing 2021年第6期10卷 483-489页

作者： Ko, Daegun Yoon, Youngmin Kim, Jinoh Choi, Haelyong Smart-City Service Group Hyundai-Autoever 510 Teheran-ro Gangnam-gu Korea Republic of Smart-Factory of Next-Generation Service Group Hyundai-Autoever 510 Teheran-ro Gangnam-gu Korea Republic of Advanced Systems Convergence Lab Department of Systems Engineering Graduate School Ajou University 206 World cup-ro Yeongtong-gu Gyeonggi-do Suwon-si Korea Republic of CEO of Core DIT Co. Ltd 21 Bangbaecheon-ro 2-gil Seocho-gu Seoul Korea Republic of

Prediction of electricity demand in homes and buildings can be used to optimize an energy management system by decreasing energy wastage. A time-series prediction system is still a challenging problem in machine learning and deep learning. Our main idea is to compare three methods. For this work, we analyzed an electricity demand prediction system using the current state-of-the-art deep-learning methods with a machine-learning method: error correction with multi-layer perceptron (eMLP) structure, autoregressive integrated moving average (ARIMA) structure, and a proposed structure named CNN-LSTM. For this, we measured and collected electricity demand data in Germany for home appliances. We report the prediction accuracy in terms of the mean square error (MSE) and mean absolute percentage error (MAPE). The experimental result indicates that CNN-LSTM outperforms eMLP and ARIMA in accuracy. Copyrights © 2021 The Institute of Electronics and Information Engineers

关键词： Error correction

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p-CuGaO2/β-Ga2O3 interfaces: A high-throughput approach for interface prediction and generation for power device applications

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Materials Today Advances 2025年 26卷

作者： Prasad, Chowdam Venkata Park, Jang Hyeok Hoai Ta, Qui Thanh Kim, Kyong Jae Jeon, Ho Jung Mir, Muzaffar Ali Raza, Mohsin Tri, Nguyen Ngoc Kim, Honggyun labed, Madani Rim, You Seung Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of Institute of Semiconductor and System IC Sejong University Seoul05006 Korea Republic of Department of Semiconductor Systems Engineering Sejong University Seoul05006 Korea Republic of Institute of Advanced Technology Vietnam Academy of Science and Technology 1A TL29 Street Thanh Loc Ward District 12 Ho Chi Minh City700000 Viet Nam Lab of Computational Chemistry and Modelling Faculty of Natural Sciences Quy Nhon University Quy Nhon City55100 Viet Nam

This study integrates a p-type copper gallium oxide (p-CuGaO2) interlayer to enhance the performance of β-Ga2O3-based power devices, addressing challenges in achieving reliable p-type doping. The p-CuGaO2 interlayer is expected to improve breakdown voltage (BV) and reduce leakage current, increasing device efficiency and reliability under high-temperature conditions. The heterojunction (HJ) system of β-Ga2O3 and p-CuGaO2 is investigated using density functional theory (DFT) via the Vienna Ab initio Simulation Package (VASP) and experimental methods, focusing on the β-Ga2O3 (001)/p-CuGaO2 (006) interface. Binding energy, electron localization, charge density difference, and electrostatic potential drop are analyzed, identifying Configuration-1 as the optimal configuration due to its high binding energy and favorable structural properties. Annealing temperature impacts the microstructure, stoichiometry, optical, electrical, and transport properties of p-CuGaO2/β-Ga2O3 HJs. Electrical measurements reveal reduced turn-on voltage (Von), on-resistance (Ron), and leakage currents, with the highest BV of 1.62 kV achieved at 800 °C compared to Pt/β-Ga2O3 Schottky barrier diode (SBD). The interface state density (NSS) is evaluated using the high-low frequency approach. Silvaco TCAD simulations explore breakdown mechanisms, demonstrating the potential of p-CuGaO2 for β-Ga2O3 HJ power devices. These findings support the development and manufacture of robust β-Ga2O3 HJ power devices. © 2025 The Authors

关键词： Heterojunctions

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Author Correction: Random laser ablated tags for anticounterfeiting purposes and towards physically unclonable functions

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Nature communications 2024年第1期15卷 8772页

作者： Srinivas Gandla Jinsik Yoon Cheol-Woong Yang HyungJune Lee Wook Park Sunkook Kim Multifunctional Nano Bio Electronics Lab Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. Institute for Wearable Convergence Electronics Department of Electronics and Information Convergence Engineering Kyung Hee University Deogyeong-daero Giheung-gu Yongin-si Gyeonggi-do 17104 Republic of Korea. Electron Microscopy Research Laboratory Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. Intelligent Networked Systems Lab Department of Computer Science and Engineering Ewha Womans University Ewhayeodae-gil Seodaemun-gu Seoul 03760 Republic of Korea. Institute for Wearable Convergence Electronics Department of Electronics and Information Convergence Engineering Kyung Hee University Deogyeong-daero Giheung-gu Yongin-si Gyeonggi-do 17104 Republic of Korea. parkwook@khu.ac.kr. Multifunctional Nano Bio Electronics Lab Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. seonkuk@skku.edu.

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud Computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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