Electric Vehicle Charging Station incorporating with an Energy Management and Demand Response Technique
Surin Khomfoi
School of Electrical Engineering,
King Mongkut's Institute of Technology Ladkrabang (KMITL)
A study of Demand Response (DR) function and Energy management for an electrical vehicle (EV) charger is presented in this paper. The proposed technique is to prevent the electrical system within the rated power by controlling electric vehicle chargers loads to the electrical system. A power management method for limiting the charging current and charging time by using state of charge (SOC) as a priority point index is developed. The power management method is also developed as a calculation algorithm for determining the charging current limit rating for electric vehicle chargers. The simulation model is also developed for validating a DR management function of the electric vehicle charger according to Provincial Electricity Authority of Thailand (PEA) load characteristic data. The results show that the proposed DR function can manage the charging current of each electric vehicle charger appropriately. Also. the proposed technique can prevent the rated power demand of the transformer distribution. The study illustrates that this method is an effective protection for the distribution transformer and can be able to apply as a DR function to manage electrical energy more efficiently
Surin Khomfoi received the B.Eng. and M.Eng. degrees in electrical engineering from the King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, in 1996 and 2000, respectively, and the Ph.D. degree in electrical engineering from The University of Tennessee, Knoxville, TN, USA, in 2007.,Since December 1997, he has been a Lecturer with the Department of Electrical Engineering, KMITL, where he is currently an Associate Professor. His current research interests include multi-level power converters, renewable energy applications, fault diagnosis, power quality, and smart grids. He is a member of the Eta Kappa Nu Honor Society. He was a recipient of the Academic Scholarship Awards, including the Full Academic Scholarship for his B.Eng., M.Eng., and Ph.D. studies from the Energy Policy and Planning Office (EPPO), Ministry of Energy, Thailand.
Order Learning and Its Applications to Computer Vision
Chang-Su Kim
School of Electrical Engineering,
Korea University
In this talk, we first discuss order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes. To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is ‘greater than,’ ‘similar to,’ or ‘smaller than" the other. Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably. Second, we present the deep repulsive clustering (DRC) algorithm of ordered data for effective order learning. To this end, we develop the order-identity decomposition (ORID) network to divide the information of an object instance into an order-related feature and an identity feature. Then, we group object instances into clusters according to their identity features using a repulsive term. Moreover, we estimate the rank of a test instance, by comparing it with references within the same cluster. Experimental results on facial age estimation, aesthetic score regression, and historical color image classification show that the proposed algorithm can cluster ordered data effectively and also yield excellent rank estimation performance.
Chang-Su Kim received a Ph.D. degree in electrical engineering from Seoul National University with a Distinguished Dissertation Award in 2000. From 2000 to 2001, he was a Visiting Scholar with the Signal and Image Processing Institute, University of Southern California, Los Angeles. From 2001 to 2003, he coordinated the 3D Data Compression Group in National Research Laboratory for 3D Visual Information Processing at SNU. From 2003 to 2005, he was an Assistant Professor in the Department of Information Engineering, Chinese University of Hong Kong. In Sept. 2005, he joined the School of Electrical Engineering, Korea University, where he is a Professor. His research topics include image processing, computer vision, and machine learning. He has published more than 300 journal and conference papers. In 2009, he received the IEIE/IEEE Joint Award for Young IT Engineer of the Year. In 2014, he received the Best Paper Award from the Journal of Visual Communication and Image Representation (JVCI). He is a member of the Multimedia Systems & Application Technical Committee (MSATC) of the IEEE Circuits and Systems Society. Also, he was an APSIPA Distinguished Lecturer for the term 2017-2018. He served as an Editorial Board Member of JVCI and an Associate Editor of IEEE Transactions on Image Processing and IEEE Transactions on Multimedia. He is a Senior Area Editor of JVCI.
Low Latency and Lightweight Video Computing in Edge Cloud Networks
Takayuki Nakachi
Information Technology Center,
University of the Ryukyus
In this talk, I will give an overview of the edge-cloud-based “high-speed video computing technologies” in the Beyond 5G (B5G) era. In the future, the wideband and low latency data transfer function of the high-frequency band Beyond 5G will be linked with various computing resources in edge and cloud networks. The purpose is to realize low latency and lightweight video processing such as video editing and compression by using software in exceeding 10 Gbps edge cloud networks. I will mainly introduce two technologies. The first is high-speed processing technologies for real-time 8K ultra-high-definition (UHD) video. We have developed a system to store, process, and deliver uncompressed 8K UHD video in real-time using PC servers in edge cloud networks. The second is seamless video coding that takes privacy into consideration. I will talk about the basic concept. It enables video compression in edge cloud networks while the video data is scrambled. It also realizes low-delay transmission and lossless archiving in a unified architecture.
Dr. Takayuki Nakachi is a Professor at the Information Technology Center at the University of the Ryukyus. He received a Ph.D. degree in electrical engineering from Keio University, Tokyo, Japan, in 1997. Since he joined Nippon Telegraph and Telephone Corporation (NTT), he has been engaged in research on super-high-definition image/video coding, and media transport technologies. From 2006 to 2007, he was a visiting scientist at Stanford University. He also actively participates in MPEG international standardization. His current research interests include secure signal processing, and small-data-driven AI technologies based on sparse modeling and the Gaussian process. He received the 26th TELECOM System Technology Award, the 6th Paper Award of the Journal of Signal Processing, and the Best Paper Award of IEEE ISPACS2015. He currently serves as an adviser of the IEICE Technical Committee on Smart Info-Media Systems (SIS), and a member of the IEEE ISPACS International Steering Committee. He served as an associate editor of IEICE Transaction Fundamentals (2005-2010), an associate editor of IEICE Fundamentals Review (2009-2010), and editor-in-chief of the special section on Smart Multimedia & Communication Systems, IEICE Transactions (2015-2016), etc. Dr. Nakachi is a member of the Institute of Electrical and Electronics Engineers the Institute of Electronics (IEEE) and the Information and Communication Engineers (IEICE) of Japan.