Prof. Dr. Dong Hwa Kim
Department of Instrumentation and Control Engineering, Hanbat National University, South Korea
He got Ph.D degree at Dept. of Computational
Intelligence and Systems Science (K. Hirota Lab.), Interdisciplinary
Graduate School of Science and Engineering, TIT (Tokyo Institute of
Technology, K.), Tokyo, Japan as the title (Genetic Algorithm Combined
with Particle Swarm Optimization/Bacterial Foraging and Its Application
to PID Controller Tuning).
He has many work experiences, Professor, Director, Korean Experts Center of TDT University, Vietnam, Dean, Graduate school of Huree University, Mongolia, 2015, Prof., Dept. of Control Eng., Hanbat National University, March 2, 1993-Feb. 2015, Honorary Prof. Hanbat National University (Feb 28, 2015- ), Associate fellow researcher, University Malaysia Sabah (Aug. 6, 2014 – Aug. 5, 2016), Visiting Professor, Mechanical, Optic, Engineering Informatics, Budapest University of Technology and Economic, March 20–Feb., 2013, Header of Admission office, Hanbat National University, Aug.1, 2010-July. 28,2011, President, Korea Institute HuCARE (President of Hu-CARE (Human-Centered Advanced Technology Research/Education), Nov. 2009-, EU-FP7 (EU- Framework Programme) NCP (ICT) in Korea, April 29, 2011-2015, Director, KNRF (Korea National Research Foundation), 2006-2008, Visiting Prof., University of Alberta, Canada, March 1, 1999-March 1, 2000, Inviting researcher, ANL (Algonne National Lab.), USA, Aug. 1988-Dec. 1988, Inviting Researcher, AECL (Atomic Energy Canada Lab.), Canada, Nov. 1985-Nov.1986, Korea Atomic Energy Research Institute, Nov., 1977-March, 1993, Korea-Hungary Joint Work: Aug. 1, 2010-Feb. 28, 2011, ‘Robot motion related topics of the ETOCOM project’ Consultation with research staff members and giving related lectures, President, Daedeok Korea-India Forum, March 1, 2010–2015, Vice President, Daedeok Korea-Japan Forum, March 1, 2010–2015
Director of Science Culture Research Institute, Korea Science Foundation, Sept. 8, 2006 - Jan. 31, 2008, Vice-president of the recognition board of the world congress of arts, sciences and communications, IBC, Sept. 1, 2007-2010, UK.
He also has many activities in keynote speak and lecture in many university (about 100 university) about future technology and mega trend of technology including his research results.
He publishes several papers (around 60) and English books of research results.
He has been studying and is currently interested in emotion technology as artificial intelligence for future ICT and emotional robot.
Speech Title: Current Artificial Intelligence Research
Status of Global Company and Top University, and Neural Network as
Abstract: Recently, global company’s such as Amazon, Google, Facebook, IBM, MS and Top University such as, MIT, Harvard, McGill, Toronto University and so on is going to have an initiative about artificial intelligence because that technology has an influence on economy and social situation, and gives an impact to development of new technology.
[Harvard business review]: Many experts are expecting that a big slice of the workforce is about to lose their jobs because of artificial intelligence. By Oxford’s material, 47% of jobs could be automated by 2033. Even the near-term outlook has been quite negative. A 2016 report by the OECD predicts 9% of jobs in the 21 countries that make up its membership could be automated. McKinsey’s report estimates AI-driven job losses at 5% in January 2017. Many researchers predict a net job loss of between 4% and 7% in key business functions by the year 2020 due to AI.
Our research terms will be shorter because of change of ICT and AI. This lecture will deal with current AI research status of global company and University such as MIT, Harvard, and other top Universities. And this lecture also will give an importance and principle of AI research and neural network such as deep leading or optimization. Also this lecture will introduce current other AI research topic for SMEs business, researcher, Ph. D students, and master course through my research materials.
Yow Kin Choong
GIST College, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
Yow Kin Choong obtained his B.Eng (Elect) with 1st Class Honours from
the National University of Singapore in 1993, and his Ph.D. from
Cambridge University, UK in 1998. He joined the Gwangju Institute of
Science and Technology (GIST) in March 2013, where he is presently a
Professor in the GIST College. Prior to joining GIST, he was a Professor
at the Shenzhen Institutes of Advanced Technology (SIAT), P.R. China
(2012-2013), and Associate Professor at the Nanyang Technological
University (NTU), Singapore (1998-2012). In 1999-2005, he served as the
Sub-Dean of Computer Engineering in NTU, and in 2006-2008, he served as
the Associate Dean of Admissions in NTU.
Yow Kin Choong’s research interest is in Ambient Intelligence which includes passive remote sensing such as Computer Vision, wireless communications such as Ad hoc and Sensor Networks, and computational intelligence such as Fuzzy-Neuro Inference Systems. He has published over 80 top quality international journal and conference papers, and he has served as reviewer for a number of premier journals and conferences, including the IEEE Wireless Communications and the IEEE Transactions on Education. He has been invited to give presentations at various scientific meetings and workshops, such as the CNET Networks Event (2002) as well as the Microsoft Windows Server 2003 Launch (2003). He is also a member of the IEEE, ACM, and the Singapore Computer Society (SCS).
His pioneering work in Mobile and Interactive Learning won the HP Philanthropy grant in 2003 for applying Mobile Technologies in a Learning Environment. Only 7 awards were given to the 21 Asia Pacific Countries who were invited, and his project was the only one from Singapore to win it. Also, in 2003, he was one of the only 2 Singaporeans to be awarded participation to the ASEAN Technology Program on Multi Robot Cooperation Development held in KAIST, Korea.
He was the winner of the NTU Excellence in Teaching Award 2005, and he won the Most Popular SCE Year 1 lecturer for 4 consecutive years 2004-2007. He has led numerous student teams to National and International victories such as the IEEE Computer Society International Design Competition (CSIDC) (2001), the Microsoft Imagine Cup (2002, 2003 and 2005), and the Wireless Challenge (2003).
Speech Title: Deep Convolutional Generative Adversarial Networks for Automatic Image Coloring
Abstract: Image colorization, which makes a grayscale image colored, is one of the classical topics of computer vision. Due to the indeterminate nature of the problem, image colorization techniques currently rely heavily on human intuition. The traditional scribble-based approach requires the user to provide local image color information, which is a highly labor intensive task. More recently, example-induced approaches using neural networks that do not rely on user guidance became increasingly popular. Such approaches require learning unique color information from a large number of images, and transfers the color composition from the full-color image to the grayscale image. However, in practice, it is unrealistic to expect that one would have a large number of colored images that is similar to the target grayscale image. In this talk, I will describe what is a Deep Convolutional Generative Adversarial Network (DCGAN) and how we can use it to build a system that achieves one-to-one image colorization, i.e. we use only one source image to color the target grayscale image.
Assoc. Prof. Moo K. Chung
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
Biography: Moo K. Chung, Ph.D. is an Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison (http://www.stat.wisc.edu/~mchung). He is also affiliated with the Department of Statistics and Waisman Laboratory for Brain Imaging and Behavior. Dr. Chung received Ph.D. in Statistics from McGill University under Keith J. Worsley and James O. Ramsay on Computational Neuroanatomy. His research concentrates on the methodological development required for quantifying and contrasting anatomical shape and network variations in both normal and clinical populations using various mathematical, statistical and computational techniques. Recently he won NIH Brain Initiative Award for three years between 2017-2019 for building large-scale brain networks and uses it for mapping the baseline heritability. He has written two books on brain image analysis and working on the third book on brain network analysis that will be published in 2018 through Cambridge University Press.
Speech Title: Heat Kernel Smoothing in Irregular Image
Abstract: Heat kernel smoothing was originally introduced in the context of filtering out surface data defined on mesh vertices obtained from 3D medical images in 2005. The formulation uses the tangent space projection in approximating the heat kernel by iteratively applying Gaussian kernel with smaller bandwidth. Recently proposed spectral formulation to heat kernel smoothing constructs the heat kernel analytically using the eigenfunctions of the Laplace-Beltrami operator, avoiding the need for the linear approximation in the tangent space approximation. In this talk, we present the discrete version of heat kernel smoothing on graph data structure. The method is used to smooth data in irregularly shaped domains in 3D medical images. As an application, we show how to filter out the human lung blood vessel trees and mandibles obtained from computed tomography for shape quantification. The talk is in part based on http://doi.org/10.1016/j.media.2015.02.003