• Home
  • Introduction to KI
  • Research Highlight
  • Activity
Tag: KI for Artificial Intelligence
  • Research Highlight

    Quantification of Oceanic Energy Injection Scales and Elucidation of Primary Drivers Through Submesoscale Observations

    Professor Sung Yong Kim and his team reported an injection spatial scale and primary driver in the oceanic submesoscale processes as O(10)-km scale baroclinic instability for the first time ever in the world through the ‘big data’ analysis of O(1) km and hourly surface current and chlorophyll concentration maps, observed by remote sensing instruments of high-frequency radars and geostationary ocean color imagery for at least one year up to five years. This work elucidates the pathways of oceanic energy cascades and will enhance studies of bio-physical interactions and improve the performance of regional and global climate models through realistic parameterization at submesoscale. The scientific outcomes have been published as two companion papers in the Journal of Geophysical Research-Oceans, a prestigious, top-shelf journal in earth science and geophysical fluid dynamics....read more

    AI Applications Baroclinic Instability Big Data Analysis Energy Injection KI for Artificial Intelligence Ocean Physics Ocean Submesoscale Processes Remote Sensing
  • Research Highlight

    Development of Machine Reading Framework for Knowledge Base Population

    The SWRC (Semantic Web Research Center, led by Prof. Choi) aims to construct and expand its knowledge base through self-machine-learning from unstructured big data (natural language), and to develop a novel technology to further verify the knowledge base....read more

    AI Applications Deep Learning Information Extraction KI for Artificial Intelligence Knowledge Base
  • Research Highlight Top Story

    Development of VR Sickness Assessment Deep Network Considering Exceptional Motion for VR Video

    Viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness can occur when watching immersive VR content. To deal with viewing safety for VR content, objective assessment of VR sickness is of great importance. In this work, based on a deep generative model, we propose a novel objective VR sickness assessment (VRSA) network to automatically predict VR sickness. The proposed method takes into account motion patterns of VR videos in which exceptional motion is a critical factor inducing excessive VR sickness in human motion perception. The proposed VRSA network consists of two parts, the VR video generator and VR sickness score predictor. For the evaluation of VRSA performance, we performed comprehensive experiments with 360° videos (stimuli), corresponding physiological signals, and subjective questionnaires. We demonstrated that the proposed VRSA achieved a high correlation with human perceptual score for VR sickness....read more

    360-degree Video AI Applications Deep Learning Human Perception Image and Video sYstems (IVY) Laboratory KI for Artificial Intelligence Motion Mismatch Objective Assessment Virtual Reality (VR) VR Sickness
  • Research Highlight

    3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents

    Professor Jong-Hwan Kim’s research team defined a 3-D scene graph, which represents physical environments in a sparse and semantic way....read more

    3-D Scene Graph AI AI Applications ASIT RITL (Robot Intelligent Technology Lab) Deep Learning Environment Model Human Robot Interaction Intelligent Agent KI for Artificial Intelligence Robot Intelligence Scene Graph Scene Understanding
KAIST Institutes for Interdisciplinary and Integrative Research
KmatriX
Copyright © 2015 KAIST MATRIX. All rights reserved.
291 Daehak-ro Yuseong-gu Daejeon, 34141, Republic of Korea
Partnered with KAIST Breakthroughs and KAIST Compass