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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
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Research Highlight
Explainable AI can predict prognosis-specific genotype of brain tumor without invasive biopsy
Professor Bumseok Jeong has developed an explainable artificial intelligence model with high diagnostic performance that predicts the IDH genotype of gliomas; this is crucial in treatment planning and prognosis prediction....read more
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Research Highlight
Microfluidic Smart Blood Typing by Finger Actuation
A microfluidic smart blood-typing device operated by finger actuation is reported. The blood-typing results are displayed by means of microfluidic channels with the letter and the symbol of the corresponding blood type. To facilitate the mixing of blood and reagents in the device, the two sample inlets are connected to a single actuation chamber. According to the agglutination aspect in the mixture, the fluids are directed to both microslit filter channels and bypass channels, or only to bypass channels. With this device, blood typing was successfully performed by seven button pushes, using less than 10 μL of blood within 30 s....read more
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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
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Research Highlight
3-Dimensional Catalysts Facilitate Conversion of CO2 into Useful Fuels
KAIST team developed multi-stacked 3-dimensional Au catalyst architecture to enhance the electrochemical CO2 reduction reaction....read more
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Research Highlight
KAIST Fourth Industrial Revolution Intelligence Center (FIRIC) Held Joint Workshop with EU JRC
FIRIC discussed regional innovation strategies based on major technologies of the Fourth Industrial Revolution, such as Artificial Intelligence (AI) and Blockchain, and agreed to expand collaboration including joint research projects for Smart Specialization in the era of the Fourth Industrial Revolution (4IR)....read more
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Research Highlight
A Novel Mechanism of Nucleosome Recognition and Destabilization by Leukemia Related Protein
Ji-Joon Song’s research team revealed the working mechanism of DOT1L-Leukemia Related Protein, which recognize the histone code and destabilizes nucleosome....read more
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Research Highlight
Development of an innovative image reconstruction method for highly reliable digital breast tomosynthesis imaging
Medical Imaging and Radiotherapy (MIR) Lab, led by Prof. Seungryong Cho, a faculty member of KI for IT Convergence, has developed a novel and efficient image reconstruction method for producing clinically reliable digital breast tomosynthesis (DBT) images. DBT images are often subject to high-density object artifacts such as ripple and undershoot, which can degrade overall image quality and may lead to misdiagnosis. The newly developed method has been shown to successfully remove those artifacts, thereby improving lesion detectability....read more
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Research Highlight
Mole-bot: Development of bio-inspired embedded directional drilling robot
A research team led by Prof. Hyun Myung developed a bio-inspired digging robot called Mole-bot that simulates the mole's biological structure and behavior for resource exploration on Earth and other planets. In addition, a localization algorithm that can estimate the robot position and orientation in the underground environment was developed. The developed platform was verified through experiments on a testbed filled with soil....read more
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Research Highlight
Design of Metal Nanocatalysts for High-Performance Hydrogen Fuel Cells
New analytical platform enables evaluation of true electrocatalytic value of metal nanoparticles....read more
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