Intel
-
Professor Minsoo Rhu Recognized as Facebook Research Scholar
Professor Minsoo Rhu from the School of Electrical Engineering was selected as the recipient of the Systems for Machine Learning Research Awards presented by Facebook.
Facebook launched the award last year with the goal of funding impactful solutions in the areas of developer tookits, compilers and code generation, system architecture, memory technologies, and machine learning accelerator support.
A total of 167 scholars from 100 universities representing 26 countries submitted research proposals, and Facebook selected final 10 scholars. Professor Rhu made the list with his research topic ‘A Near-Memory Processing Architecture for Training Recommendation Systems.’ He will receive 5,000 USD in research funds at the award ceremony which will take place during this year’s AI Systems Faculty Summit at the Facebook headquarters in Menlo Park, California.
Professor Rhu’s submission was based on research on ‘Memory-Centric Deep Learning System Architecture’ that he carried out for three years under the auspices of Samsung Science and Technology Foundation from 2017. It was an academic-industrial cooperation research project in which leading domestic companies like Samsung Electronics and SK Hynix collaborated to make a foray into the global memory-centric smart system semiconductor market.
Professor Rhu who joined KAIST in 2018 has led various systems research projects to accelerate the AI computing technology while working at NVIDIA headquarters from 2014.
(END)
2020.02.21 View 9941 -
New Insights into How the Human Brain Solves Complex Decision-Making Problems
A new study on meta reinforcement learning algorithms helps us understand how the human brain learns to adapt to complexity and uncertainty when learning and making decisions. A research team, led by Professor Sang Wan Lee at KAIST jointly with John O’Doherty at Caltech, succeeded in discovering both a computational and neural mechanism for human meta reinforcement learning, opening up the possibility of porting key elements of human intelligence into artificial intelligence algorithms. This study provides a glimpse into how it might ultimately use computational models to reverse engineer human reinforcement learning.
This work was published on Dec 16, 2019 in the journal Nature Communications. The title of the paper is “Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning.”
Human reinforcement learning is an inherently complex and dynamic process, involving goal setting, strategy choice, action selection, strategy modification, cognitive resource allocation etc. This a very challenging problem for humans to solve owing to the rapidly changing and multifaced environment in which humans have to operate. To make matters worse, humans often need to often rapidly make important decisions even before getting the opportunity to collect a lot of information, unlike the case when using deep learning methods to model learning and decision-making in artificial intelligence applications.
In order to solve this problem, the research team used a technique called 'reinforcement learning theory-based experiment design' to optimize the three variables of the two-stage Markov decision task - goal, task complexity, and task uncertainty. This experimental design technique allowed the team not only to control confounding factors, but also to create a situation similar to that which occurs in actual human problem solving.
Secondly, the team used a technique called ‘model-based neuroimaging analysis.’ Based on the acquired behavior and fMRI data, more than 100 different types of meta reinforcement learning algorithms were pitted against each other to find a computational model that can explain both behavioral and neural data. Thirdly, for the sake of a more rigorous verification, the team applied an analytical method called ‘parameter recovery analysis,’ which involves high-precision behavioral profiling of both human subjects and computational models.
In this way, the team was able to accurately identify a computational model of meta reinforcement learning, ensuring not only that the model’s apparent behavior is similar to that of humans, but also that the model solves the problem in the same way as humans do.
The team found that people tended to increase planning-based reinforcement learning (called model-based control), in response to increasing task complexity. However, they resorted to a simpler, more resource efficient strategy called model-free control, when both uncertainty and task complexity were high. This suggests that both the task uncertainty and the task complexity interact during the meta control of reinforcement learning. Computational fMRI analyses revealed that task complexity interacts with neural representations of the reliability of the learning strategies in the inferior prefrontal cortex.
These findings significantly advance understanding of the nature of the computations being implemented in the inferior prefrontal cortex during meta reinforcement learning as well as providing insight into the more general question of how the brain resolves uncertainty and complexity in a dynamically changing environment. Identifying the key computational variables that drive prefrontal meta reinforcement learning, can also inform understanding of how this process might be vulnerable to break down in certain psychiatric disorders such as depression and OCD. Furthermore, gaining a computational understanding of how this process can sometimes lead to increased model-free control, can provide insights into how under some situations task performance might break down under conditions of high cognitive load.
Professor Lee said, “This study will be of enormous interest to researchers in both the artificial intelligence and human/computer interaction fields since this holds significant potential for applying core insights gleaned into how human intelligence works with AI algorithms.”
This work was funded by the National Institute on Drug Abuse, the National Research Foundation of Korea, the Ministry of Science and ICT, Samsung Research Funding Center of Samsung Electronics.
Figure 1 (modified from the figures of the original paper doi:10.1038/s41467-019-13632-1). Computations implemented in the inferior prefrontal cortex during meta reinforcement learning. (A) Computational model of human prefrontal meta reinforcement learning (left) and the brain areas whose neural activity patterns are explained by the latent variables of the model. (B) Examples of behavioral profiles. Shown on the left is choice bias for different goal types and on the right is choice optimality for task complexity and uncertainty. (C) Parameter recoverability analysis. Compared are the effect of task uncertainty (left) and task complexity (right) on choice optimality.
-Profile
Professor Sang Wan Lee
sangwan@kaist.ac.kr
Department of Bio and Brain Engineering
Director, KAIST Center for Neuroscience-inspired AI
KAIST Institute for Artificial Intelligence (http://aibrain.kaist.ac.kr)
KAIST Institute for Health, Science, and Technology
KAIST (https://www.kaist.ac.kr)
2020.01.31 View 6070 -
Professor Junil Choi Receives Stephen O. Rice Prize
< Professor Junil Choi (second from the left) >
Professor Junil Choi from the School of Electrical Engineering received the Stephen O. Rice Prize at the Global Communications Conference (GLOBECOM) hosted by the Institute of Electrical and Electronics Engineers (IEEE) in Hawaii on December 10, 2019.
The Stephen O. Rice Prize is awarded to only one paper of exceptional merit every year. The IEEE Communications Society evaluates all papers published in the IEEE Transactions on Communications journal within the last three years, and marks each paper by aggregating its scores on originality, the number of citations, impact, and peer evaluation.
Professor Choi won the prize for his research on one-bit analog-to-digital converters (ADCs) for multiuser massive multiple-input and multiple-output (MIMO) antenna systems published in 2016. In his paper, Professor Choi proposed a technology that can drastically reduce the power consumption of the multiuser massive MIMO antenna systems, which are the core technology for 5G and future wireless communication. Professor Choi’s paper has been cited more than 230 times in various academic journals and conference papers since its publication, and multiple follow-up studies are actively ongoing.
In 2015, Professor Choi received the IEEE Signal Processing Society Best Paper Award, an award equals to the Stephen O. Rice Prize. He was also selected as the winner of the 15th Haedong Young Engineering Researcher Award presented by the Korean Institute of Communications and Information Sciences (KICS) on December 6, 2019 for his outstanding academic achievements, including 34 international journal publications and 26 US patent registrations.
(END)
2019.12.23 View 11333 -
Professor Shin-Hyun Kim Receives the Young Scientist Award
Professor Shin-Hyun Kim from the Department of Chemical and Biomolecular Engineering received the Young Scientist Award from the Korean Academy of Science and Technology.
The Young Scientist Award is presented to a promising young Korean scientist under the age of 40 who shows significant potential, passion, and remarkable achievement.
Professor Kim was lauded for his research of intelligent soft materials. By applying his research, he developed a capsule sensor material that can not only be used for sensors, but also for displays, color aesthetics, anti-counterfeit technology, residual drug detection, and more.
The award ceremony took place on December 14 at the Gwacheon National Science Museum.
The Korean minister of Science and ICT delivered words of encouragement, reminding everyone that “the driving force behind creative performance of scientists is the provision of continuous support.” He added, “Researchers of Korea deserve greater public attention and support.”
(END)
2019.12.21 View 8070 -
Two Professors Receive Awards from the Korea Robotics Society
< Professor Jee-Hwan Ryu and Professor Ayoung Kim >
The Korea Robotics Society (KROS) conferred awards onto two KAIST professors from the Department of Civil and Environmental Engineering in recognition of their achievements and contributions to the development of the robotics industry in 2019. Professor Jee-Hwan Ryu has been actively engaged in researching the field of teleoperation, and this led him to win the KROS Robotics Innovation (KRI) Award. The KRI Award was newly established in 2019 by the KROS, in order to encourage researchers who have made innovative achievements in robotics. Professor Ryu shared the honor of being the first winner of this award with Professor Jaeheung Park of Seoul National University. Professor Ayoung Kim, from the same department, received the Young Investigator Award presented to emerging robitics researchers under 40 years of age. (END)
2019.12.19 View 10373 -
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging.
As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE.
He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA.
Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
2019.12.18 View 9893 -
KAIST Awarded the IPBC R&D Institution Team of the Year
KAIST was awarded the R&D Institution Team of the Year during the annual IPBC (Intellectual Property Business Congress) Asia 2019 held in Tokyo October 28-30. IPBC is a conference dedicated to IP value creation strategies hosted by IAM Media, a world’s leading IP business media platform.
IPBC Asia 2019 recognized the institutions and businesses that employed innovative IP strategies and management to produce the greatest IP value in 11 categories covering automotive, electronics, healthcare and biotechnology, internet and software, R&D institutions, semiconductors, industrials, mobile and telecommunications, Asia IP deals, Asia teams, and Asia individuals. This year, KAIST was recognized as one of the most active patentees in the Asia-Pacific region by significantly increasing its IP value through licensing and tech transfers.
Associate Vice President Kyung Cheol Choi of the Office of University-Industry Cooperation remarked, “We are so delighted to prove the strong research capacity of KAIST. This will help us accomplish our vision of being a leading university that creates global impact.”
2019.12.04 View 8010 -
KAIST and Google Jointly Develop AI Curricula
KAIST selected the two professors who will develop AI curriculum under the auspices of the KAIST-Google Partnership for AI Education and Research. The Graduate School of AI announced the two authors among the 20 applicants who will develop the curriculum next year. They will be provided 7,500 USD per subject.
Professor Changho Suh from the School of Electrical Engineering and Professor Yong-Jin Yoon from the Department of Mechanical Engineering will use Google technology such as TensorFlow, Google Cloud, and Android to create the curriculum.
Professor Suh’s “TensorFlow for Information Theory and Convex Optimization “will be used for curriculum in the graduate courses and Professor Yoon’s “AI Convergence Project Based Learning (PBL)” will be used for online courses. Professor Yoon’s course will explore and define problems by utilizing AI and experiencing the process of developing products that use AI through design thinking, which involves product design, production, and verification. Professor Suh’s course will discus“information theory and convergence,” which uses basic sciences and engineering as well as AI, machine learning, and deep learning.
2019.12.04 View 13819 -
‘Carrier-Resolved Photo-Hall’ to Push Semiconductor Advances
(Professor Shin and Dr. Gunawan (left))
An IBM-KAIST research team described a breakthrough in a 140-year-old mystery in physics. The research reported in Nature last month unlocks the physical characteristics of semiconductors in much greater detail and aids in the development of new and improved semiconductor materials.
Research team under Professor Byungha Shin at the Department of Material Sciences and Engineering and Dr. Oki Gunawan at IBM discovered a new formula and technique that enables the simultaneous extraction of both majority and minority carrier information such as their density and mobility, as well as gain additional insights about carrier lifetimes, diffusion lengths, and the recombination process. This new discovery and technology will help push semiconductor advances in both existing and emerging technologies.
Semiconductors are the basic building blocks of today’s digital electronics age, providing us with a multitude of devices that benefit our modern life. To truly appreciate the physics of semiconductors, it is very important to understand the fundamental properties of the charge carriers inside the materials, whether those particles are positive or negative, their speed under an applied electric field, and how densely they are packed into the material.
Physicist Edwin Hall found a way to determine those properties in 1879, when he discovered that a magnetic field will deflect the movement of electronic charges inside a conductor and that the amount of deflection can be measured as a voltage perpendicular to the flow of the charge. Decades after Hall’s discovery, researchers also recognized that they can measure the Hall effect with light via “photo-Hall experiments”. During such experiments, the light generates multiple carriers or electron–hole pairs in the semiconductors.
Unfortunately, the basic Hall effect only provided insights into the dominant charge carrier (or majority carrier). Researchers were unable to extract the properties of both carriers (the majority and minority carriers) simultaneously. The property information of both carriers is crucial for many applications that involve light such as solar cells and other optoelectronic devices.
In the photo-Hall experiment by the KAIST-IBM team, both carriers contribute to changes in conductivity and the Hall coefficient. The key insight comes from measuring the conductivity and Hall coefficient as a function of light intensity. Hidden in the trajectory of the conductivity, the Hall coefficient curve reveals crucial new information: the difference in the mobility of both carriers. As discussed in the paper, this relationship can be expressed elegantly as: Δµ = d (σ²H)/dσ
The research team solved for both majority and minority carrier mobility and density as a function of light intensity, naming the new technique Carrier-Resolved Photo Hall (CRPH) measurement. With known light illumination intensity, the carrier lifetime can be established in a similar way.
Beyond advances in theoretical understanding, advances in experimental techniques were also critical for enabling this breakthrough. The technique requires a clean Hall signal measurement, which can be challenging for materials where the Hall signal is weak due to low mobility or when extra unwanted signals are present, such as under strong light illumination.
The newly developed photo-Hall technique allows the extraction of an astonishing amount of information from semiconductors. In contrast to only three parameters obtained in the classic Hall measurements, this new technique yields up to seven parameters at every tested level of light intensity. These include the mobility of both the electron and hole; their carrier density under light; the recombination lifetime; and the diffusion lengths for electrons, holes, and ambipolar types. All of these can be repeated N times (i.e. the number of light intensity settings used in the experiment).
Professor Shin said, “This novel technology sheds new light on understanding the physical characteristics of semiconductor materials in great detail.” Dr. Gunawan added, “This will will help accelerate the development of next-generation semiconductor technology such as better solar cells, better optoelectronics devices, and new materials and devices for artificial intelligence technology.”
Profile:
Professor Byungha Shin
Department of Materials Science and Engineering
KAIST
byungha@kaist.ac.kr
http://energymatlab.kaist.ac.kr/
2019.11.18 View 14342 -
AI to Determine When to Intervene with Your Driving
(Professor Uichin Lee (left) and PhD candidate Auk Kim)
Can your AI agent judge when to talk to you while you are driving? According to a KAIST research team, their in-vehicle conservation service technology will judge when it is appropriate to contact you to ensure your safety.
Professor Uichin Lee from the Department of Industrial and Systems Engineering at KAIST and his research team have developed AI technology that automatically detects safe moments for AI agents to provide conversation services to drivers.
Their research focuses on solving the potential problems of distraction created by in-vehicle conversation services. If an AI agent talks to a driver at an inopportune moment, such as while making a turn, a car accident will be more likely to occur.
In-vehicle conversation services need to be convenient as well as safe. However, the cognitive burden of multitasking negatively influences the quality of the service. Users tend to be more distracted during certain traffic conditions. To address this long-standing challenge of the in-vehicle conversation services, the team introduced a composite cognitive model that considers both safe driving and auditory-verbal service performance and used a machine-learning model for all collected data.
The combination of these individual measures is able to determine the appropriate moments for conversation and most appropriate types of conversational services. For instance, in the case of delivering simple-context information, such as a weather forecast, driver safety alone would be the most appropriate consideration. Meanwhile, when delivering information that requires a driver response, such as a “Yes” or “No,” the combination of driver safety and auditory-verbal performance should be considered.
The research team developed a prototype of an in-vehicle conversation service based on a navigation app that can be used in real driving environments. The app was also connected to the vehicle to collect in-vehicle OBD-II/CAN data, such as the steering wheel angle and brake pedal position, and mobility and environmental data such as the distance between successive cars and traffic flow.
Using pseudo-conversation services, the research team collected a real-world driving dataset consisting of 1,388 interactions and sensor data from 29 drivers who interacted with AI conversational agents. Machine learning analysis based on the dataset demonstrated that the opportune moments for driver interruption could be correctly inferred with 87% accuracy.
The safety enhancement technology developed by the team is expected to minimize driver distractions caused by in-vehicle conversation services. This technology can be directly applied to current in-vehicle systems that provide conversation services. It can also be extended and applied to the real-time detection of driver distraction problems caused by the use of a smartphone while driving.
Professor Lee said, “In the near future, cars will proactively deliver various in-vehicle conversation services. This technology will certainly help vehicles interact with their drivers safely as it can fairly accurately determine when to provide conversation services using only basic sensor data generated by cars.”
The researchers presented their findings at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp’19) in London, UK. This research was supported in part by Hyundai NGV and by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
(Figure: Visual description of safe enhancement technology for in-vehicle conversation services)
2019.11.13 View 17406 -
Image Analysis to Automatically Quantify Gender Bias in Movies
Many commercial films worldwide continue to express womanhood in a stereotypical manner, a recent study using image analysis showed. A KAIST research team developed a novel image analysis method for automatically quantifying the degree of gender bias in films.
The ‘Bechdel Test’ has been the most representative and general method of evaluating gender bias in films. This test indicates the degree of gender bias in a film by measuring how active the presence of women is in a film. A film passes the Bechdel Test if the film (1) has at least two female characters, (2) who talk to each other, and (3) their conversation is not related to the male characters.
However, the Bechdel Test has fundamental limitations regarding the accuracy and practicality of the evaluation. Firstly, the Bechdel Test requires considerable human resources, as it is performed subjectively by a person. More importantly, the Bechdel Test analyzes only a single aspect of the film, the dialogues between characters in the script, and provides only a dichotomous result of passing the test, neglecting the fact that a film is a visual art form reflecting multi-layered and complicated gender bias phenomena. It is also difficult to fully represent today’s various discourse on gender bias, which is much more diverse than in 1985 when the Bechdel Test was first presented.
Inspired by these limitations, a KAIST research team led by Professor Byungjoo Lee from the Graduate School of Culture Technology proposed an advanced system that uses computer vision technology to automatically analyzes the visual information of each frame of the film. This allows the system to more accurately and practically evaluate the degree to which female and male characters are discriminatingly depicted in a film in quantitative terms, and further enables the revealing of gender bias that conventional analysis methods could not yet detect.
Professor Lee and his researchers Ji Yoon Jang and Sangyoon Lee analyzed 40 films from Hollywood and South Korea released between 2017 and 2018. They downsampled the films from 24 to 3 frames per second, and used Microsoft’s Face API facial recognition technology and object detection technology YOLO9000 to verify the details of the characters and their surrounding objects in the scenes.
Using the new system, the team computed eight quantitative indices that describe the representation of a particular gender in the films. They are: emotional diversity, spatial staticity, spatial occupancy, temporal occupancy, mean age, intellectual image, emphasis on appearance, and type and frequency of surrounding objects.
Figure 1. System Diagram
Figure 2. 40 Hollywood and Korean Films Analyzed in the Study
According to the emotional diversity index, the depicted women were found to be more prone to expressing passive emotions, such as sadness, fear, and surprise. In contrast, male characters in the same films were more likely to demonstrate active emotions, such as anger and hatred.
Figure 3. Difference in Emotional Diversity between Female and Male Characters
The type and frequency of surrounding objects index revealed that female characters and automobiles were tracked together only 55.7 % as much as that of male characters, while they were more likely to appear with furniture and in a household, with 123.9% probability.
In cases of temporal occupancy and mean age, female characters appeared less frequently in films than males at the rate of 56%, and were on average younger in 79.1% of the cases. These two indices were especially conspicuous in Korean films.
Professor Lee said, “Our research confirmed that many commercial films depict women from a stereotypical perspective. I hope this result promotes public awareness of the importance of taking prudence when filmmakers create characters in films.”
This study was supported by KAIST College of Liberal Arts and Convergence Science as part of the Venture Research Program for Master’s and PhD Students, and will be presented at the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) on November 11 to be held in Austin, Texas.
Publication:
Ji Yoon Jang, Sangyoon Lee, and Byungjoo Lee. 2019. Quantification of Gender Representation Bias in Commercial Films based on Image Analysis. In Proceedings of the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW). ACM, New York, NY, USA, Article 198, 29 pages. https://doi.org/10.1145/3359300
Link to download the full-text paper:
https://files.cargocollective.com/611692/cscw198-jangA--1-.pdf
Profile: Prof. Byungjoo Lee, MD, PhD
byungjoo.lee@kaist.ac.kr
http://kiml.org/
Assistant Professor
Graduate School of Culture Technology (CT)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr Daejeon 34141, Korea
Profile: Ji Yoon Jang, M.S.
yoone3422@kaist.ac.kr
Interactive Media Lab
Graduate School of Culture Technology (CT)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr Daejeon 34141, Korea
Profile: Sangyoon Lee, M.S. Candidate
sl2820@kaist.ac.kr
Interactive Media Lab
Graduate School of Culture Technology (CT)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr Daejeon 34141, Korea
(END)
2019.10.17 View 25471 -
KAIST to Transfer Core Tech to Domestic Companies amid Japan's Export Curbs
< Associate Vice President Kyung-Cheol Choi of the Office of University-Industry Cooperation (OUIC) at KAIST >
KAIST will transfer four core technologies related to materials, parts, and equipment to domestic companies to help them combat the latest export curbs triggered by Korea’s removal from Japan’s ‘white list’ of preferential trade partners. In addition, KAIST’s five patented technologies in the field of artificial intelligence (AI) and materials and parts will also be transferred to the companies in order to reduce the reliance on Japan and achieve technological independence through the ‘localization’ of key technologies.
KAIST announced these university-industry cooperation promotion plans at the ‘2019 KAIST Core Tech Transfer Day Conference’ held in Seoul on September 17. More than 200 entrepreneurs and investors attended the briefing and on-site consulting sessions delivered by nine KAIST professors who led the development of the technologies.
The four technologies were presented at the conference as those that can replace Japanese technologies subject to the export curbs. They include:
1. ‘Transparent fluorinated polyimide with low thermal expansion’ developed by Professor Sang-Youl Kim of the Department of Chemistry
2. ‘A non-destructive electromagnetic performance testing system’ developed by Professor Jung-Ryul Lee of the Department of Aerospace Engineering
3. ‘A nanotechnology-based electrode material for use in advanced secondary batteries’ developed by Professor Do-Kyung Kim of the Department of Materials Science and Engineering
4. ‘A high-resolution photoresist’ developed by Professor Emeritus Jin-Baek Kim of the Department of Chemistry.
Of particular interest is the non-destructive electromagnetic performance testing system technology developed by Professor Jung-Ryul Lee. This new cost-effective technology enables tests that were impossible to carry out using conventional technologies and yields a cost reduction of more than 50 percent compared to foreign technologies.
By introducing Professor Do-Kyung Kim’s new electrode material technology, the efficiency of electric vehicles can be increased. As this technology uses relatively low-cost sodium ion batteries, industries can prepare for the possible jump from the more expensive lithium batteries currently being used.
Another five patented AI and materials and parts technologies disclosed at the conference include:
1. ‘Enhanced HTTP adaptive streaming with CNN-based super-resolution’ developed by Professor Dong-soo Han of the School of Electrical Engineering
2. ‘Method and apparatus of brain-computer interface design for estimating choice behavior and decision strategy’ developed by Professor Sang-Wan Lee of the Department of Bio and Brain Engineering
3. ‘Eco-friendly fabrication of metal oxide nanoparticles and fabrication of non-toxic polymer sunscreen ingredients by electron irradiation’ developed by Professor Sung-Oh Cho of the Department of Nuclear and Quantum Engineering
4. ‘High-density nanofiber yarn-based coloricmetric gas sensors’ developed by Professor Il-Doo Kim of the Department of Materials Science and Engineering
5. ‘Silicon-pocket energy storage electrode with high energy density and its manufacturing technology’ developed by Professor Jeung-Ku Kang of the Graduate school of EEWS.
The patented nanofiber-based coloricmetric gas sensor technology developed by Professor Il-Doo Kim allows for the diagnosis of diseases by only using the patient’s respiration. Due to its high productivity and processability, it is expected to be applied to various fields in the fast-growing disease diagnosis sensor market, which includes mobile devices and wearable sensors.
Moreover, Professor Dong-soo Han’s patented adaptive streaming technology attracted attention along with the ever-growing Over The Top (OTT) and Video On Demand (VOD) service markets, since it has significant potential for improving the streaming quality of videos and reducing costs for video providers.
Professor Kyung-Cheol Choi, the Associate Vice President of the Office of University-Industry Cooperation (OUIC) at KAIST, said, “KAIST OUIC and KAIST Advisors on Materials and Parts (KAMP) have been working tirelessly to help Korean companies cope with the recent Japanese export restrictions. KAIST’s efforts will enhance the competitiveness and growth of the Korean industry and economy, turning this national crisis into opportunity.”
(END)
2019.09.20 View 7166