GNI
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No More Touch Issues on Rainy Days! KAIST Develops Human-Like Tactile Sensor
Recent advancements in robotics have enabled machines to handle delicate objects like eggs with precision, thanks to highly integrated pressure sensors that provide detailed tactile feedback. However, even the most advanced robots struggle to accurately detect pressure in complex environments involving water, bending, or electromagnetic interference. A research team at KAIST has successfully developed a pressure sensor that operates stably without external interference, even on wet surfaces like a smartphone screen covered in water, achieving human-level tactile sensitivity.
KAIST (represented by President Kwang Hyung Lee) announced on the 10th of March that a research team led by Professor Jun-Bo Yoon from the School of Electrical Engineering has developed a high-resolution pressure sensor that remains unaffected by external interference such as "ghost touches" caused by moisture on touchscreens.
Capacitive pressure sensors, widely used in touch systems due to their simple structure and durability, are essential components of human-machine interface (HMI) technologies in smartphones, wearable devices, and robots. However, they are prone to malfunctions caused by water droplets, electromagnetic interference, and curves.
To address these issues, the research team investigated the root causes of interference in capacitive pressure sensors. They identified that the "fringe field" generated at the sensor’s edges is particularly susceptible to external disturbances.
The researchers concluded that, to fundamentally resolve this issue, suppressing the fringe field was necessary. Through theoretical analysis, they determined that reducing the electrode spacing to the nanometer scale could effectively minimize the fringe field to below a few percent.
Utilizing proprietary micro/nanofabrication techniques, the team developed a nanogap pressure sensor with an electrode spacing of 900 nanometers (nm). This newly developed sensor reliably detected pressure regardless of the material exerting force and remained unaffected by bending or electromagnetic interference.
Furthermore, the team successfully implemented an artificial tactile system utilizing the developed sensor’s characteristics. Human skin contains specialized pressure receptors called Merkel’s disks. To artificially mimic them, the exclusive detection of pressure was necessary, but hadn’t been achieved by conventional sensors.
Professor Yoon’s research team overcame these challenges, developing a sensor achieving a density comparable to Merkel’s discs and enabling wireless, high-precision pressure sensing.
To explore potential applications, the researcher also developed a force touch pad system, demonstrating its ability to capture pressure magnitude and distribution with high resolution and without interference.
Professor Yoon stated, “Our nanogap pressure sensor operates reliably even in rainy conditions or sweaty environments, eliminating common touch malfunctions. We believe this innovation will significantly enhance everyday user experiences.”
He added, “This technology has the potential to revolutionize various fields, including precision tactile sensors for robotics, medical wearable devices, and next-generation augmented reality (AR) and virtual reality (VR) interfaces.”
The study was led by Jae-Soon Yang (Ph.D.), Myung-Kun Chung (Ph.D. candidate), and Jae-Young Yoo (Assistant Professor at Sungkyunkwan University, a KAIST Ph.D. graduate). The research findings were published in Nature Communications on February 27, 2025. (Paper title: “Interference-Free Nanogap Pressure Sensor Array with High Spatial Resolution for Wireless Human-Machine Interface Applications”, DOI: 10.1038/s41467-025-57232-8)
This study was supported by the National Research Foundation of Korea’s Mid-Career Researcher Program and Leading Research Center Support Program.
2025.03.14 View 1017 -
KAIST to Collaborate with AT&C to Take Dominance over Dementia
< Photo 1. (From left) KAIST Dean of the College of Natural Sciences Daesoo Kim, KAIST President Kwang Hyung Lee, AT&C Chairman Ki Tae Lee, AT&C CEO Jong-won Lee >
KAIST (President Kwang Hyung Lee) announced on January 9th that it signed a memorandum of understanding for a comprehensive mutual cooperation with AT&C (CEO Jong-won Lee) at its Seoul Dogok Campus to expand research investment and industry-academia cooperation in preparation for the future cutting-edge digital bio era.
Senile dementia is a rapidly increasing brain disease that affects 10% of the elderly population aged 65 and older, and approximately 38% of those aged 85 and older suffer from dementia. Alzheimer's disease is the most common dementia in the elderly and its prevalence has been increasing rapidly in the population of over 40 years of age. However, an effective treatment is yet to be found.
The Korean government is investing a total of KRW 1.1 trillion in dementia R&D projects from 2020 to 2029, with the goal of reducing the rate of increase of dementia patients by 50%. Since it takes a lot of time and money to develop effective and affordable medicinal dementia treatments, it is urgent to work on the development of digital treatments for dementia that can be applied more quickly.
AT&C, a digital healthcare company, has already received approval from the Ministry of Food and Drug Safety (MFDS) for its device for antidepressant treatment based on transcranial magnetic stimulation (TMS) using magnetic fields and is selling it domestically and internationally. In addition, it has developed the first Alzheimer's dementia treatment device in Korea and received MFDS approval for clinical trials. After passing phase 1 to evaluate safety and phase 2 to test efficacy on some patients, it is currently conducting phase 3 clinical trials to test efficacy on a larger group of patients.
This dementia treatment device is equipped with a system that combines non-invasive electronic stimulations (TMS electromagnetic stimulator) and digital therapeutic prescription (cognitive learning programs) to provide precise, automated treatment by applying AI image analysis and robotics technology.
Through this agreement, KAIST and AT&C have agreed to cooperate with each other in the development of innovative digital treatment equipment for brain diseases. Through research collaboration with KAIST, AT&C will be able to develop technology that can be widely applied to Parkinson's disease, stroke, mild cognitive impairment, sleep disorders, etc., and will develop portable equipment that can improve brain function and prevent dementia at home by utilizing KAIST's wearable technology.
To this end, AT&C plans to establish a digital healthcare research center at KAIST by supporting research personnel and research expenses worth approximately 3 billion won with the goal of developing cutting-edge digital equipment within 3 years.
The digital equipment market is expected to grow at a compounded annual growth rate of 22.1% from 2023 to 2033, reaching a market size of $1.9209 trillion by 2033.
< Photo 2. (From left) Dean of the KAIST College of Natural Sciences Daesoo Kim, Professor Young-joon Lee, Professor Minee Choi of the KAIST Department of Brain and Cognitive Sciences, KAIST President Kwang Hyung Lee, Chairman Ki Tae Lee, CEO Jong-won Lee, and Headquarters Director Ki-yong Na of AT&C >
CEO Jong-won Lee said, “AT&C is playing a leading role in the treatment of Alzheimer’s disease using TMS (transcranial magnetic stimulation) technology. Through this agreement with KAIST, we will do our best to create a new paradigm for brain disease treatment and become a platform company that can lead future medical devices and medical technology.”
Former Samsung Electronics Vice Chairman Ki Tae Lee, a strong supporter of this R&D project, said, “Through this agreement with KAIST, we plan to prepare for a new future by combining the technologies AT&C has developed so far with KAIST’s innovative and differentiated technologies.”
KAIST President Kwang Hyung Lee emphasized, “Through this collaboration, KAIST expects to build a world-class digital therapeutics infrastructure for treating brain diseases and contribute greatly to further strengthening Korea’s competitiveness in the biomedical field.”
The signing ceremony was attended by KAIST President Kwang Hyung Lee, the Dean of KAIST College of Natural Sciences Daesoo Kim, AT&C CEO Lee Jong-won, and the current Chairman of AT&C, Ki Tae Lee, former Vice Chairman of Samsung Electronics.
2025.01.09 View 2079 -
KAIST Opens Newly Expanded Center for Contemplative Research in Collaboration with Brain and Cognitive Sciences Department
KAIST (represented by President Kwang Hyung Lee) announced on January 2nd that it would hold an opening ceremony for the expanded KAIST Center for Contemplative Research (Director Wan Doo Kim) at the Creativity Learning Building on its Daejeon campus on January 3 (Friday).
Established in 2018 with the mission of "integrating meditation and science for the happiness and prosperity of humanity," the KAIST Center for Contemplative Research has been expanding its scope of research into the neuroscience of meditation and training empathetic educators who will lead the field of meditation science in collaboration with the Brain and Cognitive Sciences Department, which was established in 2022.
Supported by the Plato Academy Foundation and with funding from SK Discovery for the facility’s expansion, the center now occupies an extended space on the 5th floor of the Creativity Learning Center. The new facilities include: ▲ Advanced Research Equipment ▲ Meditation Science Laboratories ▲ VR/XR-Based Meditation Experience Rooms ▲ A Large Digital Art Meditation Hall ▲ Personal Meditation Halls.
Particularly, the center plans to conduct next-generation meditation research using cutting-edge technologies such as: ▲ Brain-Computer Interface Technology ▲ Meditation Wearable Devices ▲ Metaverse-Based Meditation Environments.
The opening ceremony, scheduled for the morning of January 3 (Friday), was attended by key figures, including Plato Academy Foundation Chairman Chang-Won Choi, MindLab CEO Professor Seong-Taek Cho, Bosung Group Vice President Byung-Chul Lee, and KAIST President Kwang Hyung Lee.
The event began with a national moment of silence to honor the victims of the recent Jeju Air passenger accident. It included a progress report by the center director, a lecture by Professor Jaeseung Jeong, panel discussions, and more.
Following a tour of the expanded facilities, the center hosted a 20-minute hands-on meditation science session using *Looxid Labs EEG devices for the first 50 participants.
*Looxid Labs EEG Device: A real-time brainwave measurement device developed by KAIST startup Looxid Labs that enables users to experience efficient and AI-powered data-driven meditation science practice (Looxid Labs website: https://looxidlabs.com/).
During the ceremony, Director of the Center for Contemplative Research Wan Doo Kim presented on "The Mission, Vision, and Future of the KAIST Center for Contemplative Research." Yujin Lee, a combined master’s and doctoral researcher from the Brain and Cognitive Sciences Department, shared insights on "The Latest Trends in Meditation Science Research."
A panel discussion and Q&A session on "The Convergence of Meditation and Brain and Cognitive Sciences" followed featuring Professors Jaeseung Jeong, HyungDong Park (Brain and Cognitive Sciences), and Jiyoung Park (Digital Humanities and Social Sciences).
Director Wan Doo Kim commented, “With this expanded opening, we aim to offer advanced meditation programs integrating brain and cognitive sciences and cutting-edge technology not only to KAIST members but also to the general public interested in meditation. We will continue to dedicate ourselves to interdisciplinary research between meditation and science.”
2025.01.03 View 1584 -
KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 4349 -
Deep Learning Framework to Enable Material Design in Unseen Domain
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation
A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.
Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training data-set size. This study was reported in npj Computational Materials last month.
“We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering.
Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain.
Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps.
First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search.
As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient.
Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework.
The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project.
-Publication
Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2)
-Profile
Professor Seunghwa Ryu
Mechanics & Materials Modeling Lab
Department of Mechanical Engineering
KAIST
2021.09.29 View 11216 -
Prof. Sang Wan Lee Selected for 2021 IBM Academic Award
Professor Sang Wan Lee from the Department of Bio and Brain Engineering was selected as the recipient of the 2021 IBM Global University Program Academic Award. The award recognizes individual faculty members whose emerging science and technology contains significant interest for universities and IBM.
Professor Lee, whose research focuses on artificial intelligence and computational neuroscience, won the award for his research proposal titled A Neuroscience-Inspired Approach for Metacognitive Reinforcement Learning. IBM provides a gift of $40,000 to the recipient’s institution in recognition of the selection of the project but not as a contract for services.
Professor Lee’s project aims to exploit the unique characteristics of human reinforcement learning. Specifically, he plans to examines the hypothesis that metacognition, a human’s ability to estimate their uncertainty level, serves to guide sample-efficient and near-optimal exploration, making it possible to achieve an optimal balance between model-based and model-free reinforcement learning.
He was also selected as the winner of the Google Research Award in 2016 and has been working with DeepMind and University College London to conduct basic research on decision-making brain science to establish a theory on frontal lobe meta-enhance learning.
"We plan to conduct joint research for utilizing brain-based artificial intelligence technology and frontal lobe meta-enhanced learning technology modeling in collaboration with an international research team including IBM, DeepMind, MIT, and Oxford,” Professor Lee said.
2021.06.25 View 11321 -
‘Urban Green Space Affects Citizens’ Happiness’
Study finds the relationship between green space, the economy, and happiness
A recent study revealed that as a city becomes more economically developed, its citizens’ happiness becomes more directly related to the area of urban green space.
A joint research project by Professor Meeyoung Cha of the School of Computing and her collaborators studied the relationship between green space and citizen happiness by analyzing big data from satellite images of 60 different countries.
Urban green space, including parks, gardens, and riversides not only provides aesthetic pleasure, but also positively affects our health by promoting physical activity and social interactions. Most of the previous research attempting to verify the correlation between urban green space and citizen happiness was based on few developed countries. Therefore, it was difficult to identify whether the positive effects of green space are global, or merely phenomena that depended on the economic state of the country. There have also been limitations in data collection, as it is difficult to visit each location or carry out investigations on a large scale based on aerial photographs.
The research team used data collected by Sentinel-2, a high-resolution satellite operated by the European Space Agency (ESA) to investigate 90 green spaces from 60 different countries around the world. The subjects of analysis were cities with the highest population densities (cities that contain at least 10% of the national population), and the images were obtained during the summer of each region for clarity. Images from the northern hemisphere were obtained between June and September of 2018, and those from the southern hemisphere were obtained between December of 2017 and February of 2018.
The areas of urban green space were then quantified and crossed with data from the World Happiness Report and GDP by country reported by the United Nations in 2018. Using these data, the relationships between green space, the economy, and citizen happiness were analyzed.
The results showed that in all cities, citizen happiness was positively correlated with the area of urban green space regardless of the country’s economic state. However, out of the 60 countries studied, the happiness index of the bottom 30 by GDP showed a stronger correlation with economic growth. In countries whose gross national income (GDP per capita) was higher than 38,000 USD, the area of green space acted as a more important factor affecting happiness than economic growth. Data from Seoul was analyzed to represent South Korea, and showed an increased happiness index with increased green areas compared to the past.
The authors point out their work has several policy-level implications. First, public green space should be made accessible to urban dwellers to enhance social support. If public safety in urban parks is not guaranteed, its positive role in social support and happiness may diminish. Also, the meaning of public safety may change; for example, ensuring biological safety will be a priority in keeping urban parks accessible during the COVID-19 pandemic.
Second, urban planning for public green space is needed for both developed and developing countries. As it is challenging or nearly impossible to secure land for green space after the area is developed, urban planning for parks and green space should be considered in developing economies where new cities and suburban areas are rapidly expanding.
Third, recent climate changes can present substantial difficulty in sustaining urban green space. Extreme events such as wildfires, floods, droughts, and cold waves could endanger urban forests while global warming could conversely accelerate tree growth in cities due to the urban heat island effect. Thus, more attention must be paid to predict climate changes and discovering their impact on the maintenance of urban green space.
“There has recently been an increase in the number of studies using big data from satellite images to solve social conundrums,” said Professor Cha. “The tool developed for this investigation can also be used to quantify the area of aquatic environments like lakes and the seaside, and it will now be possible to analyze the relationship between citizen happiness and aquatic environments in future studies,” she added.
Professor Woo Sung Jung from POSTECH and Professor Donghee Wohn from the New Jersey Institute of Technology also joined this research. It was reported in the online issue of EPJ Data Science on May 30.
-PublicationOh-Hyun Kwon, Inho Hong, Jeasurk Yang, Donghee Y. Wohn, Woo-Sung Jung, andMeeyoung Cha, 2021. Urban green space and happiness in developed countries. EPJ Data Science. DOI: https://doi.org/10.1140/epjds/s13688-021-00278-7
-ProfileProfessor Meeyoung ChaData Science Labhttps://ds.ibs.re.kr/
School of Computing
KAIST
2021.06.21 View 11073 -
Deep Learning-Based Cough Recognition Model Helps Detect the Location of Coughing Sounds in Real Time
The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis.
Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %.
Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room.
Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image.
To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else.
In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places.
The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances.
The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment.
In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer.
The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image.
A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms.
Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient's condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff."
This study was conducted in collaboration with SM Instruments Inc.
Profile: Yong-Hwa Park, Ph.D.
Associate Professor
yhpark@kaist.ac.kr
http://human.kaist.ac.kr/
Human-Machine Interaction Laboratory (HuMaN Lab.)
Department of Mechanical Engineering (ME)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr/en/
Daejeon 34141, Korea
Profile: Gyeong Tae Lee
PhD Candidate
hansaram@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Seong Hu Kim
PhD Candidate
tjdgnkim@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Hyeonuk Nam
PhD Candidate
frednam@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Young-Key Kim
CEO
sales@smins.co.kr
http://en.smins.co.kr/
SM Instruments Inc.
Daejeon 34109, Korea
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2020.08.13 View 15704 -
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster.
The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics.
Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain.
In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community.
During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence.
Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women.
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2020.05.26 View 11774 -
A Study Finds Neuropeptide Somatostatin Enhances Visual Processing
Researchers have confirmed that neuropeptide somatostatin can improve cognitive function in the brain. A research group of Professor Seung-Hee Lee from the Department of Biological Sciences at KAIST found that the application of neuropeptide somatostatin improves visual processing and cognitive behaviors by reducing excitatory inputs to parvalbumin-positive interneurons in the cortex.
This study, reported at Science Advances on April 22nd (EST), sheds a new light on the therapeutics of neurodegenerative diseases. According to a recent study in Korea, one in ten seniors over 65 is experiencing dementia-related symptoms in their daily lives such like memory loss, cognitive decline, and motion function disorders. Professor Lee believes that somatostatin treatment can be directly applied to the recovery of cognitive functions in Alzheimer’s disease patients.
Professor Lee started this study noting the fact that the level of somatostatin expression was dramatically decreased in the cerebral cortex and cerebrospinal fluid of Alzheimer’s disease patients
Somatostatin-expressing neurons in the cortex are known to exert the dendritic inhibition of pyramidal neurons via GABAergic transmission. Previous studies focused on their inhibitory effects on cortical circuits, but somatostatin-expressing neurons can co-release somatostatin upon activation. Despite the abundant expression of somatostatin and its receptors in the cerebral cortex, it was not known if somatostatin could modulate cognitive processing in the cortex.
The research team demonstrated that the somatostatin treatment into the cerebral cortex could enhance visual processing and cognitive behaviors in mice. The research team combined behaviors, in vivo and in vitro electrophysiology, and electron microscopy techniques to reveal how the activation of somatostatin receptors in vivo enhanced the ability of visual recognition in animals. Interestingly, somatostatin release can reduce excitatory synaptic transmission to another subtype of GABAergic interneurons, parvalbumin (PV)-expressing neurons.
As somatostatin is a stable and safe neuropeptide expressed naturally in the mammalian brain, it was safe to be injected into the cortex and cerebrospinal fluid, showing a potential application to drug development for curing cognitive disorders in humans.
Professor Lee said, “Our research confirmed the key role of the neuropeptide SST in modulating cortical function and enhancing cognitive ability in the mammalian brain. I hope new drugs can be developed based on the function of somatostatin to treat cognitive disabilities in many patients suffering from neurological disorders.”
This study was supported by the National Research Foundation of Korea.
Publication:
Song, Y. H et al. (2020) ‘Somatostatin enhances visual processing and perception by suppressing excitatory inputs to parvalbumin-positive interneurons in V1’, Science Advances, 6(17). Available online at https://doi.org/10.1126/sciadv.aaz0517
Profile:
Seung-Hee Lee
Associate Professor
shlee1@kaist.ac.kr
https://sites.google.com/site/leelab2013/
Sensory Processing Lab (SPL)
Department of Biological Sciences (BIO)
Korea Advanced Institute of Science and Technology (KAIST)
Profile:
You-Hyang Song
Researcher (Ph.D.)
dbgidtm17@kaist.ac.kr
SPL, KAIST BIO
Profile:
Yang-Sun Hwang
Researcher (M.S.)
hys940129@kaist.ac.kr
SPL, KAIST BIO
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2020.04.23 View 12865 -
Team KAT Wins the Autonomous Car Challenge
(Team KAT receiving the Presidential Award)
A KAIST team won the 2018 International Autonomous Car Challenge for University Students held in Daegu on November 2.
Professor Seung-Hyun Kong from the ChoChunShik Graduate School of Green Transportation and his team participated in this contest with the team named KAT (KAIST Autonomous Technologies). The team received the Presidential Award with a fifty million won cash prize and an opportunity for a field trip abroad.
The competition was conducted on actual roads with Connected Autonomous Vehicles (CAV), which incorporate autonomous driving technologies and vehicle-to-everything (V2X) communication system.
In this contest, the autonomous vehicles were given a mission to pick up passengers or parcels. Through the V2X communication, the contest gave current location of the passengers or parcels, their destination, and service profitability according to distance and level of service difficulty.
The participating vehicles had to be equipped very accurate and robust navigation system since they had to drive on narrow roads as well as go through tunnels where GPS was not available. Moreover, they had to use camera-based recognition technology that was invulnerable to backlight as the contest was in the late afternoon.
The contest scored the mission in the following way: the vehicles get points if they pick up passengers and safely drop them off at their destination; on the other hand, points are deducted when they violate lanes or traffic lights. It will be a major black mark if a participant sitting in the driver’s seat needs to get involved in driving due to a technical issue.
Youngbo Shim of KAT said, “We believe that we got major points for technical superiority in autonomous driving and our algorithm for passenger selection.”
This contest, hosted by Ministry of Trade, Industry and Energy, was the first international competition for autonomous driving on actual roads. A total of nine teams participated in the final contest, four domestic teams and five teams allied with overseas universities such as Tsinghua University, Waseda University, and Nanyang Technological University.
Professor Kong said, “There is still a long way to go for fully autonomous vehicles that drive flexibly under congested traffic conditions. However, we will continue to our research in order to achieve high-quality autonomous driving technology.”
(Team KAT getting ready for the challenge)
2018.11.06 View 11220 -
KAIST to Open the Meditation Research Center
KAIST announced that it will open its Meditation Research Center next June. The center will serve as a place for the wellness of KAIST community as well as for furthering the cognitive sciences and its relevant convergence studies.
For facilitating the center, KAIST signed an MOU with the Foundation Academia Platonica in Seoul, an academy working for enriching the humanities and insight meditation on Aug.31. The Venerable Misan, a Buddhist monk well-known for his ‘Heart Smile Meditation’ program, will head the center.
The center will also conduct convergence research on meditation, which will translate into brain imaging, cognitive behavior, and its psychological effects. Built upon the research, the center expects to publish textbooks on meditation and will distribute them to the public and schools in an effort to widely disseminate the benefits of meditation.
As mindful meditation has become mainstream and more extensively studied, growing evidence suggests multiple psychological and physical benefits of these mindfulness exercises as well as for similar practices. Mind-body practices like meditation have been shown to reduce the body’s stress response by strengthening the relaxation response and lowering stress hormones.
The Venerable Misan, a Ph.D in philosophy from Oxford University, also serves as the director of the Sangdo Meditation Center and a professor at Joong-Ang Sangha University, a higher educational institution for Buddhist monks.
Monk Misan said that meditation will play a crucial part in educating creative students with an empathetic mindset. He added, “Hi-tech companies in Silicon Valley such as Google and Intel have long introduced meditation programs for stress management. Such practices will enhance the wellness of employees as well as their working efficiency.”
President Sung-Chul Shin said of the opening of the center, “From long ago, many universities in foreign countries including Havard, Stanford, Oxfor universities and the Max Planck Institute in Germany have applied scientific approaches to meditation and installed meditation centers. I am pleased to open our own center next year and I believe that it will bring more diverse opportunities for advancing convergent studies in AI and cognitive sciences.
2017.08.31 View 6143