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KAIST Develops AI-Driven Performance Prediction Model to Advance Space Electric Propulsion Technology
< (From left) PhD candidate Youngho Kim, Professor Wonho Choe, and PhD candidate Jaehong Park from the Department of Nuclear and Quantum Engineering >
Hall thrusters, a key space technology for missions like SpaceX's Starlink constellation and NASA's Psyche asteroid mission, are high-efficiency electric propulsion devices using plasma technology*. The KAIST research team announced that the AI-designed Hall thruster developed for CubeSats will be installed on the KAIST-Hall Effect Rocket Orbiter (K-HERO) CubeSat to demonstrate its in-orbit performance during the fourth launch of the Korean Launch Vehicle called Nuri rocket (KSLV-2) scheduled for November this year.
*Plasma is one of the four states of matter, where gases are heated to high energies, causing them to separate into charged ions and electrons. Plasma is used not only in space electric propulsion but also in semiconductor manufacturing, display processes, and sterilization devices.
On February 3rd, the research team from the KAIST Department of Nuclear and Quantum Engineering’s Electric Propulsion Laboratory, led by Professor Wonho Choe, announced the development of an AI-based technique to accurately predict the performance of Hall thrusters, the engines of satellites and space probes.
Hall thrusters provide high fuel efficiency, requiring minimal propellant to achieve significant acceleration of spacecrafts or satellites while producing substantial thrust relative to power consumption. Due to these advantages, Hall thrusters are widely used in various space missions, including the formation flight of satellite constellations, deorbiting maneuvers for space debris mitigation, and deep space missions such as asteroid exploration.
As the space industry continues to grow during the NewSpace era, the demand for Hall thrusters suited to diverse missions is increasing. To rapidly develop highly efficient, mission-optimized Hall thrusters, it is essential to predict thruster performance accurately from the design phase.
However, conventional methods have limitations, as they struggle to handle the complex plasma phenomena within Hall thrusters or are only applicable under specific conditions, leading to lower prediction accuracy.
The research team developed an AI-based performance prediction technique with high accuracy, significantly reducing the time and cost associated with the iterative design, fabrication, and testing of thrusters. Since 2003, Professor Wonho Choe’s team has been leading research on electric propulsion development in Korea. The team applied a neural network ensemble model to predict thruster performance using 18,000 Hall thruster training data points generated from their in-house numerical simulation tool.
The in-house numerical simulation tool, developed to model plasma physics and thrust performance, played a crucial role in providing high-quality training data. The simulation’s accuracy was validated through comparisons with experimental data from ten KAIST in-house Hall thrusters, with an average prediction error of less than 10%.
< Figure 1. This research has been selected as the cover article for the March 2025 issue (Volume 7, Issue 3) of the AI interdisciplinary journal, Advanced Intelligent Systems. >
The trained neural network ensemble model acts as a digital twin, accurately predicting the Hall thruster performance within seconds based on thruster design variables.
Notably, it offers detailed analyses of performance parameters such as thrust and discharge current, accounting for Hall thruster design variables like propellant flow rate and magnetic field—factors that are challenging to evaluate using traditional scaling laws.
This AI model demonstrated an average prediction error of less than 5% for the in-house 700 W and 1 kW KAIST Hall thrusters and less than 9% for a 5 kW high-power Hall thruster developed by the University of Michigan and the U.S. Air Force Research Laboratory. This confirms the broad applicability of the AI prediction method across different power levels of Hall thrusters.
Professor Wonho Choe stated, “The AI-based prediction technique developed by our team is highly accurate and is already being utilized in the analysis of thrust performance and the development of highly efficient, low-power Hall thrusters for satellites and spacecraft. This AI approach can also be applied beyond Hall thrusters to various industries, including semiconductor manufacturing, surface processing, and coating, through ion beam sources.”
< Figure 2. The AI-based prediction technique developed by the research team accurately predicts thrust performance based on design variables, making it highly valuable for the development of high-efficiency Hall thrusters. The neural network ensemble processes design variables, such as channel geometry and magnetic field information, and outputs key performance metrics like thrust and prediction accuracy, enabling efficient thruster design and performance analysis. >
Additionally, Professor Choe mentioned, “The CubeSat Hall thruster, developed using the AI technique in collaboration with our lab startup—Cosmo Bee, an electric propulsion company—will be tested in orbit this November aboard the K-HERO 3U (30 x 10 x 10 cm) CubeSat, scheduled for launch on the fourth flight of the KSLV-2 Nuri rocket.”
This research was published online in Advanced Intelligent Systems on December 25, 2024 with PhD candidate Jaehong Park as the first author and was selected as the journal’s cover article, highlighting its innovation.
< Figure 3. Image of the 150 W low-power Hall thruster for small and micro satellites, developed in collaboration with Cosmo Bee and the KAIST team. The thruster will be tested in orbit on the K-HERO CubeSat during the KSLV-2 Nuri rocket’s fourth launch in Q4 2025. >
This research was supported by the National Research Foundation of Korea’s Space Pioneer Program (200mN High Thrust Electric Propulsion System Development).
(Paper Title: Predicting Performance of Hall Effect Ion Source Using Machine Learning, DOI: https://doi.org/10.1002/aisy.202400555 )
< Figure 4. Graphs of the predicted thrust and discharge current of KAIST’s 700 W Hall thruster using the AI model (HallNN). The left image shows the Hall thruster operating in KAIST Electric Propulsion Laboratory’s vacuum chamber, while the center and right graphs present the prediction results for thrust and discharge current based on anode mass flow rate. The red lines represent AI predictions, and the blue dots represent experimental results, with a prediction error of less than 5%. >
2025.02.03 View 2771 -
KAIST Uncovers the Principles of Gene Expression Regulation in Cancer and Cellular Functions
< (From left) Professor Seyun Kim, Professor Gwangrog Lee, Dr. Hyoungjoon Ahn, Dr. Jeongmin Yu, Professor Won-Ki Cho, and (below) PhD candidate Kwangmin Ryu of the Department of Biological Sciences>
A research team at KAIST has identified the core gene expression networks regulated by key proteins that fundamentally drive phenomena such as cancer development, metastasis, tissue differentiation from stem cells, and neural activation processes. This discovery lays the foundation for developing innovative therapeutic technologies.
On the 22nd of January, KAIST (represented by President Kwang Hyung Lee) announced that the joint research team led by Professors Seyun Kim, Gwangrog Lee, and Won-Ki Cho from the Department of Biological Sciences had uncovered essential mechanisms controlling gene expression in animal cells.
Inositol phosphate metabolites produced by inositol metabolism enzymes serve as vital secondary messengers in eukaryotic cell signaling systems and are broadly implicated in cancer, obesity, diabetes, and neurological disorders.
The research team demonstrated that the inositol polyphosphate multikinase (IPMK) enzyme, a key player in the inositol metabolism system, acts as a critical transcriptional activator within the core gene expression networks of animal cells. Notably, although IPMK was previously reported to play an important role in the transcription process governed by serum response factor (SRF), a representative transcription factor in animal cells, the precise mechanism of its action was unclear.
SRF is a transcription factor directly controlling the expression of at least 200–300 genes, regulating cell growth, proliferation, apoptosis, and motility, and is indispensable for organ development, such as in the heart.
The team discovered that IPMK binds directly to SRF, altering the three-dimensional structure of the SRF protein. This interaction facilitates the transcriptional activity of various genes through the SRF activated by IPMK, demonstrating that IPMK acts as a critical regulatory switch to enhance SRF's protein activity.
< Figure 1. The serum response factor (SRF) protein, a key transcription factor in animal cells, directly binds to inositol polyphosphate multikinase (IPMK) enzyme and undergoes structural change to acquire DNA binding ability, and precisely regulates growth and differentiation of animal cells through transcriptional activation. >
The team further verified that disruptions in the direct interaction between IPMK and SRF lead to the reduced functionality and activity of SRF, causing severe impairments in gene expression.
By highlighting the significance of the intrinsically disordered region (IDR) in SRF, the researchers underscored the biological importance of intrinsically disordered proteins (IDPs). Unlike most proteins that adopt distinct structures through folding, IDPs, including those with IDRs, do not exhibit specific structures but play crucial biological roles, attracting significant attention in the scientific community.
Professor Seyun Kim commented, "This study provides a vital mechanism proving that IPMK, a key enzyme in the inositol metabolism system, is a major transcriptional activator in the core gene expression network of animal cells. By understanding fundamental processes such as cancer development and metastasis, tissue differentiation from stem cells, and neural activation through SRF, we hope this discovery will lead to the broad application of innovative therapeutic technologies."
The findings were published on January 7th in the international journal Nucleic Acids Research (IF=16.7, top 1.8% in Biochemistry and Molecular Biology), under the title “Single-molecule analysis reveals that IPMK enhances the DNA-binding activity of the transcription factor SRF" (DOI: 10.1093/nar/gkae1281).
This research was supported by the National Research Foundation of Korea's Mid-career Research Program, Leading Research Center Program, and Global Research Laboratory Program, as well as by the Suh Kyungbae Science Foundation and the Samsung Future Technology Development Program.
2025.01.24 View 6460 -
KAIST Awarded Presidential Commendation for Contributions in Software Industry
- At the “25th Software Industry Day” celebration held in the afternoon on Monday, December 2nd, 2024 at Yangjae L Tower in Seoul
- KAIST was awarded the “Presidential Commendation” for its contributions for the advancement of the Software Industry in the Group Category
- Korea’s first AI master’s and doctoral degree program opened at KAIST Kim Jaechul Graduate School of AI
- Focus on training non-major developers through SW Officer Training Academy "Jungle", Machine Learning Engineer Bootcamp, etc., talents who can integrate development and collaboration, and advanced talents in the latest AI technologies.
- Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI received Prime Minister’s Commendation for his contributions for the advancement of the software industry.
< Photo 1. Professor Kyung-soo Kim, the Senior Vice President for Planning and Budget (second from the left) and the Manager of Planning Team, Mr. Sunghoon Jung, stand at the stage after receiving the Presidential Commendation as KAIST was selected as one of the groups that contributed to the advancement of the software industry at the "25th Software Industry Day" celebration. >
“KAIST has been leading the way in achieving the grand goal of fostering 1 million AI talents in Korea by services that pan from providing various educational opportunities, from developing the capabilities of experts with no computer science specialty to fostering advanced professionals. I would like to thank all members of KAIST community who worked hard to achieve the great feat of receiving the Presidential Commendations.” (KAIST President Kwang Hyung Lee)
KAIST (President Kwang Hyung Lee) announced on December 3rd that it was selected as a group that contributed to the advancement of the software industry at the “2024 Software Industry Day” celebration held at the Yangjae El Tower in Seoul on the 2nd of December and received a presidential commendation.
The “Software Industry Day”, hosted by the Ministry of Science and ICT and organized by the National IT Industry Promotion Agency and the Korea Software Industry Association, is an event designed to promote the status of software industry workers in Korea and to honor their achievements.
Every year, those who have made significant contributions to policy development, human resource development, and export growth for industry revitalization are selected and awarded the ‘Software Industry Development Contribution Award.’
KAIST was recognized for its contribution to developing a demand-based, industrial field-centric curriculum and fostering non-major developers and convergence talents with the goal of expanding software value and fostering excellent human resources.
< Photo 2. Senior Vice President for Planning and Budget Kyung-soo Kim receiving the commendation as the representative of KAIST >
Specifically, it first opened the SW Officer Training Academy "Jungle" to foster convergent program developers equipped with the abilities to handle both the computer coding and human interactions for collaborations. This is a non-degree program that provides intensive study and assignments for 5 months for graduates and intellectuals without prior knowledge of computer science.
KAIST Kim Jaechul Graduate School of AI opened and operated Korea’s first master's and doctoral degree program in the field of artificial intelligence. In addition, it planned a “Machine Learning Engineers’ Boot Camp” and conducted lectures and practical training for a total of 16 weeks on the latest AI technologies such as deep learning basics and large language models. It aims to strengthen the practical capabilities of start-up companies while lowering the threshold for companies to introduce AI technology.
Also, KAIST was selected to participate in the 1st and 2nd stages of the Software-centered University Project and has been taking part in the project since 2016. Through this, it was highly evaluated for promoting curriculum based on latest technology, an autonomous system where students directly select integrated education, and expansion of internships.
< Photo 3. Professor Minjoon Seo of Kim Jaechul Graduate School of AI, who received the Prime Minister's Commendation for his contribution to the advancement of the software industry on the same day >
At the awards ceremony that day, Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI also received the Prime Minister's Commendation for his contribution to the advancement of the software industry. Professor Seo was recognized for his leading research achievements in the fields of AI and natural language processing by publishing 28 papers in top international AI conferences over the past four years.
At the same time, he was noted for his contributions to enhancing the originality and innovation of language model research, such as △knowledge encoding, △knowledge access and utilization, and △high-dimensional inference performance, and for demonstrating leadership in the international academic community.
President Kwang Hyung Lee of KAIST stated, “Our university will continue to do its best to foster software talents with global competitiveness through continuous development of cutting-edge curriculum and innovative degree systems.”
2024.12.03 View 3085 -
KAIST Proposes AI Training Method that will Drastically Shorten Time for Complex Quantum Mechanical Calculations
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model
- Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices
The close relationship between AI and high-performance scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for their AI-related research contributions in their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel AI approach.
KAIST (President Kwang-Hyung Lee) announced on the 30th of October that Professor Yong-Hoon Kim's team from the School of Electrical Engineering developed a 3D computer vision artificial neural network-based computation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations traditionally performed using supercomputers to derive the properties of materials.
< Figure 1. Various methodologies are utilized in the simulation of materials and materials, such as quantum mechanical calculations at the nanometer (nm) level, classical mechanical force fields at the scale of tens to hundreds of nanometers, continuum dynamics calculations at the macroscopic scale, and calculations that mix simulations at different scales. These simulations are already playing a key role in a wide range of basic research and application development fields in combination with informatics techniques. Recently, there have been active efforts to introduce machine learning techniques to radically accelerate simulations, but research on introducing machine learning techniques to quantum mechanical electronic structure calculations, which form the basis of high-scale simulations, is still insufficient. >
The quantum mechanical density functional theory (DFT) calculations using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow fast and accurate prediction of material properties.
*Density functional theory (DFT): A representative theory of ab initio (first principles) calculations that calculate quantum mechanical properties from the atomic level.
However, practical DFT calculations require generating 3D electron density and solving quantum mechanical equations through a complex, iterative self-consistent field (SCF)* process that must be repeated tens to hundreds of times. This restricts its application to systems with only a few hundred to a few thousand atoms.
*Self-consistent field (SCF): A scientific computing method widely used to solve complex many-body problems that must be described by a number of interconnected simultaneous differential equations.
Professor Yong-Hoon Kim’s research team questioned whether recent advancements in AI techniques could be used to bypass the SCF process. As a result, they developed the DeepSCF model, which accelerates calculations by learning chemical bonding information distributed in a 3D space using neural network algorithms from the field of computer vision.
< Figure 2. The deepSCF methodology developed in this study provides a way to rapidly accelerate DFT calculations by avoiding the self-consistent field process (orange box) that had to be performed repeatedly in traditional quantum mechanical electronic structure calculations through artificial neural network techniques (green box). The self-consistent field process is a process of predicting the 3D electron density, constructing the corresponding potential, and then solving the quantum mechanical Cohn-Sham equations, repeating tens to hundreds of times. The core idea of the deepSCF methodology is that the residual electron density (δρ), which is the difference between the electron density (ρ) and the sum of the electron densities of the constituent atoms (ρ0), corresponds to chemical bonding information, so the self-consistent field process is replaced with a 3D convolutional neural network model. >
The research team focused on the fact that, according to density functional theory, electron density contains all quantum mechanical information of electrons, and that the residual electron density — the difference between the total electron density and the sum of the electron densities of the constituent atoms — contains chemical bonding information. They used this as the target for machine learning.
They then adopted a dataset of organic molecules with various chemical bonding characteristics, and applied random rotations and deformations to the atomic structures of these molecules to further enhance the model’s accuracy and generalization capabilities. Ultimately, the research team demonstrated the validity and efficiency of the DeepSCF methodology on large, complex systems.
< Figure 3. An example of applying the deepSCF methodology to a carbon nanotube-based DNA sequence analysis device model (top left). In addition to classical mechanical interatomic forces (bottom right), the residual electron density (top right) and quantum mechanical electronic structure properties such as the electronic density of states (DOS) (bottom left) containing information on chemical bonding are rapidly predicted with an accuracy corresponding to the standard DFT calculation results that perform the SCF process. >
Professor Yong-Hoon Kim, who supervised the research, explained that his team had found a way to map quantum mechanical chemical bonding information in a 3D space onto artificial neural networks. He noted, “Since quantum mechanical electron structure calculations underpin materials simulations across all scales, this research establishes a foundational principle for accelerating material calculations using artificial intelligence.”
Ryong-Gyu Lee, a PhD candidate in the School of Electrical Engineering, served as the first author of this research, which was published online on October 24 in Npj Computational Materials, a prestigious journal in the field of material computation. (Paper title: “Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints”)
This research was conducted with support from the KAIST High-Risk Research Program for Graduate Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.
2024.10.30 View 3361 -
KAIST develops an artificial muscle device that produces force 34 times its weight
- Professor IlKwon Oh’s research team in KAIST’s Department of Mechanical Engineering developed a soft fluidic switch using an ionic polymer artificial muscle that runs with ultra-low power to lift objects 34 times greater than its weight.
- Its light weight and small size make it applicable to various industrial fields such as soft electronics, smart textiles, and biomedical devices by controlling fluid flow with high precision, even in narrow spaces.
Soft robots, medical devices, and wearable devices have permeated our daily lives. KAIST researchers have developed a fluid switch using ionic polymer artificial muscles that operates at ultra-low power and produces a force 34 times greater than its weight. Fluid switches control fluid flow, causing the fluid to flow in a specific direction to invoke various movements.
KAIST (President Kwang-Hyung Lee) announced on the 4th of January that a research team under Professor IlKwon Oh from the Department of Mechanical Engineering has developed a soft fluidic switch that operates at ultra-low voltage and can be used in narrow spaces.
Artificial muscles imitate human muscles and provide flexible and natural movements compared to traditional motors, making them one of the basic elements used in soft robots, medical devices, and wearable devices. These artificial muscles create movements in response to external stimuli such as electricity, air pressure, and temperature changes, and in order to utilize artificial muscles, it is important to control these movements precisely.
Switches based on existing motors were difficult to use within limited spaces due to their rigidity and large size. In order to address these issues, the research team developed an electro-ionic soft actuator that can control fluid flow while producing large amounts of force, even in a narrow pipe, and used it as a soft fluidic switch.
< Figure 1. The separation of fluid droplets using a soft fluid switch at ultra-low voltage. >
The ionic polymer artificial muscle developed by the research team is composed of metal electrodes and ionic polymers, and it generates force and movement in response to electricity. A polysulfonated covalent organic framework (pS-COF) made by combining organic molecules on the surface of the artificial muscle electrode was used to generate an impressive amount of force relative to its weight with ultra-low power (~0.01V).
As a result, the artificial muscle, which was manufactured to be as thin as a hair with a thickness of 180 µm, produced a force more than 34 times greater than its light weight of 10 mg to initiate smooth movement. Through this, the research team was able to precisely control the direction of fluid flow with low power.
< Figure 2. The synthesis and use of pS-COF as a common electrode-electrolyte host for electroactive soft fluid switches. A) The synthesis schematic of pS-COF. B) The schematic diagram of the operating principle of the electrochemical soft switch. C) The schematic diagram of using a pS-COF-based electrochemical soft switch to control fluid flow in dynamic operation. >
Professor IlKwon Oh, who led this research, said, “The electrochemical soft fluidic switch that operate at ultra-low power can open up many possibilities in the fields of soft robots, soft electronics, and microfluidics based on fluid control.” He added, “From smart fibers to biomedical devices, this technology has the potential to be immediately put to use in a variety of industrial settings as it can be easily applied to ultra-small electronic systems in our daily lives.”
The results of this study, in which Dr. Manmatha Mahato, a research professor in the Department of Mechanical Engineering at KAIST, participated as the first author, were published in the international academic journal Science Advances on December 13, 2023. (Paper title: Polysulfonated Covalent Organic Framework as Active Electrode Host for Mobile Cation Guests in Electrochemical Soft Actuator)
This research was conducted with support from the National Research Foundation of Korea's Leader Scientist Support Project (Creative Research Group) and Future Convergence Pioneer Project.
* Paper DOI: https://www.science.org/doi/abs/10.1126/sciadv.adk9752
2024.01.11 View 8563 -
KAIST’s Robo-Dog “RaiBo” runs through the sandy beach
KAIST (President Kwang Hyung Lee) announced on the 25th that a research team led by Professor Jemin Hwangbo of the Department of Mechanical Engineering developed a quadrupedal robot control technology that can walk robustly with agility even in deformable terrain such as sandy beach.
< Photo. RAI Lab Team with Professor Hwangbo in the middle of the back row. >
Professor Hwangbo's research team developed a technology to model the force received by a walking robot on the ground made of granular materials such as sand and simulate it via a quadrupedal robot. Also, the team worked on an artificial neural network structure which is suitable in making real-time decisions needed in adapting to various types of ground without prior information while walking at the same time and applied it on to reinforcement learning. The trained neural network controller is expected to expand the scope of application of quadrupedal walking robots by proving its robustness in changing terrain, such as the ability to move in high-speed even on a sandy beach and walk and turn on soft grounds like an air mattress without losing balance.
This research, with Ph.D. Student Soo-Young Choi of KAIST Department of Mechanical Engineering as the first author, was published in January in the “Science Robotics”. (Paper title: Learning quadrupedal locomotion on deformable terrain).
Reinforcement learning is an AI learning method used to create a machine that collects data on the results of various actions in an arbitrary situation and utilizes that set of data to perform a task. Because the amount of data required for reinforcement learning is so vast, a method of collecting data through simulations that approximates physical phenomena in the real environment is widely used.
In particular, learning-based controllers in the field of walking robots have been applied to real environments after learning through data collected in simulations to successfully perform walking controls in various terrains.
However, since the performance of the learning-based controller rapidly decreases when the actual environment has any discrepancy from the learned simulation environment, it is important to implement an environment similar to the real one in the data collection stage. Therefore, in order to create a learning-based controller that can maintain balance in a deforming terrain, the simulator must provide a similar contact experience.
The research team defined a contact model that predicted the force generated upon contact from the motion dynamics of a walking body based on a ground reaction force model that considered the additional mass effect of granular media defined in previous studies.
Furthermore, by calculating the force generated from one or several contacts at each time step, the deforming terrain was efficiently simulated.
The research team also introduced an artificial neural network structure that implicitly predicts ground characteristics by using a recurrent neural network that analyzes time-series data from the robot's sensors.
The learned controller was mounted on the robot 'RaiBo', which was built hands-on by the research team to show high-speed walking of up to 3.03 m/s on a sandy beach where the robot's feet were completely submerged in the sand. Even when applied to harder grounds, such as grassy fields, and a running track, it was able to run stably by adapting to the characteristics of the ground without any additional programming or revision to the controlling algorithm.
In addition, it rotated with stability at 1.54 rad/s (approximately 90° per second) on an air mattress and demonstrated its quick adaptability even in the situation in which the terrain suddenly turned soft.
The research team demonstrated the importance of providing a suitable contact experience during the learning process by comparison with a controller that assumed the ground to be rigid, and proved that the proposed recurrent neural network modifies the controller's walking method according to the ground properties.
The simulation and learning methodology developed by the research team is expected to contribute to robots performing practical tasks as it expands the range of terrains that various walking robots can operate on.
The first author, Suyoung Choi, said, “It has been shown that providing a learning-based controller with a close contact experience with real deforming ground is essential for application to deforming terrain.” He went on to add that “The proposed controller can be used without prior information on the terrain, so it can be applied to various robot walking studies.”
This research was carried out with the support of the Samsung Research Funding & Incubation Center of Samsung Electronics.
< Figure 1. Adaptability of the proposed controller to various ground environments. The controller learned from a wide range of randomized granular media simulations showed adaptability to various natural and artificial terrains, and demonstrated high-speed walking ability and energy efficiency. >
< Figure 2. Contact model definition for simulation of granular substrates. The research team used a model that considered the additional mass effect for the vertical force and a Coulomb friction model for the horizontal direction while approximating the contact with the granular medium as occurring at a point. Furthermore, a model that simulates the ground resistance that can occur on the side of the foot was introduced and used for simulation. >
2023.01.26 View 14146 -
Yuji Roh Awarded 2022 Microsoft Research PhD Fellowship
KAIST PhD candidate Yuji Roh of the School of Electrical Engineering (advisor: Prof. Steven Euijong Whang) was selected as a recipient of the 2022 Microsoft Research PhD Fellowship.
< KAIST PhD candidate Yuji Roh (advisor: Prof. Steven Euijong Whang) >
The Microsoft Research PhD Fellowship is a scholarship program that recognizes outstanding graduate students for their exceptional and innovative research in areas relevant to computer science and related fields. This year, 36 people from around the world received the fellowship, and Yuji Roh from KAIST EE is the only recipient from universities in Korea. Each selected fellow will receive a $10,000 scholarship and an opportunity to intern at Microsoft under the guidance of an experienced researcher.
Yuji Roh was named a fellow in the field of “Machine Learning” for her outstanding achievements in Trustworthy AI. Her research highlights include designing a state-of-the-art fair training framework using batch selection and developing novel algorithms for both fair and robust training. Her works have been presented at the top machine learning conferences ICML, ICLR, and NeurIPS among others. She also co-presented a tutorial on Trustworthy AI at the top data mining conference ACM SIGKDD. She is currently interning at the NVIDIA Research AI Algorithms Group developing large-scale real-world fair AI frameworks.
The list of fellowship recipients and the interview videos are displayed on the Microsoft webpage and Youtube.
The list of recipients: https://www.microsoft.com/en-us/research/academic-program/phd-fellowship/2022-recipients/
Interview (Global): https://www.youtube.com/watch?v=T4Q-XwOOoJc
Interview (Asia): https://www.youtube.com/watch?v=qwq3R1XU8UE
[Highlighted research achievements by Yuji Roh: Fair batch selection framework]
[Highlighted research achievements by Yuji Roh: Fair and robust training framework]
2022.10.28 View 11784 -
Anonymous Donor Makes a Gift of Property Valued at 30 Billion KRW
The KAIST Development Foundation announced on May 9 that an anonymous donor in his 50s made a gift of real estate valued at 30 billion KRW. This is the first donation from an anonymous benefactor on such a grand scale. The benefactor expressed his wishes to fund scholarships for students in need and R&D for medical and bio sciences.
According to the Development Foundation official, the benefactor is reported to have said that he felt burdened that he earned much more than he needed and was looking for the right way to share his assets. The benefactor refused to hold an official donation ceremony and meeting with high-level university administrators.
The donor believes that KAIST is filled with young and dynamic energy, saying, “I would like to help KAIST move forward and create breakthroughs that will benefit the nation as well as all humanity.”
Before making up his mind to give his asset to KAIST, he had planned to establish his own social foundation but he changed his mind. “I decided that an investment in education would be the best investment,” he said. He explained that he was inspired by his KAIST graduate friend who is running a company. He was deeply motivated to help KAIST after witnessing the KAIST graduate’s passion for conducting his business.
After receiving the gift, KAIST President Kwang Hyung Lee was thankful for the full support and trust of the benefactor. “We will spare no effort to foster next-generation talents and advance science and technology in the field of biomedicine.”
2022.05.11 View 5247 -
Nanoscale Self-Assembling Salt-Crystal ‘Origami’ Balls Envelop Liquids
Mechanical engineers have devised a ‘crystal capillary origami’ technique where salt crystals spontaneously encapsulate liquid droplets
Researchers have developed a technique whereby they can spontaneously encapsulate microscopic droplets of water and oil emulsion in a tiny sphere made of salt crystals—sort of like a minute, self-constructing origami soccer ball filled with liquid. The process, which they are calling ‘crystal capillary origami,’ could be used in a range of fields from more precise drug delivery to nanoscale medical devices.The technique is described in a paper appearing in the journal Nanoscale on September 21.
Capillary action, or ‘capillarity,’ will be familiar to most people as the way that water or other liquids can move up narrow tubes or other porous materials seemingly in defiance of gravity (for example within the vascular systems of plants, or even more simply, the drawing up of paint between the hairs of a paintbrush). This effect is due to the forces of cohesion (the tendency of a liquid’s molecules to stick together), which results in surface tension, and adhesion (their tendency to stick to the surface of other substances). The strength of the capillarity depends on the chemistry of the liquid, the chemistry of the porous material, and on the other forces acting on them both. For example, a liquid with lower surface tension than water would not be able to hold up a water strider insect.
Less well known is a related phenomenon, elasto-capillarity, that takes advantage of the relationship between capillarity and the elasticity of a very tiny flat sheet of a solid material. In certain circumstances, the capillary forces can overcome the elastic bending resistance of the sheet.
This relationship can be exploited to create ‘capillary origami,’ or three-dimensional structures. When a liquid droplet is placed on the flat sheet, the latter can spontaneously encapsulate the former due to surface tension. Capillary origami can take on other forms including wrinkling, buckling, or self-folding into other shapes. The specific geometrical shape that the 3D capillary origami structure ends up taking is determined by both the chemistry of the flat sheet and that of the liquid, and by carefully designing the shape and size of the sheet.
There is one big problem with these small devices, however. “These conventional self-assembled origami structures cannot be completely spherical and will always have discontinuous boundaries, or what you might call ‘edges,’ as a result of the original two-dimensional shape of the sheet,” said Kwangseok Park, a lead researcher on the project. He added, “These edges could turn out to be future defects with the potential for failure in the face of increased stress.” Non-spherical particles are also known to be more disadvantageous than spherical particles in terms of cellular uptake.
Professor Hyoungsoo Kim from the Department of Mechanical Engineering explained, “This is why researchers have long been on the hunt for substances that could produce a fully spherical capillary origami structure.”
The authors of the study have demonstrated such an origami sphere for the first time. They showed how instead of a flat sheet, the growth of salt-crystals can perform capillary origami action in a similar manner. What they call ‘crystal capillary origami’ spontaneously constructs a smooth spherical shell capsule from these same surface tension effects, but now the spontaneous encapsulation of a liquid is determined by the elasto-capillary conditions of growing crystals.
Here, the term ‘salt’ refers to a compound of one positively charged ion and another negatively charged. Table salt, or sodium chloride, is just one example of a salt. The researchers used four other salts: calcium propionate, sodium salicylate, calcium nitrate tetrahydrate, and sodium bicarbonate to envelop a water-oil emulsion. Normally, a salt such as sodium chloride has a cubical crystal structure, but these four salts form plate-like structures as crystallites or ‘grains’ (the microscopic shape that forms when a crystal first starts to grow) instead. These plates then self-assemble into perfect spheres.
Using scanning electron microscopy and X-ray diffraction analysis, they investigated the mechanism of such formation and concluded that it was ‘Laplace pressure’ that drives the crystallite plates to cover the emulsion surface. Laplace pressure describes the pressure difference between the interior and exterior of a curved surface caused by the surface tension at the interface between the two substances, in this case between the salt water and the oil.
The researchers hope that these self-assembling nanostructures can be used for encapsulation applications in a range of sectors, from the food industry and cosmetics to drug delivery and even tiny medical devices.
-Publication
Kwangseok Park, Hyoungsoo Kim “Crystal capillary origami capsule with self-assembled nanostructure,” Nanoscale, 13(35), 14656-14665 (DOI: 10.1039/d1nr02456f)
-Profile
Professor Hyoungsoo Kim
Fluid and Interface Laboratory
http://fil.kaist.ac.kr
Department of Mechanical Engineering
KAIST
2021.11.04 View 9034 -
Wearable Device to Monitor Sweat in Real Time
An on-skin platform for the wireless monitoring of flow rate, cumulative loss, and temperature of sweat in real time
An electronic patch can monitor your sweating and check your health status. Even more, the soft microfluidic device that adheres to the surface of the skin, captures, stores, and performs biomarker analysis of sweat as it is released through the eccrine glands.
This wearable and wireless electronic device developed by Professor Kyeongha Kwon and her collaborators is a digital and wireless platform that could help track the so-called ‘filling process’ of sweat without having to visually examine the device. The platform was integrated with microfluidic systems to analyze the sweat’s components.
To monitor the sweat release rate in real time, the researchers created a ‘thermal flow sensing module.’ They designed a sophisticated microfluidic channel to allow the collected sweat to flow through a narrow passage and a heat source was placed on the outer surface of the channel to induce a heat exchange between the sweat and the heated channel.
As a result, the researchers could develop a wireless electronic patch that can measure the temperature difference in a specific location upstream and downstream of the heat source with an electronic circuit and convert it into a digital signal to measure the sweat release rate in real time. The patch accurately measured the perspiration rate in the range of 0-5 microliters/minute (μl/min), which was considered physiologically significant. The sensor can measure the flow of sweat directly and then use the information it collected to quantify total sweat loss. Moreover, the device features advanced microfluidic systems and colorimetric chemical reagents to gather pH measurements and determine the concentration of chloride, creatinine, and glucose in a user's sweat.
Professor Kwon said that these indicators could be used to diagnose various diseases related with sweating such as cystic fibrosis, diabetes, kidney dysfunction, and metabolic alkalosis. “As the sweat flowing in the microfluidic channel is completely separated from the electronic circuit, the new patch overcame the shortcomings of existing flow rate measuring devices, which were vulnerable to corrosion and aging,” she explained.
The patch can be easily attached to the skin with flexible circuit board printing technology and silicone sealing technology. It has an additional sensor that detects changes in skin temperature. Using a smartphone app, a user can check the data measured by the wearable patch in real time.
Professor Kwon added, “This patch can be widely used for personal hydration strategies, the detection of dehydration symptoms, and other health management purposes. It can also be used in a systematic drug delivery system, such as for measuring the blood flow rate in blood vessels near the skin’s surface or measuring a drug’s release rate in real time to calculate the exact dosage.”
-PublicationKyeongha Kwon, Jong Uk Kim, John A. Rogers, et al. “An on-skin platform for wireless monitoring of flow rate, cumulative loss and temperature of sweat in real time.” Nature Electronics (doi.org/10.1038/s41928-021-00556-2)
-ProfileProfessor Kyeongha KwonSchool of Electrical EngineeringKAIST
2021.06.25 View 9963 -
‘Game&Art: Auguries of Fantasy’ Features Future of the Metaverse
‘Game & Art: Auguries of Fantasy,’ a special exhibition combining art and technology will feature the new future of metaverse fantasy. The show will be hosted at the Daejeon Creative Center at the Daejeon Museum of Art through September 5.
This show exhibits a combination of science and technology with culture and arts, and introduces young artists whose creativity will lead to new opportunities in games and art. The Graduate School of Culture Technology was designated as a leading culture content academy in 2020 by the Ministry of Culture, Sports & Tourism and the Korea Creative Content Agency for fostering the R&D workforce in creative culture technology.
NCsoft sponsored the show and also participated as an artist. It combined its game-composing elements and technologies with other genres, including data for game construction, scenarios for forming a worldview, and game art and sound. All of the contents can be experienced online in a virtual space as well as offline, and can be easily accessed through personal devices.
Characterized by the themes ‘timeless’ and ‘spaceless’ which connect the past, present, and future, and space created in the digital world. The exhibition gives audience members an opportunity to experience freedom beyond the constraints of time and space under the theme of a fantasy reality created by games and art.
"Computer games, which began in the 1980s, have become cultural content that spans generations, and games are now the fusion field for leading-edge technologies including computer graphics, sound, human-computer interactions, big data, and AI. They are also the best platform for artistic creativity by adding human imagination to technology," said Professor Joo-Han Nam from the Graduate School of Culture Technology, who led the project.
"Our artists wanted to convey various messages to our society through works that connect the past, present, and future through games."
Ju-young Oh's "Unexpected Scenery V2" and "Hope for Rats V2" display game-type media work that raises issues surrounding technology, such as the lack of understanding behind various scientific achievements, the history of accidental achievements, and the side effects of new conveniences.
Tae-Wan Kim, in his work themed ‘healing’ combined the real-time movement of particles which follows the movements of people recorded as digital data. Metadata is collected by sensors in the exhibition space, and floating particle forms are evolved into abstract graphic designs according to audio-visual responses.
Meanwhile, ‘SOS’ is a collaboration work from six KAIST researchers (In-Hwa Yeom, Seung-Eon Lee, Seong-Jin Jeon, Jin-Seok Hong, Hyung-Seok Yoon, and Sang-Min Lee). SOS is based on diverse perspectives embracing phenomena surrounding contemporary natural resources. Audience members follow a gamified path between the various media-elements composing the art’s environment. Through this process, the audience can experience various emotions such as curiosity, suspicion, and recovery.
‘Diversity’ by Sung-Hyun Kim uses devices that recognize the movements of hands and fingers to provide experiences exploring the latent space of game play images learned by deep neural networks. Image volumes generated by neural networks are visualized through physics-based, three-dimensional, volume-rendering algorithms, and a series of processes were implemented based on the self-written code.
2021.06.21 View 8952 -
Krafton Matches Alumni Donations to Raise 11 Billion KRW for SW Developers
Alumni donations from the School of Computing, including Baemin and Devsisters, continue to grow
Alumni from the KAIST School of Computing who are current and former developers at the leading game company Krafton, established by KAIST alumna Byung-Gyu Chang, made an agreement to help raise 11 billion KRW during a ceremony on June 4. The funds raised in the matching grant will be used to nurture software developers.
Krafton Chairman Chang donated 10 billion won last January. His donation inspired other alumni working at Krafton as well as its former developers. Eleven KAIST alumni raised 5.5 billion KRW in two months and discussed the matching grant idea with Chairman Chang.
The Krafton matching grant ceremony was attended by President Kwang Hyung Lee, Provost and Executive Vice President Seung Seob Lee, Vice President for Research Sang Yup Lee, Head of the School of Computing Sukyoung Ryu, Krafton Chairman Byung-gyu Chang, and KAIST alumnus from Krafton Seung-woo Shin. Other alumni donors including Krafton CEO Changhan Kim joined the ceremony online.
Krafton CEO Changhan Kim said, “Just as our alma mater played an important role in growing our company, we hope that our donation could help support good developers. This will not only help our company, but advance our industry.”
KAIST and Krafton also signed a business agreement to foster competitive developers. Krafton said it plans to continue giving back to society through the matching grant program.
Head of the School of Computing Sukyoung Ryu thanked Chairman Chang and alumni who took part in the fund raising, saying, “To take the lead in rapidly changing computer technology, we desperately need more top students, faculty members, and facilities. We need more resources and infrastructure for interdisciplinary research.”
The School of Computing has seen significant growth recently. Its number of undergraduate students has increased from 450 in 2016 to more than 900 in 2021. With this donation, the school will expand its current buildings to provide diverse educational and mentoring programs in more spacious facilities.
Seung-woo Shin (Class of ’92), who joined Krafton’s matching grant, said, “I have always been thankful for the people I met and what I learned at KAIST. I was moved by the idea of giving back to the school.”
Seong-jung Ryu (Class of ’97) said, “This donation reminded me of the good times I had back then. I thought it was crucial that the department’s facilities be extended, so I naturally wanted to take part.”
Alumni donations, especially from the School of Computing, have also continued to grow more recently. Woowa Brothers Corp. CEO Beom-Jun Kim, the developer of the meal delivery app ‘Baemin’ donated 100 million KRW in April. Baemin became the most used app in the country during the COVID-19 pandemic.
He explained, “I have been thinking about ways to give something to the next generation, rather than ‘paying back’ those who helped me in the past.”
Encouraged by Baemin’s donation, alumni couple Ha-Yeon Seo and Dong-Hun Hahn from the School of Computing and eleven alumni engineers working at Devsisters Corp. also followed suit.
2021.06.09 View 10103