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The 3rd Global Entrepreneurship Summer School (GESS 2024) Successfully Completed in Silicon Valley
The 2024 Global Entrepreneurship Summer School (2024 KAIST GESS), hosted by the Office of Global Initiatives under the KAIST International Office (Director Man-Sung Yim), was held for the third time. This program allows students to visit Silicon Valley, a global startup hub, to directly experience its famous startup ecosystem and develop their capabilities for global expansion. A total of 20 students were selected through applications, interviews, final presentations, mentoring, and peer evaluations. Additionally, 17 students from the KAIST Impact MBA course at the KAIST Business School also participated. Before starting the Silicon Valley program, participants received mentoring on business model development and pitching advice from a senior entrepreneur at KAIST for about two months, beginning last May. Afterward, they developed business items for each team at KAIST’s main campus in Daejeon. For seven days, starting from June 23rd, workshops were held under the themes of global entrepreneurship, learning through failure, capital and network, and startup culture at KOTRA Silicon Valley Trade Center, JP Morgan, and Plug and Play Tech Center. This program's lecture series provided prospective entrepreneurs with the opportunity to systematically learn the mindset and gain the experience needed to start a global business. The participants also visited local companies and gained experience in the field of global technology startups. Visits included Bear Robotics (CEO John Ha), Soundable Health (CEO Cathering Song), ImpriMed (CEO Sungwon Lim), Phantom AI (CEO Hyunggi Cho), B Garage (CEO Aiden Kim), and Simple Steps (CEO Doyeon Kim). Lectures contained vivid experiences from Silicon Valley CEOs and company tours boosted the students' passion for entrepreneurship. In particular, Doyeon Kim, CEO of Simple Steps, which helps prevent career breaks for Korean female immigrants in Silicon Valley and allows talented female immigrants to demonstrate their abilities in society, said, “As a KAIST alumna entrepreneur, it was meaningful to share my experience with this generation of students who dream of starting a global business and creating social enterprises in the United States.” This program also included a tour of Silicon Valley's big tech companies that have made a significant impact on the digital ecosystem through technological advancement and innovation. This included Broadcom, which maintains a strong global presence in the semiconductor and infrastructure software technology fields. At the invitation of Chairman Hock Tan, GESS participants had the opportunity to attend his lecture and ask questions. Chairman Tan, who received an honorary doctorate in engineering from KAIST last February, emphasized that experiencing failure and giving consistent effort over a long period of time are more important than anything else in order to grow as a global entrepreneur, and that technologies influencing the global market evolve over generations. < Photo. Group photo of GESS 2024 participants at Broadcom with Chairman Hock Tan (center) ⓒBroadcom> As part of this program, participants conducted a volunteer program called 'Let's play with AI+ Tech' with the Sunnyvale community in Silicon Valley and Foothill College to help grow together with the community. Through this program, GESS participants cultivated the virtues of a global leader. In this volunteer activity, low-income elementary school students and parents from the Sunnyvale community participated in chatbot training led by KAIST students, providing an opportunity to work with underprivileged groups in the local community. In the final pitching event, the highlight of the program, local venture investors from Silicon Valley were invited as judges and evaluated the pitches for each team's business items. The participating students, who developed their own business models while receiving advice through face-to-face mentoring from a professional accelerator in Silicon Valley, showcased their creative and innovative ideas, presenting themselves as future global entrepreneurs. Merey Makhmutova (BS in Civil and Environmental Engineering) from the K-Bridge team, who won the final pitch, expressed her ambition: “Even before GESS pitch day, our team kept refining the pitch deck as we attended the lectures and benefitted from the mentoring. Our intense teamwork was a significant reason why we ultimately won first prize.” She added that K-Bridge aims to win an award at the upcoming UKC Pitching Competition and expressed her gratitude for being able to participate in this program. Arseniy Kan (BS in Electrical Engineering) from the KAIST Enablers team, who took second place, said, “The 2024 KAIST GESS Program became the most unforgettable and precious opportunity of my lifetime, and I dream of using this opportunity as a stepping stone to becoming a global entrepreneur.“ Additionally, Kangster (CEO Kang Kim), who won the Impact MBA final pitching session, had the opportunity to secure a meeting with a local investment company after their GESS final pitch. The 2024 KAIST GESS was held in cooperation with the KAIST International Office, the KAIST College of Business, and Startup KAIST. Director Man-Sung Yim from the Office of Global Initiatives, who hosted the event, said, “KAIST students will grow into leaders with global influence and contribute to the international community by creating global value. At the same time, we hope to raise the international status of our university.” Professor Sangchan Park, who led the 17 Impact MBA students in this educational program, added, “Meeting with companies leading the global market and visiting Silicon Valley has been a valuable learning experience for students aiming to start a global startup.” KAIST plans to continue promoting its global entrepreneurship education program by enriching its curriculum each year and helping students grow into entrepreneurs with the virtues of global leaders.
2024.07.03
View 229
A 20-year-old puzzle solved: KAIST research team reveals the 'three-dimensional vortex' of zero-dimensional ferroelectrics
Materials that can maintain a magnetized state by themselves without an external magnetic field (i.e., permanent magnets) are called ferromagnets. Ferroelectrics can be thought of as the electric counterpart to ferromagnets, as they maintain a polarized state without an external electric field. It is well-known that ferromagnets lose their magnetic properties when reduced to nano sizes below a certain threshold. What happens when ferroelectrics are similarly made extremely small in all directions (i.e., into a zero-dimensional structure such as nanoparticles) has been a topic of controversy for a long time. < (From left) Professor Yongsoo Yang, the corresponding author, and Chaehwa Jeong, the first author studying in the integrated master’s and doctoral program, of the KAIST Department of Physics > The research team led by Dr. Yongsoo Yang from the Department of Physics at KAIST has, for the first time, experimentally clarified the three-dimensional, vortex-shaped polarization distribution inside ferroelectric nanoparticles through international collaborative research with POSTECH, SNU, KBSI, LBNL and University of Arkansas. About 20 years ago, Prof. Laurent Bellaiche (currently at University of Arkansas) and his colleagues theoretically predicted that a unique form of polarization distribution, arranged in a toroidal vortex shape, could occur inside ferroelectric nanodots. They also suggested that if this vortex distribution could be properly controlled, it could be applied to ultra-high-density memory devices with capacities over 10,000 times greater than existing ones. However, experimental clarification had not been achieved due to the difficulty of measuring the three-dimensional polarization distribution within ferroelectric nanostructures. The research team at KAIST successfully solved this 20-year-old challenge by implementing a technique called atomic electron tomography. This technique works by acquiring atomic-resolution transmission electron microscope images of the nanomaterials from multiple tilt angles, and then reconstructing them back into three-dimensional structures using advanced reconstruction algorithms. Electron tomography can be understood as essentially the same method with the CT scans used in hospitals to view internal organs in three dimensions; the KAIST team adapted it uniquely for nanomaterials, utilizing an electron microscope at the single-atom level. < Figure 1. Three-dimensional polarization distribution of BaTiO3 nanoparticles revealed by atomic electron tomography. >(Left) Schematic of the electron tomography technique, which involves acquiring transmission electron microscope images at multiple tilt angles and reconstructing them into 3D atomic structures.(Center) Experimentally determined three-dimensional polarization distribution inside a BaTiO3 nanoparticle via atomic electron tomography. A vortex-like structure is clearly visible near the bottom (blue dot).(Right) A two-dimensional cross-section of the polarization distribution, thinly sliced at the center of the vortex, with the color and arrows together indicating the direction of the polarization. A distinct vortex structure can be observed. Using atomic electron tomography, the team completely measured the positions of cation atoms inside barium titanate (BaTiO3) nanoparticles, a well-known ferroelectric material, in three dimensions. From the precisely determined 3D atomic arrangements, they were able to further calculate the internal three-dimensional polarization distribution at the single-atom level. The analysis of the polarization distribution revealed, for the first time experimentally, that topological polarization orderings including vortices, anti-vortices, skyrmions, and a Bloch point occur inside the 0-dimensional ferroelectrics, as theoretically predicted 20 years ago. Furthermore, it was also found that the number of internal vortices can be controlled depending on their sizes. Prof. Sergey Prosandeev and Prof. Bellaiche (who proposed with other co-workers the polar vortex ordering theoretically 20 years ago), joined this collaboration and further proved that the vortex distribution results obtained from experiments are consistent with theoretical calculations. By controlling the number and orientation of these polarization distributions, it is expected that this can be utilized into next-generation high-density memory device that can store more than 10,000 times the amount of information in the same-sized device compared to existing ones. Dr. Yang, who led the research, explained the significance of the results: “This result suggests that controlling the size and shape of ferroelectrics alone, without needing to tune the substrate or surrounding environmental effects such as epitaxial strain, can manipulate ferroelectric vortices or other topological orderings at the nano-scale. Further research could then be applied to the development of next-generation ultra-high-density memory.” This research, with Chaehwa Jeong from the Department of Physics at KAIST as the first author, was published online in Nature Communications on May 8th (Title: Revealing the Three-Dimensional Arrangement of Polar Topology in Nanoparticles). The study was mainly supported by the National Research Foundation of Korea (NRF) Grants funded by the Korean Government (MSIT).
2024.05.31
View 1112
Revolutionary 'scLENS' Unveiled to Decode Complex Single-Cell Genomic Data
Unlocking biological information from complex single-cell genomic data has just become easier and more precise, thanks to the innovative 'scLENS' tool developed by the Biomedical Mathematics Group within the IBS Center for Mathematical and Computational Sciences led by Chief Investigator Jae Kyoung Kim, who is also a professor at KAIST. This new finding represents a significant leap forward in the field of single-cell transcriptomics. Single-cell genomic analysis is an advanced technique that measures gene expression at the individual cell level, revealing cellular changes and interactions that are not observable with traditional genomic analysis methods. When applied to cancer tissues, this analysis can delineate the composition of diverse cell types within a tumor, providing insights into how cancer progresses and identifying key genes involved during each stage of progression. Despite the immense potential of single-cell genomic analysis, handling the vast amount of data that it generates has always been challenging. The amount of data covers the expression of tens of thousands of genes across hundreds to thousands of individual cells. This not only results in large datasets but also introduces noise-related distortions, which arise in part due to current measurement limitations. < Figure 1. Overview of scLENS (single-cell Low-dimensional embedding using the effective Noise Subtract) > (Left) Current dimensionality reduction methods for scRNA-seq data involve conventional data preprocessing steps, such as log normalization, followed by manual selection of signals from the scaled data. However, this study reveals that the high levels of sparsity and variability in scRNA-seq data can lead to signal distortion during the data preprocessing, compromising the accuracy of downstream analyses. (Right) To address this issue, the researchers integrated L2 normalization into the conventional preprocessing pipeline, effectively mitigating signal distortion. Moreover, they developed a novel signal detection algorithm that eliminates the need for user intervention by leveraging random matrix theory-based noise filtering and signal robustness testing. By incorporating these techniques, scLENS enables accurate and automated analysis of scRNA-seq data, overcoming the limitations of existing dimensionality reduction methods. Corresponding author Jae Kyoung Kim highlighted, “There has been a remarkable advancement in experimental technologies for analyzing single-cell transcriptomes over the past decade. However, due to limitations in data analysis methods, there has been a struggle to fully utilize valuable data obtained through extensive cost and time." Researchers have developed numerous analysis methods over the years to discern biological signals from this noise. However, the accuracy of these methods has been less than satisfactory. A critical issue is that determining signal and noise thresholds often depends on subjective decisions from the users. The newly developed scLENS tool harnesses Random Matrix Theory and Signal robustness test to automatically differentiate signals from noise without relying on subjective user input. First author Hyun Kim stated, "Previously, users had to arbitrarily decide the threshold for signal and noise, which compromised the reproducibility of analysis results and introduced subjectivity. scLENS eliminates this problem by automatically detecting signals using only the inherent structure of the data." During the development of scLENS, researchers identified the fundamental reasons for inaccuracies in existing analysis methods. They found that commonly used data preprocessing methods distort both biological signals and noise. The new preprocessing approach that scLENS offers is free from such distortions. By resolving issues related to noise threshold determined by subjective user choice and signal distortion in conventional data preprocessing, scLENS significantly outperforms existing methods in accuracy. Additionally, scLENS automates the laborious process of signal dimension selection, allowing researchers to extract biological signals conveniently and automatically. CI Kim added, "scLENS solves major issues in single-cell transcriptome data analysis, substantially improving the accuracy and efficiency throughout the analysis process. This is a prime example of how fundamental mathematical theories can drive innovation in life sciences research, allowing researchers to more quickly and accurately answer biological questions and uncover secrets of life that were previously hidden." This research was published in the international journal 'Nature Communications' on April 27. Terminology * Single-cell RNA sequencing (scRNA-seq): A technique used to measure gene expression levels in individual cells, providing insights into cell heterogeneity and rare cell types. * Dimensionality reduction: A method to reduce the number of features or variables in a dataset while preserving the most important information, making data analysis more manageable and interpretable. * Random matrix theory: A mathematical framework used to model and analyze the properties of large, random matrices, which can be applied to filter out noise in high-dimensional data. * Signal robustness test: Among the signals, this test selects signals that are robust to the slight perturbation in data because real biological signals should be invariant for such slight modification in the data.
2024.05.09
View 1075
KAIST debuts “DreamWaQer” - a quadrupedal robot that can walk in the dark
- The team led by Professor Hyun Myung of the School of Electrical Engineering developed “DreamWaQ”, a deep reinforcement learning-based walking robot control technology that can walk in an atypical environment without visual and/or tactile information - Utilization of “DreamWaQ” technology can enable mass production of various types of “DreamWaQers” - Expected to be used in exploration of atypical environment involving unique circumstances such as disasters by fire. A team of Korean engineering researchers has developed a quadrupedal robot technology that can climb up and down the steps and moves without falling over in uneven environments such as tree roots without the help of visual or tactile sensors even in disastrous situations in which visual confirmation is impeded due to darkness or thick smoke from the flames. KAIST (President Kwang Hyung Lee) announced on the 29th of March that Professor Hyun Myung's research team at the Urban Robotics Lab in the School of Electrical Engineering developed a walking robot control technology that enables robust 'blind locomotion' in various atypical environments. < (From left) Prof. Hyun Myung, Doctoral Candidates I Made Aswin Nahrendra, Byeongho Yu, and Minho Oh. In the foreground is the DreamWaQer, a quadrupedal robot equipped with DreamWaQ technology. > The KAIST research team developed "DreamWaQ" technology, which was named so as it enables walking robots to move about even in the dark, just as a person can walk without visual help fresh out of bed and going to the bathroom in the dark. With this technology installed atop any legged robots, it will be possible to create various types of "DreamWaQers". Existing walking robot controllers are based on kinematics and/or dynamics models. This is expressed as a model-based control method. In particular, on atypical environments like the open, uneven fields, it is necessary to obtain the feature information of the terrain more quickly in order to maintain stability as it walks. However, it has been shown to depend heavily on the cognitive ability to survey the surrounding environment. In contrast, the controller developed by Professor Hyun Myung's research team based on deep reinforcement learning (RL) methods can quickly calculate appropriate control commands for each motor of the walking robot through data of various environments obtained from the simulator. Whereas the existing controllers that learned from simulations required a separate re-orchestration to make it work with an actual robot, this controller developed by the research team is expected to be easily applied to various walking robots because it does not require an additional tuning process. DreamWaQ, the controller developed by the research team, is largely composed of a context estimation network that estimates the ground and robot information and a policy network that computes control commands. The context-aided estimator network estimates the ground information implicitly and the robot’s status explicitly through inertial information and joint information. This information is fed into the policy network to be used to generate optimal control commands. Both networks are learned together in the simulation. While the context-aided estimator network is learned through supervised learning, the policy network is learned through an actor-critic architecture, a deep RL methodology. The actor network can only implicitly infer surrounding terrain information. In the simulation, the surrounding terrain information is known, and the critic, or the value network, that has the exact terrain information evaluates the policy of the actor network. This whole learning process takes only about an hour in a GPU-enabled PC, and the actual robot is equipped with only the network of learned actors. Without looking at the surrounding terrain, it goes through the process of imagining which environment is similar to one of the various environments learned in the simulation using only the inertial sensor (IMU) inside the robot and the measurement of joint angles. If it suddenly encounters an offset, such as a staircase, it will not know until its foot touches the step, but it will quickly draw up terrain information the moment its foot touches the surface. Then the control command suitable for the estimated terrain information is transmitted to each motor, enabling rapidly adapted walking. The DreamWaQer robot walked not only in the laboratory environment, but also in an outdoor environment around the campus with many curbs and speed bumps, and over a field with many tree roots and gravel, demonstrating its abilities by overcoming a staircase with a difference of a height that is two-thirds of its body. In addition, regardless of the environment, the research team confirmed that it was capable of stable walking ranging from a slow speed of 0.3 m/s to a rather fast speed of 1.0 m/s. The results of this study were produced by a student in doctorate course, I Made Aswin Nahrendra, as the first author, and his colleague Byeongho Yu as a co-author. It has been accepted to be presented at the upcoming IEEE International Conference on Robotics and Automation (ICRA) scheduled to be held in London at the end of May. (Paper title: DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning) The videos of the walking robot DreamWaQer equipped with the developed DreamWaQ can be found at the address below. Main Introduction: https://youtu.be/JC1_bnTxPiQ Experiment Sketches: https://youtu.be/mhUUZVbeDA0 Meanwhile, this research was carried out with the support from the Robot Industry Core Technology Development Program of the Ministry of Trade, Industry and Energy (MOTIE). (Task title: Development of Mobile Intelligence SW for Autonomous Navigation of Legged Robots in Dynamic and Atypical Environments for Real Application) < Figure 1. Overview of DreamWaQ, a controller developed by this research team. This network consists of an estimator network that learns implicit and explicit estimates together, a policy network that acts as a controller, and a value network that provides guides to the policies during training. When implemented in a real robot, only the estimator and policy network are used. Both networks run in less than 1 ms on the robot's on-board computer. > < Figure 2. Since the estimator can implicitly estimate the ground information as the foot touches the surface, it is possible to adapt quickly to rapidly changing ground conditions. > < Figure 3. Results showing that even a small walking robot was able to overcome steps with height differences of about 20cm. >
2023.05.18
View 4476
Using light to throw and catch atoms to open up a new chapter for quantum computing
The technology to move and arrange atoms, the most basic component of a quantum computer, is very important to Rydberg quantum computing research. However, to place the atoms at the desired location, the atoms must be captured and transported one by one using a highly focused laser beam, commonly referred to as an optical tweezer. and, the quantum information of the atoms is likely to change midway. KAIST (President Kwang Hyung Lee) announced on the 27th that a research team led by Professor Jaewook Ahn of the Department of Physics developed a technology to throw and receive rubidium atoms one by one using a laser beam. The research team developed a method to throw and receive atoms which would minimize the time the optical tweezers are in contact with the atoms in which the quantum information the atoms carry may change. The research team used the characteristic that the rubidium atoms, which are kept at a very low temperature of 40μK below absolute zero, move very sensitively to the electromagnetic force applied by light along the focal point of the light tweezers. The research team accelerated the laser of an optical tweezer to give an optical kick to an atom to send it to a target, then caught the flying atom with another optical tweezer to stop it. The atom flew at a speed of 65 cm/s, and traveled up to 4.2 μm. Compared to the existing technique of guiding the atoms with the optical tweezers, the technique of throwing and receiving atoms eliminates the need to calculate the transporting path for the tweezers, and makes it easier to fix the defects in the atomic arrangement. As a result, it is effective in generating and maintaining a large number of atomic arrangements, and when the technology is used to throw and receive flying atom qubits, it will be used in studying new and more powerful quantum computing methods that presupposes the structural changes in quantum arrangements. "This technology will be used to develop larger and more powerful Rydberg quantum computers," says Professor Jaewook Ahn. “In a Rydberg quantum computer,” he continues, “atoms are arranged to store quantum information and interact with neighboring atoms through electromagnetic forces to perform quantum computing. The method of throwing an atom away for quick reconstruction the quantum array can be an effective way to fix an error in a quantum computer that requires a removal or replacement of an atom.” The research, which was conducted by doctoral students Hansub Hwang and Andrew Byun of the Department of Physics at KAIST and Sylvain de Léséleuc, a researcher at the National Institute of Natural Sciences in Japan, was published in the international journal, Optica, 0n March 9th. (Paper title: Optical tweezers throw and catch single atoms). This research was carried out with the support of the Samsung Science & Technology Foundation. <Figure 1> A schematic diagram of the atom catching and throwing technique. The optical tweezer on the left kicks the atom to throw it into a trajectory to have the tweezer on the right catch it to stop it.
2023.03.28
View 2689
KAIST team develops smart immune system that can pin down on malignant tumors
A joint research team led by Professor Jung Kyoon Choi of the KAIST Department of Bio and Brain Engineering and Professor Jong-Eun Park of the KAIST Graduate School of Medical Science and Engineering (GSMSE) announced the development of the key technologies to treat cancers using smart immune cells designed based on AI and big data analysis. This technology is expected to be a next-generation immunotherapy that allows precision targeting of tumor cells by having the chimeric antigen receptors (CARs) operate through a logical circuit. Professor Hee Jung An of CHA Bundang Medical Center and Professor Hae-Ock Lee of the Catholic University of Korea also participated in this research to contribute joint effort. Professor Jung Kyoon Choi’s team built a gene expression database from millions of cells, and used this to successfully develop and verify a deep-learning algorithm that could detect the differences in gene expression patterns between tumor cells and normal cells through a logical circuit. CAR immune cells that were fitted with the logic circuits discovered through this methodology could distinguish between tumorous and normal cells as a computer would, and therefore showed potentials to strike only on tumor cells accurately without causing unwanted side effects. This research, conducted by co-first authors Dr. Joonha Kwon of the KAIST Department of Bio and Brain Engineering and Ph.D. candidate Junho Kang of KAIST GSMSE, was published by Nature Biotechnology on February 16, under the title Single-cell mapping of combinatorial target antigens for CAR switches using logic gates. An area in cancer research where the most attempts and advances have been made in recent years is immunotherapy. This field of treatment, which utilizes the patient’s own immune system in order to overcome cancer, has several methods including immune checkpoint inhibitors, cancer vaccines and cellular treatments. Immune cells like CAR-T or CAR-NK equipped with chimera antigen receptors, in particular, can recognize cancer antigens and directly destroy cancer cells. Starting with its success in blood cancer treatment, scientists have been trying to expand the application of CAR cell therapy to treat solid cancer. But there have been difficulties to develop CAR cells with effective killing abilities against solid cancer cells with minimized side effects. Accordingly, in recent years, the development of smarter CAR engineering technologies, i.e., computational logic gates such as AND, OR, and NOT, to effectively target cancer cells has been underway. At this point in time, the research team built a large-scale database for cancer and normal cells to discover the exact genes that are expressed only from cancer cells at a single-cell level. The team followed this up by developing an AI algorithm that could search for a combination of genes that best distinguishes cancer cells from normal cells. This algorithm, in particular, has been used to find a logic circuit that can specifically target cancer cells through cell-level simulations of all gene combinations. CAR-T cells equipped with logic circuits discovered through this methodology are expected to distinguish cancerous cells from normal cells like computers, thereby minimizing side effects and maximizing the effects of chemotherapy. Dr. Joonha Kwon, who is the first author of this paper, said, “this research suggests a new method that hasn’t been tried before. What’s particularly noteworthy is the process in which we found the optimal CAR cell circuit through simulations of millions of individual tumors and normal cells.” He added, “This is an innovative technology that can apply AI and computer logic circuits to immune cell engineering. It would contribute greatly to expanding CAR therapy, which is being successfully used for blood cancer, to solid cancers as well.” This research was funded by the Original Technology Development Project and Research Program for Next Generation Applied Omic of the Korea Research Foundation. Figure 1. A schematic diagram of manufacturing and administration process of CAR therapy and of cancer cell-specific dual targeting using CAR. Figure 2. Deep learning (convolutional neural networks, CNNs) algorithm for selection of dual targets based on gene combination (left) and algorithm for calculating expressing cell fractions by gene combination according to logical circuit (right).
2023.03.09
View 3882
A Quick but Clingy Creepy-Crawler that will MARVEL You
Engineered by KAIST Mechanics, a quadrupedal robot climbs steel walls and crawls across metal ceilings at the fastest speed that the world has ever seen. < Photo 1. (From left) KAIST ME Prof. Hae-Won Park, Ph.D. Student Yong Um, Ph.D. Student Seungwoo Hong > - Professor Hae-Won Park's team at the Department of Mechanical Engineering developed a quadrupedal robot that can move at a high speed on ferrous walls and ceilings. - It is expected to make a wide variety of contributions as it is to be used to conduct inspections and repairs of large steel structures such as ships, bridges, and transmission towers, offering an alternative to dangerous or risky activities required in hazardous environments while maintaining productivity and efficiency through automation and unmanning of such operations. - The study was published as the cover paper of the December issue of Science Robotics. KAIST (President Kwang Hyung Lee) announced on the 26th that a research team led by Professor Hae-Won Park of the Department of Mechanical Engineering developed a quadrupedal walking robot that can move at high speed on steel walls and ceilings named M.A.R.V.E.L. - rightly so as it is a Magnetically Adhesive Robot for Versatile and Expeditious Locomotion as described in their paper, “Agile and Versatile Climbing on Ferromagnetic Surfaces with a Quadrupedal Robot.” (DOI: 10.1126/scirobotics.add1017) To make this happen, Professor Park's research team developed a foot pad that can quickly turn the magnetic adhesive force on and off while retaining high adhesive force even on an uneven surface through the use of the Electro-Permanent Magnet (EPM), a device that can magnetize and demagnetize an electromagnet with little power, and the Magneto-Rheological Elastomer (MRE), an elastic material made by mixing a magnetic response factor, such as iron powder, with an elastic material, such as rubber, which they mounted on a small quadrupedal robot they made in-house, at their own laboratory. These walking robots are expected to be put into a wide variety of usage, including being programmed to perform inspections, repairs, and maintenance tasks on large structures made of steel, such as ships, bridges, transmission towers, large storage areas, and construction sites. This study, in which Seungwoo Hong and Yong Um of the Department of Mechanical Engineering participated as co-first authors, was published as the cover paper in the December issue of Science Robotics. < Image on the Cover of 2022 December issue of Science Robotics > Existing wall-climbing robots use wheels or endless tracks, so their mobility is limited on surfaces with steps or irregularities. On the other hand, walking robots for climbing can expect improved mobility in obstacle terrain, but have disadvantages in that they have significantly slower moving speeds or cannot perform various movements. In order to enable fast movement of the walking robot, the sole of the foot must have strong adhesion force and be able to control the adhesion to quickly switch from sticking to the surface or to be off of it. In addition, it is necessary to maintain the adhesion force even on a rough or uneven surface. To solve this problem, the research team used the EPM and MRE for the first time in designing the soles of walking robots. An EPM is a magnet that can turn on and off the electromagnetic force with a short current pulse. Unlike general electromagnets, it has the advantage that it does not require energy to maintain the magnetic force. The research team proposed a new EPM with a rectangular structure arrangement, enabling faster switching while significantly lowering the voltage required for switching compared to existing electromagnets. In addition, the research team was able to increase the frictional force without significantly reducing the magnetic force of the sole by covering the sole with an MRE. The proposed sole weighs only 169 g, but provides a vertical gripping force of about *535 Newtons (N) and a frictional force of 445 N, which is sufficient gripping force for a quadrupedal robot weighing 8 kg. * 535 N converted to kg is 54.5 kg, and 445 N is 45.4 kg. In other words, even if an external force of up to 54.5 kg in the vertical direction and up to 45.4 kg in the horizontal direction is applied (or even if a corresponding weight is hung), the sole of the foot does not come off the steel plate. MARVEL climbed up a vertical wall at high speed at a speed of 70 cm per second, and was able to walk while hanging upside down from the ceiling at a maximum speed of 50 cm per second. This is the world's fastest speed for a walking climbing robot. In addition, the research team demonstrated that the robot can climb at a speed of up to 35 cm even on a surface that is painted, dirty with dust and the rust-tainted surfaces of water tanks, proving the robot's performance in a real environment. It was experimentally demonstrated that the robot not only exhibited high speed, but also can switch from floor to wall and from wall to ceiling, and overcome 5-cm high obstacles protruding from walls without difficulty. The new climbing quadrupedal robot is expected to be widely used for inspection, repair, and maintenance of large steel structures such as ships, bridges, transmission towers, oil pipelines, large storage areas, and construction sites. As the works required in these places involves risks such as falls, suffocation and other accidents that may result in serious injuries or casualties, the need for automation is of utmost urgency. One of the first co-authors of the paper, a Ph.D. student, Yong Um of KAIST’s Department of Mechanical Engineering, said, "By the use of the magnetic soles made up of the EPM and MRE and the non-linear model predictive controller suitable for climbing, the robot can speedily move through a variety of ferromagnetic surfaces including walls and ceilings, not just level grounds. We believe this would become a cornerstone that will expand the mobility and the places of pedal-mobile robots can venture into." He added, “These robots can be put into good use in executing dangerous and difficult tasks on steel structures in places like the shipbuilding yards.” This research was carried out with support from the National Research Foundation of Korea's Basic Research in Science & Engineering Program for Mid-Career Researchers and Korea Shipbuilding & Offshore Engineering Co., Ltd.. < Figure 1. The quadrupedal robot (MARVEL) walking over various ferrous surfaces. (A) vertical wall (B) ceiling. (C) over obstacles on a vertical wall (D) making floor-to-wall and wall-to-ceiling transitions (E) moving over a storage tank (F) walking on a wall with a 2-kg weight and over a ceiling with a 3-kg load. > < Figure 2. Description of the magnetic foot (A) Components of the magnet sole: ankle, Square Eletro-Permanent Magnet(S-EPM), MRE footpad. (B) Components of the S-EPM and MRE footpad. (C) Working principle of the S-EPM. When the magnetization direction is aligned as shown in the left figure, magnetic flux comes out of the keeper and circulates through the steel plate, generating holding force (ON state). Conversely, if the magnetization direction is aligned as shown in the figure on the right, the magnetic flux circulates inside the S-EPM and the holding force disappears (OFF state). > Video Introduction: Agile and versatile climbing on ferromagnetic surfaces with a quadrupedal robot - YouTube
2022.12.30
View 9741
Tomographic Measurement of Dielectric Tensors
Dielectric tensor tomography allows the direct measurement of the 3D dielectric tensors of optically anisotropic structures A research team reported the direct measurement of dielectric tensors of anisotropic structures including the spatial variations of principal refractive indices and directors. The group also demonstrated quantitative tomographic measurements of various nematic liquid-crystal structures and their fast 3D nonequilibrium dynamics using a 3D label-free tomographic method. The method was described in Nature Materials. Light-matter interactions are described by the dielectric tensor. Despite their importance in basic science and applications, it has not been possible to measure 3D dielectric tensors directly. The main challenge was due to the vectorial nature of light scattering from a 3D anisotropic structure. Previous approaches only addressed 3D anisotropic information indirectly and were limited to two-dimensional, qualitative, strict sample conditions or assumptions. The research team developed a method enabling the tomographic reconstruction of 3D dielectric tensors without any preparation or assumptions. A sample is illuminated with a laser beam with various angles and circularly polarization states. Then, the light fields scattered from a sample are holographically measured and converted into vectorial diffraction components. Finally, by inversely solving a vectorial wave equation, the 3D dielectric tensor is reconstructed. Professor YongKeun Park said, “There were a greater number of unknowns in direct measuring than with the conventional approach. We applied our approach to measure additional holographic images by slightly tilting the incident angle.” He said that the slightly tilted illumination provides an additional orthogonal polarization, which makes the underdetermined problem become the determined problem. “Although scattered fields are dependent on the illumination angle, the Fourier differentiation theorem enables the extraction of the same dielectric tensor for the slightly tilted illumination,” Professor Park added. His team’s method was validated by reconstructing well-known liquid crystal (LC) structures, including the twisted nematic, hybrid aligned nematic, radial, and bipolar configurations. Furthermore, the research team demonstrated the experimental measurements of the non-equilibrium dynamics of annihilating, nucleating, and merging LC droplets, and the LC polymer network with repeating 3D topological defects. “This is the first experimental measurement of non-equilibrium dynamics and 3D topological defects in LC structures in a label-free manner. Our method enables the exploration of inaccessible nematic structures and interactions in non-equilibrium dynamics,” first author Dr. Seungwoo Shin explained. -PublicationSeungwoo Shin, Jonghee Eun, Sang Seok Lee, Changjae Lee, Herve Hugonnet, Dong Ki Yoon, Shin-Hyun Kim, Jongwoo Jeong, YongKeun Park, “Tomographic Measurement ofDielectric Tensors at Optical Frequency,” Nature Materials March 02, 2022 (https://doi.org/10/1038/s41563-022-01202-8) -ProfileProfessor YongKeun ParkBiomedical Optics Laboratory (http://bmol.kaist.ac.kr)Department of PhysicsCollege of Natural SciencesKAIST
2022.03.22
View 4705
Label-Free Multiplexed Microtomography of Endogenous Subcellular Dynamics Using Deep Learning
AI-based holographic microscopy allows molecular imaging without introducing exogenous labeling agents A research team upgraded the 3D microtomography observing dynamics of label-free live cells in multiplexed fluorescence imaging. The AI-powered 3D holotomographic microscopy extracts various molecular information from live unlabeled biological cells in real time without exogenous labeling or staining agents. Professor YongKeum Park’s team and the startup Tomocube encoded 3D refractive index tomograms using the refractive index as a means of measurement. Then they decoded the information with a deep learning-based model that infers multiple 3D fluorescence tomograms from the refractive index measurements of the corresponding subcellular targets, thereby achieving multiplexed micro tomography. This study was reported in Nature Cell Biology online on December 7, 2021. Fluorescence microscopy is the most widely used optical microscopy technique due to its high biochemical specificity. However, it needs to genetically manipulate or to stain cells with fluorescent labels in order to express fluorescent proteins. These labeling processes inevitably affect the intrinsic physiology of cells. It also has challenges in long-term measuring due to photobleaching and phototoxicity. The overlapped spectra of multiplexed fluorescence signals also hinder the viewing of various structures at the same time. More critically, it took several hours to observe the cells after preparing them. 3D holographic microscopy, also known as holotomography, is providing new ways to quantitatively image live cells without pretreatments such as staining. Holotomography can accurately and quickly measure the morphological and structural information of cells, but only provides limited biochemical and molecular information. The 'AI microscope' created in this process takes advantage of the features of both holographic microscopy and fluorescence microscopy. That is, a specific image from a fluorescence microscope can be obtained without a fluorescent label. Therefore, the microscope can observe many types of cellular structures in their natural state in 3D and at the same time as fast as one millisecond, and long-term measurements over several days are also possible. The Tomocube-KAIST team showed that fluorescence images can be directly and precisely predicted from holotomographic images in various cells and conditions. Using the quantitative relationship between the spatial distribution of the refractive index found by AI and the major structures in cells, it was possible to decipher the spatial distribution of the refractive index. And surprisingly, it confirmed that this relationship is constant regardless of cell type. Professor Park said, “We were able to develop a new concept microscope that combines the advantages of several microscopes with the multidisciplinary research of AI, optics, and biology. It will be immediately applicable for new types of cells not included in the existing data and is expected to be widely applicable for various biological and medical research.” When comparing the molecular image information extracted by AI with the molecular image information physically obtained by fluorescence staining in 3D space, it showed a 97% or more conformity, which is a level that is difficult to distinguish with the naked eye. “Compared to the sub-60% accuracy of the fluorescence information extracted from the model developed by the Google AI team, it showed significantly higher performance,” Professor Park added. This work was supported by the KAIST Up program, the BK21+ program, Tomocube, the National Research Foundation of Korea, and the Ministry of Science and ICT, and the Ministry of Health & Welfare. -Publication Hyun-seok Min, Won-Do Heo, YongKeun Park, et al. “Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning,” Nature Cell Biology (doi.org/10.1038/s41556-021-00802-x) published online December 07 2021. -Profile Professor YongKeun Park Biomedical Optics Laboratory Department of Physics KAIST
2022.02.09
View 6212
Team KAIST Makes Its Presence Felt in the Self-Driving Tech Industry
Team KAIST finishes 4th at the inaugural CES Autonomous Racing Competition Team KAIST led by Professor Hyunchul Shim and Unmanned Systems Research Group (USRG) placed fourth in an autonomous race car competition in Las Vegas last week, making its presence felt in the self-driving automotive tech industry. Team KAIST, beat its first competitor, Auburn University, with speeds of up to 131 mph at the Autonomous Challenge at CES held at the Las Vegas Motor Speedway. However, the team failed to advance to the final round when it lost to PoliMOVE, comprised of the Polytechnic University of Milan and the University of Alabama, the final winner of the $150,000 USD race. A total of eight teams competed in the self-driving race. The race was conducted as a single elimination tournament consisting of multiple rounds of matches. Two cars took turns playing the role of defender and attacker, and each car attempted to outpace the other until one of them was unable to complete the mission. Each team designed the algorithm to control its racecar, the Dallara-built AV-21, which can reach a speed of up to 173 mph, and make it safely drive around the track at high speeds without crashing into the other. The event is the CES version of the Indy Autonomous Challenge, a competition that took place for the first time in October last year to encourage university students from around the world to develop complicated software for autonomous driving and advance relevant technologies. Team KAIST placed 4th at the Indy Autonomous Challenge, which qualified it to participate in this race. “The technical level of the CES race is much higher than last October’s and we had a very tough race. We advanced to the semifinals for two consecutive races. I think our autonomous vehicle technology is proving itself to the world,” said Professor Shim. Professor Shim’s research group has been working on the development of autonomous aerial and ground vehicles for the past 12 years. A self-driving car developed by the lab was certified by the South Korean government to run on public roads. The vehicle the team used cost more than 1 million USD to build. Many of the other teams had to repair their vehicle more than once due to accidents and had to spend a lot on repairs. “We are the only one who did not have any accidents, and this is a testament to our technological prowess,” said Professor Shim. He said the financial funding to purchase pricy parts and equipment for the racecar is always a challenge given the very tight research budget and absence of corporate sponsorships. However, Professor Shim and his research group plan to participate in the next race in September and in the 2023 CES race. “I think we need more systemic and proactive research and support systems to earn better results but there is nothing better than the group of passionate students who are taking part in this project with us,” Shim added.
2022.01.12
View 6516
Two Researchers Designated as SUHF Fellows
Professor Taeyun Ku from the Graduate School of Medical Science and Engineering and Professor Hanseul Yang from the Department of Biological Sciences were nominated as 2021 fellows of the Suh Kyungbae Foundation (SUHF). SUHF selected three young promising scientists from 53 researchers who are less than five years into their careers. A panel of judges comprised of scholars from home and abroad made the final selection based on the candidates’ innovativeness and power to influence. Professor You-Bong Hyun from Seoul National University also won the fellowship. Professor Ku’s main topic is opto-connectomics. He will study ways to visualize the complex brain network using innovative technology that transforms neurons into optical elements. Professor Yang will research the possibility of helping patients recover from skin diseases or injuries without scars by studying spiny mouse genes. SUHF was established by Amorepacific Group Chairman Suh Kyungbae in 2016 with 300 billion KRW of his private funds. Under the vision of ‘contributing to humanity by supporting innovative discoveries of bioscience researchers,’ the foundation supports promising Korean scientists who pioneer new fields of research in biological sciences. From 2017 to this year, SUHF has selected 20 promising scientists in the field of biological sciences. Selected scientists are provided with up to KRW 500 million each year for five years. The foundation has provided a total of KRW 48.5 billion in research funds to date.
2021.09.15
View 5284
3D Visualization and Quantification of Bioplastic PHA in a Living Bacterial Cell
3D holographic microscopy leads to in-depth analysis of bacterial cells accumulating the bacterial bioplastic, polyhydroxyalkanoate (PHA) A research team at KAIST has observed how bioplastic granule is being accumulated in living bacteria cells through 3D holographic microscopy. Their 3D imaging and quantitative analysis of the bioplastic ‘polyhydroxyalkanoate’ (PHA) via optical diffraction tomography provides insights into biosynthesizing sustainable substitutes for petroleum-based plastics. The bio-degradable polyester polyhydroxyalkanoate (PHA) is being touted as an eco-friendly bioplastic to replace existing synthetic plastics. While carrying similar properties to general-purpose plastics such as polyethylene and polypropylene, PHA can be used in various industrial applications such as container packaging and disposable products. PHA is synthesized by numerous bacteria as an energy and carbon storage material under unbalanced growth conditions in the presence of excess carbon sources. PHA exists in the form of insoluble granules in the cytoplasm. Previous studies on investigating in vivo PHA granules have been performed by using fluorescence microscopy, transmission electron microscopy (TEM), and electron cryotomography. These techniques have generally relied on the statistical analysis of multiple 2D snapshots of fixed cells or the short-time monitoring of the cells. For the TEM analysis, cells need to be fixed and sectioned, and thus the investigation of living cells was not possible. Fluorescence-based techniques require fluorescence labeling or dye staining. Thus, indirect imaging with the use of reporter proteins cannot show the native state of PHAs or cells, and invasive exogenous dyes can affect the physiology and viability of the cells. Therefore, it was difficult to fully understand the formation of PHA granules in cells due to the technical limitations, and thus several mechanism models based on the observations have been only proposed. The team of metabolic engineering researchers led by Distinguished Professor Sang Yup Lee and Physics Professor YongKeun Park, who established the startup Tomocube with his 3D holographic microscopy, reported the results of 3D quantitative label-free analysis of PHA granules in individual live bacterial cells by measuring the refractive index distributions using optical diffraction tomography. The formation and growth of PHA granules in the cells of Cupriavidus necator, the most-studied native PHA (specifically, poly(3-hydroxybutyrate), also known as PHB) producer, and recombinant Escherichia coli harboring C. necator PHB biosynthesis pathway were comparatively examined. From the reconstructed 3D refractive index distribution of the cells, the team succeeded in the 3D visualization and quantitative analysis of cells and intracellular PHA granules at a single-cell level. In particular, the team newly presented the concept of “in vivo PHA granule density.” Through the statistical analysis of hundreds of single cells accumulating PHA granules, the distinctive differences of density and localization of PHA granules in the two micro-organisms were found. Furthermore, the team identified the key protein that plays a major role in making the difference that enabled the characteristics of PHA granules in the recombinant E. coli to become similar to those of C. necator. The research team also presented 3D time-lapse movies showing the actual processes of PHA granule formation combined with cell growth and division. Movies showing the living cells synthesizing and accumulating PHA granules in their native state had never been reported before. Professor Lee said, “This study provides insights into the morphological and physical characteristics of in vivo PHA as well as the unique mechanisms of PHA granule formation that undergo the phase transition from soluble monomers into the insoluble polymer, followed by granule formation. Through this study, a deeper understanding of PHA granule formation within the bacterial cells is now possible, which has great significance in that a convergence study of biology and physics was achieved. This study will help develop various bioplastics production processes in the future.” This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (Grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) and the Bio & Medical Technology Development Program (Grant No. 2021M3A9I4022740) from the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea to S.Y.L. This work was also supported by the KAIST Cross-Generation Collaborative Laboratory project. -PublicationSo Young Choi, Jeonghun Oh, JaeHwang Jung, YongKeun Park, and Sang Yup Lee. Three-dimensional label-free visualization and quantification of polyhydroxyalkanoates in individualbacterial cell in its native state. PNAS(https://doi.org./10.1073/pnas.2103956118) -ProfileDistinguished Professor Sang Yup LeeMetabolic Engineering and Synthetic Biologyhttp://mbel.kaist.ac.kr/ Department of Chemical and Biomolecular Engineering KAIST Endowed Chair Professor YongKeun ParkBiomedical Optics Laboratoryhttps://bmokaist.wordpress.com/ Department of PhysicsKAIST
2021.07.28
View 8080
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