Team USRG’s Winning Streak Continues at the AI Gra..
< Team USRG won the AI Grand Challenge 2020 for the second consecutive year. > Team USRG (Unmanned Systems Research Group) led by Professor Hyunchul Shim from the School of Electrical Engineering has won the AI Grand Challenge 2020 held on Nov. 23 at Kintex in Ilsan, Kyonggi-do for the second consecutive year. The team received 7.7 million KRW in research funding from the Ministry of Science and ICT, the organizer of the challenge. The team took a little over two minutes to complete the rescue operation mission of the challenge. The mission included swerving around seven obstacles, airdropping an aid package, and safely landing after identifying the landing spot. Their drone is the only one that successfully passed through a 10-meter tunnel out of five pre-qualified teams: three from universities and two from companies. The AI Grand Challenge, which began in 2017, was designed to promote AI technology and its applications for addressing high-risk technical challenges, especially for conducting complex disaster relief operations. For autonomous flying drones, swerving to avoid objects has always been an essential skill and a big challenge. For their flawless performance in the rescue operation, the team loaded an AI algorithm and upgraded their drone by improving the LiDAR-based localization system and a stronger propulsion system to carry more sensors. The drone weighs 2.4 kg and carries a small yet powerful computer with a GPU. This AI-powered drone can complete rescue missions more efficiently in complicated and disastrous environments by precisely comprehending where the drone should go without needing GPS. The team also designed an all-in-one prop guard and installed a gripper onto the bottom of the drone to hold the aid package securely. “We tried hard to improve our localization system better to resolve issues we had in the previous event,” said Professor Shim. Two PhD candidates, Han-Sob Lee and Bo-Sung Kim played a critical role in developing this drone. After their two-year winning streak, their prize money now totals 2.4 billion KRW, equivalent to the winning prize of the DARPA Challenge. As the winning team, they will collaborate with other champions at the AI track challenge to develop rescue mission technology for a more complex environment. “The importance of AI technology is continuing to grow and the government is providing large amounts of funding for research in this field. We would like to develop very competitive technology that will work in the real world,” Professor Shim added. His group is investigating a wide array of AI technologies applicable to unmanned vehicles including indoor flying drones, self-driving cars, delivery robots, and a tram that circles the campus. < Two PhD candidates, Han-Sob Lee and Bo-Sung Kim make a final touch for the competition. >
Feel the Force with ElaStick
ElaStick, a handheld variable stiffness display, renders the dynamic haptic response of a flexible object Haptic controllers play an important role in providing rich and immersive virtual reality experiences. Professor Andrea Bianchi’s team in the Department of Industrial Design recreated the haptic response of flexible objects made of different materials and with different shapes by changing the stiffness of a custom-controller – ElaStick. ElaStick is a portable hand-held force-feedback controller that is capable of rendering the illusion of how flexible and deformable objects feel when held in the hand. This VR haptic controller can change its stiffness in two directions independently and continuously. Since providing haptic feedback enhances the VR experience, researchers have suggested numerous approaches for rendering the physical properties of virtual objects - such as weights, the movement of mass, impacts, and damped oscillations. The research team designed a new mechanism based on a quaternion joint and four variable-stiffness tendons. The quaternion joint is a two-DoF bending joint that enables ElaStick to bend and oscillate in any direction using a pair of tendons with varying stiffness. In fact, each tendon around the joint is made of a series of elastic rubber bands and inelastic fishing lines and can vary its stiffness by changing the proportion of the two materials. Thanks to these structures, each pair of tendons can behave independently, controlling the anisotropic characteristics of the entire device. “The main challenge was to implement the mechanism to control the stiffness while maintaining independence between deformations in two perpendicular directions,” said Professor Bianchi. The research team successfully measured the relative threshold of human perception on the stiffness of a handheld object. The results showed that the just-noticeable difference (JND) of human perception of stiffness is at most about 30％ of the change from the initial value. It also found that appropriate haptic responses significantly enhance the quality of the VR experience. The research team surveyed the perceived realism, immersion, and enjoyment of participants after they played with various flexible objects in VR. “It is meaningful that the haptic feedback of a flexible object was mechanically reproduced and its effectiveness in VR was proven. ElaStick has succeeded in implementing a novel mechanism to recreate the dynamic response of flexible objects that mimic real ones, suggesting a new category of haptic feedback that can be provided in VR,” explained Professor Bianchi. The team plans to extend the ElaStick’s applications, from being used merely as a game controller to driving simulations, medical training, and many other digital contexts. This research, led by MS candidate Neung Ryu, won the Best Paper Award at the ACM UIST 2020 (the ACM Symposium on User Interface Software & Technology) last month. -Profile Professor Andrea Bianchi Makinteract.kaist.ac.kr Department of Industrial Design KAIST
‘WalkON Suit 4’ Releases Paraplegics from Wheelcha..
- KAIST Athletes in ‘WalkON Suit 4’ Dominated the Cybathlon 2020 Global Edition. - Paraplegic athletes Byeong-Uk Kim and Joohyun Lee from KAIST’s Team Angel Robotics won a gold and a bronze medal respectively at the Cybathlon 2020 Global Edition last week. ‘WalkON Suit 4,’ a wearable robot developed by the Professor Kyoungchul Kong’s team from the Department of Mechanical Engineering topped the standings at the event with double medal success. Kim, the former bronze medallist, clinched his gold medal by finishing all six tasks in 3 minutes and 47 seconds, whereas Lee came in third with a time of 5 minutes and 51 seconds. TWIICE, a Swiss team, lagged 53 seconds behind Kim’s winning time to be the runner-up. < Byeong-Uk Kim (left) and Joohyun Lee (right) > Cybathlon is a global championship, organized by ETH Zurich, which brings together people with physical disabilities to compete using state-of-the-art assistive technologies to perform everyday tasks. The first championship was held in 2016 in Zurich, Switzerland. Due to the COVID-19 pandemic, the second championship was postponed twice and held in a new format in a decentralized setting. A total of 51 teams from 20 countries across the world performed the events in their home bases in different time zones instead of traveling to Zurich. Under the supervision of a referee and timekeeper, all races were filmed and then reviewed by judges. KAIST’s Team Angel Robotics participated in the Powered Exoskeleton Race category, where nine pilots representing five nations including Korea, Switzerland, the US, Russia, and France competed against each other. The team installed their own arena and raced at the KAIST Main Campus in Daejeon according to the framework, tasks, and rules defined by the competition committee. The two paraplegic pilots were each equipped with exoskeletal devices, the WalkON Suit 4, and undertook six tasks related to daily activities. The WalkON Suit 4 recorded the fastest walking speed for a complete paraplegic ever reported. For a continuous walk, it achieved a maximum speed of 40 meters per minute. This is comparable to the average walking pace of a non-disabled person, which is around two to four kilometers per hour. The research team raised the functionality of the robot by adding technology that can observe the user’s level of anxiety and external factors like the state of the walking surface, so it can control itself intelligently. The assistive functions a robot should provide vary greatly with the environment, and the WalkON Suit 4 made it possible to analyze the pace of the user within 30 steps and provide a personally optimized walking pattern, enabling a high walking speed. < WalkON Suit 4 > The six tasks that Kim and Lee had to complete were:1) sitting and standing back up, 2) navigating around obstacles while avoiding collisions, 3) stepping over obstacles on the ground, 4) going up and down stairs, 5) walking across a tilted path, and 6) climbing a steep slope, opening and closing a door, and descending a steep slope. < Overview of the Powered Exoskeleton Race. > Points were given based on the accuracy of each completed task, and the final scores were calculated by adding all of the points that were gained in each attempt, which lasted 10 minutes. Each pilot was given three opportunities and used his/her highest score. Should pilots have the same final score, the pilot who completed the race in the shortest amount of time would win. Kim said in his victory speech that he was so thrilled to see all his and fellow researchers’ years of hard work paying off. “This will be a good opportunity to show how outstanding Korean wearable robot technologies are,” he added. Lee, who participated in the competition for the first time, said, “By showing that I can overcome my physical disabilities with robot technology, I’d like to send out a message of hope to everyone who is tired because of COVID-19”. Professor Kong’s team collaborated in technology development and pilot training with their colleagues from Angel Robotics Co., Ltd., Severance Rehabilitation Hospital, Yeungnam University, Stalks, and the Institute of Rehabilitation Technology. Footage from the competition is available at the Cybathlon’s official website. (END)
Researchers Control Multiple Wavelengths of Light ..
KAIST researchers have synthesized a collection of nanoparticles, known as carbon dots, capable of emitting multiple wavelengths of light from a single particle. Additionally, the team discovered that the dispersion of the carbon dots, or the interparticle distance between each dot, influences the properties of the light the carbon dots emit. The discovery will allow researchers to understand how to control these carbon dots and create new, environmentally responsible displays, lighting, and sensing technology. Research into nanoparticles capable of emitting light, such as quantum dots, has been an active area of interest for the last decade and a half. These particles, or phosphors, are nanoparticles made out of various materials that are capable of emitting light at specific wavelengths by leveraging quantum mechanical properties of the materials. This provides new ways to develop lighting and display solutions as well as more precise detection and sensing in instruments. As technology becomes smaller and more sophisticated, the usage of fluorescent nanoparticles has seen a dramatic increase in many applications due to the purity of the colors emitting from the dots as well as their tunability to meet desired optical properties. Carbon dots, a type of fluorescent nanoparticles, have seen an increase in interest from researchers as a candidate to replace non-carbon dots, the construction of which requires heavy metals that are toxic to the environment. Since they are made up of mostly carbon, the low toxicity is an extremely attractive quality when coupled with the tunability of their inherent optical properties. Another striking feature of carbon dots is their capability to emit multiple wavelengths of light from a single nanoparticle. This multi-wavelength emission can be stimulated under a single excitation source, enabling the simple and robust generation of white light from a single particle by emitting multiple wavelengths simultaneously. Carbon dots also exhibit a concentration-dependent photoluminescence. In other words, the distance between individual carbon dots affects the light that the carbon dots subsequently emit under an excitation source. These combined properties make carbon dots a unique source that will result in extremely accurate detection and sensing. This concentration-dependency, however, had not been fully understood. In order to fully utilize the capabilities of carbon dots, the mechanisms that govern the seemingly variable optical properties must first be uncovered. It was previously theorized that the concentration-dependency of carbon dots was due to a hydrogen bonding effect. Now, a KAIST research team, led by Professor Do Hyun Kim of the Department of Chemical and Biomolecular Engineering has posited and demonstrated that the dual-color-emissiveness is instead due to the interparticle distances between each carbon dot. The research was published in the 36th Issue of Physical Chemistry Chemical Physics. First author of the paper, PhD candidate Hyo Jeong Yoo, along with Professor Kim and researcher Byeong Eun Kwak, examined how the relative light intensity of the red and blue colors changed when varying the interparticle distances, or concentration, of the carbon dots. They found that as the concentration was adjusted, the light emitted from the carbon dots would transform. By varying the concentration, the team was able to control the relative intensity of the colors, as well as emit them simultaneously to generate a white light from a single source (See Figure). “The concentration-dependence of the photoluminescence of carbon dots on the change of the emissive origins for different interparticle distances has been overlooked in previous research. With the analysis of the dual-color-emission phenomenon of carbon dots, we believe that this result may provide a new perspective to investigate their photoluminescence mechanism,” Yoo explained. The newly analyzed ability to control the photoluminescence of carbon dots will likely be heavily utilized in the continued development of solid-state lighting applications and sensing. < Figure. Photoluminescence change of dual-color-emissive carbon dots (CDs) depending on their concentration. Blue- and red-emissions show different contributions with different interparticle distances. > Publication: Yoo, H. J., Kwak, B. E., and Kim. D. H. (2020) Interparticle distance as a key factor for controlling the dual-emission properties of carbon dots. Physical Chemistry Chemical Physics, Issue 36, Pages 20227-20237. Available online at https://doi.org/10.1039/d0cp02120b Profile: Do Hyun Kim, Sc.D. Professor dokim＠kaist.ac.kr http://procal.kaist.ac.kr/ Process Analysis Laboratory Department of Chemical and Biomolecular Engineering https://www.kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea (END)
Professor Kyu-Young Whang Donates Toward the 50th ..
< Professor Euijong Whang on behalf of his father Professor Kyu-Young Whang pose with President Shin (right) at the donation ceremony. > Distinguished Professor Kyu-Young Whang from the School of Computing made a gift of 100 million KRW toward the construction of the 50th Anniversary Memorial Building during a ceremony on November 3 at the Daejeon campus. Distinguished Professor Whang’s son Professor Euijong Whang from the School of Electrical Engineering presented the gift at the ceremony. “As a member of the first class of KAIST, I feel very delighted to play a part in the fundraising campaign for the 50th anniversary celebration. This is also a token of appreciation to my alma mater and I look forward to alumni and the KAIST community joining this campaign,” Professor Whang said on behalf of his father. Distinguished Professor Whang was a member of the first graduating class of KAIST who served as the president of KAIST Alumni Association. He completed his master’s degree at KAIST in 1975 and his PhD at Stanford University in 1984. Joining KAIST faculty in 1990, he led the Advanced IT Research Center and ERC of the National Research Foundation of Korea. He is a life fellow at IEEE and an ACM fellow. KAIST will name the “Kyu-Young Whang and Jong-Hye Song Seminar Room” at the 50th Anniversary Memorial Building. The ground will be broken in 2022 for construction of the building.
To Talk or Not to Talk： Smart Speaker Determines O..
A KAIST research team has developed a new context-awareness technology that enables AI assistants to determine when to talk to their users based on user circumstances. This technology can contribute to developing advanced AI assistants that can offer pre-emptive services such as reminding users to take medication on time or modifying schedules based on the actual progress of planned tasks. Unlike conventional AI assistants that used to act passively upon users’ commands, today’s AI assistants are evolving to provide more proactive services through self-reasoning of user circumstances. This opens up new opportunities for AI assistants to better support users in their daily lives. However, if AI assistants do not talk at the right time, they could rather interrupt their users instead of helping them. The right time for talking is more difficult for AI assistants to determine than it appears. This is because the context can differ depending on the state of the user or the surrounding environment. A group of researchers led by Professor Uichin Lee from the KAIST School of Computing identified key contextual factors in user circumstances that determine when the AI assistant should start, stop, or resume engaging in voice services in smart home environments. Their findings were published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) in September. The group conducted this study in collaboration with Professor Jae-Gil Lee’s group in the KAIST School of Computing, Professor Sangsu Lee’s group in the KAIST Department of Industrial Design, and Professor Auk Kim’s group at Kangwon National University. After developing smart speakers equipped with AI assistant function for experimental use, the researchers installed them in the rooms of 40 students who live in double-occupancy campus dormitories and collected a total of 3,500 in-situ user response data records over a period of a week. The smart speakers repeatedly asked the students a question, “Is now a good time to talk?” at random intervals or whenever a student’s movement was detected. Students answered with either “yes” or “no” and then explained why, describing what they had been doing before being questioned by the smart speakers. Data analysis revealed that 47％ of user responses were “no” indicating they did not want to be interrupted. The research team then created 19 home activity categories to cross-analyze the key contextual factors that determine opportune moments for AI assistants to talk, and classified these factors into ‘personal,’ ‘movement,’ and ‘social’ factors respectively. Personal factors, for instance, include: 1. the degree of concentration on or engagement in activities, 2. the degree urgency and busyness, 3. the state of user’s mental or physical condition, and 4. the state of being able to talk or listen while multitasking. While users were busy concentrating on studying, tired, or drying hair, they found it difficult to engage in conversational interactions with the smart speakers. Some representative movement factors include departure, entrance, and physical activity transitions. Interestingly, in movement scenarios, the team found that the communication range was an important factor. Departure is an outbound movement from the smart speaker, and entrance is an inbound movement. Users were much more available during inbound movement scenarios as opposed to outbound movement scenarios. In general, smart speakers are located in a shared place at home, such as a living room, where multiple family members gather at the same time. In Professor Lee’s group’s experiment, almost half of the in-situ user responses were collected when both roommates were present. The group found social presence also influenced interruptibility. Roommates often wanted to minimize possible interpersonal conflicts, such as disturbing their roommates' sleep or work. Narae Cha, the lead author of this study, explained, “By considering personal, movement, and social factors, we can envision a smart speaker that can intelligently manage the timing of conversations with users.” She believes that this work lays the foundation for the future of AI assistants, adding, “Multi-modal sensory data can be used for context sensing, and this context information will help smart speakers proactively determine when it is a good time to start, stop, or resume conversations with their users.” This work was supported by the National Research Foundation (NRF) of Korea. < Image 1. In-situ experience sampling of user availability for conversations with AI assistants > < Image 2. Key Contextual Factors that Determine Optimal Timing for AI Assistants to Talk > Publication: Cha, N, et al. (2020) “Hello There！ Is Now a Good Time to Talk?”: Opportune Moments for Proactive Interactions with Smart Speakers. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 4, No. 3, Article No. 74, pp. 1-28. Available online at https://doi.org/10.1145/3411810 Link to Introductory Video: https://youtu.be/AA8CTi2hEf0 Profile: Uichin Lee Associate Professor uclee＠kaist.ac.kr http://ic.kaist.ac.kr Interactive Computing Lab. School of Computing https://www.kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea (END)
Chemical Scissors Snip 2D Transition Metal Dichalc..
New ‘nanoribbon’ catalyst should slash cost of hydrogen production for clean fuels Researchers have identified a potential catalyst alternative – and an innovative way to produce them using chemical ‘scissors’ – that could make hydrogen production more economical. The research team led by Professor Sang Ouk Kim at the Department of Materials Science and Engineering published their work in Nature Communications. Hydrogen is likely to play a key role in the clean transition away from fossil fuels and other processes that produce greenhouse gas emissions. There is a raft of transportation sectors such as long-haul shipping and aviation that are difficult to electrify and so will require cleanly produced hydrogen as a fuel or as a feedstock for other carbon-neutral synthetic fuels. Likewise, fertilizer production and the steel sector are unlikely to be “de-carbonized” without cheap and clean hydrogen. The problem is that the cheapest methods by far of producing hydrogen gas is currently from natural gas, a process that itself produces the greenhouse gas carbon dioxide–which defeats the purpose. Alternative techniques of hydrogen production, such as electrolysis using an electric current between two electrodes plunged into water to overcome the chemical bonds holding water together, thereby splitting it into its constituent elements, oxygen and hydrogen are very well established. But one of the factors contributing to the high cost, beyond being extremely energy-intensive, is the need for the very expensive precious and relatively rare metal platinum. The platinum is used as a catalyst–a substance that kicks off or speeds up a chemical reaction–in the hydrogen production process. As a result, researchers have long been on the hunt for a substitution for platinum -- another catalyst that is abundant in the earth and thus much cheaper. Transition metal dichalcogenides, or TMDs, in a nanomaterial form, have for some time been considered a good candidate as a catalyst replacement for platinum. These are substances composed of one atom of a transition metal (the elements in the middle part of the periodic table) and two atoms of a chalcogen element (the elements in the third-to-last column in the periodic table, specifically sulfur, selenium and tellurium). What makes TMDs a good bet as a platinum replacement is not just that they are much more abundant, but also their electrons are structured in a way that gives the electrodes a boost. In addition, a TMD that is a nanomaterial is essentially a two-dimensional super-thin sheet only a few atoms thick, just like graphene. The ultrathin nature of a 2-D TMD nanosheet allows for a great many more TMD molecules to be exposed during the catalysis process than would be the case in a block of the stuff, thus kicking off and speeding up the hydrogen-making chemical reaction that much more. However, even here the TMD molecules are only reactive at the four edges of a nanosheet. In the flat interior, not much is going on. In order to increase the chemical reaction rate in the production of hydrogen, the nanosheet would need to be cut into very thin – almost one-dimensional strips, thereby creating many edges. In response, the research team developed what are in essence a pair of chemical scissors that can snip TMD into tiny strips. “Up to now, the only substances that anyone has been able to turn into these ‘nano-ribbons’ are graphene and phosphorene,” said Sang Professor Kim, one of the researchers involved in devising the process. “But they’re both made up of just one element, so it’s pretty straightforward. Figuring out how to do it for TMD, which is made of two elements was going to be much harder.” The ‘scissors’ involve a two-step process involving first inserting lithium ions into the layered structure of the TMD sheets, and then using ultrasound to cause a spontaneous ‘unzipping’ in straight lines. “It works sort of like how when you split a plank of plywood: it breaks easily in one direction along the grain,” Professor Kim continued. “It’s actually really simple.” The researchers then tried it with various types of TMDs, including those made of molybdenum, selenium, sulfur, tellurium and tungsten. All worked just as well, with a catalytic efficiency as effective as platinum’s. Because of the simplicity of the procedure, this method should be able to be used not just in the large-scale production of TMD nanoribbons, but also to make similar nanoribbons from other multi-elemental 2D materials for purposes beyond just hydrogen production. < Schematic view of scissoring 2D sheets to nanoribbon. > -Profile Professor Sang Ouk Kim Soft Nanomaterials Laboratory (http://snml.kaist.ac.kr) Department of Materials Science and Engineering KAIST
E. coli Engineered to Grow on CO₂ and Formic Acid ..
- An E. coli strain that can grow to a relatively high cell density solely on CO₂ and formic acid was developed by employing metabolic engineering. - < From left Jong An Lee, Distinguished Professor Sang Yup Lee, Dr. Junho Bang, Dr.Jung Ho Ahn. > Most biorefinery processes have relied on the use of biomass as a raw material for the production of chemicals and materials. Even though the use of CO₂ as a carbon source in biorefineries is desirable, it has not been possible to make common microbial strains such as E. coli grow on CO₂. Now, a metabolic engineering research group at KAIST has developed a strategy to grow an E. coli strain to higher cell density solely on CO₂ and formic acid. Formic acid is a one carbon carboxylic acid, and can be easily produced from CO₂ using a variety of methods. Since it is easier to store and transport than CO₂, formic acid can be considered a good liquid-form alternative of CO₂. With support from the C1 Gas Refinery R&D Center and the Ministry of Science and ICT, a research team led by Distinguished Professor Sang Yup Lee stepped up their work to develop an engineered E. coli strain capable of growing up to 11-fold higher cell density than those previously reported, using CO₂ and formic acid as sole carbon sources. This work was published in Nature Microbiology on September 28. Despite the recent reports by several research groups on the development of E. coli strains capable of growing on CO₂ and formic acid, the maximum cell growth remained too low (optical density of around 1) and thus the production of chemicals from CO₂ and formic acid has been far from realized. The team previously reported the reconstruction of the tetrahydrofolate cycle and reverse glycine cleavage pathway to construct an engineered E. coli strain that can sustain growth on CO₂ and formic acid. To further enhance the growth, the research team introduced the previously designed synthetic CO₂ and formic acid assimilation pathway, and two formate dehydrogenases. Metabolic fluxes were also fine-tuned, the gluconeogenic flux enhanced, and the levels of cytochrome bo3 and bd-I ubiquinol oxidase for ATP generation were optimized. This engineered E. coli strain was able to grow to a relatively high OD600 of 7~11, showing promise as a platform strain growing solely on CO₂ and formic acid. Professor Lee said, “We engineered E. coli that can grow to a higher cell density only using CO₂ and formic acid. We think that this is an important step forward, but this is not the end. The engineered strain we developed still needs further engineering so that it can grow faster to a much higher density.” Professor Lee’s team is continuing to develop such a strain. “In the future, we would be delighted to see the production of chemicals from an engineered E. coli strain using CO₂ and formic acid as sole carbon sources,” he added. < Figure: Metabolic engineering strategies and central metabolic pathways of the engineered E. coli strain that grows on CO2 and formic acid. Carbon assimilation and reducing power regeneration pathways are described. Engineering strategies and genetic modifications employed in the engineered strain are also described. Figure from Nature Microbiology. > Profile: Distinguished Professor Sang Yup Lee leesy＠kaist.ac.kr http://mbel.kaist.ac.kr Department of Chemical and Biomolecular Engineering KAIST
Professor Jungwon Kim Named Scientist of October
< Professor Jungwon Kim > Professor Jungwon Kim from the Department of Mechanical Engineering was selected as the ‘Scientist of the Month’ for October 2020 by the Ministry of Science and ICT and the National Research Foundation of Korea. Professor Kim was recognized for his contributions to expanding the horizons of the basics of precision engineering through his research on multifunctional ultrahigh-speed, high-resolution sensors. He received 10 million KRW in prize money. Professor Kim was selected as the recipient of this award in celebration of “Measurement Day”, which commemorates October 26 as the day in which King Sejong the Great established a volume measurement system. Professor Kim discovered that the time difference between the pulse of light created by a laser and the pulse of the current produced by a light-emitting diode was as small as 100 attoseconds (10-16 seconds). He then developed a unique multifunctional ultrahigh-speed, high-resolution Time-of-Flight (TOF) sensor that could take measurements of multiple points at the same time by sampling electric light. The sensor, with a measurement speed of 100 megahertz (100 million vibrations per second), a resolution of 180 picometers (1/5.5 billion meters), and a dynamic range of 150 decibels, overcame the limitations of both existing TOF techniques and laser interferometric techniques at the same time. The results of this research were published in Nature Photonics on February 10, 2020. Professor Kim said, “I’d like to thank the graduate students who worked passionately with me, and KAIST for providing an environment in which I could fully focus on research. I am looking forward to the new and diverse applications in the field of machine manufacturing, such as studying the dynamic phenomena in microdevices, or taking ultraprecision measurement of shapes for advanced manufacturing.” (END)
Sturdy Fabric-Based Piezoelectric Energy Harvester..
KAIST researchers presented a highly flexible but sturdy wearable piezoelectric harvester using the simple and easy fabrication process of hot pressing and tape casting. This energy harvester, which has record high interfacial adhesion strength, will take us one step closer to being able to manufacture embedded wearable electronics. A research team led by Professor Seungbum Hong said that the novelty of this result lies in its simplicity, applicability, durability, and its new characterization of wearable electronic devices. Wearable devices are increasingly being used in a wide array of applications from small electronics to embedded devices such as sensors, actuators, displays, and energy harvesters. Despite their many advantages, high costs and complex fabrication processes remained challenges for reaching commercialization. In addition, their durability was frequently questioned. To address these issues, Professor Hong’s team developed a new fabrication process and analysis technology for testing the mechanical properties of affordable wearable devices. For this process, the research team used a hot pressing and tape casting procedure to connect the fabric structures of polyester and a polymer film. Hot pressing has usually been used when making batteries and fuel cells due to its high adhesiveness. Above all, the process takes only two to three minutes. The newly developed fabrication process will enable the direct application of a device into general garments using hot pressing just as graphic patches can be attached to garments using a heat press. In particular, when the polymer film is hot pressed onto a fabric below its crystallization temperature, it transforms into an amorphous state. In this state, it compactly attaches to the concave surface of the fabric and infiltrates into the gaps between the transverse wefts and longitudinal warps. These features result in high interfacial adhesion strength. For this reason, hot pressing has the potential to reduce the cost of fabrication through the direct application of fabric-based wearable devices to common garments. In addition to the conventional durability test of bending cycles, the newly introduced surface and interfacial cutting analysis system proved the high mechanical durability of the fabric-based wearable device by measuring the high interfacial adhesion strength between the fabric and the polymer film. Professor Hong said the study lays a new foundation for the manufacturing process and analysis of wearable devices using fabrics and polymers. He added that his team first used the surface and interfacial cutting analysis system (SAICAS) in the field of wearable electronics to test the mechanical properties of polymer-based wearable devices. Their surface and interfacial cutting analysis system is more precise than conventional methods (peel test, tape test, and microstretch test) because it qualitatively and quantitatively measures the adhesion strength. Professor Hong explained, “This study could enable the commercialization of highly durable wearable devices based on the analysis of their interfacial adhesion strength. Our study lays a new foundation for the manufacturing process and analysis of other devices using fabrics and polymers. We look forward to fabric-based wearable electronics hitting the market very soon.” The results of this study were registered as a domestic patent in Korea last year, and published in Nano Energy this month. This study has been conducted through collaboration with Professor Yong Min Lee in the Department of Energy Science and Engineering at DGIST, Professor Kwangsoo No in the Department of Materials Science and Engineering at KAIST, and Professor Seunghwa Ryu in the Department of Mechanical Engineering at KAIST. This study was supported by the High-Risk High-Return Project and the Global Singularity Research Project at KAIST, the National Research Foundation, and the Ministry of Science and ICT in Korea. < Figure 1. Fabrication process, structures, and output signals of a fabric-based wearable energy harvester. > < Figure 2. Measurement of an interfacial adhesion strength using SAICAS > -Publication: Jaegyu Kim, Seoungwoo Byun, Sangryun Lee, Jeongjae Ryu, Seongwoo Cho, Chungik Oh, Hongjun Kim, Kwangsoo No, Seunghwa Ryu, Yong Min Lee, Seungbum Hong＊, Nano Energy 75 (2020), 104992. https://doi.org/10.1016/j.nanoen.2020.104992 -Profile: Professor Seungbum Hong seungbum＠kaist.ac.kr http://mii.kaist.ac.kr/ Department of Materials Science and Engineering KAIST
[arirang TV] COVID-19 PANDEMIC： How can Korea deve..
Deep Learning Helps Explore the Structural and Str..
Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person’s behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the “bible” of mental health diagnosis. However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult. But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability. The technique, or rather suite of techniques, has enjoyed remarkable success in recent years in fields as diverse as voice recognition, translation, autonomous vehicles, and drug discovery. A group of researchers from KAIST in collaboration with the Yonsei University College of Medicine has applied these deep learning techniques to autism diagnosis. Their findings were published on August 14 in the journal IEEE Access. Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal grey and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition. These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy, the number of participants in these studies has been small, often under 50, and diagnostic performance drops markedly when applied to large sample sizes or on datasets that include people from a wide variety of populations and locations. “There was something as to what defines autism that human researchers and clinicians must have been overlooking,” said Keun-Ah Cheon, one of the two corresponding authors and a professor in Department of Child and Adolescent Psychiatry at Severance Hospital of the Yonsei University College of Medicine. “And humans poring over thousands of MRI scans won’t be able to pick up on what we’ve been missing,” she continued. “But we thought AI might be able to.” So the team applied five different categories of deep learning models to an open-source dataset of more than 1,000 MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) initiative, which has collected brain imaging data from laboratories around the world, and to a smaller, but higher-resolution MRI image dataset (84 images) taken from the Child Psychiatric Clinic at Severance Hospital, Yonsei University College of Medicine. In both cases, the researchers used both structural MRIs (examining the anatomy of the brain) and functional MRIs (examining brain activity in different regions). < Visualization of logics of classification learned by recurrent attention model (RAM). > The models allowed the team to explore the structural bases of ASD brain region by brain region, focusing in particular on many structures below the cerebral cortex, including the basal ganglia, which are involved in motor function (movement) as well as learning and memory. Crucially, these specific types of deep learning models also offered up possible explanations of how the AI had come up with its rationale for these findings. “Understanding the way that the AI has classified these brain structures and dynamics is extremely important,” said Sang Wan Lee, the other corresponding author and an associate professor at KAIST. “It’s no good if a doctor can tell a patient that the computer says they have autism, but not be able to say why the computer knows that.” The deep learning models were also able to describe how much a particular aspect contributed to ASD, an analysis tool that can assist psychiatric physicians during the diagnosis process to identify the severity of the autism. “Doctors should be able to use this to offer a personalized diagnosis for patients, including a prognosis of how the condition could develop,” Lee said. “Artificial intelligence is not going to put psychiatrists out of a job,” he explained. “But using AI as a tool should enable doctors to better understand and diagnose complex disorders than they could do on their own.” -Profile Professor Sang Wan Lee Department of Bio and Brain Engineering Laboratory for Brain and Machine Intelligence https://aibrain.kaist.ac.kr/ KAIST