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‘WalkON Suit 4’ Releases Paraplegics from Wheelchairs
- 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.
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.
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.
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)
2020.11.20 View 9808 -
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.”
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2020.10.15 View 11857 -
Slippery When Wet: Fish and Seaweed Inspire Ships to Reduce Fluid Friction
Faster ships could be on the horizon after KAIST scientists develop a slippery surface inspired by fish and seaweed to reduce the hull's drag through the water.
Long-distance cargo ships lose a significant amount of energy due to fluid friction. Looking to the drag reduction mechanisms employed by aquatic life can provide inspiration on how to improve efficiency.
Fish and seaweed secrete a layer of mucus to create a slippery surface, reducing their friction as they travel through water. A potential way to mimic this is by creating lubricant-infused surfaces covered with cavities. As the cavities are continuously filled with the lubricant, a layer is formed over the surface.
Though this method has previously been shown to work, reducing drag by up to 18%, the underlying physics is not fully understood. KAIST researchers in collaboration with a team of researchers from POSTECH conducted simulations of this process to help explain the effects, and their findings were published in the journal Physics of Fluids on September 15.
The group looked at the average speed of a cargo ship with realistic material properties and simulated how it behaves under various lubrication setups. Specifically, they monitored the effects of the open area of the lubricant-filled cavities, as well as the thickness of the cavity lids.
They found that for larger open areas, the lubricant spreads more than it does with smaller open areas, leading to a slipperier surface. On the other hand, the lid thickness does not have much of an effect on the slip, though a thicker lid does create a thicker lubricant buildup layer.
Professor Emeritus Hyung Jin Sung from the KAIST Department of Mechanical Engineering who led this study said, “Our investigation of the hydrodynamics of a lubricant layer and how it results in drag reduction with a slippery surface in a basic configuration has provided significant insight into the benefits of a lubricant-infused surface.”
Now that they have worked on optimizing the lubricant secretion design, the authors hope it can be implemented in real-life marine vehicles.
“If the present design parameters are adopted, the drag reduction rate will increase significantly,” Professor Sung added.
This work was supported by the National Research Foundation (NRF) of Korea.
Source:
Materials provided by American Institute of Physics.
Publication:
Kim, Seung Joong, et al. (2020). A lubricant-infused slip surface for drag reduction. Physics of Fluids. Available online at https://doi.org/10.1063/5.0018460
Profile:
Hyung Jin Sung
Professor Emeritus
hyungjin@kaist.ac.kr
http://flow.kaist.ac.kr/index.php
Flow Control Lab. (FCL)
Department of Mechanical Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2020.10.12 View 7573 -
Deep Learning Helps Explore the Structural and Strategic Bases of Autism
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).
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.”
-ProfileProfessor Sang Wan LeeDepartment of Bio and Brain EngineeringLaboratory for Brain and Machine Intelligence https://aibrain.kaist.ac.kr/
KAIST
2020.09.23 View 11411 -
Sturdy Fabric-Based Piezoelectric Energy Harvester Takes Us One Step Closer to Wearable Electronics
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.
-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
2020.09.17 View 13863 -
Advanced NVMe Controller Technology for Next Generation Memory Devices
KAIST researchers advanced non-volatile memory express (NVMe) controller technology for next generation information storage devices, and made this new technology named ‘OpenExpress’ freely available to all universities and research institutes around the world to help reduce the research cost in related fields.
NVMe is a communication protocol made for high-performance storage devices based on a peripheral component interconnect-express (PCI-E) interface. NVMe has been developed to take the place of the Serial AT Attachment (SATA) protocol, which was developed to process data on hard disk drives (HDDs) and did not perform well in solid state drives (SSDs).
Unlike HDDs that use magnetic spinning disks, SSDs use semiconductor memory, allowing the rapid reading and writing of data. SSDs also generate less heat and noise, and are much more compact and lightweight.
Since data processing in SSDs using NVMe is up to six times faster than when SATA is used, NVMe has become the standard protocol for ultra-high speed and volume data processing, and is currently used in many flash-based information storage devices.
Studies on NVMe continue at both the academic and industrial levels, however, its poor accessibility is a drawback. Major information and communications technology (ICT) companies around the world expend astronomical costs to procure intellectual property (IP) related to hardware NVMe controllers, necessary for the use of NVMe. However, such IP is not publicly disclosed, making it difficult to be used by universities and research institutes for research purposes.
Although a small number of U.S. Silicon Valley startups provide parts of their independently developed IP for research, the cost of usage is around 34,000 USD per month. The costs skyrocket even further because each copy of single-use source code purchased for IP modification costs approximately 84,000 USD.
In order to address these issues, a group of researchers led by Professor Myoungsoo Jung from the School of Electrical Engineering at KAIST developed a next generation NVMe controller technology that achieved parallel data input/output processing for SSDs in a fully hardware automated form.
The researchers presented their work at the 2020 USENIX Annual Technical Conference (USENIX ATC ’20) in July, and released it as an open research framework named ‘OpenExpress.’
This NVMe controller technology developed by Professor Jung’s team comprises a wide range of basic hardware IP and key NVMe IP cores. To examine its actual performance, the team made an NVMe hardware controller prototype using OpenExpress, and designed all logics provided by OpenExpress to operate at high frequency.
The field-programmable gate array (FPGA) memory card prototype developed using OpenExpress demonstrated increased input/output data processing capacity per second, supporting up to 7 gigabit per second (GB/s) bandwidth. This makes it suitable for ultra-high speed and volume next generation memory device research.
In a test comparing various storage server loads on devices, the team’s FPGA also showed 76% higher bandwidth and 68% lower input/output delay compared to Intel’s new high performance SSD (Optane SSD), which is sufficient for many researchers studying systems employing future memory devices. Depending on user needs, silicon devices can be synthesized as well, which is expected to further enhance performance.
The NVMe controller technology of Professor Jung’s team can be freely used and modified under the OpenExpress open-source end-user agreement for non-commercial use by all universities and research institutes. This makes it extremely useful for research on next-generation memory compatible NVMe controllers and software stacks.
“With the product of this study being disclosed to the world, universities and research institutes can now use controllers that used to be exclusive for only the world’s biggest companies, at no cost,ˮ said Professor Jung. He went on to stress, “This is a meaningful first step in research of information storage device systems such as high-speed and volume next generation memory.”
This work was supported by a grant from MemRay, a company specializing in next generation memory development and distribution.
More details about the study can be found at http://camelab.org.
Image credit: Professor Myoungsoo Jung, KAIST
Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only.
-Publication:
Myoungsoo Jung. (2020). OpenExpress: Fully Hardware Automated Open Research Framework for Future Fast NVMe Devices. Presented in the Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), Available online at https://www.usenix.org/system/files/atc20-jung.pdf
-Profile: Myoungsoo Jung
Associate Professor
m.jung@kaist.ac.kr
http://camelab.org
Computer Architecture and Memory Systems Laboratory
School of Electrical Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2020.09.04 View 11250 -
Virtual Commencement Ceremony Honors the Class of 2020
The KAIST community gathered online to celebrate the 2020 graduating class. The blended ceremony conferred their hard-earned degrees on August 28. The belated celebration, which was postponed from February 21 due to the COVID-19 outbreak, honored the 2846 graduates with live streaming on YouTube beginning at 2:00 pm. The graduates include 721 PhDs and 1399 master’s degree holders.
The government raised its social distancing guidelines to level two out of three on August 23 as the second wave of the virus hit the nation. Level two guidelines prohibit the gathering of more than 50 persons indoors or 100 persons outdoors.
For the virtual ceremony, the Office of Student Affairs and Policy announced a list of 67 graduates who signed up to participate in the graduation ceremony. Graduates were divided into three groups to attend at three different places and watch the ceremony via Zoom. No family and friends of the graduates were allowed to participate at the campus.
This year’s valedictorian, Kon-Yong Lee from the Department of Chemical and Biomolecular Engineering, received the Award of Minister of Science and Technology. Salutorian Hee-Kwang Roh from the Department of Chemistry received the Award of the KAIST Board of Trustees, while the recipient of the KAIST Presidential Award was Hong Jae-Min from the School of Computing.
President Sung-Chul Shin, Chairman of the Board of Trustees Woo-Sik Kim, former Minister of Science and Technology and former Provost at KAIST Dr. KunMo Chung, and a very limited number of faculty and staff members officiated the commencement ceremony from the KAIST auditorium.
President Shin in his commencement speech applauded the graduates’ hard work and dedication and delivered a very special congratulatory message to them. He encouraged the new graduates to be courageous enough to deal with these new challenges as well as future uncertainties, during the greatest transformation brought about by COVID-19.
“Instead of following behind others as a fast follower, we should take the initiative and walk down new paths as a first mover.” He also stressed, “We can transform this crisis into an opportunity by practicing the C3 values KAIST pursues: Challenging, Creating, and Caring.”
As new alumni of Korea’s top science and technology university, he said, “Our graduates should focus on creating the world’s best, first, or only one in their research or their work.” However, he also pointed out the importance of a caring mind for others when working together.
At the ceremony, KAIST conferred an honorary doctorate degree to Dr. Younghoon David Kim, CEO and Chairman of Daesung Group, in recognition of his lifetime dedication to making innovations in the energy industry.
Daesung Group is a leading energy company in Korea which manufactures and supplies natural gas for industries and home users. Dr. Kim is committed to making efficient energy sources by advancing cutting-energy sciences and disruptive technologies. He has served as chairman of the World Energy Council since 2016.
In his acceptance speech, Kim stressed the Grand Energy Transition as a new driving force in the future energy industry for maximizing energy efficiency.
“Since energy is the most basic foundation for all industries, improvements in energy efficiency translate into benefits for all related industries in terms of its efficiency and productivity.”
“The Grand Energy Transition is progressing widely and rapidly across the entire value chain of energy production, distribution, and consumption with decarbonization, decentralization, and digitalization serving as its driving force.”
He went on, “We should regard energy efficiency not as the fifth fuel but the first primary fuel.”
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2020.08.28 View 11572 -
Professor Jaehyouk Choi, IT Young Engineer of the Year
Professor Jaehyouk Choi from the KAIST School of Electrical Engineering won the ‘IT Young Engineer Award’ for 2020. The award was co-presented by the Institute of Electrical and Electronics Engineers (IEEE) and the Institute of Electronics Engineers of Korea (IEIE), and sponsored by the Haedong Science and Culture Foundation.
The ‘IT Young Engineer Award’ selects only one mid-career scientist or engineer 40 years old or younger every year, who has made a great contribution to academic or technological advancements in the field of IT.
Professor Choi’s research topics include high-performance semiconductor circuit design for ultrahigh-speed communication systems including 5G communication. In particular, he is widely known for his field of the ‘ultra-low-noise, high-frequency signal generation circuit,’ key technology for next-generation wired and wireless communications, as well as for memory systems. He has published 64 papers in SCI journals and at international conferences, and applied for and registered 25 domestic and international patents.
Professor Choi is also an active member of the Technical Program Committee of international symposiums in the field of semiconductor circuits including the International Solid-State Circuits Conference (ISSCC) and the European Solid-State Circuit Conference (ESSCIRC). Beginning this year, he also serves as a distinguished lecturer at the IEEE Solid-State Circuit Society (SSCS).
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2020.08.20 View 11344 -
KAIST Technology Value Tops in Commercialization Market
KAIST became the first Korean university to achieve 10.183 billion KRW in annual technology royalties, and was also selected as an ‘Institution of Outstanding Patent Quality Management’ and an ‘Institution of Outstanding Public Patent Technology Transfer’ for 2020.
KAIST earns its technology royalties through 56 technology transfer contracts. Following KAIST in the rankings were Seoul National University (SNU) in second place with 8.8 billion KRW from 87 contracts and Korea University (KU) in the third with 5.4 billion KRW from 133 contracts. The data shows the high value of KAIST-created technology in the market.
The Korean Intellectual Property Office (KIPO) started to recognize the Institution of Outstanding Patent Quality Management this year to encourage profit-driven patent management at universities and public research institutes, and KAIST was selected as one of the four first recipients of this distinction.
In addition, KAIST was selected as an Institution of Outstanding Public Patent Technology Transfer, a title given by KIPO to three universities and public research institutes this year with outstanding achievements in technology transfers and commercialization to encourage patent utilization.
Director of the KAIST Institute of Technology Value Creation (ITVC) Professor Kyung-cheol Choi said that KAIST’s achievement in annual technology royalties and technology transfers and commercialization were prime examples of accelerating competitiveness in intellectual property through innovative R&D investment.
In April, KAIST expanded and reorganized its Industry-Academia Collaboration Team into the ITVC to support technology transfers and commercialization. Specialized organizations such as the Intellectual Property and Technology Transfer Center and Industrial Liaison Center have been established under the ITVC, and industry experts have been recruited as special professors focusing on industry-academia collaborations to enhance its specialized functions.
KAIST also operates an enterprise membership system and technology consulting system, aimed at sharing its outstanding intellectual property within domestic industries. In 2019, it secured a technology transfer commercialization fund of 1.2 billion KRW available for three years under KIPO’s Intellectual Property Profit Reinvestment Support Program (formerly the Korean Patent Gap Fund Creation Project).
This program was introduced to bridge the gap between the technology developed in universities and the level of technology required by industry. Under the program, bold investments are made in early-stage technologies at the research paper or experiment phase.
The program encourages enterprises to take active steps for the transfer of technologies by demonstrating their commercial potential through prototype production, testing and certification, and standard patent filing. KAIST is currently funding approximately 20 new technologies under this program as of July 2020.
KAIST’s outstanding intellectual property management has also received international recognition, with its selection as Asia’s leading institution in university R&D intellectual property at the Intellectual Property Business Congress (IPBC) Asia 2019 held in Tokyo, Japan last October.
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2020.08.18 View 10324 -
Deep Learning-Based Cough Recognition Model Helps Detect the Location of Coughing Sounds in Real Time
The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis.
Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %.
Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room.
Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image.
To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else.
In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places.
The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances.
The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment.
In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer.
The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image.
A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms.
Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient's condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff."
This study was conducted in collaboration with SM Instruments Inc.
Profile: Yong-Hwa Park, Ph.D.
Associate Professor
yhpark@kaist.ac.kr
http://human.kaist.ac.kr/
Human-Machine Interaction Laboratory (HuMaN Lab.)
Department of Mechanical Engineering (ME)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr/en/
Daejeon 34141, Korea
Profile: Gyeong Tae Lee
PhD Candidate
hansaram@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Seong Hu Kim
PhD Candidate
tjdgnkim@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Hyeonuk Nam
PhD Candidate
frednam@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Young-Key Kim
CEO
sales@smins.co.kr
http://en.smins.co.kr/
SM Instruments Inc.
Daejeon 34109, Korea
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2020.08.13 View 16390 -
Tinkering with Roundworm Proteins Offers Hope for Anti-aging Drugs
- The somatic nuclear protein kinase VRK-1 increases the worm’s lifespan through AMPK activation, and this mechanism can be applied to promoting human longevity, the study reveals. -
KAIST researchers have been able to dial up and down creatures’ lifespans by altering the activity of proteins found in roundworm cells that tell them to convert sugar into energy when their cellular energy is running low. Humans also have these proteins, offering up the intriguing possibilities for developing longevity-promoting drugs. These new findings were published on July 1 in Science Advances.
The roundworm Caenorhabditis elegans (C. elegans), a millimeter-long nematode commonly used in lab testing, enjoyed a boost in its lifespan when researchers tinkered with a couple of proteins involved in monitoring the energy use by its cells.
The proteins VRK-1 and AMPK work in tandem in roundworm cells, with the former telling the latter to get to work by sticking a phosphate molecule, composed of one phosphorus and four oxygen atoms, on it. In turn, AMPK’s role is to monitor energy levels in cells, when cellular energy is running low. In essence, VRK-1 regulates AMPK, and AMPK regulates the cellular energy status.
Using a range of different biological research tools, including introducing foreign genes into the worm, a group of researchers led by Professor Seung-Jae V. Lee from the Department of Biological Sciences at KAIST were able to dial up and down the activity of the gene that tells cells to produce the VRK-1 protein. This gene has remained pretty much unchanged throughout evolution. Most complex organisms have this same gene, including humans.
Lead author of the study Sangsoon Park and his colleagues confirmed that the overexpression, or increased production, of the VRK-1 protein boosted the lifespan of the C. elegans, which normally lives just two to three weeks, and the inhibition of VRK-1 production reduced its lifespan.
The research team found that the activity of the VRK-1-to-AMPK cellular-energy monitoring process is increased in low cellular energy status by reduced mitochondrial respiration, the set of metabolic chemical reactions that make use of the oxygen the worm breathes to convert macronutrients from food into the energy “currency” that cells spend to do everything they need to do.
It is already known that mitochondria, the energy-producing engine rooms in cells, play a crucial role in aging, and declines in the functioning of mitochondria are associated with age-related diseases. At the same time, the mild inhibition of mitochondrial respiration has been shown to promote longevity in a range of species, including flies and mammals.
When the research team performed similar tinkering with cultured human cells, they found they could also replicate this ramping up and down of the VRK-1-to-AMPK process that occurs in roundworms.
“This raises the intriguing possibility that VRK-1 also functions as a factor in governing human longevity, and so perhaps we can start developing longevity-promoting drugs that alter the activity of VRK-1,” explained Professor Lee.
At the very least, the research points us in an interesting direction for investigating new therapeutic strategies to combat metabolic disorders by targeting the modulation of VRK-1. Metabolic disorders involve the disruption of chemical reactions in the body, including diseases of the mitochondria.
But before metabolic disorder therapeutics or longevity drugs can be contemplated by scientists, further research still needs to be carried out to better understand how VRK-1 works to activate AMPK, as well as figure out the precise mechanics of how AMPK controls cellular energy.
This work was supported by the National Research Foundation (NRF), and the Ministry of Science and ICT (MSIT) of Korea.
Image credit: Seung-Jae V. LEE, KAIST.
Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only.
Publication:
Park, S., et al. (2020) ‘VRK-1 extends life span by activation of AMPK via phosphorylation’. Science Advances, Volume 6. No. 27, eaaw7824. Available online at https://doi.org/10.1126/sciadv.aaw7824
Profile: Seung-Jae V. Lee, Ph.D.
Professor
seungjaevlee@kaist.ac.kr
https://sites.google.com/view/mgakaist
Molecular Genetics of Aging Laboratory
Department of Biological Sciences
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.krDaejeon 34141, Korea
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2020.07.31 View 12404 -
Atomic Force Microscopy Reveals Nanoscale Dental Erosion from Beverages
KAIST researchers used atomic force microscopy to quantitatively evaluate how acidic and sugary drinks affect human tooth enamel at the nanoscale level. This novel approach is useful for measuring mechanical and morphological changes that occur over time during enamel erosion induced by beverages.
Enamel is the hard-white substance that forms the outer part of a tooth. It is the hardest substance in the human body, even stronger than bone. Its resilient surface is 96 percent mineral, the highest percentage of any body tissue, making it durable and damage-resistant. The enamel acts as a barrier to protect the soft inner layers of the tooth, but can become susceptible to degradation by acids and sugars.
Enamel erosion occurs when the tooth enamel is overexposed to excessive consumption of acidic and sugary food and drinks. The loss of enamel, if left untreated, can lead to various tooth conditions including stains, fractures, sensitivity, and translucence. Once tooth enamel is damaged, it cannot be brought back. Therefore, thorough studies on how enamel erosion starts and develops, especially at the initial stages, are of high scientific and clinical relevance for dental health maintenance.
A research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering at KAIST reported a new method of applying atomic force microscopy (AFM) techniques to study the nanoscale characterization of this early stage of enamel erosion. This study was introduced in the Journal of the Mechanical Behavior of Biomedical Materials (JMBBM) on June 29.
AFM is a very-high-resolution type of scanning probe microscopy (SPM), with demonstrated resolution on the order of fractions of a nanometer (nm) that is equal to one billionth of a meter. AFM generates images by scanning a small cantilever over the surface of a sample, and this can precisely measure the structure and mechanical properties of the sample, such as surface roughness and elastic modulus.
The co-lead authors of the study, Dr. Panpan Li and Dr. Chungik Oh, chose three commercially available popular beverages, Coca-Cola®, Sprite®, and Minute Maid® orange juice, and immersed tooth enamel in these drinks over time to analyze their impacts on human teeth and monitor the etching process on tooth enamel.
Five healthy human molars were obtained from volunteers between age 20 and 35 who visited the KAIST Clinic. After extraction, the teeth were preserved in distilled water before the experiment. The drinks were purchased and opened right before the immersion experiment, and the team utilized AFM to measure the surface topography and elastic modulus map.
The researchers observed that the surface roughness of the tooth enamel increased significantly as the immersion time increased, while the elastic modulus of the enamel surface decreased drastically. It was demonstrated that the enamel surface roughened five times more when it was immersed in beverages for 10 minutes, and that the elastic modulus of tooth enamel was five times lower after five minutes in the drinks.
Additionally, the research team found preferential etching in scratched tooth enamel. Brushing your teeth too hard and toothpastes with polishing particles that are advertised to remove dental biofilms can cause scratches on the enamel surface, which can be preferential sites for etching, the study revealed.
Professor Hong said, “Our study shows that AFM is a suitable technique to characterize variations in the morphology and mechanical properties of dental erosion quantitatively at the nanoscale level.”
This work was supported by the National Research Foundation (NRF), the Ministry of Science and ICT (MSIT), and the KUSTAR-KAIST Institute of Korea.
A dentist at the KAIST Clinic, Dr. Suebean Cho, Dr. Sangmin Shin from the Smile Well Dental, and Professor Kack-Kyun Kim at the Seoul National University School of Dentistry also collaborated in this project.
Publication:
Li, P., et al. (2020) ‘Nanoscale effects of beverages on enamel surface of human teeth: An atomic force microscopy study’. Journal of the Mechanical Behavior of Biomedical Materials (JMBBM), Volume 110. Article No. 103930. Available online at https://doi.org/10.1016/j.jmbbm.2020.103930
Profile: Seungbum Hong, Ph.D.
Associate Professor
seungbum@kaist.ac.kr
http://mii.kaist.ac.kr/
Materials Imaging and Integration (MII) Lab.
Department of Materials Science and Engineering (MSE)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Korea
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2020.07.21 View 12733