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Chemical Scissors Snip 2D Transition Metal Dichalcogenides into Nanoribbon
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.
-ProfileProfessor Sang Ouk KimSoft Nanomaterials Laboratory (http://snml.kaist.ac.kr)Department of Materials Science and EngineeringKAIST
2020.10.29 View 9075 -
'Mini-Lungs' Reveal Early Stages of SARS-CoV-2 Infection
Researchers in Korea and the UK have successfully grown miniature models of critical lung structures called alveoli, and used them to study how the coronavirus that causes COVID-19 infects the lungs.
To date, there have been more than 40 million cases of COVID-19 and almost 1.13 million deaths worldwide. The main target tissues of SARS-CoV-2, the virus that causes COVID-19, especially in patients that develop pneumonia, appear to be alveoli – tiny air sacs in the lungs that take up the oxygen we breathe and exchange it with carbon dioxide to exhale.
To better understand how SARS-CoV-2 infects the lungs and causes disease, a team of Professor Young Seok Ju from the Graduate School of Medical Science and Engineering at KAIST in collaboration with the Wellcome-MRC Cambridge Stem Cell Institute at the University of Cambridge turned to organoids – ‘mini-organs’ grown in three dimensions to mimic the behaviour of tissue and organs.
The team used tissue donated to tissue banks at the Royal Papworth Hospital NHS Foundation Trust and Addenbrooke’s Hospital, Cambridge University NHS Foundations Trust, UK, and Seoul National University Hospital to extract a type of lung cell known as human lung alveolar type 2 cells. By reprogramming these cells back to their earlier ‘stem cell’ stage, they were able to grow self-organizing alveolar-like 3D structures that mimic the behaviour of key lung tissue.
“The research community now has a powerful new platform to study precisely how the virus infects the lungs, as well as explore possible treatments,” said Professor Ju, co-senior author of the research.
Dr. Joo-Hyeon Lee, another co-senior author at the Wellcome-MRC Cambridge Stem Cell Institute, said: “We still know surprisingly little about how SARS-CoV-2 infects the lungs and causes disease. Our approach has allowed us to grow 3D models of key lung tissue – in a sense, ‘mini-lungs’ – in the lab and study what happens when they become infected.”
The team infected the organoids with a strain of SARS-CoV-2 taken from a patient in Korea who was diagnosed with COVID-19 on January 26 after traveling to Wuhan, China. Using a combination of fluorescence imaging and single cell genetic analysis, they were able to study how the cells responded to the virus.
When the 3D models were exposed to SARS-CoV-2, the virus began to replicate rapidly, reaching full cellular infection just six hours after infection. Replication enables the virus to spread throughout the body, infecting other cells and tissue.
Around the same time, the cells began to produce interferons – proteins that act as warning signals to neighbouring cells, telling them to activate their antiviral defences. After 48 hours, the interferons triggered the innate immune response – its first line of defence – and the cells started fighting back against infection.
Sixty hours after infection, a subset of alveolar cells began to disintegrate, leading to cell death and damage to the lung tissue.
Although the researchers observed changes to the lung cells within three days of infection, clinical symptoms of COVID-19 rarely occur so quickly and can sometimes take more than ten days after exposure to appear. The team say there are several possible reasons for this. It may take several days from the virus first infiltrating the upper respiratory tract to it reaching the alveoli. It may also require a substantial proportion of alveolar cells to be infected or for further interactions with immune cells resulting in inflammation before a patient displays symptoms.
“Based on our model we can tackle many unanswered key questions, such as understanding genetic susceptibility to SARS-CoV-2, assessing relative infectivity of viral mutants, and revealing the damage processes of the virus in human alveolar cells,” said Professor Ju. “Most importantly, it provides the opportunity to develop and screen potential therapeutic agents against SARS-CoV-2 infection.”
“We hope to use our technique to grow these 3D models from cells of patients who are particularly vulnerable to infection, such as the elderly or people with diseased lungs, and find out what happens to their tissue,” added Dr. Lee.
The research was a collaboration involving scientists from KAIST, the University of Cambridge, Korea National Institute of Health, Institute for Basic Science (IBS), Seoul National University Hospital and Genome Insight in Korea.
- ProfileProfessor Young Seok JuLaboratory of Cancer Genomics https://julab.kaist.ac.kr the Graduate School of Medical Science and EngineeringKAIST
2020.10.26 View 12517 -
Experts to Help Asia Navigate the Post-COVID-19 and 4IR Eras
Risk Quotient 2020, an international conference co-hosted by KAIST and the National University of Singapore (NUS), will bring together world-leading experts from academia and industry to help Asia navigate the post-COVID-19 and Fourth Industrial Revolution (4IR) eras.
The online conference will be held on October 29 from 10 a.m. Korean time under the theme “COVID-19 Pandemic and A Brave New World”. It will be streamed live on YouTube at https://www.youtube.com/c/KAISTofficial and https://www.youtube.com/user/NUScast.
The Korea Policy Center for the Fourth Industrial Revolution (KPC4IR) at KAIST organized this conference in collaboration with the Lloyd's Register Foundation Institute for the Public Understanding of Risk (IPUR) at NUS.
During the conference, global leaders will examine the socioeconomic impacts of the COVID-19 pandemic on areas including digital innovation, education, the workforce, and the economy. They will then highlight digital and 4IR technologies that could be utilized to effectively mitigate the risks and challenges associated with the pandemic, while harnessing the opportunities that these socioeconomic effects may present. Their discussions will mainly focus on the Asian region.
In his opening remarks, KAIST President Sung-Chul Shin will express his appreciation for the Asian populations’ greater trust in and compliance with their governments, which have given the continent a leg up against the coronavirus. He will then emphasize that by working together through the exchange of ideas and global collaboration, we will be able to shape ‘a brave new world’ to better humanity. Welcoming remarks by Prof. Sang Yup Lee (Dean, KAIST Institutes) and Prof. Tze Yun Leong (Director, AI Technology at AI Singapore) will follow.
For the keynote speech, Prof. Lan Xue (Dean, Schwarzman College, Tsinghua University) will share China’s response to COVID-19 and lessons for crisis management. Prof. Danny Quah (Dean, Lee Kuan Yew School of Public Policy, NUS) will present possible ways to overcome these difficult times. Dr. Kak-Soo Shin (Senior Advisor, Shin & Kim LLC, Former Ambassador to the State of Israel and Japan, and Former First and Second Vice Minister of the Ministry of Foreign Affairs of the Republic of Korea) will stress the importance of the international community’s solidarity to ensure peace, prosperity, and safety in this new era.
Panel Session I will address the impact of COVID-19 on digital innovation. Dr. Carol Soon (Senior Research Fellow, Institute of Policy Studies, NUS) will present her interpretation of recent technological developments as both opportunities for our society as a whole and challenges for vulnerable groups such as low-income families. Dr. Christopher SungWook Chang (Managing Director, Kakao Mobility) will show how changes in mobility usage patterns can be captured by Kakao Mobility’s big data analysis. He will illustrate how the data can be used to interpret citizen’s behaviors and how risks can be transformed into opportunities by utilizing technology. Mr. Steve Ledzian’s (Vice President, Chief Technology Officer, FireEye) talk will discuss the dangers caused by threat actors and other cyber risk implications of COVID-19. Dr. June Sung Park (Chairman, Korea Software Technology Association (KOSTA)) will share how COVID-19 has accelerated digital transformations across all industries and why software education should be reformed to improve Korea’s competitiveness.
Panel Session II will examine the impact on education and the workforce. Dr. Sang-Jin Ban (President, Korean Educational Development Institute (KEDI)) will explain Korea’s educational response to the pandemic and the concept of “blended learning” as a new paradigm, and present both positive and negative impacts of online education on students’ learning experiences. Prof. Reuben Ng (Professor, Lee Kuan Yew School of Public Policy, NUS) will present on graduate underemployment, which seems to have worsened during COVID-19. Dr. Michael Fung’s presentation (Deputy Chief Executive (Industry), SkillsFuture SG) will introduce the promotion of lifelong learning in Singapore through a new national initiative known as the ‘SkillsFuture Movement’. This movement serves as an example of a national response to disruptions in the job market and the pace of skills obsolescence triggered by AI and COVID-19.
Panel Session III will touch on technology leadership and Asia’s digital economy and society. Prof. Naubahar Sharif (Professor, Division of Social Science and Division of Public Policy, Hong Kong University of Science and Technology (HKUST)) will share his views on the potential of China in taking over global technological leadership based on its massive domestic market, its government support, and the globalization process. Prof. Yee Kuang Heng (Professor, Graduate School of Public Policy, University of Tokyo) will illustrate how different legal and political needs in China and Japan have shaped the ways technologies have been deployed in responding to COVID-19. Dr. Hayun Kang (Head, International Cooperation Research Division, Korea Information Society Development Institute (KISDI)) will explain Korea’s relative success containing the pandemic compared to other countries, and how policy leaders and institutions that embrace digital technologies in the pursuit of public welfare objectives can produce positive outcomes while minimizing the side effects.
Prof. Kyung Ryul Park (Graduate School of Science and Technology Policy, KAIST) will be hosting the entire conference, whereas Prof. Alice Hae Yun Oh (Director, MARS Artificial Intelligence Research Center, KAIST), Prof. Wonjoon Kim (Dean, Graduate School of Innovation and Technology Management, College of Business, KAIST), Prof. Youngsun Kwon (Dean, KAIST Academy), and Prof. Taejun Lee (Korea Development Institute (KDI) School of Public Policy and Management) are to chair discussions with the keynote speakers and panelists.
Closing remarks will be delivered by Prof. Chan Ghee Koh (Director, NUS IPUR), Prof. So Young Kim (Director, KAIST KPC4IR), and Prof. Joungho Kim (Director, KAIST Global Strategy Institute (GSI)).
“This conference is expected to serve as a springboard to help Asian countries recover from global crises such as the COVID-19 pandemic through active cooperation and joint engagement among scholars, experts, and policymakers,” according to Director So Young Kim.
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2020.10.22 View 15955 -
Taesik Gong Named Google PhD Fellow
PhD candidate Taesik Gong from the School of Computing was named a 2020 Google PhD Fellow in the field of machine learning.
The Google PhD Fellowship Program has recognized and supported outstanding graduate students in computer science and related fields since 2009. Gong is one of two Korean students chosen as the recipients of Google Fellowships this year. A total of 53 students across the world in 12 fields were awarded this fellowship.
Gong’s research on condition-independent mobile sensing powered by machine learning earned him this year’s fellowship. He has published and presented his work through many conferences including ACM SenSys and ACM UbiComp, and has worked at Microsoft Research Asia and Nokia Bell Labs as a research intern. Gong was also the winner of the NAVER PhD Fellowship Award in 2018.
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2020.10.15 View 12675 -
Big Ideas on Emerging Materials Explored at EMS
Renowned scholars and editors from academic journals joined the Emerging Materials e-Symposium (EMS) held at KAIST and shared the latest breakthroughs and big ideas in new material development last month. This e-symposium was organized by Professor Il-Doo Kim from the KAIST Department of Materials Sciences and Engineering over five days from September 21 through 25 via Zoom and YouTube. Professor Kim also serves as an associate editor of ACS Nano.
Esteemed scholars and editors of academic journals including ACS Nano, Nano Energy, and Energy Storage Materials made Zoom presentations in three main categories: 1) nanostructures for next-generation applications, 2) chemistry and biotechnology for applications in the fields of environment and industry, and 3) material innovation for technological applications.
During Session I, speakers including Professor John A. Rogers of Northwestern University and Professor Zhenan Bao of Stanford University led the session on Emerging Soft Electronics and 3D printing.
In later sessions, other globally recognized scholars gave talks titled Advanced Nanostructuring for Emerging Materials, Frontiers in Emerging Materials Research, Advanced Energy Materials and Functional Nanomaterials, and Latest Advances in Nanomaterials Research.
These included 2010 Nobel Prize laureate and professor at Manchester University Andre Geim, editor-in-chief of ACS Nano and professor at UCLA Paul S. Weiss, Professor Paul Alivisatos of UC Berkeley, Professor William Chueh of Stanford University, and Professor Mircea Dinca of MIT.
KAIST President Sung-Chul Shin, who is also a materials physicist, said in his opening address, “Innovation in materials science will become an important driving force to change our way of life. All the breakthroughs in materials have extended a new paradigm that has transformed our lives.”
“Creative research projects alongside global collaborators like all of you will allow the breakthroughs that will deliver us from these crises,” he added.
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2020.10.06 View 16493 -
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 12110 -
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 11965 -
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 12522 -
Microscopy Approach Poised to Offer New Insights into Liver Diseases
Researchers have developed a new way to visualize the progression of nonalcoholic fatty liver disease (NAFLD) in mouse models of the disease. The new microscopy method provides a high-resolution 3D view that could lead to important new insights into NAFLD, a condition in which too much fat is stored in the liver.
“It is estimated that a quarter of the adult global population has NAFLD, yet an effective treatment strategy has not been found,” said professor Pilhan Kim from the Graduate School of Medical Science and Engineering at KAIST. “NAFLD is associated with obesity and type 2 diabetes and can sometimes progress to liver failure in serious case.”
In the Optical Society (OSA) journal Biomedical Optics Express, Professor Kim and colleagues reported their new imaging technique and showed that it can be used to observe how tiny droplets of fat, or lipids, accumulate in the liver cells of living mice over time.
“It has been challenging to find a treatment strategy for NAFLD because most studies examine excised liver tissue that represents just one timepoint in disease progression,” said Professor Kim. “Our technique can capture details of lipid accumulation over time, providing a highly useful research tool for identifying the multiple parameters that likely contribute to the disease and could be targeted with treatment.”
Capturing the dynamics of NAFLD in living mouse models of the disease requires the ability to observe quickly changing interactions of biological components in intact tissue in real-time. To accomplish this, the researchers developed a custom intravital confocal and two-photon microscopy system that acquires images of multiple fluorescent labels at video-rate with cellular resolution.
“With video-rate imaging capability, the continuous movement of liver tissue in live mice due to breathing and heart beating could be tracked in real time and precisely compensated,” said Professor Kim. “This provided motion-artifact free high-resolution images of cellular and sub-cellular sized individual lipid droplets.”
The key to fast imaging was a polygonal mirror that rotated at more than 240 miles per hour to provide extremely fast laser scanning. The researchers also incorporated four different lasers and four high-sensitivity optical detectors into the setup so that they could acquire multi-color images to capture different color fluorescent probes used to label the lipid droplets and microvasculature in the livers of live mice.
“Our approach can capture real-time changes in cell behavior and morphology, vascular structure and function, and the spatiotemporal localization of biological components while directly visualizing of lipid droplet development in NAFLD progression,” said Professor Kim. “It also allows the analysis of the highly complex behaviors of various immune cells as NAFLD progresses.”
The researchers demonstrated their approach by using it to observe the development and spatial distribution of lipid droplets in individual mice with NAFLD induced by a methionine and choline-deficient diet. Next, they plan to use it to study how the liver microenvironment changes during NAFLD progression by imaging the same mouse over time. They also want to use their microscope technique to visualize various immune cells and lipid droplets to better understand the complex liver microenvironment in NAFLD progression.
2020.08.21 View 10211 -
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 11288 -
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 17632 -
‘SoundWear’ a Heads-Up Sound Augmentation Gadget Helps Expand Children’s Play Experience
In this digital era, there has been growing concern that children spend most of their playtime watching TV, playing computer games, and staring at mobile phones with ‘head-down’ posture even outdoors.
To counter such concerns, KAIST researchers designed a wearable bracelet using sound augmentation to leverage play benefits by employing digital technology. The research team also investigated how sound influences children’s play experiences according to their physical, social, and imaginative aspects.
Playing is a large part of enjoyable and rewarding lives, especially for children. Previously, a large part of children’s playtime used to take place outdoors, and playing outdoors has long been praised for playing an essential role in providing opportunities to perform physical activity, improve social skills, and boost imaginative thinking.
Motivated by these concerns, a KAIST research team led by Professor Woohun Lee and his researcher Jiwoo Hong from the Department of Industrial Design made use of sound augmentation, which is beneficial for motivating playful experiences by facilitating imagination and enhancing social awareness with its ambient and omnidirectional characteristics.
Despite the beneficial characteristics of sound augmentation, only a few studies have explored sound interaction as a technology to augment outdoor play due to its abstractness when conveying information in an open space outdoors. There is also a lack of empirical evidence regarding its effect on children's play experiences.
Professor Lee’s team designed and implemented an original bracelet-type wearable device called SoundWear. This device uses non-speech sound as a core digital feature for children to broaden their imaginations and improvise their outdoor games.
Children equipped with SoundWear were allowed to explore multiple sounds (i.e., everyday and instrumental sounds) on SoundPalette, pick a desired sound, generate the sound with a swinging movement, and transfer the sound between multiple devices for their outdoor play.
Both the quantitative and qualitative results of a user study indicated that augmenting playtime with everyday sounds triggered children’s imagination and resulted in distinct play behaviors, whereas instrumental sounds were transparently integrated with existing outdoor games while fully preserving play benefits in physical, social, and imaginative ways.
The team also found that the gestural interaction of SoundWear and the free sound choice on SoundPalette helped children to gain a sense of achievement and ownership toward sound. This led children to be physically and socially active while playing.
PhD candidate Hong said, “Our work can encourage the discussion on using digital technology that entails sound augmentation and gestural interactions for understanding and cultivating creative improvisations, social pretenses, and ownership of digital materials in digitally augmented play experiences.”
Professor Lee also envisioned that the findings being helpful to parents and educators saying, “I hope the verified effect of digital technology on children’s play informs parents and educators to help them make more informed decisions and incorporate the playful and creative usage of new media, such as mobile phones and smart toys, for young children.”
This research titled “SoundWear: Effect of Non-speech Sound Augmentation on the Outdoor Play Experience of Children” was presented at DIS 2020 (the ACM Conference on Designing Interactive Systems) taking place virtually in Eindhoven, Netherlands, from July 6 to 20. This work received an Honorable Mention Award for being in the top 5% of all the submissions to the conference.
Publication:
Hong, J., et al. (2020) ‘SoundWear: Effect of Non-speech Sound Augmentation on the Outdoor Play Experience of Children’. Proceedings of the 2020 ACM Designing Interactive Systems Conference (DIS'20), Pages 2201-2213. Available online at https://doi.org/10.1145/3357236.3395541
Profile: Professor Woohun Leewoohun.lee@kaist.ac.krhttp://wonderlab.kaist.ac.kr
Department of Industrial Design (ID) KAIST
2020.07.28 View 9319