GNI
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KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 2506 -
Deep Learning Framework to Enable Material Design in Unseen Domain
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation
A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.
Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training data-set size. This study was reported in npj Computational Materials last month.
“We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering.
Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain.
Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps.
First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search.
As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient.
Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework.
The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project.
-Publication
Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2)
-Profile
Professor Seunghwa Ryu
Mechanics & Materials Modeling Lab
Department of Mechanical Engineering
KAIST
2021.09.29 View 9326 -
Prof. Sang Wan Lee Selected for 2021 IBM Academic Award
Professor Sang Wan Lee from the Department of Bio and Brain Engineering was selected as the recipient of the 2021 IBM Global University Program Academic Award. The award recognizes individual faculty members whose emerging science and technology contains significant interest for universities and IBM.
Professor Lee, whose research focuses on artificial intelligence and computational neuroscience, won the award for his research proposal titled A Neuroscience-Inspired Approach for Metacognitive Reinforcement Learning. IBM provides a gift of $40,000 to the recipient’s institution in recognition of the selection of the project but not as a contract for services.
Professor Lee’s project aims to exploit the unique characteristics of human reinforcement learning. Specifically, he plans to examines the hypothesis that metacognition, a human’s ability to estimate their uncertainty level, serves to guide sample-efficient and near-optimal exploration, making it possible to achieve an optimal balance between model-based and model-free reinforcement learning.
He was also selected as the winner of the Google Research Award in 2016 and has been working with DeepMind and University College London to conduct basic research on decision-making brain science to establish a theory on frontal lobe meta-enhance learning.
"We plan to conduct joint research for utilizing brain-based artificial intelligence technology and frontal lobe meta-enhanced learning technology modeling in collaboration with an international research team including IBM, DeepMind, MIT, and Oxford,” Professor Lee said.
2021.06.25 View 9168 -
‘Urban Green Space Affects Citizens’ Happiness’
Study finds the relationship between green space, the economy, and happiness
A recent study revealed that as a city becomes more economically developed, its citizens’ happiness becomes more directly related to the area of urban green space.
A joint research project by Professor Meeyoung Cha of the School of Computing and her collaborators studied the relationship between green space and citizen happiness by analyzing big data from satellite images of 60 different countries.
Urban green space, including parks, gardens, and riversides not only provides aesthetic pleasure, but also positively affects our health by promoting physical activity and social interactions. Most of the previous research attempting to verify the correlation between urban green space and citizen happiness was based on few developed countries. Therefore, it was difficult to identify whether the positive effects of green space are global, or merely phenomena that depended on the economic state of the country. There have also been limitations in data collection, as it is difficult to visit each location or carry out investigations on a large scale based on aerial photographs.
The research team used data collected by Sentinel-2, a high-resolution satellite operated by the European Space Agency (ESA) to investigate 90 green spaces from 60 different countries around the world. The subjects of analysis were cities with the highest population densities (cities that contain at least 10% of the national population), and the images were obtained during the summer of each region for clarity. Images from the northern hemisphere were obtained between June and September of 2018, and those from the southern hemisphere were obtained between December of 2017 and February of 2018.
The areas of urban green space were then quantified and crossed with data from the World Happiness Report and GDP by country reported by the United Nations in 2018. Using these data, the relationships between green space, the economy, and citizen happiness were analyzed.
The results showed that in all cities, citizen happiness was positively correlated with the area of urban green space regardless of the country’s economic state. However, out of the 60 countries studied, the happiness index of the bottom 30 by GDP showed a stronger correlation with economic growth. In countries whose gross national income (GDP per capita) was higher than 38,000 USD, the area of green space acted as a more important factor affecting happiness than economic growth. Data from Seoul was analyzed to represent South Korea, and showed an increased happiness index with increased green areas compared to the past.
The authors point out their work has several policy-level implications. First, public green space should be made accessible to urban dwellers to enhance social support. If public safety in urban parks is not guaranteed, its positive role in social support and happiness may diminish. Also, the meaning of public safety may change; for example, ensuring biological safety will be a priority in keeping urban parks accessible during the COVID-19 pandemic.
Second, urban planning for public green space is needed for both developed and developing countries. As it is challenging or nearly impossible to secure land for green space after the area is developed, urban planning for parks and green space should be considered in developing economies where new cities and suburban areas are rapidly expanding.
Third, recent climate changes can present substantial difficulty in sustaining urban green space. Extreme events such as wildfires, floods, droughts, and cold waves could endanger urban forests while global warming could conversely accelerate tree growth in cities due to the urban heat island effect. Thus, more attention must be paid to predict climate changes and discovering their impact on the maintenance of urban green space.
“There has recently been an increase in the number of studies using big data from satellite images to solve social conundrums,” said Professor Cha. “The tool developed for this investigation can also be used to quantify the area of aquatic environments like lakes and the seaside, and it will now be possible to analyze the relationship between citizen happiness and aquatic environments in future studies,” she added.
Professor Woo Sung Jung from POSTECH and Professor Donghee Wohn from the New Jersey Institute of Technology also joined this research. It was reported in the online issue of EPJ Data Science on May 30.
-PublicationOh-Hyun Kwon, Inho Hong, Jeasurk Yang, Donghee Y. Wohn, Woo-Sung Jung, andMeeyoung Cha, 2021. Urban green space and happiness in developed countries. EPJ Data Science. DOI: https://doi.org/10.1140/epjds/s13688-021-00278-7
-ProfileProfessor Meeyoung ChaData Science Labhttps://ds.ibs.re.kr/
School of Computing
KAIST
2021.06.21 View 9258 -
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 13663 -
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster.
The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics.
Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain.
In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community.
During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence.
Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women.
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2020.05.26 View 9923 -
A Study Finds Neuropeptide Somatostatin Enhances Visual Processing
Researchers have confirmed that neuropeptide somatostatin can improve cognitive function in the brain. A research group of Professor Seung-Hee Lee from the Department of Biological Sciences at KAIST found that the application of neuropeptide somatostatin improves visual processing and cognitive behaviors by reducing excitatory inputs to parvalbumin-positive interneurons in the cortex.
This study, reported at Science Advances on April 22nd (EST), sheds a new light on the therapeutics of neurodegenerative diseases. According to a recent study in Korea, one in ten seniors over 65 is experiencing dementia-related symptoms in their daily lives such like memory loss, cognitive decline, and motion function disorders. Professor Lee believes that somatostatin treatment can be directly applied to the recovery of cognitive functions in Alzheimer’s disease patients.
Professor Lee started this study noting the fact that the level of somatostatin expression was dramatically decreased in the cerebral cortex and cerebrospinal fluid of Alzheimer’s disease patients
Somatostatin-expressing neurons in the cortex are known to exert the dendritic inhibition of pyramidal neurons via GABAergic transmission. Previous studies focused on their inhibitory effects on cortical circuits, but somatostatin-expressing neurons can co-release somatostatin upon activation. Despite the abundant expression of somatostatin and its receptors in the cerebral cortex, it was not known if somatostatin could modulate cognitive processing in the cortex.
The research team demonstrated that the somatostatin treatment into the cerebral cortex could enhance visual processing and cognitive behaviors in mice. The research team combined behaviors, in vivo and in vitro electrophysiology, and electron microscopy techniques to reveal how the activation of somatostatin receptors in vivo enhanced the ability of visual recognition in animals. Interestingly, somatostatin release can reduce excitatory synaptic transmission to another subtype of GABAergic interneurons, parvalbumin (PV)-expressing neurons.
As somatostatin is a stable and safe neuropeptide expressed naturally in the mammalian brain, it was safe to be injected into the cortex and cerebrospinal fluid, showing a potential application to drug development for curing cognitive disorders in humans.
Professor Lee said, “Our research confirmed the key role of the neuropeptide SST in modulating cortical function and enhancing cognitive ability in the mammalian brain. I hope new drugs can be developed based on the function of somatostatin to treat cognitive disabilities in many patients suffering from neurological disorders.”
This study was supported by the National Research Foundation of Korea.
Publication:
Song, Y. H et al. (2020) ‘Somatostatin enhances visual processing and perception by suppressing excitatory inputs to parvalbumin-positive interneurons in V1’, Science Advances, 6(17). Available online at https://doi.org/10.1126/sciadv.aaz0517
Profile:
Seung-Hee Lee
Associate Professor
shlee1@kaist.ac.kr
https://sites.google.com/site/leelab2013/
Sensory Processing Lab (SPL)
Department of Biological Sciences (BIO)
Korea Advanced Institute of Science and Technology (KAIST)
Profile:
You-Hyang Song
Researcher (Ph.D.)
dbgidtm17@kaist.ac.kr
SPL, KAIST BIO
Profile:
Yang-Sun Hwang
Researcher (M.S.)
hys940129@kaist.ac.kr
SPL, KAIST BIO
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2020.04.23 View 11354 -
Team KAT Wins the Autonomous Car Challenge
(Team KAT receiving the Presidential Award)
A KAIST team won the 2018 International Autonomous Car Challenge for University Students held in Daegu on November 2.
Professor Seung-Hyun Kong from the ChoChunShik Graduate School of Green Transportation and his team participated in this contest with the team named KAT (KAIST Autonomous Technologies). The team received the Presidential Award with a fifty million won cash prize and an opportunity for a field trip abroad.
The competition was conducted on actual roads with Connected Autonomous Vehicles (CAV), which incorporate autonomous driving technologies and vehicle-to-everything (V2X) communication system.
In this contest, the autonomous vehicles were given a mission to pick up passengers or parcels. Through the V2X communication, the contest gave current location of the passengers or parcels, their destination, and service profitability according to distance and level of service difficulty.
The participating vehicles had to be equipped very accurate and robust navigation system since they had to drive on narrow roads as well as go through tunnels where GPS was not available. Moreover, they had to use camera-based recognition technology that was invulnerable to backlight as the contest was in the late afternoon.
The contest scored the mission in the following way: the vehicles get points if they pick up passengers and safely drop them off at their destination; on the other hand, points are deducted when they violate lanes or traffic lights. It will be a major black mark if a participant sitting in the driver’s seat needs to get involved in driving due to a technical issue.
Youngbo Shim of KAT said, “We believe that we got major points for technical superiority in autonomous driving and our algorithm for passenger selection.”
This contest, hosted by Ministry of Trade, Industry and Energy, was the first international competition for autonomous driving on actual roads. A total of nine teams participated in the final contest, four domestic teams and five teams allied with overseas universities such as Tsinghua University, Waseda University, and Nanyang Technological University.
Professor Kong said, “There is still a long way to go for fully autonomous vehicles that drive flexibly under congested traffic conditions. However, we will continue to our research in order to achieve high-quality autonomous driving technology.”
(Team KAT getting ready for the challenge)
2018.11.06 View 9410 -
KAIST to Open the Meditation Research Center
KAIST announced that it will open its Meditation Research Center next June. The center will serve as a place for the wellness of KAIST community as well as for furthering the cognitive sciences and its relevant convergence studies.
For facilitating the center, KAIST signed an MOU with the Foundation Academia Platonica in Seoul, an academy working for enriching the humanities and insight meditation on Aug.31. The Venerable Misan, a Buddhist monk well-known for his ‘Heart Smile Meditation’ program, will head the center.
The center will also conduct convergence research on meditation, which will translate into brain imaging, cognitive behavior, and its psychological effects. Built upon the research, the center expects to publish textbooks on meditation and will distribute them to the public and schools in an effort to widely disseminate the benefits of meditation.
As mindful meditation has become mainstream and more extensively studied, growing evidence suggests multiple psychological and physical benefits of these mindfulness exercises as well as for similar practices. Mind-body practices like meditation have been shown to reduce the body’s stress response by strengthening the relaxation response and lowering stress hormones.
The Venerable Misan, a Ph.D in philosophy from Oxford University, also serves as the director of the Sangdo Meditation Center and a professor at Joong-Ang Sangha University, a higher educational institution for Buddhist monks.
Monk Misan said that meditation will play a crucial part in educating creative students with an empathetic mindset. He added, “Hi-tech companies in Silicon Valley such as Google and Intel have long introduced meditation programs for stress management. Such practices will enhance the wellness of employees as well as their working efficiency.”
President Sung-Chul Shin said of the opening of the center, “From long ago, many universities in foreign countries including Havard, Stanford, Oxfor universities and the Max Planck Institute in Germany have applied scientific approaches to meditation and installed meditation centers. I am pleased to open our own center next year and I believe that it will bring more diverse opportunities for advancing convergent studies in AI and cognitive sciences.
2017.08.31 View 5463 -
New Building Endowed in Bio and Brain Engineering Department
An endowment from the former Chairman of Mirae Industries, Moon Soul Chung, was used to establish the Yang Bun Soon Building in the Bio and Brain Engineering Department at KAIST. The opening ceremony for the building took place on February 8 and was attended by President Sung-Mo Kang, KAIST administrators, faculty, and students.
The Yang Bun Soon Building, named after the wife of Chairman Chung, is a new addition to the Bio and Brain Engineering Department complex. The five-story building was erected next to the 11-story Chung Moon Soul Building, which was completed in 2003 using a portion of his first endowment to KAIST. Chairman Chung donated approximately 30 billion KRW for funding a convergence research for IT and BT in 2001.
The new building was completed with financing from Chung’s second endowment of 21.5 billion KRW in support of the fields of brain and cognitive sciences in 2014. The building will accommodate both lab facilities and lecture halls.
At the ceremony, President Kang thanked the Chungs for their continuing generosity to KAIST. He commended Chung for showing how entrepreneurs can fulfill their social responsibility by supporting Korea’s future through donations and support.
(Photo caption: Chung Moon Soul Building (left) and Yang Bun Soon Building(right))
2017.02.09 View 5239 -
Yang-Hann Kim named recipient of the Rossing Prize in Acoustics Education by the Acoustical Society of America
Courtesy of the Acoustical Society of America (ASA)
Press release issued by ASA on October 8, 2015:
Yang-Hann Kim named recipient of the Rossing Prize in Acoustics Education by the Acoustical Society of America
Melville (NY), 8 October 2015—Yang-Hann Kim, Professor at KAIST (Korea Advanced Institute of Science and Technology), Daejeon, has been named recipient of the Acoustical Society of America (ASA) Rossing Prize in Acoustics Education. The Rossing Prize is awarded to an individual who has made significant contributions toward furthering acoustics education through distinguished teaching, creation of educational materials, textbook writing and other activities. The Prize will be presented at the 170th meeting of the ASA on 4 November 2015 in Jacksonville, Florida.
“It is my great honor to receive the Rossing Prize, which has been given to outstanding scholar members of ASA since 2003. I never dreamed to be one of them.” said Kim. “I must express my deep respect and love to my friend Thomas Rossing: I have known him more than 20 years, always respect what he has done for teaching, writing books, and pioneering work in musical acoustics.”
Yang-Hann Kim is a Fellow of the Acoustical Society of America. He received a Ph.D. from the Massachusetts Institute of Technology. His main research interests in acoustics began with “sound visualization” resulted in the development of the “sound camera” which makes any sound visible instantly. Then he moved to “sound manipulation.” Using his manipulation technology, one can move any sound in space and time, positioning sound, and can create a private sound zone. Sound Visualization and Manipulation, (Wiley, 2013), summarizes these two fields. Dr. Kim’s textbook, Sound Propagation: An Impedance Based Approach (John Wiley and Sons, 2010), is well acknowledged by the associated professional communities as one of best acoustics textbooks. Using his teaching experience at KAIST, he created a YouTube lecture on acoustics and vibration which is also available in MOOC (Massive Open Online Course). He has also presented lectures to over 500 engineers and technicians for the past 30 years.
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The Acoustical Society of America (ASA) is the premier international scientific society in acoustics devoted to the science and technology of sound. Its 7000 members worldwide represent a broad spectrum of the study of acoustics. ASA publications include the Journal of the Acoustical Society of America—the world’s leading journal on acoustics, Acoustics Today magazine, books, and standards on acoustics. The Society also holds two major scientific meetings per year. For more information about the Society visit our website, www.acousticalsociety.org.
2015.10.06 View 7960 -
The Acoustical Society of America Names Yang Hann Kim of KAIST the Recipient of the 2015 Rossing Prize in Acoustics Education
The award, given to Dr. Kim in recognition of his contributions to the advancement of acoustics education, will be presented during the 170th Meeting of the Acoustical Society of America on November 2-6, 2015 in Jacksonville, Florida.
The Acoustical Society of America (ASA) announced today that Professor Yang Hann Kim of the Mechanical Engineering Department at the Korea Advanced Institute of Science and Technology (KAIST) was the 12th recipient of the Rossing Prize in Acoustics Education. Dr. Kim is the first recipient selected from a non-English-speaking nation.
The Rossing Prize in Acoustics Education was established in 2003 from a generous gift made to the ASA Foundation by Thomas D. Rossing to recognize an individual who has made significant contributions to the advancement of acoustics education through distinguished teaching, creation of educational materials, textbook writing, and other activities.
During 25 years of teaching and conducting research in acoustics, noise, and vibration at KAIST, Dr. Kim has advised 26 doctorates and published over 200 research papers in journals such as Journal of Acoustical Society of America, Journal of Sound and Vibration, and Journal of Mechanical Systems and Signal Processing. He also wrote two acoustics textbooks for university education, which has been widely read worldwide. The textbook titles are: Sound Propagation: An Impedance Based Approach (Wiley, July 2010) and with the co-author, Dr. Jung-Woo Choi, Sound Visualization and Manipulation (Wiley, September 2013).
Since 2009, Professor Kim has lectured an online course entitled “Introduction to Acoustics,” offering students and the general public throughout the world guidance to study acoustics through the basic concept of impedance, for example, on vibrations and waves.
Dr. Kim will receive the award during ASA’s 170th conference to be held on November 2-6, 2015 at the Hyatt Regency Jacksonville Riverfront Hotel in Jacksonville, Florida, USA.
For the list of previous recipients of the Rossing Prize in Acoustics Education,
see:http://acousticalsociety.org/funding_resources/prizes#rossing
2015.06.04 View 7767