recognition
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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 2597 -
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
(END)
2020.08.13 View 13750 -
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 9508 -
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 8016 -
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 7836 -
Professor Ji-Yun Lee, Received FAA Recognition Award
Professor Ji-Yun Lee, from the Department of Aerospace Engineering at KAIST, received the US Federal Aviation Administration (FAA) Recognition Award for her Ground-Based Augmentation System (GBAS) and her contribution to the development of satellite navigation technology.
GBAS contributes to the safety of aircraft navigation by providing flawless information with real-time location accuracy within one meter.
Professor Lee developed the monitoring software to improve the safety of GBAS users in her paper published in the International Journal of Radio Science in July of 2012.
The software will be distributed and used by many organizations including Eurocontrol following verification from the FAA technical center. It is expected to be standardized after discussions among international organizations.Professor Lee said, “As satellite navigation is applied to the infrastructure of air, marine, and ground transportation, as well as information & communications and finance, ensuring the performance and safety of the system is the most important factor. GBAS will be further developed and applied as a part of a global service system through international collaboration.”
2013.11.15 View 10200 -
Lecture Hall Named After Venture Businessman Min-Hwa Lee
A lecture hall in the Alumni Start-Up Building on the KAIST campus was named Min-Hwa Lee Hall in a ceremony on Tuesday to pay tribute to KAIST alumnus Min-Hwa Lee"s contributions to the development of Korean venture business.
On hand at the ceremony were Sung-Woo Hong, head of the Small and Medium Business Administration, KAIST President Nam-Pyo Suh, dozens of KAIST alumni representatives, and figures from government research institutes.
Lee, who obtained his M.S. (1978) and Ph.D. (1985) in Electrical Engineering from KAIST, established a fund of 10 billion won along with other KAIST alumni in 2001 and donated it for the construction of the Alumni Start-Up Building for aspiring entrepreneurs. To remember his lofty vision, KAIST decided to name a lecture hall after him.
As a venture businessman, Lee founded the Madison, Ltd., one of the earliest venture companies in Korea, in 1985. Lee then played a leading role in the creation of the Korea Venture Industry Association in 1995, and in the establishment of KOSDAQ and the enactment of a special law for venture enterprises.
KAIST will appoint Lee as an adjunct professor in recognition of his expertise in venture business and commercialization of new inventions. Lee will teach entrepreneurship at the Graduate School of Management and the Institute for Gifted Students, a KAIST affiliate.
"Dr. Lee has made a great contribution to the development of Korean venture business. At a time when commercialization of new inventions was at an infant stage, he nurtured technology ventures and built the foundation for the proliferation of technology venture," President Suh said. "We expect that he will strive to open the generation of technologies which will lead the development of Korea in the future and become a mentor of aspiring entrepreneurs," Suh added.
2009.06.30 View 14052 -
KAIST Prof. Park Selected as Winner of Clemson Award
Professor Tae-Gwan Park of the Department of Biological Sciences, KAIST, was chosen as the winner of the 2009 Clemson Award for Fundamental Research, university authorities said on Tuesday (April 7).
The award is the highest recognition of the Society for Biomaterials, an international organization of more than 3,000 members that promotes research in the field of biomaterials. Prof. Park is cited for his outstanding achievements in interdisciplinary research covering gene transferring, gene therapy and neogenesis. It is rare for a non-U.S. national to win the prize in the 36-year history of the award.
The award will be given to Professor Park at the Annual Meeting of the society which will be held in San Antonio, Texas, on April 22.
2009.04.09 View 11757