Kaist

Distinguished Professor Sang Yup Lee Announced as ..

(Distinguished Professor Sang Yup Lee) Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering will be awarded the 2018 Eni Advanced Environmental Solutions Prize in recognition of his innovations in the fields of energy and environment. The award ceremony will take place at the Quirinal Palace, the official residence of Italian President Sergio Mattarella, who will also be attending on October 22. Eni, an Italian multinational energy corporation established the Eni Award in 2008 to promote technological and research innovation of efficient and sustainable energy resources. The Advanced Environmental Solutions Prize is one of the three categories of the Eni Award. The other two categories are Energy Transition and Energy Frontiers. The Award for Advanced Environmental Solutions recognizes a researcher or group of scientists that has achieved internationally significant R&D results in the field of environmental protection and recovery. The Eni Award is referred to as the Nobel Award in the fields of energy and environment. Professor Lee, a pioneering leader in systems metabolic engineering was honored with the award for his developing engineered bacteria to produce chemical products, fuels, and non-food biomass materials sustainably and with a low environmental impact. He has leveraged the technology to develop microbial bioprocesses for the sustainable and environmentally friendly production of chemicals, fuels, and materials from non-food renewable biomass. The award committee said that they considered the following elements in assessing Professor Lee’s achievement: the scientific relevance and the research innovation level; the impact on the energy system in terms of sustainability as well as fairer and broader access to energy; and the adequacy between technological and economic aspects. Professor Lee, who already won two other distinguished prizes such as the George Washington Carver Award and the PV Danckwerts Memorial Lecture Award this year, said, “I am so glad that the international academic community as well as global industry leaders came to recognize our work that our students and research team has made for decades.” Dr. Lee’s lab has been producing a lot of chemicals in environmentally friendly ways. Among them, many were biologically produced for the first time and some of these processes have been already commercialized. “We will continue to strive for research outcomes with two objectives: First, to develop bio-based processes suitable for sustainable chemical industry. The other is to contribute to the human healthcare system through development of platform technologies integrating medicine and nutrition,” he added.

AI-based Digital Watermarking to Beat Fake News

(from left: PhD candidates Ji-Hyeon Kang, Seungmin Mun, Sangkeun Ji and Professor Heung-Kyu Lee)   The illegal use of images has been a prevalent issue along with the rise of distributing fake news, which all create social and economic problems. Here, a KAIST team succeeded in embedding and detecting digital watermarks based on deep neural learning artificial intelligence, which adaptively responds to a variety of attack types, such as removing watermarks and hacking. Their research shows that this technology reached a level of reliability for technology commercialization.    Conventional watermarking technologies show limitations in terms of practicality, technology scalability, and usefulness because they require a predetermined set of conditions, such as the attack type and intensity. They are designed and implemented in a way to satisfy specific conditions.    In addition to those limitations, the technology itself is vulnerable to security issues because upgraded hacking technologies are constantly emerging, such as watermark removal, copying, and substitution.    Professor Heung-Kyu Lee from the School of Computing and his team provided a web service that responds to new attacks through deep neural learning artificial intelligence. It also serves as a two-dimensional image watermarking technique based on neural networks with high security derived from the nonlinear characteristics of artificial neural networks. To protect images from varying viewpoints, the service offers a depth-image-based rendering (DIBR) three-dimensional image watermarking technique.    Lastly, they provided a stereoscopic three-dimensional (S3D) image watermarking technique that minimizes visual fatigue due to the embedded watermarks. Their two-dimensional image watermarking technology is the first of its kind to be based upon artificial neural works. It acquires robustness through educating the artificial neural networking on various attack scenarios.    At the same time, the team has greatly improved on existing security vulnerabilities by acquiring high security against watermark hacking through the deep structure of artificial neural networks. They have also developed a watermarking technique embedded whenever needed to provide proof during possible disagreements.    Users can upload their images to the web service and insert the watermarks. When necessary, they can detect the watermarks for proof in any dispute.    Moreover, this technology provides services, including simulation tools, watermark adjustment, and image quality comparisons before and after the watermark is embedded.   This study maximized the usefulness of watermarking technology by facilitating additional editing and demonstrating robustness against hacking.    Hence, this technology can be applied in a variety of contents for certification, authentication, distinction tracking, and copyrights. It can contribute to spurring the content industry and promoting a digital society by reducing the socio-economic losses caused by the use of various illegal image materials in the future.   Professor Lee said, “Disputes related to images are now beyond the conventional realm of copyrights. Recently, their interest has rapidly expanded due to the issues of authentication, certification, integrity inspection, and distribution tracking because of the fake video problem. We will lead digital watermarking research that can overcome the technical limitations of conventional watermarking techniques.”    This technology has only been conducted in labs thus far, but it is now open to the public after years of study. His team has been conducting a test run on the webpage (click).Moving forward from testing the technology under specific lab conditions, it will be applied to a real environment setting where constant changes pervade.    1. Figure. 2D image using the watermarking technique: a) original image b) watermark-embedded image c) signal from the embedded watermark   Figure 2. Result of watermark detection according to the password     Figure 3. Example of a center image using the DIBR 3D image watermarking technique: a) original image b) depth image c) watermark-embedded image d) signal from the embedded watermark   Figure 4. Example of using the S3D image watermarking technique: a) original left image b) original right image c) watermark-embedded left image d) watermark-embedded right image e) signal from the embedded watermark (left) f) signal from the embedded watermark (right)

Rh Ensemble Catalyst for Effective Automobile Exha..

(from left: Professor Hyunjoo Lee and PhD candidate Hojin Jeong)   A KAIST research team has developed a fully dispersed Rh ensemble catalyst (ENS) that shows better performance than commercial diesel oxidation catalyst (DOC). This newly developed ENSs could improve low-temperature automobile exhaust treatment.   Precious metals have been used for various heterogeneous reactions, but it is crucial to maximize efficiency of catalysts due to their high cost. Single-atom catalysts (SACs) have received much attention because it is possible for all of the metal atoms to be used for reactions, yet they do not show catalytic activity for reactions that require ensemble sites.   Meanwhile, hydrocarbons, such as propylene (C3H6) and propane (C3H8) are typical automobile exhaust gas pollutants and must be converted to carbon dioxide (CO2) and water (H2O) before they are released as exhaust. Since the hydrocarbon oxidation reaction proceeds only during carbon-carbon (C-C) or carbon-hydrogen (C-H) bond cleavage, it is essential to secure the metal ensemble site for the catalytic reaction. Therefore, precious metal catalysts with high dispersion and ensemble sites are greatly needed.   To solve this issue, Professor Hyunjoo Lee from the Department of Chemical and Biomolecular Engineering and Professor Jeong Woo Han from POSTECH developed an Rh ensemble catalyst with 100％ dispersion, and applied it to automobile after-treatment. Having a 100％ dispersion means that every metal atom is used for the reaction since it is exposed on the surface.   SACs also have 100％ dispersion, but the difference is that ENSs have the unique advantage of having an ensemble site with two or more atoms.   As a result of the experiment, the ENSs showed excellent catalytic performance in CO, NO, propylene, and propane oxidation at low temperatures. This complements the disadvantage of nanoparticle catalyst (NPs) that perform catalysis poorly at low temperatures due to low metal dispersion, or SACs without hydrocarbon oxidation.   In particular, the ENSs have superior low-temperature activity even better than commercial DOC, hence they are expected to be applied to automobile exhaust treatment.   Professor Lee said, “I believe that the ENSs have given academic contribution for proposing a new concept of metal catalysts, differentiating from conventional SACs and NPs. At the same time, they are of great value in the industry of exhaust treatment catalysts.”   This research, led by PhD candidate Hojin Jeong, was published in the Journal of the American Chemical Society on July 5.    Figure 1. Concept of Rh ensemble catalyst for automobile exhaust treatment     Figure 2. Structure and performance comparison of single-atom catalyst and ensemble catalyst Figure 3. Energy-dispersive X-ray spectroscopy (EDS) mapping images for SAC, ENS, and NP, respectively (green, Eh; red, Ce)

Skin Hardness to Estimate Better Human Thermal Sta..

(Professor Young-Ho Cho and Researcher Sunghyun Yoon)   Under the same temperature and humidity, human thermal status may vary due to individual body constitution and climatic environment. A KAIST research team previously developed a wearable sweat rate sensor for human thermal comfort monitoring. Furthering the development, this time they proposed skin hardness as an additional, independent physiological sign to estimate human thermal status more accurately. This novel approach can be applied to developing systems incorporating human-machine interaction, which requires accurate information about human thermal status.    Professor Young-Ho Cho and his team from the Department of Bio and Brain Engineering had previously studied skin temperature and sweat rate to determine human thermal comfort, and developed a watch-type sweat rate sensor that accurately and steadily detects thermal comfort last February (title: Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring ).   However, skin temperature and sweat rate are still not enough to estimate exact human thermal comfort. Hence, an additional indicator is required for enhancing the accuracy and reliability of the estimation and the team selected skin hardness. When people feel hot or cold, arrector pili muscles connected to hair follicles contract and expand, and skin hardness comes from this contraction and relaxation of the muscles. Based on the phenomenon of changing skin hardness, the team proposed skin hardness as a new indicator for measuring human thermal sensation.    With this new estimation model using three physiological signs for estimating human thermal status, the team conducted human experiments and verified that skin hardness is effective and independent from the two conventional physiological signs. Adding skin hardness to the conventional model can reduce errors by 23.5％, which makes its estimation more reliable.    The team will develop a sensor that detects skin hardness and applies it to cognitive air-conditioning and heating systems that better interact with humans than existing systems.    Professor Cho said, “Introducing this new indicator, skin hardness, elevates the reliability of measuring human thermal comfort regardless of individual body constitution and climatic environment. Based on this method, we can develop a personalized air conditioning and heating system that will allow affective interaction between humans and machines by sharing both physical and mental health conditions and emotions.”   This research, led by researchers Sunghyun Yoon and Jai Kyoung Sim, was published in Scientific Reports, Vol.8, Article No.12027 on August 13, 2018. (pp.1-6)   Figure 1. Measuring human thermal status through skin hardness   Figure 2. The instrument used for measuring human thermal status through skin hardness

KAIST Introduces Faster and More Powerful Aqueous ..

(Professor Jeung Ku Kang from the Graduate School of EEWS)   A KAIST research team made it one step closer to realizing safe energy storage with high energy density, high power density, and a longer cycle life. This hybrid storage alternative shows power density 100 times faster than conventional batteries, allowing it to be charged within a few seconds. Hence, it is suitable for small portable electronic devices.    Conventional electrochemical energy storage systems, including lithium-ion batteries (LIBs), have a high voltage range and energy density, but are subject to safety issues raised by flammable organic electrolytes, which are used to ensure the beneficial properties. Additionally, they suffer from slow electrochemical reaction rates, which lead to a poor charging rate and low power density with a capacity that fades quickly, resulting in a short cycle life.    On the other hand, capacitors based on aqueous electrolytes are receiving a great deal of attention because they are considered to be safe and environmentally friendly alternatives. However, aqueous electrolytes lag behind energy storage systems based on organic electrolytes in terms of energy density due to their limited voltage range and low capacitance.    Hence, developing aqueous energy storage with high energy density and a long cycle life in addition to the high power density that enables fast charging is the most challenging task for advancing next-generation electrochemical energy storage devices.    Here, Professor Jeung Ku Kang from the Graduate School of Energy, Environment, Water and Sustainability and his team developed an aqueous hybrid capacitor (AHC) that boasts high energy density, high power, and excellent cycle stability by synthesizing two types of porous metal oxide nanoclusters on graphene to create positive and negative electrodes for AHCs.    The porous metal oxide nanoparticles are composed of nanoclusters as small as two to three nanometers and have mesopores that are smaller than five nanometers. In these porous structures, ions can be rapidly transferred to the material surfaces and a large number of ions can be stored inside the metal oxide particles very quickly due to their small particle size and large surface area.   The team applied porous manganese oxide on graphene for positive electrodes and porous iron oxide on graphene for negative electrodes to design an aqueous hybrid capacitor that can operate at an extended voltage range of 2V.    Professor Kang said, “This newly developed AHC with high capacity and power density driven from porous metal oxide electrodes will contribute to commercializing a new type of energy storage system. This technology allows ultra-fast charging within several seconds, making it suitable as a power source for mobile devices or electric vehicles where solar energy is directly stored as electricity.”   This research, co-led by Professor Hyung Mo Jeong from Kangwon National University, was published in Advanced Functional Materials on August 15, 2018.    Figure 1. Image that shows properties of porous metal oxide nanoparticles formed on graphene in the aqueous hybrid capacitor 

KAIST Identifies the Trigger of the Hyperactivatio..

(Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering) Scientists have been investigating the negative effects that the hyperactivation of fibrosis has on fibrotic diseases and cancer. A KAIST research team unveiled a positive feedback loop that bi-stably activates fibroblasts in collaboration with Samsung Medical Center. This finding will contribute to developing therapeutic targets for both fibrosis and cancer.    Human fibroblasts are dormant in normal tissue, but show radical activation during wound healing. However, the principle that induces their explosive activation has not yet been identified.    Here, Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering, in collaboration with Professor Seok-Hyung Kim from Samsung Medical Center, discovered the principle of a circuit that continuously activates fibroblasts.   They constructed a positive feedback loops (PFLs) where Twist1, Prrx1, and Tenascin-C (TNC) molecules consecutively activate fibroblasts. They confirmed that these are the main inducers of fibroblast activation by conducting various experiments, including molecular biological tests, mathematical modeling, animal testing, and computer simulations to conclude that they are the main inducers of fibroblast activation.    According to their research, Twist 1 is a key regulator of cancer-associated fibroblasts, which directly upregulates Prrx1 and then triggers TNC, which also increases Twist1 expression. This circuit consequently forms a Twist-Prrx1-TNC positive feedback loop.   Activated fibroblasts need to be deactivated after wounds are healed. However, if the PFLs continue, the fibroblasts become the major cause of worsening fibrotic diseases and cancers.   Therefore, the team expects that Twist1-Prrx1-TNC positive PFLs will be applied for novel and effective therapeutic targeting of fibrotic diseases and cancers.    This research was published in Nature Communications on August 1, 2018.    Figure 1. Twist1 increases tenascin-c expression in cancer-associated fibroblasts. Twist1 is a potent but indirect inducer of tenascin-c (TNC), which is essential for maintaining Twist1 expression in cancer-associated fibroblasts (CAFs).   Figure 2. Summary of the study. The Twist1-Prrx1-TNC positive feedback regulation provides clues for understanding the activation of fibroblasts during wound healing under normal conditions, as well as abnormally activated fibroblasts in pathological conditions such as cancerous and fibrotic diseases. Under normal conditions, the PFL acts as a reversible bistable switch by which the activation of fibroblasts is “ON" above a sufficient level of stimulation and “OFF" for the withdrawal of the stimulus. However, this switch can be permanently turned on under pathologic conditions by continued activation of the PFL, resulting in sustained proliferation of fibroblasts.

Levitating 2D semiconductor for better performance

(from top: Professor Yeon Sik Jung and PhD candidate Soomin Yim)   Atomically thin 2D semiconductors have been drawing attention for their superior physical properties over silicon semiconductors; nevertheless, they are not the most appealing materials due to their structural instability and costly manufacturing process. To shed some light on these limitations, a KAIST research team suspended a 2D semiconductor on a dome-shaped nanostructure to produce a highly efficient semiconductor at a low cost.   2D semiconducting materials have emerged as alternatives for silicon-based semiconductors because of their inherent flexibility, high transparency, and excellent carrier transport properties, which are the important characteristics for flexible electronics.   Despite their outstanding physical and chemical properties, they are oversensitive to their environment due to their extremely thin nature. Hence, any irregularities in the supporting surface can affect the properties of 2D semiconductors and make it more difficult to produce reliable and well performing devices. In particular, it can result in serious degradation of charge-carrier mobility or light-emission yield.   To solve this problem, there have been continued efforts to fundamentally block the substrate effects. One way is to suspend a 2D semiconductor; however, this method will degrade mechanical durability due to the absence of a supporter underneath the 2D semiconducting materials.   Professor Yeon Sik Jung from the Department of Materials Science and Engineering and his team came up with a new strategy based on the insertion of high-density topographic patterns as a nanogap-containing supporter between 2D materials and the substrate in order to mitigate their contact and to block the substrate-induced unwanted effects.    More than 90％ of the dome-shaped supporter is simply an empty space because of its nanometer scale size. Placing a 2D semiconductor on this structure creates a similar effect to levitating the layer. Hence, this method secures the mechanical durability of the device while minimizing the undesired effects from the substrate. By applying this method to the 2D semiconductor, the charge-carrier mobility was more than doubled, showing a significant improvement of the performance of the 2D semiconductor.   Additionally, the team reduced the price of manufacturing the semiconductor. In general, constructing an ultra-fine dome structure on a surface generally involves costly equipment to create individual patterns on the surface. However, the team employed a method of self-assembling nanopatterns in which molecules assemble themselves to form a nanostructure. This method led to reducing production costs and showed good compatibility with conventional semiconductor manufacturing processes.    Professor Jung said, “This research can be applied to improve devices using various 2D semiconducting materials as well as devices using graphene, a metallic 2D material. It will be useful in a broad range of applications, such as the material for the high speed transistor channels for next-generation flexible displays or for the active layer in light detectors.”   This research, led by PhD candidate Soomin Yim, was published in Nano Letters in April.      Figure 1. Image of a 2D semiconductor using dome structures

Improved Efficiency and Stability of CQD Solar Cel..

(from left: Professor Jung-Yong Lee and Dr. Se-Woong Baek)   Recently, the power conversion efficiency (PCE) of colloidal quantum dot (CQD)-based solar cells has been enhanced, paving the way for their commercialization in various fields; nevertheless, they are still a long way from being commercialized due to their efficiency not matching their stability. In this research, a KAIST team achieved highly stable and efficient CQD-based solar cells by using an amorphous organic layer to block oxygen and water permeation.   CQD-based solar cells are light-weight, flexible, and they boost light harvesting by absorbing near-infrared lights. Especially, they draw special attention for their optical properties controlled efficiently by changing the quantum dot sizes. However, they are still incompatible with existing solar cells in terms of efficiency, stability, and cost. Therefore, there is great demand for a novel technology that can simultaneously improve both PCE and stability while using an inexpensive electrode material.   Responding to this demand, Professor Jung-Yong Lee from the Graduate School of Energy, Environment, Water and Sustainability and his team introduced a technology to improve the efficiency and stability of CQD-based solar cells.   The team found that an amorphous organic thin film has a strong resistance to oxygen and water. Using these properties, they employed this doped organic layer as a top-hole selective layer (HSL) for the PbS CQD solar cells, and confirmed that the hydro/oxo-phobic properties of the layer efficiently protected the PbS layer. According to the molecular dynamics simulations, the layer significantly postponed the oxygen and water permeation into the PbS layer. Moreover, the efficient injection of the holes in the layer reduced interfacial resistance and improved performance.   With this technology, the team finally developed CQD-based solar cells with excellent stability. The PCE of their device stood at 11.7％ and maintained over 90％ of its initial performance when stored for one year under ambient conditions.    Professor Lee said, “This technology can be also applied to QD LEDs and Perovskite devices. I hope this technology can hasten the commercialization of CQD-based solar cells.”   This research, led by Dr. Se-Woong Baek and a Ph.D. student, Sang-Hoon Lee, was published in Energy & Environmental Science on May 10.    Figure 1. The schematic of the equilibrated structure of the amorphous organic film   Figure 2. Schematic illustration of CQD-based solar cells and graphs showing their performance

KAIST Reveals Mathematical Principle behind AI’s ‘..

(from left: Professor Jong Chul Ye, PhD candidates Yoseob Han and Eunju Cha)   A KAIST research team identified the geometrical structure of artificial intelligence (AI) and discovered the mathematical principles of highly performing artificial neural networks, which can be applicable in fields such as medical imaging.    Deep neural networks are an exemplary method of implementing deep learning, which is at the core of the AI technology, and have shown explosive growth in recent years. This technique has been used in various fields, such as image and speech recognition as well as image processing.    Despite its excellent performance and usefulness, the exact working principles of deep neural networks has not been well understood, and they often suffer from unexpected results or errors. Hence, there is an increasing social and technical demand for interpretable deep neural network models.   To address these issues, Professor Jong Chul Ye from the Department of Bio & Brain Engineering and his team attempted to find the geometric structure in a higher dimensional space where the structure of the deep neural network can be easily understood. They proposed a general deep learning framework, called deep convolutional framelets, to understand the mathematical principle of a deep neural network in terms of the mathematical tools in Harmonic analysis.    As a result, it was found that deep neural networks’ structure appears during the process of decomposition of high dimensionally lifted signal via Hankel matrix, which is a high-dimensional structure formerly studied intensively in the field of signal processing.   In the process of decomposing the lifted signal, two bases categorized as local and non-local basis emerge. The researchers found that non-local and local basis functions play a role in pooling and filtering operation in convolutional neural network, respectively.    Previously, when implementing AI, deep neural networks were usually constructed through empirical trial and errors. The significance of the research lies in the fact that it provides a mathematical understanding on the neural network structure in high dimensional space, which guides users to design an optimized neural network.   They demonstrated improved performance of the deep convolutional framelets’ neural networks in the applications of image denoising, image pixel in painting, and medical image restoration.     Professor Ye said, “Unlike conventional neural networks designed through trial-and-error, our theory shows that neural network structure can be optimized to each desired application and are easily predictable in their effects by exploiting the high dimensional geometry. This technology can be applied to a variety of fields requiring interpretation of the architecture, such as medical imaging.”   This research, led by PhD candidates Yoseob Han and Eunju Cha, was published in the April 26th issue of the SIAM Journal on Imaging Sciences.    Figure 1. The design of deep neural network using mathematical principles   Figure 2. The results of image noise cancelling   Figure 3. The artificial neural network restoration results in the case where 80％ of the pixels are lost

Professor Emeritus Jung Ki Park Won the IBA Techno..

(Professor Emeritus Jung Ki Park) Professor Emeritus Jung Ki Park from the Department of Chemical and Biomolecular Engineering received the IBA Technology Award from the International Battery Association (IBA).   IBA 2018 was held from March 11 to 16 on Jeju Island, which was the first time it was hosted in Korea. The conference was an excellent opportunity to let the world know the level of the Korean rechargeable battery industry and its technology.    Professor Park delivered his keynote speech titled Advances in Lithium Batteries in Korea at the conference and received the IBA Technology Award as the first Korean recipient.   ?Professor Park is a world-renowned scholar who was a groundbreaker in the rechargeable battery industry. He was recognized by the IBA Award Committee for his contributions carrying out research and development, fostering competent people, and enhancing the lithium rechargeable battery industry in Korea over the last 30 years.    Professor Park said, “It is my great honor to receive this award, which is the best international award in the field of rechargeable batteries. I would like to share this with my colleagues and students. As competition in the rechargeable industry intensifies, systemic cooperation among industries, academia, and government is needed for the continued development of the battery industry in Korea.

Seong-Tae Kim Wins Robert-Wagner All-Conference Be..

(Ph.D. candidate Seong-Tae Kim) Ph.D. candidate Seong-Tae Kim from the School of Electrical Engineering won the Robert Wagner All-Conference Best Student Paper Award during the 2018 International Society for Optics and Photonics (SPIE) Medical Imaging Conference, which was held in Houston last month.   Kim, supervised by Professor Yong Man Ro, received the award for his paper in the category of computer-aided diagnosis. His paper, titled “ICADx: Interpretable Computer-Aided Diagnosis of Breast Masses”, was selected as the best paper out of 900 submissions. The conference selects the best paper in nine different categories. His research provides new insights on diagnostic technology to detect breast cancer powered by deep learning.

Recognizing Seven Different Face Emotions on a Mob..

(Professor Hoi-Jun Yoo)   A KAIST research team succeeded in achieving face emotion recognition on a mobile platform by developing an AI semiconductor IC that processes two neural networks on a single chip.   Professor Hoi-Jun Yoo and his team (Primary researcher: Jinmook Lee Ph. D. student) from the School of Electrical Engineering developed a unified deep neural network processing unit (UNPU).    Deep learning is a technology for machine learning based on artificial neural networks, which allows a computer to learn by itself, just like a human.   The developed chip adjusts the weight precision (from 1 bit to 16 bit) of a neural network inside of the semiconductor in order to optimize energy efficiency and accuracy. With a single chip, it can process a convolutional neural network (CNN) and recurrent neural network (RNN) simultaneously. CNN is used for categorizing and recognizing images while RNN is for action recognition and speech recognition, such as time-series information.    Moreover, it enables an adjustment in energy efficiency and accuracy dynamically while recognizing objects. To realize mobile AI technology, it needs to process high-speed operations with low energy, otherwise the battery can run out quickly due to processing massive amounts of information at once. According to the team, this chip has better operation performance compared to world-class level mobile AI chips such as Google TPU. The energy efficiency of the new chip is 4 times higher than the TPU.   In order to demonstrate its high performance, the team installed UNPU in a smartphone to facilitate automatic face emotion recognition on the smartphone. This system displays a user’s emotions in real time. The research results for this system were presented at the 2018 International Solid-State Circuits Conference (ISSCC) in San Francisco on February 13.   Professor Yoo said, “We have developed a semiconductor that accelerates with low power requirements in order to realize AI on mobile platforms. We are hoping that this technology will be applied in various areas, such as object recognition, emotion recognition, action recognition, and automatic translation. Within one year, we will commercialize this technology.”