Kaist

Winning Best in Theme Award in NASA RASC-AL

〈 KAIST team of the Department of Aerospace Engineering poses after winning the Best in Theme Award in NASA's RASC-AL) 〉 〈 Prof. Jaemyung Ahn 〉   A students team from the Department of Aerospace Engineering won the Best in  Theme Award for moon exploration system design at Revolutionary Aerospace Systems Concepts - Academic Linkage (RASC-AL), an aerospace mission system design competition organized by NASA in the USA. The KAIST team, consisting of Jaeyoul Ko, Jongeun Suh, Juseong Lee, Sukmin Choi, and Eunkwang Lee, and supervised by Professor Jaemyung Ahn, competed as a joint team with Texas Tech University and the Royal Melbourne Institute of Technology in Australia, The joint team was selected as one of the 14 finalists after two preliminary rounds. The finals of RASC-AL Forum took place from May 30 to June 3 in Florida. The team received the top prize with their design entitled ‘Earth to Lunar Interchangeable Transportation Environment (ELITE) for Logistics Delivery Systems’, one of the four themes of the competition. Since 2002, RASC-AL competitions, managed by NASA, have been held with themes on innovative aerospace system and missions, in which world-class undergraduate and graduate students have participated. This year’s themes were ▲ Lightweight Exercise Suite ▲ Airlock Design ▲ Commercially Enabled LEO/Mars Habitable Module and ▲ Logistics Delivery System. Moon exploration requires a great deal of time and supplies. The KAIST team has been researching supply delivery systems in space for long-term manned moon exploration with their joint team for the last eight months. In particular, incidents can occur during the initial stages of long-term manned moon exploration missions that are unpredictable during system design and planning. Therefore, to cope with such unpredictability in the mission, the KAIST team deduced a system and an operational concept with increased flexibility to maximize the cost effectiveness of the supply transport. The spacecraft was divided into propulsion and transport modules based on their functionalities, and can allow the flexibility by switching the transport module according to the demands of the moon base. The operational flexibility and cost effectiveness are further increased by introducing multiple departure orbits from the Earth (e.g. low Earth orbit vs. geosynchronous Earth orbit) enabled by utilization of various launch vehicles. Professor Ahn, the advisor for the team, said, “I am proud of the students who collaborated with the international joint teams and achieved great result.” He continued, “I believe this to be the result of continuous efforts and initiatives of the department for system design-centered education. We will keep providing high-quality system design and education through various opportunities such as international cooperation in design education.”

Face Recognition System “K-Eye” Presented by KAIST

    Artificial intelligence (AI) is one of the key emerging technologies. Global IT companies are competitively launching the newest technologies and competition is heating up more than ever. However, most AI technologies focus on software and their operating speeds are low, making them a poor fit for mobile devices. Therefore, many big companies are investing to develop semiconductor chips for running AI programs with low power requirements but at high speeds. A research team led by Professor Hoi-Jun Yoo of the Department of Electrical Engineering has developed a semiconductor chip, CNNP (CNN Processor), that runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. The system was made in collaboration with a start-up company, UX Factory Co. The K-Eye series consists of two types: a wearable type and a dongle type. The wearable type device can be used with a smartphone via Bluetooth, and it can operate for more than 24 hours with its internal battery. Users hanging K-Eye around their necks can conveniently check information about people by using their smartphone or smart watch, which connects K-Eye and allows users to access a database via their smart devices. A smartphone with K-EyeQ, the dongle type device, can recognize and share information about users at any time. When recognizing that an authorized user is looking at its screen, the smartphone automatically turns on without a passcode, fingerprint, or iris authentication. Since it can distinguish whether an input face is coming from a saved photograph versus a real person, the smartphone cannot be tricked by the user’s photograph. The K-Eye series carries other distinct features. It can detect a face at first and then recognize it, and it is possible to maintain “Always-on” status with low power consumption of less than 1mW. To accomplish this, the research team proposed two key technologies: an image sensor with “Always-on” face detection and the CNNP face recognition chip. The first key technology, the “Always-on” image sensor, can determine if there is a face in its camera range. Then, it can capture frames and set the device to operate only when a face exists, reducing the standby power significantly. The face detection sensor combines analog and digital processing to reduce power consumption. With this approach, the analog processor, combined with the CMOS Image Sensor array, distinguishes the background area from the area likely to include a face, and the digital processor then detects the face only in the selected area. Hence, it becomes effective in terms of frame capture, face detection processing, and memory usage. The second key technology, CNNP, achieved incredibly low power consumption by optimizing a convolutional neural network (CNN) in the areas of circuitry, architecture, and algorithms. First, the on-chip memory integrated in CNNP is specially designed to enable data to be read in a vertical direction as well as in a horizontal direction. Second, it has immense computational power with 1024 multipliers and accumulators operating in parallel and is capable of directly transferring the temporal results to each other without accessing to the external memory or on-chip communication network. Third, convolution calculations with a two-dimensional filter in the CNN algorithm are approximated into two sequential calculations of one-dimensional filters to achieve higher speeds and lower power consumption. With these new technologies, CNNP achieved 97％ high accuracy but consumed only 1/5000 power of the GPU. Face recognition can be performed with only 0.62mW of power consumption, and the chip can show higher performance than the GPU by using more power. These chips were developed by Kyeongryeol Bong, a Ph. D. student under Professor Yoo and presented at the International Solid-State Circuit Conference (ISSCC) held in San Francisco in February. CNNP, which has the lowest reported power consumption in the world, has achieved a great deal of attention and has led to the development of the present K-Eye series for face recognition. Professor Yoo said “AI - processors will lead the era of the Fourth Industrial Revolution. With the development of this AI chip, we expect Korea to take the lead in global AI technology.” The research team and UX Factory Co. are preparing to commercialize the K-Eye series by the end of this year. According to a market researcher IDC, the market scale of the AI industry will grow from ＄127 billion last year to ＄165 billion in this year.   (Photo caption: Schematic diagram of K-Eye system)

KAIST Team Wins Bronze Medal at Int＇l Programming ..

  〈 Professor Taisook Han and his students 〉     A KAIST Team consisting of undergraduate students from the School of Computing and Department of Mathematical Science received a bronze medal and First Problem Solver award at an international undergraduate programming competition, The Association for Computing Machinery-International Collegiate Programming Contest (ACM-ICPC) World Finals. The 41st ACM-ICPC hosted by ACM and funded by IBM was held in South Dakota in the US on May 25. The competition, first held in 1977, is aimed at undergraduate students from around the world. A total of 50,000 students from 2900 universities and 103 countries participated in the regional competition and 400 students competed in the finals. The competition required teams of three to solve 12 problems. The KAIST team was coached by Emeritus Professor Sung-Yong Shin and Professor Taisook Han. The student contestants were Jihoon Ko and Hanpil Kang from the School of Computing and Jongwoon Lee from the Department of Mathematical Science. The team finished ranked 9th, receiving a bronze medal and a ＄3000 prize. Additionally, the team was the first to solve all the problems and received the First Problem Solver award. Detailed score information can be found on. https://icpc.baylor.edu/scoreboard/