Seoul National University Department of Industrial Engineering, won the Grand Prize and Excellence Prize at ‘The 13^th Master’s Thesis Competition’


▲ (From the left) A graduate student Sung-Woo Kim of Department of Industrial Engineering, Supervisor Ilkyeong Moon, A graduated student Se-Hwa Kim, Supervisor Kyungsik Lee

 
Seoul National University College of Engineering (Dean Kookheon Char) announced that graduate student Sung-Woo Kim of Department of Industrial Engineering and graduate student Se-Hwa Kim (LG Display) won the Grand Prize and the Excellence Prize at the ‘13^th Master’s Thesis Competition’, which was hosted by Korean Institute of Industrial Engineers (KIIE). The competition was held on November 4^th at Daejeon KAIST.
 
Sung-woo Kim, a graduate student who won the Grand Prize, presented a new truck-drone delivery system using the drone station through a paper titled "Truck-drone Routing Problem with a Drone Station". He developed a new delivery system that can run multiple drones in urban areas, which can compensate for the shortcomings of existing systems. Unmanned delivery boxes, street lights, and trucks can also be used as a drone station if additional equipment is installed.
 
Sung-Woo Kim said, "During my research, Professor Ilkyeong Moon’s guidance and help from my colleagues have been a great help. I would like to continue research on drone delivery system to contribute to the establishment of next generation delivery system and the commercialization of drone.”
 
A graduated student Se-Hwa Kim proposed a new technique for single class classification problem under the supervision of Professor Kyungsik Lee through a paper titled 'One Class Classification by Norm Ball Covering'. The single class classifier proposed by Se-Hwa Kim identifies the target data by finding the minimum number of norm balls that suitably cover the target data through integer optimization. This technology can be applied to the 'edge computing' area where the hardware such as a mobile phone is inserted into a weak terminal and performs data classification processing.
 
Se-Haw Kim said, "The two-year effort has made a good result thanks to the professor's guidance and advice from our lab colleagues. I want to contribute to the ‘detecting abnormal processes’, which classifies false data, by applying the research result to the smart factory environment.