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이차원 배열 데이터에서 유사 구역의 효율적인 탐색 기법
최연정 ( Yeonjeong Choe ),이기용 ( Ki Yong Lee ) 한국정보처리학회 2016 한국정보처리학회 학술대회논문집 Vol.23 No.2
첨단 과학 장비를 이용한 시뮬레이션의 결과로 데이터의 정확도 및 정밀도가 향상되어 대용량의 이차원 배열 데이터가 생성되고 있다. 대용량의 이차원 배열 데이터에서 유사 구역(similar region)을 찾아내는 것은 매우 의미 있는 일이다. 따라서 본 논문에서는 대용량의 이차원 배열데이터에서 유사 구역을 잦는 단순 방법(naive method)과 효율적으로 탐색할 수 있는 알고리즘을 제안한다. 또한 단순 방법과 제안 알고리즘의 시간 복잡도(time complexity)# 분석하고 실험을 통해 제안 방법이 단순 방법보다 더 빠르게 처리함을 보인다.
2차원 배열 데이터에서 유사 구역의 효율적인 탐색 기법
최연정 ( Yeonjeong Choe ),이기용 ( Ki Yong Lee ) 한국정보처리학회 2017 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.6 No.4
In various fields of science, 2-dimensional array data is being generated actively as a result of measurements and simulations. Although various query processing techniques for array data are being studied, the problem of finding similar regions, whose sizes are not known in advance, in 2-dimensional array has not been addressed yet. Therefore, in this paper, we propose an efficient method for finding regions with similar element values, whose size is larger than a user-specified value, for a given 2-dimensional array data. The proposed method, for each pair of elements in the array, expands the corresponding two regions, whose initial size is 1, along the right and down direction in stages, keeping the shape of the two regions the same. If the difference between the elements values in the two regions becomes larger than a user-specified value, the proposed method stops the expansion. Consequently, the proposed method can find similar regions efficiently by accessing only those parts that are likely to be similar regions. Through theoretical analysis and various experiments, we show that the proposed method can find similar regions very efficiently.
통계적 기계학습 기술을 이용한 시뮬레이션 결과 예측 시스템 개발
이기용 ( Ki Yong Lee ),신윤재 ( Yoonjae Shin ),최연정 ( Yeonjeong Choe ),김선정 ( Seonjeong Kim ),서영균 ( Young-kyoon Suh ),사정환 ( Jeong Hwan Sa ),이종숙 ( Jongsuk Luth Lee ),조금원 ( Kum Won Cho ) 한국정보처리학회 2016 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.5 No.11
Computer simulation is widely used in a variety of computational science and engineering fields, including computational fluid dynamics, nano physics, computational chemistry, structural dynamics, and computer-aided optimal design, to simulate the behavior of a system. As the demand for the accuracy and complexity of the simulation grows, however, the cost of executing the simulation is rapidly increasing. It, therefore, is very important to lower the total execution time of the simulation especially when that simulation makes a huge number of repetitions with varying values of input parameters. In this paper we develop a simulation service system that provides the ability to predict the result of the requested simulation without actual execution for that simulation: by recording and then returning previously obtained or predicted results of that simulation. To achieve the goal of avoiding repetitive simulation, the system provides two main functionalities: (1) storing simulation-result records into database and (2) predicting from the database the result of a requested simulation using statistical machine learning techniques. In our experiments we evaluate the prediction performance of the system using real airfoil simulation result data. Our system on average showed a very low error rate at a minimum of 0.9% for a certain output variable. Using the system any user can receive the predicted outcome of her simulation promptly without actually running it, which would otherwise impose a heavy burden on computing and storage resources.