• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.1-11
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• 2015

This paper is focused on how the trajectory of launch vehicle could be optimally estimated by the quadratic regression of trajectory data mining for the operation of telemetry ground system in NARO space center during real-time. To receive the telemetry data, the telemetry ground system has to track the space launch vehicle without tracking loss, and it is possible by the well-designed algorithm to estimate a flight position in real-time. For this reason, the quadratic regression model instead of interpolation was considered to estimate the exact position data of launch vehicle and the improvement of antenna performance. For analysis, the real trajectory data which had been logged during NARO 1st launch mission were used, the estimation result of launcher current position was analyzed by the mathematical modeling. In conclusion, the algorithm using quadratic regression based on trajectory data mining showed the better performance than previous interpolation algorithm to estimate the next flight position and the antenna driving performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.27-30
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• 2004

이동 객체의 위치 정보는 현재 위치뿐만이 아니라 과거 움직였던 궤적 데이터 역시 데이터마이닝과 같은 응용분야에서 중요하다. 대부분의 기존 연구에서 사용하는 GPS(Global Positioning Service) 위치정보는 이 차원 유클리디안 공간의 한 점과 시간만으로 표현된다. 우리는 이러한 표현이 가지는 내재적인 문제점들을 지적하고 이를 해결할 수 있는 새로운 방법을 제시한다. 본 논문에서 우리는 이동 객체의 움직임은 기본속도모델을 따라 움직인다고 가정하고 이틀 이용하여 다양한 움직임의 형태를 표현하고 이러한 표현에 의하여 달성할 수 있는 저장 및 처리의 효율성에 대하여 연구한다. 실제 데이터를 이용한 분석에서 우리는 이동 객체가 우리가 제시하는 속도 모델에 따라 움직인다는 사실을 보여주고, 제시하는 표현 방법이 저장뿐만이 아니라 성능적인 면에서도 기존 GPS 위치정보 표현보다 월등하다는 사실을 보여 준다.

• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.76-87
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• 2008

Prediction of travel time on road network is one of crucial research issue in dynamic route guidance system. A new approach based on Rule-Based classification is proposed for predicting travel time. This approach departs from many existing prediction models in that it explicitly consider traffic patterns during day time as well as week day. We can predict travel time accurately by considering both traffic condition of time range in a day and traffic patterns of vehicles in a week. We compare the proposed method with the existing prediction models like Link-based, Micro-T* and Switching model. It is also revealed that proposed method can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.