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이원하(Won-Ha Lee),김재중(Jae-Jung Kim),곽계달(Kae-Dal Kwack) 대한기계학회 2009 대한기계학회 춘추학술대회 Vol.2009 No.11
This paper presents an estimating lifetime of Automotive Brake bulb using Accelerated Life Test (ALT). Voltage fluctuation, temperature and on/off are main bulb failure factors of field. Failure mode and mechanism of Automotive Brake bulb are bulb open and burnout of tungsten filament respectively. To realize field stress, estimate lifetime of Automotive Brake bulb when stress applied. We applied stress that has one factor(voltage) and three levels. Failure mode and mechanism of Automotive Brake bulb in experiment is same as in field. This paper estimate B10 Life of Automotive Brake bulb.
최승용 ( Seung Yong Chol ),이원하 ( Won Ha Lee ),한건연 ( Kun Yeun Han ),김극수 ( Keuk Soo Kim ) 한국지리정보학회 2010 한국지리정보학회지 Vol.13 No.4
본 연구의 목적은 GIS 기반 다방향 흐름 분배 모형의 적용성을 평가하는데 있다. 개발된 모형의 적용성을 평가하기 위해서 평창강, 소양강 유역을 포함한 실제 유역에 대해 적용하고 모의 결과를 실측치와 비교하였다. 모의 결과를 실측치와 비교한 결과 실측치와 비교적 잘 일치함을 확 인할 수 있었다. 또한 다방향 흐름분배의 적용을 통해 정확도의 향상과 계산 소요시간의 단축을 확인할 수 있었다. 향후 유역 유출 산정에 있어 본 연구에서 개발된 다방향 흐름 분배 알고리즘을 적용하면 조금 더 정확한 유출량을 계산할 수 있을 것으로 판단된다. The objective of this study is to evaluate the applicability of GIS based multi-directional flow allocation model. In order to evaluate the suggested model in this study, it was applied to real watersheds, Pyeongchang and Soyang river basin. The simulation results were compared with observed values, and showed good agreements. The improvement of accuracy and reduction of simulation time were carried out by applying multi-directional flow allocation. Accordingly, the applied methodologies presented in this study will be used to predict accurate runoff, which plays a major role in integrated flood management. If this model is combined with the techniques of rainfall forecasting, it will contribute to the real-time flood forecasting and warning in the future.
김풍민(Poong-Min Kim),하영렬(Young-Lyol Ha),이원하(Won-Ha Lee) 한국정보과학회 1995 한국정보과학회 학술발표논문집 Vol.22 No.2A
시각장애인이 문서화 작업을 하기 위해 사용하는 점자타자기나 점필의 속도한계와 오타수정의 불편함등을 해소하고 컴퓨터환경에 쉽게 접근할 수 있는 방법의 연구결과로써 점자입력 제어모듈을 개발하게 되었다. 이 모듈을 개발함으로써 시각장애인이 컴퓨터를 이용하여 편리하게 점자를 사용할 수 있는 워드프로세서의 개발이 가능하며, 나아가 시각장애인에게 보다 적합한 컴퓨터 제어환경을 제공하였다. 본 논문에서는 시각장애인이 컴퓨터 환경을 이용하기 위한 입력디바이스 개발에 있어 점자입력 제어모듈의 개발방법과 설계 그리고 점자입력용 Keypad에 관해 연구한 결과들을 논하였다.
시계열 데이터의 성격과 예측 모델의 예측력에 관한 연구
이원하,최종욱 한국지능정보시스템학회 1998 지능정보연구 Vol.4 No.1
Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deteministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or AR1MA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministieally chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model appropriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.