• ETRI Journal
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• 제5권3호
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• pp.9-15
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• 1983

본 연구에서는 MIL-HDBK-2l7D의 Part stress 해석방법을 이용하여 부품의 고장률을 계산하였다. 이 방법은 운용시 주위환경, 주위온도에 의한 stress등 많은 양의 자세한 정보가필요하다. 본고에서는 part stress 방법을 적용한 컴퓨터 프로그램을 개발하여 부품의 고장률 계산에 이용하였다. Fortran V로 쓰여진 이 프로그램은 다음의 4개 부분으로 구성되었고 그 기능및구조를 제시하였다. (1) Raw data file (2) 부품별 연산 프로그램 (3) 신뢰도 modelling (직렬구조) (4) New data file

• 응용통계연구
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• 제6권2호
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• pp.253-263
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• 1993

최근 다구찌 실험에 대한 관심이 고조되어 일반화 선형모형에서 평균과 분산의 동시모형화가 연구되고 있다. 하나의 자료 변환만으로는 자료분석에 필요한 모든 조건들을 만족시킬 수 없기 때문에 다구찌 품질실험의 자료들을 일반화 선형모형으로 분석하는 것이 바람직하다. 본 논문에서는 이 자료변환법과 일반선형모형을 이용한 분석법을 소개, 비교하고 일반화 선형모형을 다구찌 자료에 적용할 수 있는 GLIM 프로그램을 제시한다.

• 대한기계학회논문집A
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• 제20권4호
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• pp.2752-2759
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• 1996

This paper presents crack growth analysis approach on the basis of neural networks, a branch of cognitive science to high temperature low cycle fatigue that shows strong nonlinearity in material behavior. As the number of data patterns on crack growth increase, pattern classification occurs well and two point representation scheme with gradient of crack growth curve simulates crack growth rate better than one point representation scheme. Optimal number of learning data exists and excessive number of learning data increases estimated mean error with remarkable learning time J-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.179-179
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• 2015

Citarum River is one of the important river in West Java, Indonesia. During the rainy season, flood happens almost every year in Upper Citarum Watershed, hence, it is necessary to establish the countermeasure in order to prevent and mitigate flood damages. Since the lack of hydrological data for the modelling is common problem in this area, it is difficult to prepare the countermeasures. Therefore, we used Rainfall-Runoff-Inundation (RRI) Model developed by Sayama et al. (2010) as the hydrological and inundation modelling for evaluating the inundation case happened in Upper Citarum Watershed, West Java, Indonesia and the satellite based information such as rainfall (GSMaP), landuse and so on instead of the limited hydrological data. In addition, 3 arc-second HydroSHEDS Digital Elevation Model (DEM) is used. To verify the model, the observed data of Nanjung water stage gauging station and the daily observation data are used. Simulated inundation areas are compared with the flood extent figure from Upper Citarum Basin Flood Management Project (UCBFM).

• Wind and Structures
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• 제28권6호
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• pp.369-380
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• 2019

Statistical distributions are very useful in describing wind speed characteristics and in predicting wind power potential of a specified region. Although the Weibull distribution is the most popular one in wind energy literature, it does not seem to be able to perfectly fit all the investigated wind speed data in nature. Thus, many studies are still being conducted to find flexible distribution for modelling wind speed data. In this study, we propose a new Odd-Burr Rayleigh distribution for wind speed characterization. The Odd-Burr Rayleigh distribution with two shape parameters is flexible enough to model different shapes of wind speed data and thus it can be an alternative wind speed distribution for the assessment of wind energy potential. Therefore, suitability of the Odd-Burr Rayleigh distribution is investigated on real wind speed data taken from different regions in the South Africa. Numerical results of the conducted analysis confirm that the new Odd-Burr Rayleigh distribution is suitable for modelling most of the considered real wind speed cases and it also can be used for predicting wind power.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2012년도 학술발표회
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• pp.39-39
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• 2012

Data assimilation techniques have received growing attention due to their capability to improve prediction in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modelling software are based on a deterministic approach. In this study, we developed a hydrological modelling framework for sequential data assimilation, namely MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modelling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. In this software framework, sequential data assimilation based on the particle filters is available for any hydrologic models considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and radar rainfall estimates is assessed simultaneously in sequential data assimilation.

• Smart Structures and Systems
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• 제32권4호
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• 대한지리학회지
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• 제39권4호
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• pp.633-642
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• 2004

본 연구의 목적은 규칙기반 모델링을 구성하여 하계망을 일반화하고자 하였다. 그 동안 지도 일반화에 대한 연구는 제한된 지도요소를 대상으로 선형사상의 형태변형을 위한 알고리즘 개발과 평가에 집중되었다. 규칙 기반 모델링은 지도제작 원리와 공간현상의 분포패턴을 분석하여, 그 결과를 일반화 과정에 적용하기 때문에 기존의 일반화 알고리즘 개선에 도움이 된다. 규칙기반 모델링은 다양한 지도요소들을 대상으로 일반화를 적용할 수 있고, 디지털 환경하에서 다축척 지도제작에 효과적이다. 본 연구에서 개발된 하계망 규칙기반 모델링은 일반화 규칙, 중심선 추출 그리고 선형사상 일반화 알고리즘으로 구성된다. 일반화를 적용하기 앞서, 하계망은 논리적 오류를 최소화하기 위해 저수지와의 연결관계를 분석하였다. 모델을 적용한 결과, 108개의 실폭 하천 중 17개 하천이 중심선으로 추출되었다. 하천의 총길이는 1:25,000에서 17%, 1:50,000에서는 29%로 감소하였다. 선형사상 일반화를 위해 개발된 Simoo 알고리즘은 Douglas-Peucker 알고리즘과 비교하였다. Doug]as-Peucker 알고리즘은 자료점 간격과 편각이 커지게 되어 선의 형태가 거칠어지는 반면, Simoo 알고리즘에서 선형사상은 축척이 감소함에 따라 보다 완만해진다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2009년도 춘계학술발표회 논문집
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• pp.163-167
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• 2009

A discussion of a method that has been used with success in terrain modelling to estimate the height at any point on the land surface from irregularly distributed samples. The special requirements of terrain modelling are discussed as well as a detailed description of the algorithm and an example of its application.