• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.927-929
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• 2003

Using received northeast region in China of Terra/MODIS data at Tokyo University of information Sciences. Make monthly division Land Surface Temperature maximum composite image. Using monthly division Land Surface Temperature maximum composite image, considered characteristic of monthly variation of Land surface temperature and relation with land covering and NDVI at the northeast region in China.

• 물과 미래
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• 제13권4호
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• pp.41-50
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• 1980

본 연구는 Gamma 분포의 이론적 검토와 이의 수공학에의 적용, 즉 Gamma 분포의 적합성 및 Gamma 모델에 의한 하천유량의 Simulation에 대한 연구와 검토를 행하는데 그 목적을 두고 있다. 분석에 있어서 우리나라 주요하천(낙동강, 한강 및 금강)의 월유량자료를 사용하였으며 분석을 간단하게 하기 위하여 자료를 Modular coefficient로 변환시켰다. 먼저 이변수 Gamma 분포형에 대한 월류량에의 적합성을 검정하였으며 이로부터 Gamma 분포형과 Monto Carlo 기법을 기초로 한 Gamma 모델에 의하여 월류량의 Simulation을 행하였다. 그 결과 기록치와 매우 근접한 Simulation 자료를 얻을 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.523-523
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• 2000

We have developed a method to build time series prediction models by Genetic Programming (GP). Our proposed CP includes two new techniques. One is the parameter optimization algorithm, and the other is the new mutation operator. In this paper, the sunspot prediction experiment by our proposed CP was performed. The sunspot prediction is good benchmark, because many researchers have predicted them with various kinds of models. We make three experiments. The first is to compare our proposed method with the conventional methods. The second is to investigate about the relation between a model-building period and prediction precision. In the first and the second experiments, the long-term data of annual sunspots are used. The third is to try the prediction using monthly sunspots. The annual sunspots are a mean of the monthly sunspots. The behaviors of the monthly sunspot cycles in tile annual sunspot data become invisible. In the long-term data of the monthly sunspots, the behavior appears and is complicated. We estimate that the monthly sunspot prediction is more difficult than the annual sunspot prediction. The usefulness of our method in time series prediction is verified by these experiments.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2000년도 학술발표회 발표논문집
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• pp.590-598
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• 2000

A multiplicative ARIMA model was tested and applied to analyze the periodicity and trends of 168 monthly oxygen demand data from the Noryanggin water quality gauging station in the downstream Han River. ARIMA model was identified to fit to the data using ACF and PACF tests, and the parameters estimated using an unconditional least square method. The residuals between the observed and forecasted data were acceptable with the Porte-Manteau test. A forecast of DO changes was made for its applications.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.491-495
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• 1999

We compared the satellite observed temperature with the radiosonde observed temperature in the Korean Peninsula. The radiosonde observed data were obtained from four upper air observation stations in the Korean Peninsula from 1981 to 1998, and that was compared with the satellite observed data of the channel-2 and channel-4 of microwave sounding unit(MSU) on board NOAA series of polar-orbiting satellites. The radiosonde data were reconstructed into monthly radiosonde T$_{b}$ using MSU weighting function. The monthly climatology shows radiosonde T$_{b2}$ is higher than MSU T$_{b2}$ in summer. The correlation between MSU T$_{b2}$ and radiosonde T$_{b2}$ is 0.72-0.76 and 0.73-0.81 between MSU T$_{b4}$ and radiosonde T$_{b4}$.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1215-1218
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training data consist of the mixed data The mixed data involves the historic data and the generated data using PARMA (1,1). And, the testing data consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• 한국농공학회지
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• 제35권3호
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• pp.100-116
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• 1993

It is experienced fact that unreasonable design criterion and unsitable operation management for the agricultural structures including reservoirs based on short terms data of monthly flows have been brought about not only loss of lives, but also enormous property damage. For the solution of this point at issue, this study was conducted to simulate long series of synthetic monthly flows by multi-season first order Markov model with selection of best fitting frequency distribution and to make a comparison of statistical parameters between observed and synthetic flows of six watersheds in Yeong San and Seom Jin river systems. The results obtained through this study can be summarized as follows. 1.Both Gamma and two parameter lognormal distribution were found to be suitable ones for monthly flows in all watersheds by Kolmogorov-Smirnov test while those distributions were judged to be unfitness in Nam Pyeong of Yeong San and Song Jeong and Ab Rog watersheds of Seom Jin river systems in the $\chi$$^2$ goodness of fit test. 2.Most of the arithmetic mean values for synthetic monthly flows simulated by Gamma distribution are much closer to the results of the observed data than those of two parameter lognomal distribution in the applied watersheds. 3.Fluctuation for the coefficient of variation derived by Gamma distribution was shown in general as better agreement with the results of the observed data than that of two parameter lognormal distribution in the applied watersheds both in Yeong San and Seom Jin river systems. Especially, coefficients of variation calculated by Gamma distribution are seemed to be much closer to those of the observed data during July and August. 4.It can be concluded that synthetic monthly flows simulated by Gamma distribution are seemed to be much closer to the observed data than those by two parameter lognormal distribution in the applied watersheds. 5.It is to be desired that multi-season first order Markov model based on Gamma distribution which is confirmed as a good fitting one in this study would be compared with Harmonic synthetic model as a continuation follows.

• 한국대기환경학회지
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• 제28권4호
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• pp.466-476
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• 2012

This study calculated monthly and sectoral (for industry, energy industry, transport, residential, commercial and public sectors) greenhouse gas (GHG) emissions of Seoul, Korea from 1999 until 2009 with following the IPCC 2006 Guideline for National Greenhouse Gas Inventories through an analysis on available monthly data of fossil fuel and electricity consumption for the period. The time series analysis showed that GHG emissions had significant cyclical pattern season by season with the highest peak in August and the lowest peak in January throughout the period. The analysis on monthly and sectoral energy consumption showed that residential, commercial and public sectors had emitted about 65% of total GHG emissions of Seoul and had consumed more energy in winter for heating. About 30% GHG of Seoul was emitted from transport sector but its monthly energy consumption showed irregular pattern and it consumed 80% petroleum (in 2009) of Seoul. Hopefully together with further study on this subject, it is expected that this study can be used as basic data for various research regarding Greenhouse gas baseline emission, energy consumption pattern and estimation for future GHG emission of Seoul.

• 한국농공학회지
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• 제35권3호
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• pp.117-129
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• 1993

This study was conducted to get reasonable and abundant hydrological time series of monthly flows simulated by a best fitting stochastic simulation model for the establishment of rational design and the rationalization of management for agricultural hydraulic structures including reservoirs. Comparative analysis carried out for both statistical characteristics and synthetic monthly flows simulated by the multi-season first order Markov model based on Gamma distribution which is confirmed as good one in the first report of this study and by Harmonic synthetic model analyzed in this report for the six watersheds of Yeong San and Seom Jin river systems. 1.Arithmetic mean values of synthetic monthly flows simulated by Gamma distribution are much closer to the results of the observed data than those of Harmonic synthetic model in the applied watersheds. 2.In comparison with the coefficients of variation, index of fluctuation for monthly flows simulated by two kinds of synthetic models, those based on Gamma distribution are appeared closer to the observed data than those of Harmonic synthetic model both in Yeong San and Seom Jin river systems. 3.It was found that synthetic monthly flows based on Gamma distribution are considered to give better results than those of Harmonic synthetic model in the applied watersheds. 4.Continuation studies by comparison with other simulation techniques are to be desired for getting reasonable generation technique of synthetic monthly flows for the various river systems in Korea.