• Journal of Korea Water Resources Association
• /
• v.35 no.1
• /
• pp.109-124
• /
• 2002

This study is an effort to develop a stochastic model of precipitation series that preserves the pattern of occurrence of precipitation events throughout the year as well as several characteristics of the duration, amount, and intensity of precipitation events. In this study an event cluster model is used to describe the occurrence of precipitation events. A logarithmic negative mixture distribution is used to describe event duration and separation. The number of events within each cluster is also described by the Poisson cluster process. The duration of each event within a cluster and the separation of events within a single cluster are described by a logarithmic negative mixture distribution. The stochastic model for hourly precipitation occurrence process is fitted to historical precipitation data by estimating the model parameters. To allow for seasonal variations in the precipitation process, the model parameters are estimated separately for each month. an analysis of thirty-four years of historical and simulated hourly precipitation data for Seoul indicates that the stochastic model preserves many features of historical precipitation. The seasonal variations in number of precipitation events in each month for the historical and simulated data are also approximately identical. The marginal distributions for event characteristics for the historical and simulated data were similar. The conditional distributions for event characteristics for the historical and simulated data showed in general good agreement with each other.

• Journal of the Korean Statistical Society
• /
• v.35 no.1
• /
• pp.79-90
• /
• 2006

We consider the subdiagonal bilinear model and ARMA model with subdiagonal bilinear errors. Sufficient conditions for geometric ergodicity of associated Markov chains are derived by using results on generalized random coefficient autoregressive models and then strict stationarity and ,a-mixing property with exponential decay rates for given processes are obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.322-325
• /
• 2003

In this paper, we propose n new fuzzy model identification of time series system using wavelet transform and genetic DNA code. Generally, it is well known that the DNA coding method is more diverse in the knowledge expression and better in the optimization performance than the genetic algorithm (GA) because it can encode more plentiful genetic information based on the biological DNA. The proposed method can construct a fuzzy model using the wavelet transform, in which the coefficients are identified by the DNA coding method. Thus, we can effectively get the fuzzy model of the nonlinear system by using the advantages of both wavelet transform and DNA coding method. In order to demonstrate the superiority of the proposed method, it is compared with modeling method using the conventional GA.

• Proceedings of the Korean Institute of Industrial Safety Conference
• /
• 2000.11a
• /
• pp.39-45
• /
• 2000

In recent years, composite materials such as fiber reinforced plastics (FRP) have gained considerable attention in the aircraft and automobile industries due to their light weight, high modulus and specific strength. In practice, control of chip formation appears to be the most serious problem since chip formation mechanism in composite machining has significant effects on the finished surface ［1,2,3,4,5］. Current study will discuss frequency analysis based on autoregressive (AR) time series model and process characterization in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized model composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The experimental correlation between the different chip formation mechanisms and model coefficients are established.(omitted)

• Journal of Navigation and Port Research
• /
• v.33 no.4
• /
• pp.277-282
• /
• 2009

This paper estimated the container volumes for the Incheon port with univariate time series. As best suited models Winters' additive model, ARIMA model,and Winters' additive model were selected by import-export, coastal, and transshipment volume respectively, based on the data of monthly volume by October 2008 since January 2001. This study supposed the import-export container volumes would be decreased by 14% against that in 2008 and would have been recovered to the increasing trend of the volumes beyond the fourth quarter of 2010. The future import-export and transshipment volumes showed the increasing trend beyond 2011, while the coastal volumes would be on the stagnation. The yearly container volumes were finally forecasted as 1,705, 2,432, and 3,341 thousand TEU in 2011, 2015, and 2020 respectively.

• Journal of the Korean Statistical Society
• /
• v.25 no.4
• /
• pp.529-544
• /
• 1996

This paper deals with the problem of estimating the autoregressive random coefficient of a first-order random coefficient autoregressive time series model applied to panel data of time series. The autoregressive random coefficients across individual units are assumed to be a random sample from a truncated normal distribution with the space (-1, 1) for stationarity. The estimates of random coefficients are obtained by an empirical Bayes procedure using the estimates of model parameters. Also, a Monte Carlo study is conducted to support the estimation procedure proposed in this paper. Finally, we apply our results to the economic panel data in Liu and Tiao(1980).

• Proceedings of the KIEE Conference
• /
• 2001.05a
• /
• pp.230-232
• /
• 2001

This paper presents a model for short-term load forecasting using multiple time-series. We made one-hour ahead load forecasting without classifying load data according to daily load patterns(e.g. weekday. weekend and holiday) To verify its effectiveness. the results are compared with those of neuro-fuzzy forecasting model(5). The results show that the proposed model has more accurate estimate in forecasting.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2003.09a
• /
• pp.213-216
• /
• 2003

Efforts for better understanding of the interaction between groundwater recharge and thermal regime of the subsurface medium is gaining momentum for its diverse applications in water resources. A numerical model is developed to simulate temperature variations of the subsurface under time varying groundwater recharge. The model utilizes MacCormack scheme for finite difference approximation of the partial differential equation describing the conductive and advective heat transport. For the estimation of recharge rate, optimization of the model is realized by searching for the unknown parameters which minimize the root-mean-square error between simulated and measured temperatures. Simulation results for 22-year time series data of temperature measurements reveal that the proposed model can accurately simulate subsurface temperature variations resulting from the redistribution of the heat due to the movement of water and it can also estimate temporal variations of recharge. Seasonal variations of recharge and a linear relationship between precipitation and recharge are clearly reflected in the simulated results.

• Communications for Statistical Applications and Methods
• /
• v.10 no.1
• /
• pp.19-30
• /
• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.10a
• /
• pp.1007-1013
• /
• 2008

In this study, a series of scaled model test were carried out in order to find factors that influence the ultimate load of underreamed anchors. Model anchors were made of arcril and 3cm in diameter. Series of tests were performed with various conditions such as density of soil, diameter of bulb, and number of bulb. Type of soil was Jumunjin sand and relative density varied 40%, 60%, 80%.