• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2193-2196
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• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1103-1112
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• 2013

Exploratory data analysis focuses mostly on data exploration instead of model fitting. We can use the recurrence plot as a graphical exploratory data analysis tool. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in time series at a glance.

• Transactions of the Korean Society of Mechanical Engineers
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• v.6 no.3
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• pp.247-255
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• 1982

The heat diffusion equation for an annular fin is analyzed by Laplace transformation. The fin has a uniform thickness, with its end insulated, and three different temperature profiles at the base such as step change, harmonic and exponential functions. The exact solutions for the temperature and heat flux of the fins are obtained with the infinite series. The series solutions converge rapidly for large values of dimensionless time, but slowly for small values. Therefore some approximate solutions are presented here to fine the temperature distribution and heat flux for small values of dimensionless time. Furthermore a simple approximate heat flux, .OMEGA.=1.13c.tau.$^{1}$2/ is found in the range of .tau. .leg. o.1/c for the exponential function at the base.

• Journal of Industrial Technology
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• v.18
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• pp.17-25
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• 1998

Recently, there are a lot of studies to use a satellite image data in order to investigate a simultaneous change of a wide range area as a lake. However, many cases of a water quality research occur as problem when we try to extract the water quality factors from the satellite image data, because of the atmosphere scattering exert as bad influence on a result of analysis. In this study, and attempt was made to select the relative atmospheric correction method for the water quality factors extraction from the satellite image data. And also, the time-series analysis of the water quality factors extraction from the satellite image data. And also, the time-series analysis of the water quality factors was performed by using the multi-temporal image data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.779-783
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• 2006

A The estimation of electrical power consumption is becoming more important to supply stabilized electrical power recently. In this paper, we propose a supplied forecasting system of electrical power using Fuzzy Compensative Algorithm to estimate electrical load accurately than the previous. We evaluate a time series of supplied electrical power have the chaotic character using quantitative and qualitative analysis, compose a forecasting system by the maximum change $rate(\alpha)$ of Fuzzy Algorithm and compensative parameter. Simulating it for obtained time series, we can obtain more accurate results than the previous proposed system.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• Journal of Climate Change Research
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• v.7 no.1
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• pp.49-57
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• 2016

In the present study, the time series of the number of days on which temperatures were not higher than $-5^{\circ}C$ in winter in Seoul was analyzed. The results showed a decreasing tendency until recently. Statistical change-point analysis was conducted to examine whether climate regime shifts existed in this time series. According to the results, the number of days on which temperatures were not higher than $-5^{\circ}C$ in winter in Seoul drastically decreased since 1988. Therefore, to find out the reason for the recent decrease in the number of days, differences between the means of large-scale environments in winder during 1988~2010 and those during 1974~1987 were analyzed. In all layers of the troposphere, anomalous anticyclones developed in regions around the Korean Peninsula and thus the Korean Peninsula was affected by westerlies or south-westerlies. This was associated with the recent a little further northward development of western North Pacific subtropical high. Therefore, environments good for warm and humid air to flow into the Korean Peninsula were formed. To examine whether relatively warm and humid air actually flowed into the Korean Peninsula recently, temperatures and specific humidity in all layers in the troposphere were analyzed and according to the results the Korean Peninsula showed warm and humid anomalies. In the analyses of sensible heat net flux and maximum temperatures at a height of 2 m that can be felt by humans, the East Asia Continent including the Korean Peninsula showed positive anomalies.

• Journal of Climate Change Research
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• v.7 no.1
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• pp.31-39
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• 2016

In this study, the time series of the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region were analyzed and they showed a strong trend of increase until recently. To determine the existence of a climate regime shift in this time series, the statistical change-point analysis was applied and it was found that the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region increased sharply since 1993. Therefore, in order to examine the cause of the sharp increase of the days with $25^{\circ}C$ or higher temperatures in the Jeju region, the differences between the averages of 1994~2013 and the averages of 1974~1993 were analyzed for the large-scale environment. In the Korean Peninsula including the Jeju region, precipitable water and total cloud cover decreased recently due to the intensification of strong anomalous anticyclones near the Korean Peninsula in the top, middle and bottom layers of the troposphere. As a result of this, the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region could increase sharply in recent years. Furthermore, in the analysis of sensible heat net flux and daily maximum temperatures at 2 m, which is the height that can be felt by people, the Korean Peninsula was included in the positive anomaly region. In addition, the frequency of typhoons affecting the Korean Peninsula decreased recently, which reduced the opportunities for air temperature drops in the Jeju region.

• Korean Management Science Review
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• v.8 no.1
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• pp.3-11
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• 1991

Non-Stationary transition probabilities models which is incorporated into a Markov framework with exogenous variables to account for some of variability are discussed, and extended for alternative procedure. Also as an application of the methodology, the size change of aggregate time-series data on the planted system of agricultural crops is estimated, and evaluated for the precision of time-varying evolution statistically.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.23-26
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• 2000

It is noted that many econometric time series have long-memory properties. A long-memory process, or strongly dependent process, is characterized by hyperbolic decaying autocorrelations and unbounded spectral density at the origin. Since the long-memory property can be observed by data obtained from rather a long period, there is some possibility of parameter change in the process. In this paper, we consider the estimation of change-point when there is a change in the variance of a long-memory process. The estimator is based on some reasonable statistic and the consistency is shown using Taqqu's strong reduction theorem