• Journal of the Korean Statistical Society
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• v.10
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• pp.140-144
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• 1981

This paper approximates to a kernel density estimate by a truncated series of expansion involving Hermite polynomials, since this could ease the computing burden involved in the kernel-based density estimation. However, this truncated series may give a multimodal estimate when we are estiamting unimodal density. In this paper we will show a way to insure the truncated series to be positive and unimodal so that the approximation to a kernel density estimator would be maeningful.

• Journal of Korean Society of Forest Science
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• v.47 no.1
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• pp.52-61
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• 1980

This study is carried out to learn the properties of forest soils in Korea and propose the reasonable management methods of forest land. Among 178 soil series surveyed until now in Korea forest soils include 64 series broken down according to the weathered products into 5 categories such as residual materials on mountain and hill, residual materials on rolling and hill, colluvial materials on local valley and fans, alluvial materials and volcanic ash soils. What discussed in this paper are classification system, parent rocks, texture class and drainage conditions of Korean forest soils. The characteristics of Korean forest soil properties classified in U.S.D.A. soil classification system are as follows: 1. Residual soils on mountain and hill (29 soil series) are almost Lithosols without any distinct soil profile development. They have loamy skeletal (11 series), coarse loamy (5 series), fine loamy (3 series), and fine clayey soils (3 series). Their drainage conditions are somewhat excessively drained in 16 series and well drained in 7 series. 2. Residual soils on rolling and hill (19 series) are Red-Yellow Podzolic soils with well developed soil profiles. They have coarse and fine loamy texture in 12 series and fine clayey texture in 5 series mostly with well drained condition. 3. Colluvial soils on local valley and fans (13 series) include mostly Regosols and some Red-Yellow Podzolic Soils and Acid Brown Forest Soils. They have loamy skeletal (4 series), coarse loamy (3 series), fine loamy (3 series), and fine clayey soils (2 series) with well drained condition. 4. Soil textures of weathered products of parent rocks are as follows: 1) Parent rocks producing coarse texture soils are rhyolite, granite gneiss, schist, shale, sandstone, siltstone, and conglomerate. 2) Parent rocks producing fine and heavy texture soils are limestone, basalt, gabbro, and andesite porphyry. 3) Granite is a parent rock producing various textured soils.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.553-568
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• 2022

The Household Income and Expenditure Survey is a representative survey of Statistics Korea, which aims to measure and analyze national income and consumption levels and their changes by understanding the current state of household balances. Recently, the disconnection problem in these time series caused by the large-scale reorganization of the survey methods in 2017 and 2019 has become an issue. In this study, we model the characteristics of the time series in the Household Income and Expenditure Survey up to 2016, and use the modeling to compute forecasts for linking the expenditures in 2017 and 2018. In order to evenly reflect the characteristics across all expenditure item series and to reduce the impact of a specific forecast model, we synthesize a total of 8 models such as regression models, time series models, and machine learning techniques. In particular, the noteworthy aspect of this study is that it improves the forecast by using the optimal combination technique that can exactly reflect the hierarchical structure of the Household Income and Expenditure Survey without loss of information as in the top-down or bottom-up methods. As a result of applying the proposed method to forecast expenditure series from 2017 to 2019, it contributed to the recovery of time series linkage and improved the forecast. In addition, it was confirmed that the hierarchical time series forecasts by the optimal combination method make linkage results closer to the actual survey series.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.147-160
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• 2020

In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.2
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• pp.110-118
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• 2008

The regulated peak power tracking (RPPT) systems such as the series structure and the parallel structure are commonly used in the satellite space power system. However, this structure processes the solar array power to the load through two regulators during one orbit cycle, which reduces the energy transfer efficiency. The series-parallel structure for the RPPT system can improve the power conversion efficiency, but an additional regulator increases the cost, size and weight of the system. In this paper, a simplified series-parallel space power system that consists of two regulators is proposed. The proposed system has the similar energy transfer efficiency with the series-parallel structure by adding one switch to the series structure, which reduces the cost, size and the weight. The large signal stability analyses is provided to understand the four main modes of system operation. In order to compare the energy efficiency with a series structure, the simulation is performed. The experimental verifications are performed using a prototype hardware with TMS320F2812 DSP and 200W solar arrays.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.39-49
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• 2017

As the Internet technology advances, data on the web is increasing sharply. Many research study about incremental learning for classifying effectively in data increasing. Web document contains the time-series data such as published date. If we reflect time-series data to classification, it will be an effective classification. In this study, we analyze the time-series variation of the words. We propose an efficient classification through dividing the dataset based on the analysis of time-series information. For experiment, we corrected 1 million online news articles including time-series information. We divide the dataset and classify the dataset using SVM and $Na{\ddot{i}}ve$ Bayes. In each model, we show that classification performance is increasing. Through this study, we showed that reflecting time-series information can improve the classification performance.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.4
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• pp.274-282
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• 2014

Time series of the dynamic response of a slender marine structure was predicted using quadratic Volterra series. The wave-structure interaction system was identified using the NARX(Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through the supervised training with the prepared datasets. The dataset used for the network training was obtained by carrying out the nonlinear finite element analysis on the freely standing riser under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of relative velocity between the water particle and structure in Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of response of the structure was predicted using the quadratic Volterra series. In order to check the applicability of the method, the response of structure under the realistic ocean wave environment with given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. It turned out that the predicted time series of the response of structure with quadratic Volterra series successfully captures the slowly varying response with reasonably good accuracy. It is expected that the method can be used in predicting the response of the slender offshore structure exposed to the Morison type load without relying on the computationally expensive time domain analysis, especially for the screening purpose.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.79-87
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• 2011

A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.

• Journal of the Korean Society for Marine Environment & Energy
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• v.14 no.2
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• pp.73-80
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• 2011

The present study deals with the propeller design for the standard new propeller series so called KF Series for 52ton class fishing vessel. The MAU or B series have been usually used for the fishing vessel's propeller, which are to be improved in consideration of the efficiency as well as the cavitation point of view. The high technology of propeller design has been applied to the design of 52ton class fishing vessel's propeller in the present study. The new designed series propellers will be validated by the experimental results whose data will be also used for the new series chart.