• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1225-1235
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• 2011

This paper suggests a method for tuning a numerically simulated wind speed data, provided by NIMR(National Institute of Meteorological Research) and generated from a numerical meteorological model to improve a wind resource map with a $1Km{\times}1Km$ resolution. To this end, "tuning factor method" is developed that consists of two procedures. First, estimate monthly wind fields based on a suitably designed statistical wind field model that covers 345,682 regions obtained by $1Km{\times}1Km$ lattice sites in South Korea. The second procedure computes the tuning factor and then tunes the generated wind speeds of each month as well as each lattice site. The second procedure is based on the wind fields estimated by the first procedure. The performance of the suggested tuning method is demonstrated by using two wind data(both TMY and numerically simulated wind speed data) of 75 weather station areas.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.791-804
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• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Journal of Communications and Networks
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• v.17 no.1
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• pp.67-74
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• 2015

In this paper, we propose a web-based content syndication system in which users can easily choose Internet protocol television (IPTV) contents. This system generates personalized content guide to provide a list of IPTV contents with respect to users' interests and statistics information of their online social community. For this, IPTV contents and relevant metadata are collected from various sources and transformed. Then, the service and content metadata are processed by user metadata including audience measurement and community metadata. The metadata flows are separated from content flows of transport network. The implementation of IPTV content syndication system demonstrates how to arrange IPTV contents efficiently from content providers to the end user's screen. We also show that the user metadata including online community information are important for the system's performance and the user's satisfaction.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.223-232
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• 2002

The present paper develops a test of the multivariate normality based on nonlinear transformations and the likelihood function. For checking the normality, we test the shape parameter which indexes the family of transformations. A score test and a parametric bootstrap test are used to evaluate the discrepancy between the data and a multivariate normal distribution. In order to compare the performance of our test with the existing tests, a simulation study was carried out for several situations where nuisance parameters have to be estimated. The results showed that the proposed method is superior to the existing methods.

• International Journal of Safety
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• v.3 no.1
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• pp.47-52
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• 2004

Current OSH system was analyzed in this paper to explain why high fatal incidents and disasters are continuously repeated for recent years in Korea. It was found that we have Dichotomous Perceptional Misconception of prevention before accident and compensation after accident and there is a significant lack of proper feed­back reward system for OSH performance. It was assumed that no reduction of accident rate and fatality rate have not been achieved recently despite of a great effort and increased resource allocations. Some statistics for proving weak punishment were analyzed. In the current system, the will of administrative agency would have been very limited particularly in the legal aspects. The Industrial Safety and Health Act is not suitable to after-injury punishment for employer and/or corporate since it is based on a framework for enforcement of prevention. Based on these analyses, it was concluded that there was a need to consider a special law for Corporate Accountability for Fatal Accidents. Because it is necessary to consider seriously for introduction of a new legal system for after injury punishment to repair the current system where it was found lack of proper feedback system. Also, there was no proper sanction measures for corporate with the current OSH legal system, and the most urgent problem in OSH area is the high fatality rate. it is necessary to consider seriously for introduction of a new legal system for after injury punishment. Also, there is no proper sanction measures for corporate with the current OSH legal system, and the most urgent problem in OSH area is the high fatality rate.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.201-206
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• 2011

FRUC (Frame Rate Up-Conversion) technique needs an effective frame interpolation algorithm using motion information between adjacent neighboring frames. In order to have good visual qualities in the interpolated frames, it is necessary to develop an effective detection and interpolation algorithms for occluded regions. For this aim, this paper proposes an effective occluded region detection algorithm through the adaptive forward and backward motion searches and also by introducing the minimum value of normalized cross-correlation coefficient (NCCC). That is, the proposed scheme looks for the location with the minimum sum of absolute differences (SAD) and this value is compared to that of the location with the maximum value of NCCC based on the statistics of those relations. And, these results are compared with the size of motion vector and then the proposed algorithm decides whether the given block is the occluded region or not. Furthermore, once the occluded regions are classified, then this paper proposes an adaptive interpolation algorithm for occluded regions, which still exist in the merged frame, by using the neighboring pixel information and the available data in the occluded block. Computer simulations show that the proposed algorithm can effectively classify the occluded region, compared to the conventional SAD-based method and the performance of the proposed interpolation algorithm has better PSNR than the conventional algorithms.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.463-473
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• 2017

How to handle missing data is a main issue in clinical trials. We impute missing data based on missing data that follows a mechanism according to the intention-to-treat rule. However, using the right imputation method for missing data is very important because this supposition is unclear. We suggest a new imputation method for missing data using agreement and maintenance introduced by Kang and Kim (1997). We give an example and adapt a Monte Carlo simulation to compare the performance between the established method and the suggested method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.13-20
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• 2005

This paper addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple recordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, withe fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.