• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.169-174
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• 2015

Permutation p-values are provided for the agreement measures for multivariate interval data among many raters. Three agreement measures, Berry and Mielke's measure, Janson and Olsson's measure, and Um's measure are described and compared. Exact and resampling permutation methods are utilized to compute p-values and empirical quantile limits for three measures. Comparisons of p-values demonstrate that resampling permutation methods provide close approximations to exact p-values, and Berry and Mielke's measure and Um's measure show similar performance in terms of measuring agreement.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.149-160
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• 2017

In hypothesis testing, the interpretation of a statistic obtained from the data analysis relies on a probabilistic distribution of the statistic constructed according to several statistical theories. For instance, the statistical significance of a mean difference between experimental conditions is determined according to a probabilistic distribution of the mean differences (e.g., Student's t) constructed under several theoretical assumptions for population characteristics. The present study explored the logic and advantages of random-resampling approach for analyzing event-related potentials (ERPs) where a hypothesis is tested according to the distribution of empirical statistics that is constructed based on randomly resampled dataset of real measures rather than a theoretical distribution of the statistics. To motivate ERP researchers' understanding of the random-resampling approach, the present study further introduced a specific example of data analyses where a random-permutation procedure was applied according to the random-resampling principle, as well as discussing several cautions ahead of its practical application to ERP data analyses.

• Journal of Korea Water Resources Association
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• v.35 no.3
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• pp.275-284
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• 2002

In this study, a method of random resampling of residuals from stochastic models such as the Monte-Carlo model, the lag-one autoregressive model(AR(1)) and the periodic lag-one autoregressive model(PAR(1)), has been adopted to generate a large number of long traces of annual and monthly steamflows. Main advantage of this resampling scheme called the Bootstrap method is that it does not rely on the assumption of population distribution. The Bootstrap is a method for estimating the statistical distribution by resampling the data. When the data are a random sample from a distribution, the Bootstrap method can be implemented (among other ways) by sampling the data randomly with replacement. This procedure has been applied to the Yongdam site to check the performance of Bootstrap method for the streamflow generation. and then the statistics between the historical and generated streamflows have been computed and compared. It has been shown that both the conventional and Bootstrap methods for the generation reproduce fairly well the mean, standard deviation, and serial correlation, but the Bootstrap technique reproduces the skewness better than the conventional ones. Thus, it has been noted that the Bootstrap method might be more appropriate for the preservation of skewness.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.665-682
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• 2005

The sample reuse bootstrap technique has been successful to attract both applied and theoretical statisticians since its origination. In recent years a good deal of attention has been focused on the applications of bootstrap methods in regression analysis. It is easier but more accurate computation methods heavily depend on high-speed computers and warrant tough mathematical justification for their validity. It is now evident that the presence of multiple unusual observations could make a great deal of damage to the inferential procedure. We suspect that bootstrap methods may not be free from this problem. We at first present few examples in favour of our suspicion and propose a new method diagnostic-before-bootstrap method for regression purpose. The usefulness of our newly proposed method is investigated through few well-known examples and a Monte Carlo simulation under a variety of error and leverage structures.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.84-89
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• 2014

Data-driven method for fault diagnostics system often use machine learning technique. To use such technique proper signal processing should be implemented such as time synchronous averaging (TSA) for ball bearing systems. However, for journal bearing diagnostics systems not much has been researched, and yet a proper signal processing method has not been studied. Therefore, in this research an optimal datum unit for a reliable journal bearing diagnostics system along with angular resampling process is being suggested. Before extracting time and frequency domain features, angular resampling is applied to each cycle of vibration data. As to preserve the characteristics of vibration signal, averaging method is replaced by finding the optimal datum unit which strengthens statistical characteristics of vibration signal. Then 20 features were extracted for various cases, and those features are being evaluated by two criteria, separability and classification accuracy.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.159-164
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• 2019

The statistical inference of Cronbach's alpha measure of internal reliability is known to be inaccurate when sample size is small and the assumption of normality is violated. In this paper, we describe the permutation method in which we compute resampling p-values for testing the difference between two or more independent Cronbach's alpha coefficients. When the over-all permutation test is significant, we also make pairwise post-hoc comparisons using permutaion method. The permutation tests for the equality of two independent Cronbach's alpha coefficients and three independent Cronbach's alpha coefficients are illustrated with an example analysis of survey data.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.367-377
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• 2010

In this study, we considered annual maximum rainfall data from 56 weather stations for rainfall frequency analysis using SIR(Sampling Important Resampling) algorithm and Bootstrap method. SIR algorithm is resampling method considering weight in extreme rainfall sample and Bootstrap method is resampling method without considering weight in rainfall sample. Therefore we can consider the difference between SIR and Bootstrap method may be due to the climate change. After the frequency analysis, we compared the results. Then we derived the results which the frequency based rainfall obtained using the data from SIR algorithm has the values of -10%~60% of the rainfall obtained using the data from Bootstrap method.

• Journal of Wetlands Research
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• v.20 no.2
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• pp.105-115
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• 2018

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results of frequency based snowfall depth show that most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. According to the results, observed data and Bootstrap method showed a difference of -19.2% to 3.9%, and the Bootstrap method and SIR(Sampling Importance Resampling) algorithm showed a difference of -7.7 to 137.8%. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.517-523
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• 2011

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), ${\alpha}-{\beta}$ filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.