• Journal of KSNVE
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• v.8 no.6
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• pp.1023-1029
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• 1998

When one attempts to construct a hologram. one finds that there are many sources of measurement errors. These errors are even amplified if one predicts the pressures close to the sources. The pressure estimation errors depend on the following parameters: the measurement spacing on the hologram plane. the prediction spacing on the prediction plane. and the distance between the hologram and the prediction plane. This raper analyzes quantitatively the errors when these are distributed irregularly on the hologram plane The sensor mismatch and inaccurate measurement location. position mismatch. are mainly addressed. In these cases. one can assume that the measurement is a sample of many measurement events. The bias and random error are derived theoretically. Then the relationship between the random error amplification ratio and the parameters mentioned above is examined quantitatively in terms of energy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.403-407
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• 2003

This paper studies the error of pressure, particle velocity, and intensity which are distributed in a space. Errors may be amplified when other sound field variables are predicted. We theoretically derive their bias error and random error. The analysis shows that many samples do not always guarantee good results. Random error of the velocity and intensity are increased when many samples are used. The characteristics of the amplification of the random error are analyzed in terms of the sample spacing. The amplification was found to be related to the spatial differential of random noise. The numerical simulations are performed to verify theoretical results.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.523-529
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• 2004

In Korea, mathematics education has been changed according to the 7th national mathematics curriculum renovated by the Ministry of Education and Human Resources Development announcement in 1997. The education of probability and Statistics has been carried out as a part of this curriculum. We analyze and compare 3 kinds of mathematics textbooks for 10-12 grade students. Descriptions of random variable, sample variance and sample standard deviation, distribution of sample mean, and etc. which are on some textbooks, are misleaded in school education. We suggest the unbiased estimator of sample variance in textbooks and distributions of sample means with normal population assumption.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.111-111
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• 2004

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2005.06a
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• pp.151-157
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• 2005

For the treatment of regulatory clearance of the soils, a procedure for the radionuclides and radioactivity concentration analysis is under development. A strategy for soil sampling including random sampling after homogenization and standardization was set up. Statistical representativeness is considered for not only sampling strategy but also sample size. In this study, designed sample size was designed with confidence interval and error bound of soil using the pilot samples which were taken following the sampling strategy.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.231-243
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• 1997

In this paper we investigate weak convergence of the intergral processes whose index set is the non-compact infinite time interval. Our first goal is to develop the empirical central limit theorem as random elements of [0, .infty.) for an integral process which is constructed from iid variables. In developing the weak convergence as random elements of D[0, .infty.), we will use a result of Ossiander(4) whose proof heavily depends on the total boundedness of the index set. Our next goal is to establish the empirical central limit theorem for the Kaplan-Meier integral process as random elements of D[0, .infty.). In achieving the the goal, we will use the above iid result, a representation of State(6) on the Kaplan-Meier integral, and a lemma on the uniform order of convergence. The first result, in some sense, generalizes the result of empirical central limit therem of Pollard(5) where the process is regarded as random elements of D[-.infty., .infty.] and the sample paths of limiting Gaussian process may jump. The second result generalizes the first result to random censorship model. The later also generalizes one dimensional central limit theorem of Stute(6) to a process version. These results may be used in the nonparametric statistical inference.

• International Journal of Reliability and Applications
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• v.1 no.2
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• pp.155-165
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• 2000

In this paper, we propose some estimators of Kullback- Leibler Information functions using the data from three step stress accelerated life tests. This acceleration model is assumed to be a tampered random variable model. Some asymptotic properties of proposed estimators are proved. Simulations are performed for comparing the small sample properties of the proposed estimators under use condition of accelerated life test.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.329-340
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• 2002

Two methods are proposed for constructing a confidence interval on the among group variance component in a unbalanced one-way random effects model. Computer simulation is used to compare these methods with alternative procedures. The results indicate that the method1 and methods2 perform well over small group size and large sample size respectively.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.241-246
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• 2000

A simple random sample is taken from a population and a particular survey item is subject to nonresponse that corresponds to random subsampling of the sampled values within adjustment cells. Our object is to estimate Bayesian probability interval of the population mean.