• 말소리와 음성과학
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• 제5권3호
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• pp.63-69
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• 2013

The purpose of this study was to evaluate the maximum and optimal listening speed of Korean TTS for the blind. Five blind participants took part in this study. The instruments used in this study were 17 sentence sets (2 sets for an excercise, 10 sets for a repeated test, and 5 sets for a random test), with short meaningful sentences (the same sentences for the repeated test, different sentences for the random test) with 15 differentiated speeds (Range=0.8-3.6, SD=0.2). Each participant's maximum and quickest listening speeds were calculated by objective recall accuracy (determined by the number of correctly recalled syllables/the total number of syllables in a sentence X 100) and subjective recall accuracy (recall accuracy judged by each participant's subjective evaluation). The results showed that the participants' recall accuracy had a tendency to increase as the TTS speed decreased. Participants' subjective recall accuracy was higher than objective recall accuracy in the repeated tests and vice versa in the random tests. The results also revealed that the participants' sentence familiarity had an influence on their Korean TTS listening speed.

• Journal of applied mathematics & informatics
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• 제27권5_6호
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• pp.1459-1463
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• 2009

Recently, Zhao et.al [European Journal of Operational Research 169 (2006) 189-201] discussed a random fuzzy renewal process based on random fuzzy theory. They considered the rate of the random fuzzy renewal process and presented a random fuzzy elementary renewal theorem. They also established Blackwell's theorem in random fuzzy sense. But all these results are invalid. We give a counter example in this note.

• Transactions on Electrical and Electronic Materials
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• 제7권4호
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.871-876
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• 2002

In this paper, we establish some characterizations of tightness for a sequence of random elements taking values in the space of upper-semicontinuous fuzzy sets with compact support in R.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.183-188
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• 2003

In this paper, we establish some results on strong convergence for weighted sums of uniformly integrable fuzzy random variables taking values in the space of upper-semicontinuous fuzzy sets in R$^{p}$.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2002년도 정기학술대회
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• pp.303-303
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• 2002

We obtain the necessary and sufficient condition of tightness for a sequence of random variables in the space of fuzzy sets with compact support in R.

• Korean Journal of Mathematics
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• 제2권
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• pp.57-64
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• 1994

• Journal of the Korean Data and Information Science Society
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• 제27권2호
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• pp.549-557
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• 2016

In this paper we propose multiclassification method for large data sets by ensembling least squares support vector machines (LS-SVM) with principal components instead of raw input vector. We use the revised one-vs-all method for multiclassification, which is one of voting scheme based on combining several binary classifications. The revised one-vs-all method is performed by using the hat matrix of LS-SVM ensemble, which is obtained by ensembling LS-SVMs trained using each random sample from the whole large training data. The leave-one-out cross validation (CV) function is used for the optimal values of hyper-parameters which affect the performance of multiclass LS-SVM ensemble. We present the generalized cross validation function to reduce computational burden of leave-one-out CV functions. Experimental results from real data sets are then obtained to illustrate the performance of the proposed multiclass LS-SVM ensemble.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권7호
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• pp.525-530
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• 2004

A classifier was constructed by using a generalized regression neural network (GRU) and random generator (RG), which was applied to classify DNA sequences. Three data sets evaluated are eukaryotic and prokaryotic sequences (Data-I), eukaryotic sequences (Data-II), and prokaryotic sequences (Data-III). For each data set, the classifier performance was examined in terms of the total classification sensitivity (TCS), individual classification sensitivity (ICS), total prediction accuracy (TPA), and individual prediction accuracy (IPA). For a given spread, the RG played a role of generating a number of sets of spreads for gaussian functions in the pattern layer Compared to the GRNN, the RG-GRNN significantly improved the TCS by more than 50%, 60%, and 40% for Data-I, Data-II, and Data-III, respectively. The RG-GRNN also demonstrated improved TPA for all data types. In conclusion, the proposed RG-GRNN can effectively be used to classify a large, multivariable promoter sequences.

• Communications for Statistical Applications and Methods
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• 제26권4호
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.