• Phonetics and Speech Sciences
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• v.7 no.2
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• pp.19-28
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• 2015

This study investigated final consonant error characteristics at word-medial position in children with functional articulation disorder. Data was collected from 11 children with functional articulation and 11 normal children, ages 4 to 5. The speech samples were collected from a naming test. Seventy-five words with every possible bi-consonants matrix at the word-medial position were used. The results of this study were as follows : First, percentage of correct word-medial final consonants of functional articulation disorder was lower than normal children. Second, there were significant differences between two groups in omission, substitution and assimilation error. Children with functional articulation disorder showed a high frequency of omission and regressive assimilation error, especially alveolarization in regressive assimilation error most. However, normal children showed a high frequency of regressive assimilation error, especially bilabialization in regressive assimilation error most. Finally, the results of error analysis according to articulation manner, articulation place and phonation type of consonants of initial consonant at word-medial, both functional articulation disorder and normal children showed a high error rate in stop sound-stop sound condition. The error rate of final consonant at word-medial position was high when initial consonant at word-medial position was alveolar sound and alveopalatal sound. Futhermore, when initial sounds were fortis and aspirated sounds, more errors occurred than linis sound was initial sound. The results of this study provided practical error characteristics of final consonant at word-medial position in children with speech sound disorder.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.189-199
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• 2020

This paper studies a Bayesian ordered multiple linear regression model with skew normal error. It is reasonable that the kind of inherent information available in an applied regression requires some constraints on the coefficients to be estimated. In addition, the assumption of normality of the errors is sometimes not appropriate in the real data. Therefore, to explain such situations more flexibly, we use the skew-normal distribution given by Sahu et al. (The Canadian Journal of Statistics, 31, 129-150, 2003) for error-terms including normal distribution. For Bayesian methodology, the Markov chain Monte Carlo method is employed to resolve complicated integration problems. Also, under the improper priors, the propriety of the associated posterior density is shown. Our Bayesian proposed model is applied to NZAPB's apple data. For model comparison between the skew normal error model and the normal error model, we use the Bayes factor and deviance information criterion given by Spiegelhalter et al. (Journal of the Royal Statistical Society Series B (Statistical Methodology), 64, 583-639, 2002). We also consider the problem of detecting an influential point concerning skewness using Bayes factors. Finally, concluding remarks are discussed.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.165-172
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• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.7
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• pp.910-914
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• 2001

The teat number of a sow plays an important role for weaning pigs and has been utilized in selection of swine breeding stock. Various linear models have been employed for genetic analyses of teat number although the teat number can be considered as a count trait. Theoretically, Poisson error mixed models are more appropriate for count traits than Normal error mixed models. In this study, the two models were compared by analyzing data simulated with Poisson error. Considering the mean square errors and correlation coefficients between observed and fitted values, the Poisson generalized linear mixed model (PGLMM) fit the data better than the Normal error mixed model. Also these two models were applied to analyzing teat numbers in four breeds of swine (Landrace, Yorkshire, crossbred of Landrace and Yorkshire, crossbred of Landrace, Yorkshire, and Chinese indigenous Min pig) collected in China. However, when analyzed with the field data, the Normal error mixed model, on the contrary, fit better for all the breeds than the PGLMM. The results from both simulated and field data indicate that teat numbers of swine might not have variance equal to mean and thus not have a Poisson distribution.

• Journal of the Korean Institute of Gas
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• v.25 no.5
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• pp.63-69
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• 2021

In this study, we established an algorithm to determine whether the work in the gas facility is a normal work or an abnormal work using the threshold of the reconstruction error of the autoencoder. This algorithm do deep learning the autoencoder only with time-series data of a normal work, and derives the optimized threshold of the reconstruction error of the normal work. We applied this algorithm to the time series data of the new work to get the reconstruction error, and then compare it with the reconstruction error threshold of the normal work to determine whether the work is normal work or abnormal work. In order to train and validate this algorithm, we defined the work in a virtual gas facility, and constructed the training data set consisting only of normal work data and the validation data set including both normal work and abnormal work data.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.46-55
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• 1996

As industrial standard data, the STL format which approximates three dimensional CAD model to triangular facets, is used for RP(Rapid Prototyping) system in recent days. Because most RP system take the only form of two dimensional line segments as an input stream inspite of its imperfectness while converting into STL format, a CAD model is converted into a standard industrial format which is composed of many triangular facets. The error verifying process is composed of four main steps, and these are 1) Remove facets with two or more vertices equal to each other. 2) Fix overlapping error such as more than three facets adjacent to anedge. 3) Fill holes in the mesh by using Delaunay triangulation method. 4) Correct the wrong direction and normal vectors. This paper is concerned with serching the mentioned errors in advance and modifying them.

• Speech Sciences
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• v.7 no.2
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• pp.7-18
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• 2000

The purpose of this study was to investigate the phonological error patterns of korean children with and without specific phonological disorders(SPD). In this study, 29 normally developing children and 10 SPD children were involved. The children were matched the percentage of consonants correct(PCC). 22 picture cards were used to elicit korean consonants in word initial syllable initial, word medial syllable initial, word medial syllable final, word final syllable final positions. The findings were as follows. First, the phonological error patterns of SPD were 1) similar to those of normal children with the same PCC, 2) similar to those of normal children with the lower PCC, or 3) unusual to those of normal children. Second,. korean children showed phonological processes reflecting the korean phonological characteristics: tensification, reduction of the word medial syllable final consonant. This study suggests that both the PCC and error patterns should be considered in assessing phonological abilities of children.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.2
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• pp.60-66
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• 2010

Normal Operations Safety Survey (NOSS), a methodology for capturing safety data during normal Air Traffic Control (ATC) operations. The NOSS methodology is based on the Threat and Error Management (TEM) framework, and is a safety management tool to monitor safety during normal aviation operations. Monitoring safety in normal operations is an essential activity within the safety management systems of Air Traffic Services (ATS) providing organizations, and NOSS is proposed as a suitable way to do this. This study, operating in the latter half of the year of 2010 in South Korea on NOSS was the understanding and to apply.

• Journal of the Korean Statistical Society
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• v.34 no.2
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• pp.99-107
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• 2005

In this paper we consider the asymptotic mean squared error of positive part James-Stein estimators. In the normal-normal example, estimators of the mean squared error of these estimators are provided which are correct asymptotically up to O($m^{-l}$). Asymptotic estimators of the MSE's which correct up to O($m^{-l}$) are also provide. Here, m denotes the number of strata. A simulation study is undertaken to evaluate the performance of these estimators.