• Journal of the Korean Society of Safety
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• v.30 no.1
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• pp.66-71
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• 2015

Safety, resilience and comfort of pedestrian were assessed by the British Pendulum Test and SB/GB factor test at 8 kinds of sidewalk pavement. Sidewalk paving materials were normal concrete, porous concrete, concrete block, soil concrete, asphalt, rubber chip/resin mixture, wood chip/resin mixture and floor tile. In addition, a survey was conducted to investigate the perception of pedestrians on the sidewalk paving material. As a result, while the skid resistance value was measured in the most 60BPN above, the floor tile showed a low value of about 30BPN. The ratios of SB factor to GB factor of the elastic pavements(rubber/resin mixture and wood chip/resin mixture) appeared to be relatively large when compared with those of the conventional sidewalks. The survey showed that respondents perceived as more safe and comfortable elastic pavements compared to conventional pavements. Approximately 50% of respondents answered that hardened soil pavement was the most environmentally friendly.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.197-206
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• 2015

Recently the automobile engine has been developed in achieving the high performance, fuel economy, and emission reduction. In a conventional spark ignition engine the fuel and air are mixed together in the intake system, inducted through the intake valve into the cylinder, and then compressed. Under normal operating conditions, the combustion is initiated towards the end of the compression stroke at the spark plug by an electric discharge. Following inflammation, a flame develops and propagates through this premixed fuel-air mixture. Therefore the state of mixture is very important in the combustion and emission characteristics. In this study the combustion and emission characteristics were tested and analyzed with changing the mixture composition and engine operating parameters in order to improve the combustion and performance in engine.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.633-645
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• 2018

Gaussian error distributions are a common choice in traditional regression models for the maximum likelihood (ML) method. However, this distributional assumption is often suspicious especially when the error distribution is skewed or has heavy tails. In both cases, the ML method under normality could break down or lose efficiency. In this paper, we consider the log-concave and Gaussian scale mixture distributions for error distributions. For the log-concave errors, we propose to use a smoothed maximum likelihood estimator for stable and faster computation. Based on this, we perform comparative simulation studies to see the performance of coefficient estimates under normal, Gaussian scale mixture, and log-concave errors. In addition, we also consider real data analysis using Stack loss plant data and Korean labor and income panel data.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.1-9
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• 2016

PURPOSES : This study aims to show the difference of the binder aging level in the hot-mix asphalt (HMA) mixture after short-term aging (SA) under different aging conditions, such as mixture temperature and duration in hour. METHODS : Three SA times (i.e., 1 h, 2 h, and 4 h) at two temperatures (i.e., $160^{\circ}C$ and $180^{\circ}C$) were used for the normal mixtures prepared using a PG64-22 asphalt. The field long-term aging (LA) was simulated by applying the same LA procedure (65 h at $110^{\circ}C$) to all compacted specimens, prepared at the air void of 7% using each SA-treated mixture, in a convection oven. The binder aging level was measured in terms of large molecular size by gel-permeation chromatography (GPC) from the mixture and the absolute viscosity (AV) from the recovered binder. The aging levels were evaluated using those two properties after SA and LA, and then compared based on the normal SA (NSA) mixture (1 h at $160^{\circ}C$). The service life reduction caused by SA in various conditions was estimated based on the aging level of the field cores from different locations in various service lives. RESULTS : The results of the laboratory evaluation indicated that the binder of the mixture, which was treated at longer SA time and higher temperature, showed a significantly higher aging level than the NSA mixture. The binder aging level from a longer time, such as 2 h and 4 h SA, or at a higher temperature ($180^{\circ}C$), were estimated to be similar to that of the mixtures, which had already been in field service for several years. CONCLUSIONS : The HMA mixture should be produced at a moderate temperature, such as $160^{\circ}C$, and placed within a limited hauling and queuing time to avoid a significant short-term aging of the binder before placement in the field pavement. The SA for a longer time at a higher temperature than the NSA condition was found to be detrimental to the service life of the asphalt pavement.

• MALSORI
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• no.64
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• pp.77-88
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• 2007

Nowadays, mel-frequency cesptral coefficients (MFCCs) and Gaussian mixture models (GMMs) are used for the pathological voice detection. This paper suggests a method to improve the performance of the pathological/normal voice classification based on the MFCC-based GMM. We analyze the characteristics of the mel frequency-based filterbank energies using the fisher discriminant ratio (FDR). And the feature vectors through the linear discriminant analysis (LDA) transformation of the filterbank energies (FBE) and the MFCCs are implemented. An accuracy is measured by the GMM classifier. This paper shows that the FBE LDA-based GMM is a sufficiently distinct method for the pathological/normal voice classification, with a 96.6% classification performance rate. The proposed method shows better performance than the MFCC-based GMM with noticeable improvement of 54.05% in terms of error reduction.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.573-578
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• 2009

We prove the consistency of a penalized maximum likelihood estimate proposed by Ahn (2001). The PMLE not only avoids the well-known problem that the ordinary likelihood of the normal mixture model is unbounded for any given sample size, but also removes redundant components.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.167-182
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• 1999

This article is concerned with the selection of subsets of predictor variables to be included in building the binary response probit regression model. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the probit regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. The appropriate posterior probability of each subset of predictor variables is obtained through the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as the one with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.305-319
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• 2003

This paper provides a non-Bayesian method based on the expanded EM algorithm for segmenting the magnetic resonance images degraded by bias field. For the images with the intensity as a pixel value, many segmentation methods often fail to segment it because of the bias field(with low frequency) as well as noise(with high frequency). Our contextual approach is appropriately designed by using normal mixture model incorporated with Markov random field for noise-corrective segmentation and by using the penalized likelihood to estimate bias field for efficient bias filed-correction.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.651-659
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• 2012

In this paper we developed a statistical model for circular data with noises. In this case, model fitting by single circular model has a lack-of-fit problem. To overcome this problem, we consider some mixture models that include circular uniform distribution and apply an EM algorithm to estimate the parameters. Both von Mises and Wrapped skew normal distributions are considered in this paper. Simulation studies are executed to assess the suggested EM algorithms. Finally, we applied the suggested method to fit 2008 EHFRS(Epidemic Hemorrhagic Fever with Renal Syndrome) data provided by the KCDC(Korea Centers for Disease Control and Prevention).