• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.729-738
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• 2015

In this paper, we compare the robustness to the assumption of normality of the single control charts to control the mean and variance simultaneously. The charts examined were semicircle control chart, max chart and MSE chart with Shewhart individuals control charts. Their in-control and out-of-control performance were studied by simulation combined with computation. We calculated false alarm rate to compare among single charts by changing subgroup size and shifting mean of quality characteristics. It turns out that max chart is more robust than any of the others if the process is in-control. In some cases max chart and MSE chart are more robust than others if the process is out-of-control.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.361-369
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• 2015

A general linear mixed-effects model is often used to analyze repeated measurement experiment data of a continuous response variable. However, a general linear mixed-effects model can give improper analysis results when simultaneously detecting heteroscedasticity and the non-normality of population distribution. To achieve a more robust estimation, we used a heavy-tailed linear mixed-effects model for a more exact and reliable analysis conclusion than a general linear mixed-effects model. We also provide reliability analysis results for further research.

• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.31-37
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• 2018

Pitch trajectories reflect a continuous variation of vocal fold movements over time. This study examined the pitch trajectories of English vowels produced by 139 American English speakers, statistically analyzing their trajectories using the Generalized Additive Mixed Models (GAMMs). First, Praat was used to read the sound data of Hillenbrand et al. (1995). A pitch analysis script was then prepared, and six pitch values at the corresponding time points within each vowel segment were collected and checked. The results showed that the group of men produced the lowest pitch trajectories, followed by the groups of women, boys, then girls. The density line showed a bimodal distribution. The pitch values at the six corresponding time points formed a single dip, which changed gradually across the vowel segment from 204 to 193 to 196 Hz. The normality tests performed on the pitch data rejected the null hypothesis. Nonparametric tests were therefore conducted to discover the significant differences in the values among the four groups. The GAMMs, which analyzed all the pitch data, produced significant results among the pitch values at the six corresponding time points but not between the two groups of boys and girls. The GAMMs also revealed that the two groups were significantly different only at the first and second time points. Accordingly, the methodology of this study and its findings may be applicable to future studies comparing curvilinear data sets elicited by experimental conditions.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.143-149
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• 2022

This excerpt delineates a quantitative measure of relationship between a research title and its respective abstract extracted from different journal articles documented through a Korean Citation Index (KCI) database published through various journals. In this paper, we propose a machine learning-based similarity metric that does not assume normality on dataset, realizes the imbalanced dataset problem, and zero-variance problem that affects most of the rule-based algorithms. The advantage of using this algorithm is that, it eliminates the limitations experienced by Pearson correlation coefficient (r) and additionally, it solves imbalanced dataset problem. A total of 107 journal articles collected from the database were used to develop a corpus with authors, year of publication, title, and an abstract per each. Based on the experimental results, the proposed algorithm achieved high correlation coefficient values compared to others which are cosine similarity, euclidean, and pearson correlation coefficients by scoring a maximum correlation of 1, whereas others had obtained non-a-number value to some experiments. With these results, we found that an effective title must have high correlation coefficient with the respective abstract.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.10
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• pp.1433-1439
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• 2000

An experiment was carried out with 48 IVRI 2CQ rats 6-8 week old, weighing 50-100 g, to study the effect of lead exposure on antioxidant defense, lipid peroxide level, status of thiol groups and concentration of lead in the liver and kidneys at the end of the exposure and also after withdrawal of lead administration. Twenty four rats were given lead at a daily dose rate of 1 mg lead/2 ml of distilled water/kg body weight as lead acetate solution intraperitoneally for a period of 30 days. Another 24 control rats received 2 ml of sterile normal saline solution (0.85% NaCl)/kg body weight in an identical manner. A many-fold increase in concentration of lead was associated with a non-significant (p>0.05) decrease in the activities of superoxide dismutase (SOD) in the liver (27%) and kidneys (12%) and catalase in kidneys (22%). A significant (p<0.05) increase in lipid peroxide level was recorded in the liver (40%) compared with control values. There were significant (p<0.05) decreases in the total thiol and protein bound thiol contents in liver and an increase in non-protein bound thiol groups in the kidneys of lead exposed rats. During the 10 day observation period after withdrawal of lead administration, no significant change was observed with respect to any of the above parameters indicating that a 10 day withdrawal period was not enough for restoration of normality. It is concluded that the magnitude of response and the resultant changes in the lipid peroxide concentration, and the activities of SOD and catalase were not identical in the liver and kidneys of lead-exposed rats.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.97-105
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• 2010

An All-red clearance interval(AR) has been operating with amber signal in order to avoid collision between vehicles which cannot come out of the intersections, vehicles coming in from the opposite direction, and pedestrians(bicycles) on the crosswalk during the signal conversion time at the intersections. Foreign nations have been analyzing AR's influences of traffic accidents. On the other hand, the similar research has not been conducted in the country. The objective of this paper, therefore, is to analyze the safety at the intersections with respect to the installation of AR through the hypothesis test. A before-and-after analysis has been performed for 10 intersections where applied AR. From the 95% of significance level, the results of Non-parametric Test show that the installation of AR improves a safety at the intersections. The results indicates that AR discharges vehicles passing through the intersections and control entering vehicles at the intersections.

• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.1
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• pp.18-25
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• 2022

Purpose: This study aimed to evaluate the color stability and antibacterial properties of the surface of polyethyl methacrylate (PEMA) resin with zirconia nanoparticles added. Materials and Methods: The control group was pure PEMA resin, and the experiment group was PEMA resin 15 mm in diameter and 2.5 mm in thickness disk-shaped specimens with 2, 4 and 8 w/v% of zirconia nanoparticles added, which were respectively divided into Group Z2, Group Z4, and Group Z8. After analyzing the surface roughness and color stability of the specimens, their antibacterial properties were evaluated using Porphyromonas gingivalis (P. gingivalis). The Statistical analysis was performed using when normality was met in the Shapiro-Wilk test, one-way ANOVA was used to test parameters, and Tukey's test was used as a post hoc test. When normality was not met, the Kruskal-Wallis test, a non-parametric test was used (P < 0.05). Results: The surface roughness measurement found that there was no significant difference between the experimental and control groups. The color stability evaluation showed that the Z2, Z4, and Z8 groups were within the color range of natural teeth. The adhesion of P. gingivalis was evaluated to be significantly reduced in Group Z2 compared to the control group (P < 0.05). In the Z2 group, Z4 group, and Z8 group, dead cells bacteria than the control group were observed. Conclusion: In conclusion, PEMA resin with zirconia nanoparticles added was within the range of natural teeth in color and reduced the adhesion of P. gingivalis.