• Journal of the Korean Society for Precision Engineering
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• v.15 no.10
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• pp.103-112
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• 1998

The evolution of semiconductor manufacturing technology has accelerated the reduction of device dimensions and the increase of integrated circuit density. In order to improve yield within a short turn around time and maintain it at high level, a system that can rapidly determine problematic processing steps is needed. The statistical process control detects abnormal process variation of key parameters. Expert systems in SPC can serve as a valuable tool to automate the analysis and interpretation of control charts. A set of IF-THEN rules was used to formalize knowledge base of special causes. This research proposes a strategy to apply expert system to SPC in semiconductor manufacturing. In analysis, the expert system accomplishes the instability detection of process parameter, In diagnosis, an engineer is supported by process analyzer program. An example has been used to demonstrate the expert system and the process analyzer.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.118-128
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• 2012

An attempt is given to the problem of analyzing the two-way binary attribute data using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the analysis of variance (ANOVA) may not be good enough, especially for the case that the proportion is very low or high. The logistic transformation of proportion data could be a help, but not sound in the statistical sense. Meanwhile, the adoption of generalized least squares (GLS) method entails much to estimate the variance-covariance matrix. On the other hand, the logistic regression methodology provides sound statistical means in estimating related confidence intervals and testing the significance of model parameters. Based on simulated data, the efficiencies of estimates are ensured with a view to demonstrate the usefulness of the methodology.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.2179-2184
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• 2008

The ride comfort is more important according to train speedup. Generally it is defined as the vehicle vibration. There are many studies on evaluation method of ride comfort for railway. But the ride comfort for Korean high speed train(HSR 350x) has been assessed by statistical method according to UIC 513R. In this paper, the ride indices, which are measured in the Korean high speed train, have been analyzed and reviewed by appling the statistical methodology such as t-test, variance analysis(ANOVA) and regression analysis.

• Journal of the Korean Society for Railway
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• v.9 no.1 s.32
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• pp.1-6
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• 2006

The purpose of this study is to calculate transmission loss of the high speed railway's wall section accurately. Transmission loss measurement of ideal case i.e. the wall in the laboratory condition was carried out in first, which results were compared with those by statistical energy method. Transmission loss values of high speed railway calculated out by experimental method are compared with those from closed form solution. Commercial statistical energy analysis was also used to predict the outside pressure level using those measured transmission loss values. Simple SEA model could estimate reasonable exterior sound pressure level.

• Genomics & Informatics
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• v.2 no.4
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• pp.180-183
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• 2004

Comparative Statistic Module(CSM) provides more reliable list of significant genes to genomics researchers by offering the commonly selected genes and a method of choice by calculating the rank of each statistical test based on the average ranking of common genes across the five statistical methods, i.e. t-test, Kruskal-Wallis (Wilcoxon signed rank) test, SAM, two sample multiple test, and Empirical Bayesian test. This statistical analysis module is implemented in Perl, and R languages.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.873-876
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• 2000

The information-theoretic approach to face recognition is based on the compact coding where face images are decomposed into a small set of basis images. Most popular method for the compact coding may be the principal component analysis (PCA) which eigenface methods are based on. PCA based methods exploit only second-order statistical structure of the data, so higher- order statistical dependencies among pixels are not considered. Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. ICA exploits high-order statistical structure of the data that contains important information. In this paper we employ the ICA for the efficient feature extraction from face images and show that ICA outperforms the PCA in the task of face recognition. Experimental results using a simple nearest classifier and multi layer perceptron (MLP) are presented to illustrate the performance of the proposed method.

• Biomaterials and Biomechanics in Bioengineering
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• v.3 no.1
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• pp.47-57
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• 2016

Results from multiple high profile experiments on the parameters influencing the impacts that cause skull fractures to the frontal, temporal, and parietal bones were gathered and analyzed. The location of the impact as a binary function of frontal or lateral strike, the velocity, the striking area of the impactor, and the force needed to cause skull fracture in each experiment were subjected to statistical analysis using the JMP statistical software pack. A novel neural network model predicting skull fracture threshold was developed with a high statistical correlation ($R^2=0.978$) and presented in this text. Despite variation within individual studies, the equation herein proposes a 3 kN greater resistance to fracture for the frontal bone when compared to the temporoparietal bones. Additionally, impacts with low velocities (<4.1 m/s) were more prone to cause fracture in the lateral regions of the skull when compared to similar velocity frontal impacts. Conversely, higher velocity impacts (>4.1 m/s) showed a greater frontal sensitivity.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.205-219
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• 1998

Statistical analyses have been perf7rm7d to find factors affecting chronic renal allograft dysfunction for 114 renal transplanted patients. Renal function was evaluated using serum creatinine values every three months during 1 year to 5 years after transplantation. Statistical models for the repeated measures were considered to evaluate factors affecting the reciprocal of serum creatinine values. This paper focuses on some common problems on the choice of correlation matrices occurred in the analysis of repeated measures.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.17 no.4
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• pp.342-349
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• 1988

The characteristics of flow pattern transitions in a horizontal cocurrent gas-liquid flow have been investigated by means of a statistical analysis of instantaneous pressure drop curves at an orifice. The dimensionless intensity of pressure drop fluctuation shows a sudden change during the course of flow transitions, indicating that it may be a good measure to identify the flow regime transitions. The probability density function of the curves feature a unique pattern depending upon the flow regimes and the statistical properties of the PDF also have particular ranges for each flow regime. In conclusion, the statistical analysis of instantaneous pressure drops may be a powerful tool for predicting the flow regime transitions.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.583-594
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• 2021

In this paper, we propose wavelet-based procedures to identify the difference between images, including portraits and handwriting. The proposed methods are based on a novel combination of multiscale methods with a regularization technique. The multiscale method extracts the local characteristics of an image, and the distinct features are obtained through the regularized regression of the local characteristics. The regularized regression approach copes with the high-dimensional problem to build the relation between the local characteristics. Lytle and Yang (2006) introduced the detection method of forged handwriting via wavelets and summary statistics. We expand the scope of their method to the general image and significantly improve the results. We demonstrate the promising empirical evidence of the proposed method through various experiments.