• MALSORI
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• no.66
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.233-241
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• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.665-674
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• 2006

This paper presents the optimal design of a high-speed (200[km/h]) Linear Synchronous Motor which will be used as a propulsion system of a dynamic tester for catenary-current collection used in railways. Motor performance, especially detent force minimization on various design schemes has been investigated in detail by using FEM (Finite Element Method). Simulation-based DOE (Design of Experiments) method is also applied in order to reduce the large number of analysis according to each design variable and consider the effect among variables. The optimal design in all aspects is proposed by an optimization algorithm using a regression equation derived from the simulation-based DOE and the performance is verified by FEM.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.641-653
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• 2022

Penalized least squares methods are important tools to simultaneously select variables and estimate parameters in linear regression. The penalized maximum likelihood can also be used for the same purpose assuming that the error distribution falls in a certain parametric family of distributions. However, the use of a certain parametric family can suffer a misspecification problem which undermines the estimation accuracy. To give sufficient flexibility to the error distribution, we propose to use the symmetric log-concave error distribution with LASSO penalty. A feasible algorithm to estimate both nonparametric and parametric components in the proposed model is provided. Some numerical studies are also presented showing that the proposed method produces more efficient estimators than some existing methods with similar variable selection performance.

• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.91-99
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• 2010

In this study, the advanced numerical algorithm is developed which can performed the static and dynamic stochastic finite element analysis by considering the effect of uncertainties included in the member stiffness of steel cable-stayed bridges and seismic load. After conducting the linear and nonlinear initial shape analysis, the advanced numerical algorithm is the assessment tool which can performed structural the response analysis considering the static linearity and non-linearity of before or after induced intial tensile force, and examined the reliability assessment more efficiently. The verification of the developed numerical algorithm is evaluated by analyzing the regression analysis and coefficient of correlation using the direct monte carlo simulation. Also, the dynamic response characteristic and coefficient of variation of the steel cable-stayed bridge is calculated by considering the uncertainty of random variables using the developed numerical algorithm. In addition, the quantitative structural safety of the steel cable-stayed bridges is evaluated by conducting the reliability assessment based upon the dynamic stochastic finite element analysis result.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.23-29
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• 2021

[Purpose] This preliminary study aimed to develop a regression model to estimate the non-exercise activity thermogenesis (NEAT) of Korean adults using various easy-to-measure dependent variables. [Methods] NEAT was measured in 71 healthy adults (male n = 29; female n = 42). Statistical analysis was performed to develop a NEAT estimation regression model using the stepwise regression method. [Results] We confirmed that age^A, weight^B, heart rate (HR)_average^C, weight × HR_average^D, weight × HR_sum^E, systolic blood pressure (SBP) × HR_rest^F, fat mass ÷ height^2G, gender × HR_average^H, and gender × weight × HR_sum^I were important variables in various NEAT activity regression models. There was no significant difference between the measured NEAT values obtained using a metabolic gas analyzer and the predicted NEAT. [Conclusion] This preliminary study developed a regression model to estimate the NEAT in healthy Korean adults. The regression model was as follows: sitting = 1.431 - 0.013 × (A) + 0.00014 × (D) - 0.00005 × (F) + 0.006 × (H); leg jiggling = 1.102 - 0.011 × (A) + 0.013 × (B) + 0.005 × (H); standing = 1.713 - 0.013 × (A) + 0.0000017 × (I); 4.5 km/h walking = 0.864 + 0.035 × (B) + 0.0000041 × (E); 6.0 km/h walking = 4.029 - 0.024 × (C) + 0.00071 × (D); climbing up 1 stair = 1.308 - 0.016 × (A) + 0.00035 × (D) - 0.000085 × (F) - 0.098 × (G); and climbing up 2 stairs = 1.442 - 0.023 × (A) - 0.000093 × (F) - 0.121 × (G) + 0.0000624 × (E).

• Journal of Biosystems Engineering
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• v.19 no.4
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• pp.380-396
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• 1994

A personal computer based color machine vision system with video camera and fluorescent lighting system was used to generate images of stationary tobacco leaves. Image processing algorithms were developed to extract both the geometric and the color features of tobacco leaves. Geometric features include area, perimeter, centroid, roundness and complex ratio. Color calibration scheme was developed to convert measured pixel values to the standard color unit using both statistics and artificial neural network algorithm. Improved back propagation algorithm showed less sum of square errors than multiple linear regression. Color features provide not only quality evaluation quantities but the accurate color measurement. Those quality features would be useful in grading tobacco automatically. This system would also be useful in measuring visual features of other agricultural products.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.177-190
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• 2006

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.1
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• pp.47-51
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• 2018

This paper is concerned with an optimization design of Incremental Granular Model(IGM) based Genetic Algorithm (GA) as an evolutionary approach. The performance of IGM has been successfully demonstrated to various examples. However, the problem of IGM is that the same number of cluster in each context is determined. Also, fuzzification factor is set as typical value. In order to solve these problems, we develop a design method for optimizing the IGM to optimize the number of cluster centers in each context and the fuzzification factor. We perform energy analysis using 12 different building shapes simulated in Ecotect. The experimental results on energy efficiency data set of building revealed that the proposed GA-based IGM showed good performance in comparison with LR and IGM.

• Computational Structural Engineering
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• v.10 no.2
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• pp.265-272
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• 1997

The purpose of this paper is to develop Neural Network models for Approximate Structural Analysis (NNASA). As an initial stage, the paper classifies the characteristics and the active role of neural networks in the numerical analysis by comparing neural networks with conventional numerical analysis algorithms. The paper proposed two methods of finding solutions of linear algebraic equations by a modified neural network algorithm, and presents that multilayer feedforward networks are a class of universal approximators by comparing the neural network with regression and interpolation techniques.