• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.287-299
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• 2022

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.372-377
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• 2001

Piezoelectric Fiber Composites with Interdigitated Electrodes (PFCIDE) were previously introduced as an alternative to monolithic wafers with conventional electrodes for applications of structural actuation. This paper is an investigation into the performance improvement of piezoelectric fiber composite actuators by changing the matrix material. This paper presents a modified micro-electromechanical model of a piezoelectric fiber/piezopolymer matrix composite actuator with interdigitated electrodes (PFPMIDE). Various concepts from different backgrounds including three-dimensional linear elastic and dielectric theories have been incorporated into the present linear piezoelectric model. The rule of mixture and the modified method to calculate the effective properties of fiber composites are extended to apply to the PFPMIDE model. The new model is validated comparing with available experimental data and other analytical results.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1076-1080
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• 2003

$CO_2$ uptake of vegetation is one of the important variables in order to estimate photosynthetic activity, plant growth and carbon budget estimations. The objective of this research was to develop a new estimation method of $CO_2$ uptake of vegetation based on airborne hyperspectral remote sensing measurements in combination with a photosynthetic rate curve model. In this study, a compact airborne spectrographic imager (CASI) was used to obtain image over a field that had been set up to study the $CO_2$ uptake of corn on August 7, 2002. Also, a field survey was conducted concurrently with the CASI overpass. As a field survey, chlorophyll a content, photosynthetic rate curve, Leaf area, dry biomass and light condition were measured. The developed estimation method for $CO_2$ uptake consists of three major parts: a linear mixture model, an enhanced big leaf model and a photosynthetic rate curve model. The Accuracy of this scheme indicates that $CO_2$ uptake of vegetation could be estimated by using airborne hyperspectral remote sensing data in combination with a physiological model.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.468-470
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• 2007

This paper is to present an approach for generating coarse resolution (MODIS data) fraction images of forested region in Korea peninsula using forest type area fraction derived from high resolution data (ASTER data) in regional forest area. A 15-m spatial resolution multi-spectral ASTER image was acquired under clear sky conditions on September 22, 2003 over the forested area near Seoul, Korea and was used to select each end-member that represent a pure reflectance of component of forest such as different forest, bare soil and water. The area fraction of selected each end-member and a 500-m spatial resolution MODIS reflectance product covering study area was applied to a linear mixture inversion model for calculating the fraction image of forest component across the South Korea. We found that the area fraction values of each end-member observed from high resolution image data could be used to separate forest cover in low resolution image data.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.57-68
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• 2017

Clustering algorithms attempt to find a partition of a finite set of objects in to a potentially predetermined number of nonempty subsets. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet prior distribution calculates posterior probabilities when the number of clusters was known. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. Examples are given to show how these models perform on real data.

• 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 Data and Information Science Society
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• v.28 no.5
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• pp.1021-1026
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• 2017

Mixture experiments are commonly encountered in many fields including food, chemical and pharmaceutical industries. In mixture experiments, measured response depends on the proportions of the components present in the mixture and not on the amount of the mixture. Statistical analysis of the data from mixture experiments has mainly focused on a continuous response variable. In the example of quantal response data in mixture experiments, however, the tumor incidence data have been analyzed in Chen et al. (1996) to study the effects of 3 dietary components on the expression of mammary gland tumor. In this paper, we compared the logistic regression models with linear predictors such as second degree Scheffe polynomial model, Becker model and Akay model in terms of classification accuracy.

• ETRI Journal
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• v.38 no.3
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• pp.487-493
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• 2016

Two new methods are proposed for an unsupervised adaptation of a language model (LM) with a single sentence for automatic transcription tasks. At the training phase, training documents are clustered by a method known as Latent Dirichlet allocation (LDA), and then a domain-specific LM is trained for each cluster. At the test phase, an adapted LM is presented as a linear mixture of the now trained domain-specific LMs. Unlike previous adaptation methods, the proposed methods fully utilize a trained LDA model for the estimation of weight values, which are then to be assigned to the now trained domain-specific LMs; therefore, the clustering and weight-estimation algorithms of the trained LDA model are reliable. For the continuous speech recognition benchmark tests, the proposed methods outperform other unsupervised LM adaptation methods based on latent semantic analysis, non-negative matrix factorization, and LDA with n-gram counting.

• Journal of Microbiology and Biotechnology
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• v.12 no.3
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• pp.449-456
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• 2002

The objective of this study was to optimize medium composition of initial pH, tryptone, glucose, yeast extract, and mineral mixture for production of bacteriocin by Lactobacillus acidophilus ATCC 4356, using response surface methodology. A response surface approach including new statistical and plotting methods was employed for design and analysis of the experiment. An interiorly augmented central composite design was used as an experimental design. A normal-distribution log-link generalized linear model based on a subset fourth-order polynomial ($R^2$=0.94, Mean Error Deviance=0.0065) was used as an analysis model. This model was statistically superior to the full second-order polynomial-based generalized linear model ($R^2$=0.80, Mean Error Deviance=0.0140). Nonlinear programming determined the optimum composition of the medium as initial pH 6.35, typtone $1.21\%$, glucose $0.9\%$, yeast extract $0.65\%$, and mineral mixture $1.17\%$. A validation experiment confirmed that the optimized medium was comparable to the MRS medium in bacteriocin production, having the advantage of economy and practicality.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.847-851
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• 2004

In the original mixture density network(MDN), which was introduced by Bishop and Nabney, the parameters of the conditional probability density function are represented by the output vector of a single multi-layer perceptron. Among the recent modification of the MDNs, there is the so-called modified mixture density network, in which each of the priors, conditional means, and covariances is represented via an independent multi-layer perceptron. In this paper, we consider a further simplification of the modified MDN, in which the conditional means are linear with respect to the input variable together with the development of the MATLAB program for the simplification. In this paper, we first briefly review the original mixture density network, then we also review the modified mixture density network in which independent multi-layer perceptrons play an important role in the learning for the parameters of the conditional probability, and finally present a further modification so that the conditional means are linear in the input. The applicability of the presented method is shown via an illustrative simulation example.