• 한국생물공학회:학술대회논문집
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• 2001.11a
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• pp.497-500
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• 2001

Adsorption characteristics on the biomass of Chlorella sp. HA -1 were investigated in binary system with $Pb^2$, $Cu^2$, $Cd^2$, and $Zn^2$ ions. For the adsorption tests of single metal, Langmuir model was showed good correlation for equilibrium data compared to Freundlich model. Maximum metal uptakes increased as follows: $Pb^2$>$Cd^2$>$Zn^2$>$Cu^2$, whereas the affinity showed different trends: $Cu^2$>>$Cd^2$>$Zn^2$>$Pb^2$. In binary metal system, $Cu^2$ ions inhibited sharply the adsorption of other metal ions except $Pb^2$ ions because of the high biosorption affinity of $Cu^2$ ions. In the case of $Cu^2$ and $Pb^2$ system, there was no significant inhibition on metal uptakes. The results of metal adsorption in the binary system could be explained well on the basis of Langmuir parameters evaluated.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.125-137
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• 2005

Many discriminant analysis models for binary data have been used in real applications, but none of the classification models dominates in all varying circumstances(Asparoukhov & Krzanowski(2001)). Lee and Hwang (2003) proposed a new classification model by using multinomial distribution with the maximum entropy estimation method. The model showed some promising results in case of small number of variables, but its performance was not satisfactory for large number of variables. This paper explores to use the iterative cross entropy minimization estimation method in replace of the maximum entropy estimation. Simulation experiments show that this method can compete with other well known existing classification models.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.139-147
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• 2005

It is interesting to locate homogeneous segments within a DNA sequence. Suppose that the DNA sequence has segments within which the observations follow the same residue frequency distribution, and between which observations have different distributions. In this setting, change points correspond to the end points of these segments. This article explores the use of a binary segmentation procedure in detecting the change points in the DNA sequence. The change points are determined using a sequence of nested hypothesis tests of whether a change point exists. At each test, we compare no change-point model with a single change-point model by using the Bayesian information criterion. Thus, the method circumvents the computational complexity one would normally face in problems with an unknown number of change points. We illustrate the procedure by analyzing the genome of the bacteriophage lambda.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.19-35
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• 2006

We use the hierarchical Bayesian approach to describe the transition probabilities of a binary nonhomogeneous Markov chain. The Markov chain is used for describing the transition behavior of emotionally disturbed children in a treatment program. The effects of covariates on transition probabilities are assessed using a logit link function. To describe the time evolution of transition probabilities, we consider two modeling strategies. The first strategy is based on the concept of exchangeabiligy, whereas the second one is based on a first order Markov property. The deviance information criterion (DIC) measure is used to compare models with two different time dependent structures. The inferences are made using the Markov chain Monte Carlo technique. The developed methodology is applied to some real data.

• 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.

• 한국가시화정보학회:학술대회논문집
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• 2006.12a
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• pp.86-91
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• 2006

In this parer, we present the bubble forming and motion in the micro channel by using the two-dimensional numerical computation and experiment. In the numerical computation, The Lattice Boltzmann method(LBM) and free-energy model is used to treat the interfacial force and deformation of binary fluid system, drawn in to a micro channel and a numerical simulation is carried out by using the parallel computation method. The urn in this investigation is to examine the applicability of LBM to numerical analysis and experimental method of binary fluid separation and motion in the micro channel.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.487-496
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• 2024

In this paper, we propose a data-driven procedure to segment a binary sequence as an alternative to the popular hidden Markov model (HMM) based procedure. Unlike the HMM, our procedure does not make any distributional or model assumption to the data. To segment the sequence, we suggest to minimize the least square distance from the observations under total variation regularization to the solution, and develop a polynomial time algorithm for it. Finally, we illustrate the algorithm using a toy example and apply it to the Gemini boat race data between Oxford and Cambridge University. Further, we numerically compare the performance of our procedure to the HMM based segmentation through these examples.

• Applied Chemistry for Engineering
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• v.17 no.3
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• pp.317-320
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• 2006

The optimum mobile phase condition for analysis of the six purine derivatives (caffeine, guanine, hypoxanthine, purine, theobromine, and theophylline) were determined by a HCI program. Reversed-phase HPLC system was used with the binary mobile phase, water and methanol. Three retention models (Snyder, Langmuir, and Binary polynomial) were considered to predict the retention factors. The elution profiles were calculated by the plate theory based on the binary polynomial retention model. From the final calculated results, the binary polynomial retention model showed the best agreements between the calculated and experimental data. In the isocratic mode, the optimum mobile phase composition of water/methanol is 93/7(v/v). However, we used step-gradient mode to decrease the run-time ($1^{st}$ mobile phase : water/methanol = 93/7 (v/v), gradient time : 5 min, $2^{nd}$ mobile phase : water/methanol = 75/25 (v/v)). The experimental and simulated profiles of above the two conditions show a good agreement.

• ETRI Journal
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• v.32 no.5
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• pp.795-800
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• 2010

Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL-based optimization using either a Kullback-Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.537-549
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• 1995

In this article, we propose a class of weighted estimators for the excess risk in additive risk model with a binary covariate. The proposed estimator is consistent and asymptotically normal. When the assumed model is inappropriate, however, the estimators with different weights converge to nonidentical constants. This fact enables us to develop a goodness-of-fit test for the excess assumption by comparing estimators with diffrent weights. It is shown that the proposed test converges in distribution to normal with mean zero and is consistent under the model misspecifications. Furthermore, the finite-sample properties of the proposed test procedure are investigated and two examples using real data are presented.