• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.161-164
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• 2005

In this paper, a new estimation algorithm for the rotor speed for SRM drives is described. The algorithm is implemented by the binary observer. The stability and robustness of the binary observer for the parameter variations of the SRM are proved by variable structure control theory. Variable speed control of the SRM is accomplished by the estimated speed. Experiment results verify that the binary observer is able to estimate the speed.

• Journal of Korean Society for Quality Management
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• v.29 no.3
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• pp.124-137
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• 2001

In general, it is very efficient and effective to use screening variables that are correlated with the performance variable in case that measuring the performance variable is impossible (destructive) or expensive. The general methodology for searching surrogate variables is regression analysis. This paper considers the inspection problem in CRT (Cathode Ray Tube) production line, in which the performance variable (dependent variable) is binary type and screening variables are continuous. The general regression with dummy variable, discriminant analysis and binary logistic regression are considered. The cost model is also formulated to determine economically inspection procedure with screening variables.

• Bulletin of the Korean Mathematical Society
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• v.48 no.2
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• pp.413-426
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• 2011

Two types of new methods with variable time steps are proposed in order to valuate binary options efficiently. Type I changes adaptively the size of the time step at each time based on the magnitude of the local error, while Type II combines two uniform meshes. The new methods are hybrid finite difference methods, namely starting the computation with a fully implicit finite difference method for a few time steps for accuracy then performing a ${\theta}$-method during the rest of computation for efficiency. Numerical experiments for standard European vanilla, binary and American options show that both Type I and II variable time step methods are much more efficient than the fully implicit method or hybrid methods with uniform time steps.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1574-1582
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• 2004

Binary manipulators have recently received much attention due to hyper-redundancy, light weight, good controllability and high reliability. The precise positioning of the manipulator end-effecter requires the use of many modules, which results in a high-dimensional workspace. When the workspace dimension is large, existing inverse kinematics methods such as the Ebert-Uphoff algorithm may require impractically large memory size in determining the binary positions of all actuators. To overcome this limitation, we propose a new inverse kinematics algorithm: the inverse kinematics problem is formulated as an optimization problem using real-valued design variables, The key procedure in this approach is to transform the integer-variable optimization problem to a real-variable optimization problem and to push the real-valued design variables as closely as possible to the permissible binary values. Since the actual optimization is performed in real-valued design variables, the design sensitivity becomes readily available, and the optimization method becomes extremely efficient. Because the proposed formulation is quite general, other design considerations such as operation power minimization can be easily considered.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.158-169
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• 1999

This paper is concerned with the application of generalized linear interactive modelling(GLIM) to the binary categorical data. To analyze the categorical data given by a contingency table, finding a good-fitting loglinear model is commonly adopted. In the case of a contingency table with a response variable, we can fit a logit model to find a good-fitting loglinear model. For a given $2^4$ contingency table with a binary response variable, we show the process of fitting a loglinear model by fitting a logit model using GLIM and SAS and then we estimate parameters to interpret the nature of associations implied by the model.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.191.1-191.1
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• 2005

• Proceedings of the Optical Society of Korea Conference
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• 2001.02a
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• pp.62-63
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• 2001

We studied a method to use a variable discrete random phase mask in 2-D binary data representation for efficient holographic data storage. The variable phase mask is realized by use of a liquid crystal display.

• The Journal of the Acoustical Society of Korea
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• v.5 no.3
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• pp.13-20
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• 1986

본 논문은 신호처리가 가능한 2진 로가리즘 상수를 발생시키기 위한 programmable variable-rate up/down 계수기의 설계를 기술한다. 2진수에 대한 밑수가 2인 로가리즘 계승의 적용은 결 과적으로 오차가 발생한다. log\sub 2\(1+χ)-χ에 의해 정의된 것처럼 log\sub 2\(1+χ)에서의 오차는 직선의 집합으로 갖게된다. 계수기 rate는 직선의 기울기에 비례한다. 그러므로 신호처리가 가능한 2진 로가리즘 상수는 programmable 계수기를 사용함으로써 쉽게 발생될 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.403-410
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• 2014

Binary logistic regression analysis is a statistical technique that explains binary response variable by quantitative or qualitative explanatory variables. In the binary logistic regression model, the probability that the response variable equals, say 1, one of the binary values is to be explained as a transformation of linear combination of explanatory variables. This is one of big barriers that non-statisticians have to overcome in order to understand the model. In this study, an educational tool is developed that explains the need of the binary logistic regression analysis using Excel VBA. More precisely, this tool explains the problems related to modeling the probability of the response variable equal to 1 as a linear combination of explanatory variables and then shows how these problems can be solved through some transformations of the linear combination.