• 에너지공학
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• 제10권3호
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• pp.183-187
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• 2001

Impedance imaging for binary mixture is a kind of nonlinear inverse problem, which is usually solved iteratively by the Newton-Raphson method. Then, the ill-posedness of Hessian matrix often requires the use of a regularization method to stabilize the solution. In this study, the Levenberg-Marquredt regularization method is introduced for the binary-mixture system with various resistivity contrasts (1:2∼1:1000). Several mixture distribution are tested and the results show that the Newton-Raphson iteration combined with the Levenberg-Marquardt regularization can reconstruct reasonably good images.

• 한국경영과학회지
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• 제14권1호
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• pp.80-87
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• 1989

Many Traditional Algebraic Analyses of preference and choice for a finite set of alternatives have been based on binary choices. They have assumed that the binary preference information given by a decision maker is transitive. However, there is considerable evidence that many relations that might occur as preference relations cannot be presented as transitive relations. To construct transitive binary comparisons from intransitive ones, we suggest the notion of superior set, which helps us to understand the structure of intransitive binary comparisons. We also provide a heuristic method to construct transitive binary comparisons. And some merits merits of the suggested method over the existing methods are also discussed.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• East Asian mathematical journal
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• 제39권5호
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• pp.529-536
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• 2023

This paper introduces the Frobenius endomophisms on the binary Hessian curves. It provides an efficient and computable homomorphism for computing point multiplication on binary Hessian curves. As an application, it is possible to construct the GLV method combined with the Frobenius endomorphism to accelerate scalar multiplication over the curve.

• 한국정보통신학회논문지
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• 제7권1호
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• pp.122-128
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• 2003

In this paper, a 16${\times}$16-bit Multiplier and Accumulator (MAC) is designed using a Redundant Binary Adder (RBA) circuit so that it can make a fast addition of the Redundant Binary Partial Products (RB_PP's) by using Wallace-tree structure. Because a RBA adds two RB numbers, it acts as a 4-2 compressor, which reduces four inputs to two output signals. We propose a method to convert the Redundant Binary (RB) representation into the 2's complement binary representation. Instead of using the conventional full adders, a more efficient RB number to binary number converter can be designed with new conversion method.

• 한국표면공학회지
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• 제47권1호
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• pp.48-52
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• 2014

Electric conductivities of $LaCl_3$-KCl binary melts have been measured by the Kohlausch bridge method over the range from their liquidus temperatures to about 1280 K. The electric conductivity increased with the content of KCl for all over the composition range of binary melts. The composition dependence of the electric conductivity and molar conductivity for the binary melt showed a non-linear relation from the additivity line, and the deviation showed a maximum value at about 60 mol.% KCl. The deviation implies the existence of complex ion of $LaCl^{4-}$ in the melt. Activation energy for electric conductivity of the binary melts decreased monotonously with increasing content of KCl.

• 한국표면공학회지
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• 제39권6호
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• pp.282-287
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• 2006

Viscosities of $LaCl_3-NaCl$ binary melts were measured by the capillary method over the range of their liquidus temperatures to about 1200K. The cell constant were determined by using pure water. The results obtained are summerized as follows: Viscosities were decreased with the content of NaCl for all over the composition range of binary melts. Composition - viscosity relation for the binary melts show a non-linear from the additivity line and the deviations shows a maximum at about 60 mol% NaCl. This suggests the existence of the complex ion of $LaCl_4^-$ in the melt. Activation energy for viscous flow of the binary melts decrease monotonously with the increasing content of NaCl.

• 한국표면공학회지
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• 제37권5호
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• pp.301-306
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• 2004

Electric Conductivities of $LaCl_3$-LiCl binary melts have been measured by the Kohlausch bridge method over the range of their liquidus temperatures to about 1200 K. The electric conductivity increases with the content of LiCl for all over the composition range of binary melts. Composition dependence of the electric conductivity and molar conductivity for the binary melt shows a non-linear relation from the additivity line, and the deviations displays a maximum value at about 60 mol % LiCl. This suggest the existence of the complex ion of$ LaCl_{4}^{-}$ in the melt. Activation energy for electric conductivity of the binary melts decrease monotonously with increasing content of LiCl.l.

• 한국경영과학회지
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• 제33권2호
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• Korean Journal of Mathematics
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• 제10권1호
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• pp.89-95
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• 2002

We present a simple but new way of decoding the binary Golay code.