• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.244-247
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• 1999

in this paper, we propose the recognition system of face using polynomial coefficients to recognize fact images using neural network. The system consists of following steps. First step, the sizes of fare images is reduced sizes of input images to 1/4 using wavelet transform. Second step, the polynomial coefficients is obtained from low frequency coefficient matrix after 3 level wavelet transform. Third step, polynomial coefficients is normalized. The of range of normalization is from -1 to 1. Last, Face images is trained and recognized using neural network with error back propagation algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.751-762
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• 2004

In this paper, we propose the method to derive new GRM(Generalized Reed-Muller) coefficients for each 2$^n$ polarities using Triangle cell. As the existing methods to generate GRM coefficients, there are Green's method to operate transform matrix with a given RM coefficient and Besslich's method to get other polarities using basic transfer matrices repeatedly. In this paper, Triangle cell is defined so as to obtain GRM coefficients efficiently. After arranging 2$^n$ given RM coefficients of a first row of Triangle cell, sequence modulo sum is peformed in parallel to low column by a fixed numerical formula. To prove the efficiency of proposed arithmetic method, it is compared with Besslich’s method. As the compared result, to calculate GRM coefficients of all polarities to n input variables, Besslich’s method needs 2$^n$$^{-1}$ ${\times}$(2$^n$-1) two-input Ex-ORs and the proposed method needs 2${\times}$(the number of Ex-ORs for n-1 variables)+3$^n$$^{-1}$ for the same system complexity - (lo $g_2$$^n$) $T_{X}$./.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.156-163
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• 2013

In this paper, we propose an algorithm for estimating media characteristics of sea water and subbottom multi-layers. The proposed algorithm for estimating reflection coefficients, uses a transmitted signal and reflected signal obtained from multiple layers of various shape and structure, and the algorithm is called Schur algorithm. The algorithm is efficient in estimating the reflection coefficients since it finds solution by converting the given inverse scattering problem into matrix factorization. To verify the proposed algorithm, we generate a transmit signal and reflected signal obtained from lattice filter model for sea water and subbottom of multi-level non-homogeneous layers, and then find that the proposed algorithm can estimate reflection coefficients efficiently.

• Journal of Korea Water Resources Association
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• v.35 no.4 s.129
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• pp.385-395
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• 2002

A stochastic model using FOEA(First-Order Error-Analysis) and Monte Carlo Method is developed to predict water quality variation in a river. A sensitivity analysis using influential matrix is performed to determine the significant reaction coefficients. Also the BFGS (Broyden-Fletcher-Goldfarb-Shanno) optimization method is applied to estimate the optimal values of the major reaction coefficients. The developed stochastic model is applied to the real study reach and the results are agreed well with those of deterministic analysis. The process for analyzing the uncertainties of the discharge, water quality and reaction coefficients of headwater and tributaries is included in the model to estimate the influence on the water quality variation at downstream. The extents of contribution of the uncertainties influencing on the total uncertainty can be evaluated from the results of the model.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.163-168
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• 2011

In this paper, we proposed non-linear gamma curve algorithm for gamma correction. The previous non-linear gamma curve algorithm is generated by the least square polynomial using the Gauss-Jordan inverse matrix. However, the previous algorithm has some weak points. When calculating coefficients using inverse matrix of higher degree, occurred truncation errors. Also, only if input sample points are existed regular interval on 10-bit scale, the least square polynomial is accurately works. To compensate weak-points, we calculated accurate coefficients of polynomial using eigenvalue and orthogonal value of mat11x from singular value decomposition (SVD) and QR decomposition of vandemond matrix. Also, we used input data part segmentation, then we performed polynomial curve fitting and merged curve fitting results. When compared the previous method and proposed method using the mean square error (MSE) and the standard deviation (STD), the proposed segmented polynomial curve fitting is highly accuracy that MSE under the least significant bit (LSB) error range is approximately $10^{-9}$ and STD is about $10^{-5}$.

• The Journal of Industrial Distribution & Business
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• v.9 no.1
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• pp.89-97
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• 2018

Purpose - The Regional Comprehensive Economic Partnership (RCEP) among 16 countries including South Korea, the largest free trade agreement in the Asia-Pacific region, will be concluded next year. The participating countries decided to pursue a comprehensive and high -quality agreement, while ensuring flexibility considering development level of each country. In this study, trade structures between nations from 2005 through 2016 were examined to see the impact that this agreement will have on Korea and to come up with effective countermeasures. Research design, data, and methodology - The method of analysis includes the analysis of the trade matrix, which is useful for identifying the dependency of the individual countries on the market in the region and the reciprocal dependency of the member countries on the market, and the index of intensity of trade, which is useful for figuring out the share of trade between the parties in total trade. Results - The results showed that first, the international trade coefficients of Vietnam and Philippines are higher than those of China and Japan. Secondly, the international inducement coefficients between China and Japan were high, and that between Indonesia and Burma were low, indicating that Korea's exports did not have much effect on export increase of these countries. Third, as a result of analyzing Korea's trade intensity, it was found that export intensity and import intensity were greater than 1 in Vietnam and Philippines, which shows that there is a high degree of relational bond with these countries. India and Laos countries still have a low level of relational bond, which indicates that there is room for improvement in economic relations when the agreement is concluded. After the signing of the agreement in the future, more diverse industrial structures should be continuously studied. Conclusions - The analysis of trade matrix, trade structure, trade inducement coefficient and trade intensity between Korea and RCEP participating countries shows that the majority of the countries have the high level of economic relationship with Korea. Korea should drive a harder bargain when negotiating the terms of the RCEP, in comparison with the level of the existing FTA agreement excluding Japan.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.385-392
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• 2015

Principal component analysis (PCA), a dimension-reduction technique, is usually implemented after the variables are standardized when the measurement unit of variables are different. To standardize a variable we divide it by its standard deviation. But there is another way to transform a variable to be independent of its measurement unit. It is to divide it by its mean rather than standard deviation. Implementing PCA on standardized variables is equivalent to implementing PCA with a correlation matrix of original variables. Similarly, implementing PCA on the transformed variables divided by their means is equivalent to implementing PCA with a matrix related to the coefficients of variation of the original variables. We explain why we need to implement PCA on the variables transformed by their means.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.85-93
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• 2015

Background: $^{241}Am$, $^{243}Am$, and $^{237}Np$ isotopes are among the most radiotoxic components of spent nuclear fuel. Recently, researchers have planned different incineration scenarios for the highly radiotoxic elements of nuclear waste in critical reactors. Computational methods are widely used to predict burnup rates of such nuclear wastes that are used under fuel matrixes in critical reactors. Methods: In this work, the Monte Carlo N-particle transport code was used to calculate the neutronic behavior of a transuranic (TRU)-bearing CANada Deuterium Uranium 6 reactor. Results: The computational data showed that the 1.0% TRU-containing thorium-based fuel matrix presents higher proliferation resistance and TRU depletion rate than the other investigated fuel Matrixes. The fuel matrix includes higher negative temperature reactivity coefficients as well. Conclusion: The investigated thorium-based fuel matrix can be successfully used to decrease the production of highly radiotoxic isotopes.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.129-135
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• 2015

In this experimental study, the thermal stability values of micrometer-sized silica particle-reinforced epoxy composite specimens were evaluated by measuring their thermal expansion coefficients and Young's moduli. For all specimens used in this study (from the baseline specimen to that containing 70 wt% silica filler), the thermal expansion coefficients and Young's moduli were gradually reduced down to 25% and increased up to 51%, respectively. The results of the experiment were compared with those of certain empirical models. The experimental results of the measurement of thermal expansion coefficients corresponded well with those of Kerner's model, which considers the bulk and shear moduli of the matrix and silica filler. However, the results of the measurement of Young's moduli using the empirical Mori-Tanaka model were observed to match better with those of the experiment. The comparison of the results of the experiment with those of the empirical models demonstrated that a reliable model for measuring the thermal expansion coefficients and Young's moduli of composite specimens needs to consider certain property variations in the composites in addition to volume fraction changes in the filler and matrix.