• Title/Summary/Keyword: Gauss-Jordan inverse matrix

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A Study on the Simultaneous Linear Equations by Computer (전자계산기에 의한 다원연립 일차방정식의 해법에 관한 연구)

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    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.8 no.12
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    • pp.127-138
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    • 1985
  • There are several methods which have been presented up to now in solving the simultaneous linear equations by computer. They are Gaussian Elimination Method, Gauss-Jordan Method, Inverse matrix Method and Gauss-Seidel iterative Method. This paper is not only discussed in their mechanisms compared with their algorithms, depicted flow charts, but also calculated the numbers of arithmetic operations and comparisons in order to criticize their availability. Inverse Matrix Method among em is founded out the smallest in the number of arithmetic operation, but is not the shortest operation time. This paper also indicates the many problems in using these methods and propose the new method which is able to applicate to even small or middle size computers.

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The Segmented Polynomial Curve Fitting for Improving Non-linear Gamma Curve Algorithm (비선형 감마 곡선 알고리즘 개선을 위한 구간 분할 다항식 곡선 접합)

  • Jang, Kyoung-Hoon;Jo, Ho-Sang;Jang, Won-Woo;Kang, Bong-Soon
    • 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 $10^{-5}$.