• 전기학회논문지
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• 제57권2호
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• pp.307-313
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• 2008

In this paper, we propose a novel defects inspection algorithm for TFT-LCD panels. We first compensate the distorted image caused by the camera distortion and the uneven illumination environment using the least squares method and the bezier surface. We find a starting point of each pattern for restricting each pattern. A clean image is compared to each pattern to find defects using modified PCSR-G algorithm. The simulation example shows that our algorithm not only inspects the defects well, but also is robust to the 1-pixel error.

• Computers and Concrete
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• 제21권5호
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• pp.505-511
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• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전력기술부문
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• pp.14-16
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• 2004

A DC railway system has low feeder voltage, The remote fault current can be smaller than the current of load starting. So it is important to discriminate between the small fault current and the load starting current. The load starting current increases step by step but the fault current increases all at once. As for the load starting current, the time constant of load current at each step is much smaller than that of the fault current. To detect faults in DC railway systems, an algorithm using the time constant calculated by the method of least squares was presented. But, It have a weakness about harmonic to calculate time constant. So in this paper, new protection algorithm for DC railway systems using estimation of time constant based on fourier transform was presented.

• Nuclear Engineering and Technology
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• 제32권5호
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• pp.485-494
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• 2000

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4160-4176
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• 2015

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.307-309
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• 2004

A DC railway system has low feeder voltage, The remote fault current can be smaller than the current of load starting. So it is important to discriminate between the small fault current and the train starting current. The train starting current increases step by step but the fault current increases all at once. So the type of $\bigtriangleup I\;relay(50F)$ was developed using the different characteristics between the load starting current and the fault current. As for the train starting current, the time constant of train current at each step is much smaller than that of the fault current. To detect faults in U railway systems, an algorithm that is independent of train starting current. This algorithm use the time constant calculated by the method of least squares is presented in this paper.

• 한국전기전자재료학회논문지
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• 제32권1호
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• pp.13-19
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• 2019

This paper proposes an easy algorithm for finding tapped-delay-line (TDL) filter coefficients in an adaptive filter algorithm using orthogonal input signals. The proposed algorithm can be used to obtain the coefficients and errors of a TDL filter without using an inverse orthogonalization process for the orthogonal input signals. The form of the proposed algorithm in this paper has the advantages of being easy to use and similar to the familiar recursive least-squares (RLS) algorithm. In order to evaluate the proposed algorithm, system identification simulation of the $11^{th}$-order finite-impulse-response (FIR) filter was performed. It is shown that the convergence characteristics of the learning curve and the tracking ability of the coefficient vectors are similar to those of the conventional RLS analysis. Also, the derived equations and computer simulation results ensure that the proposed algorithm can be used in a similar manner to the Levinson-Durbin algorithm.

• 대한전자공학회논문지SP
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• 제47권6호
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• pp.10-18
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• 2010

본 논문에서는 움직임 분리와 안정화 모드를 이용하여 근접감시용 무인항공기의 영상을 안정화 시키는 알고리즘을 제안하였다. 무인 항공기에서 촬영된 영상에는 임무에 의한 움직임과 기체의 진동에 의한 움직임이 혼합되어 나타난다. 영상을 안정화하기 위해서는 진동에 의한 움직임을 제거하여야 한다. 제안된 알고리즘에서는 연속된 두 영상의 전역움직임을 6계수 움직임 모형과 2계수 밝기변화 모형으로 모델링하고 Gauss-Newton 알고리즘에 기반한 비선형 최소 제곱법(non-linear least squares)을 이용하여 움직임을 추정하였다. 추정된 움직임에서 IIR 필터를 이용하여 진동에 의한 움직임을 분리하여 제거함으로서 영상을 안정화 하였다. 또한 안정화 영상 생성시 시점의 변화가 많은 실제 무인항공영상에 적용하기 위하여 초기화 상태와 안정화 상태의 두 가지의 상태를 가지는 안정화 모드를 제안하였다. 실험결과 99%의 정확도로 전역 움직임을 추정하였고, 90%의 진동에 의한 움직임 제거 성능을 보였다. 또한, 제안한 알고리즘을 실제 항공영상에 적용하여 영상이 안정화 되는 것을 확인하였다.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2004년도 추계학술발표대회논문집 제23권 2호
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• pp.547-550
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• 2004

본 논문은 시스템 인식에서 RLS의 성능을 높이기 위한 한 방법으로 UDU 행렬 분해법을 바탕으로 한 recursive total least squares (RTLS) algorithm을 제안한다. 기존의 RTLS는 Power Method에 의거해서 recursive하게 만든 형태이어서 RLS와 거의 같은 구조이다. 그러나 본 논문에서는 일반적인 Power Method가 rank-1 update를 이용하기 때문에 ill-condition에 빠질 가능성이 높은 점을 감안하여, UDU 행렬 분해법을 사용한 RTLS방법을 제안하고, 그를 시스템 인식에 적용한다.

• 대한수학회논문집
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• 제33권3호
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• pp.973-983
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• 2018

Fuzzy linear regression model has been widely studied with many successful applications but there have been only a few studies on the fuzzy regression model with monotonic response function as a generalization of the linear response function. In this paper, we propose the fuzzy regression model with the monotonic response function and the algorithm to construct the proposed model by using ${\alpha}-level$ set of fuzzy number and the resolution identity theorem. To estimate parameters of the proposed model, the least squares (LS) method and the least absolute deviation (LAD) method have been used in this paper. In addition, to evaluate the performance of the proposed model, two performance measures of goodness of fit are introduced. The numerical examples indicate that the fuzzy regression model with the monotonic response function is preferable to the fuzzy linear regression model when the fuzzy data represent the non-linear pattern.