• Current Research on Agriculture and Life Sciences
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• v.9
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• pp.37-49
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• 1991

This study was conducted to estimate the effects of year, age of dam at calving, farm and lactation period on milk yield with the data of 4,008 cows' records which were collected at 32 farms by Korea Animal Improvement Association from 1985 to 1989. The results obtained in this study are summarized as follows: 1. The average performance of the dairy cattle in the study were $5,959.23{\pm}2,113.03kg$ in actual milk yield, $49.19{\pm}22.77$ months in age of dam at calving, $27.11{\pm}5.13$ months in age at first calving and $255.11{\pm}79.68$ days in lactation period. 2. The percentages of variance component for different sources were 29.39% for the residuals, 1.91% for years, 4.86% for age at calving, 8.89% for farms and 54.94% for lactation period. 3. The overall mean of least-square estimate on the milk yield was 6,229.31kg. In the effects of year, the least-square means of milk yield were estimated 6,000.76kg in 1985-1987, 6,028.11kg in 1988 and 6,659.07kg in 1989. 4. The least-square means of calving age on the milk yield were estimated 5,456.01kg in less than 24 months, 6,565.48kg in 61-66 months which were the highest least-square means. This effects were gradually increased until the 61-66months and gradually decreased after the 61-66months, with highly significant differences among different months of age at calving(p<0.01). 5. In the effects of farm, the least-square means of milk yield were estimated 4,959.50 kg in the lowest farm and 7,497.07kg in the highest farm. Among the milk yield of each farm the effects showed highly significant difference(p<0.01). 6. The least-square means of milk yield in the effects of lactation period were gradually increased with the lapse of the lactation period. Among the lactation period the effects showed highly significant difference(p<0.01).

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.1
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• pp.29-39
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• 1989

In this work, a numerical and experimental study was done to get an optimum cam profile considering dynamic characteristics of a cam-valve system. First of all, a four degree of freedom dynamic model was set up for an OHV type cam-valve acceleration while not modifying original cam shape greatly. Also another optimization which aims to enlarge the valve displacement area while reducing the peak valve acceleration, was tried. The optimized cam profile was tested experimentally and found that the measured valve displacement and pushrod force show only very small error from the analytically predicted model simulation results.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.283-289
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• 2019

RLS (Recursive-least-squares) algorithm is known to have good convergence and excellent error level after convergence. However, there is a disadvantage that numerical instability is included in the algorithm due to inverse matrix calculation. In this paper, we propose an algorithm with no matrix inversion to avoid the instability aforementioned. The proposed algorithm still keeps the same convergence performance. In the proposed algorithm, we adopt an averaged gradient-based step size as a self-adjusted step size. In addition, a variable forgetting factor is introduced to provide superior performance for time-varying channel estimation. Through simulations, we compare performance with conventional RLS and show its equivalency. It also shows the merit of the variable forgetting factor in time-varying channels.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.1
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• pp.45-52
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• 2016

This paper suggests a new method of finding regularization parameter for image restoration problems. Wiener filter requires priori information such that power spectrums of original image and noise. Constrained least squares restoration also requires knowledge of the noise level. If the prior information is not available, separate optimization functions for Tikhonov regularization parameter are suggested in the literature such as generalized cross validation and L-curve criterion. In this paper, self-regularization method that connects bias term of augmented linear system and smoothing term of Tikhonov regularization is introduced in the frequency domain and applied to the image restoration problems. Experimental results show the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2310-2316
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• 1997

This paper presents a comparative performance analysis of the stochastic gradient adaptive algorithm based on the least mean absolute third (LMAT) error criterion with other widely-used competing adaptive algorithms. Under the assumption that the signals involved are zero-mean, wide-sense stationary and Gaussian, approximate expressions that characterize the steady-state mean-squared estimation error of the algorithm is dervied. The validity of our derivation is then confirement by computer simulations. The convergence speed is compared under the condition that the LMAT and other competing algorithms converge to the same value for the mean-squared estimation error in the stead-state, and superior convergence property of the LMAT algorithm is observed. In particular, it is shown that the LMAT algorithm converges faster than other algorithms even through the eignevalue spread ratio of the input signal and measurement noise power change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.691-698
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• 2003

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-l, we may compute the updated estimate of this vector at iteration n upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RLS algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the B times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.553-555
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• 2001

블라인드 등화기법은 별도의 훈련신호없이 효율적인 데이터 전송을 위한 등화기 탭 수정을 수행하는 방법이다. 본 논문에서는 이중 모드를 갖는 SCS-MMA 방법을 제안한다. 기존의 CMA와 MMA 기법들은 자승평균 오차함수(mean squared error function)를 기반으로 하는 포물선을 이루지 않는 비용함수를 사용하므로, 부적절한 국부 최소값으로 수렴할 수 있다. 제안하는 방법은 정규화된 MMA 등화 방법을 기반으로 수렴 속도의 개선과 요구되지 않은 국부 최소값으로의 수렴진행을 방지위해 SCS(soft constraint satisfaction) 알고리듬을 구현하였다. 또한, 입력 신호에 신뢰도를 주어 결정지향 알고리듬으로 자동 전환하는 방법을 적용한다. 이를 통해, 보다 빠른 수렴과 정상상태에서 결정지향 알고리듬에서와 같은 평균 오차값을 보장할 수 있다. 실험 결과 제안된 알고리듬이 기존의 방법들보다 수렴속도와 안정성에 있어 우수한 성능을 갖음을 볼 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.05a
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• pp.400-404
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• 1998

본 논문에서는 도심지 이동 통신에서 SINR 성능을 향상시키기 위해 기존의 배열 안테나에 최소 평균 자승(LMS) 알고리즘을 적용하여 실제 배열 출력과 이상적 출력간의 최소 평균 오차(MSE)를 최소화하고 안테나의 배열로부터 가중치를 결합한 신호에 의해 방향성을 적절히 제어하여 간섭신호를 효과적으로 제거한다. 배열 출력 신호 대 간섭에 추가된 잡음비(SINR) 성능 분석에 적합한 삼차원적 분석을 사용하여 적응 배열 원소를 사용한 성능과 모노폴 안테나 원소에서 배열의 성능을 비교한다. 또한, SINR 패턴 각 비(PAR)를 사용하여 적응 배열 원소 방위, 내부 원소간의 간격들 그리고 입사 신호 방향들과 같은 다른 배열 매개 변수들에서 배열 성능을 계산하고 SINR 패턴의 양적 평가를 한다. 결과로서, 적응 배열 원소가 가정된 신호 환경에 있어 4상파형(quarterwave) 모노폴(monopole) 안테나 배열보다 더 바람직하다.

• The Journal of the Acoustical Society of Korea
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• v.23 no.8
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• pp.570-575
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• 2004

In the underwater acoustic systems. we can receive signals and retrieve information about a target by using a beamforming method. The most important thing in the beamforming is finding the way to optimize the mainlobe beamwidth and the sidelobe level to the desired value. One of the prominent results of beamforming method. which has been studied. is Philip's weighting function method(1) . Philip's method adaptively adjusts its weights of array to meet the desired mainlobe beamwidth and sidelobe level. It is very similar to the design method in adaptive filter. However. this method cannot easily bring us to the desired sidelobe level due to complementary relation between mainlobe beamwidth and sidelobe level. In this paper, we propose a new algorithm using partial constrained adaptation. This method makes us circumvent the above problem and meet the specification of design easily. The proposed algorithm presents a Pattern synthesis that designer can easily control the mainlobe beamwidth and the sidelobe level to the desired value while calculation time to converge is decreasing.