• 응용통계연구
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• 제29권4호
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• pp.595-611
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• 2016

일반적으로 독성학 또는 약리학에서는 자료를 분석할 때 Hill Model과 같은 비선형 회귀모형을 사용한다. 비선형 회귀모형에서 모수의 추정량과 그것의 불확실성(uncertainty)에 대한 측도의 추정은 오차의 분산 구조에 영향을 받게 된다. 따라서 자료가 등분산인지 혹은 이분산인지에 따라 사용하여야 할 추정 방법이 달라져야 한다. 그러나 일반적으로 자료를 실제로 분석하기 전에는 오차의 분산구조에 대해서 잘 알 수 없다. 그러므로 오차의 분산구조에 로버스트한 추정 방법을 개발하는 것은 중요한 문제이다. 본 논문에서는 예비검정 방법을 기반으로 한 비선형 회귀모형에서의 모수 추정 방법을 제안하였다. 오차 분산의 등분산성에 대한 간단한 예비검정의 결과에 따라 보통 최소제곱 추정(ordinary Least Square Estimation) 방법과 반복 가중 최소제곱 추정(iterative weighted least square estimation) 방법을 사용하는 추정량을 정의하였다. 제안된 추정량은 모의실험 연구를 통하여 기존의 표준적인 추정량들과 그 성능을 비교하였다. 또한 미국의 National Toxicology Program으로부터 얻어진 실제자료를 사용하여 추정 방법들을 비교하였다.

• 한국통신학회논문지
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• 제25권5B호
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• pp.830-841
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• 2000

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-1, we may compute the updated estimate of this vector at iteration a 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 RL 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+1)times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• 한국통신학회논문지
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• 제32권3C호
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• pp.338-347
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• 2007

본 논문에서는 순환 보호 구긴(cyclic-prefix)을 사용하는 시공간 블록 부호 (STBC: Space-Time Block-Coding) 단일 반송파 시스템에서 향상된 채널 성능을 위한 가중된 블록 적응형 주파수 영역 채널 추정기를 제안한다. 제안된 채널 추정기 구조는 필터 입력 신호에 대해 STBC로 구성된 블록을 형성하며, 이후 형성된 입력 블록에 대해 사후 오차 (a posteriori error)를 이용하는 가중된 LS (least-square) 규준을 적용하여 알고리즘을 유도한다. 또한 정적 채널에서 steady-state EMSE (excess mean-square error) 분석을 통해 블록 길이가 늘어남에 따라 EMSE를 분석한다. 전산 모의실험에서는 시변 TU (typical urban) 채널에서 블록 길이를 증가시킬수록 제안한 채널 추정기는 기존 NLMS와 RLS 채널 추정기들 보다 우수한 성능을 나타냄을 확인 할 수 있다.

• 약학회지
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• 제47권5호
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• pp.261-265
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• 2003

Near-infrared (NIR) system was used to determine rapidly and simply indomethacin in buffer solution for a dissolution test of tablets and capsules. Indomethacin standards were prepared ranging from 10 to 50 ppm using the mixture of phosphate buffer (pH 7.2) and water (1 : 4). The near-infrared (NIR) transmittance spectra of indomethacin standard solutions were collected by using a quartz cell in 1 mm and 2 mm pathlength. Partial least square regression (PLSR) was explored to develop calibration models over the spectral range 1100∼1700 nm. The model using 1 mm quartz cell was better than that using 2 mm quartz cell. The PLSR models developed gave standard error of prediction (SEP) of 0.858 ppm. In order to validate the developed calibration model, routine analysis was performed using another standard solutions. The NIR routine analysis showed good correlation with actual values. Standard error of prediction (SEP) is 1.414 ppm for 7 indomethacin samples in routine analysis and its error was permeable in the regulation of Korean Pharmacopoeia (VII). These results show the potential use of the real time monitoring for indomethacin during a dissolution test.

• 소음진동
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• 제4권1호
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• pp.59-69
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• 1994

A new LMS algorithm titled constrained LMS' is proposed for problems with constrained structure. The conventional LMS algorithm can not be used because it destroys the constrained structures of the weights or parameters. Proposed method uses error-back propagation, which is popular in training neural networks, for error minimization. The illustrative examplesare shown to demonstrate the applicability of the proposed algorithm.

• 디지털산업정보학회논문지
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• 제10권3호
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• pp.197-205
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• 2014

In applications of adaptive noise control or active noise control, the presence of a transfer function in the secondary path following the adaptive controller and the error path, been shown to generally degrade the performance of the Least Mean Square (LMS) algorithm. Thus, the convergence rate is lowered, the residual power is increased, and the algorithm can become unstable. In general, in order to solve these problems, the filtered-x LMS (FX-LMS) type algorithms can be used. But these algorithms have slow convergence speed and weakness in the environment that the secondary path and error path are varied. Therefore, I present the new algorithm called the "Bi-directional Filtered-x (BFX) LMS" algorithm with nearly equal computation complexity. Through experimental study, the proposed BFX-LMS algorithm has better convergence speed and better performance than the conventional FX-LMS algorithm, especially when the secondary path or error path is varied and the impulsive disturbance is flow in.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.14-15
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• 2006

A saturation of magnetic flux in the core may occur when a large primary current flows when the iron-cored current transformer is used. This saturation makes the distorted secondary current of the CT. the distorted secondary current may cause the mal-operation or operation time delay of protective relays. CT compensation algorithm using The LSQ(Least Square) fitting method has a problem. It needs to acquire enough data for executing this algorithm without an error. In this paper, an enhanced algorithm using interpolation based LSQ(Least Square) Fitting Method is proposed. The Lagrange Interpolation Method is used for the interpolation and CT is simulated by EMTP. The results show that the proposed algorithm can accurately compensate a distorted secondary current more than existing Algorithm when the saturation severely occurs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권6호
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• The Journal of the Acoustical Society of Korea
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• 제27권2E호
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• pp.64-68
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• 2008

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square(VEDNSS LMS) algorithm. Adopting VEDNSS LMS results in higher system complexity, but noise is reduced providing fast convergence speed Mathematical analysis demonstrates that tap coefficient misadjustment converges. This is confirmed by computer simulation with the proposed algorithm.

• 한국전자통신학회논문지
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• 제9권5호
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• pp.549-554
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• 2014

본 논문에서 ICS(Interference Cancellation System) 중계기를 위한 Signed LMS(Least Mean Square) 알고리즘을 제안한다. 제안된 Signed LMS 알고리즘은 스텝 사이즈를 조절함에 따라 성능이 개선된다. 제안된 Signed LMS 알고리즘에서 스텝사이즈가 0.067인 경우 수렴횟수 1000 회 일 때 평균 자승 에러는 기존 CMA 알고리즘보다 약 3 ~ 18 dB정도 더 낮다. 그리고, 평균 자승 에러 -25 dB 일 때 LMS(Least Mean Square)와 CMA보다 수렴횟수가 500 ~ 4000 회 정도 줄어든다.