• 한국통신학회논문지
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• 제29권10C호
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• pp.1425-1432
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• 2004

기존의 LSE(Least Square Estimation)를 이용한 영상보간 방법은 다른 방법들 보다 고주파 성분인 에지 부분의 보간 성능이 뛰어나고 월등한 주관적 화질의 향상을 보인다. 하지만 같은 고주파 성분인 잡음 성분의 증폭으로 인해 보간 된 일부 영상은 자연스럽지 못하는 문제점이 있다. 또한 연산량과 메모리 요구량이 상당히 높아 실시간이나 고속 구현에 어려움이 따른다. 본 논문에서 제안하는 방법은 단순한 샘플윈도우와 Direction Detector를 사용하여 보간 연산에 사용되는 참조 화소의 개수를 화질의 열화 없이 줄임으로써 연산량과 메모리 요구량을 획기적으로 줄였다. 또한 Bi-Linear 보간 방법을 선택적으로 적용함으로써 기존 방법에서 에러가 생기는 부분을 보완하였다. 컴퓨터 모의 실험 결과 기존의 LSE를 이용한 보간 방법 보다 주관적인 화질이나 객관적인 화질에서도 보다 나은 결과를 보여 주는 것을 확인하였다.

• 대한산업공학회지
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• 제42권4호
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• pp.256-262
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• 2016

The Weibull is widely used in reliability analysis, and several studies have attempted to improve estimation of the distribution's parameters. least squares estimation (LSE) or Maximum likelihood estimation (MLE) are often used to estimate distribution parameters. However, it has been proven that Bayesian methods are more suitable for small sample sizes than LSE and MLE. In this work, the Weibull parameter estimation accuracy of LSE, MLE, and Bayesian method are compared for sample sets with 3 to 30 data points. The Bayesian method was most accurate for sample sizes under 25, and the accuracy of the Bayesian method was similar to LSE and MLE as the sample size increased.

• 품질경영학회지
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• 제38권4호
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• pp.480-490
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• 2010

In this paper, two estimation methods(least square estimation and maximum likelihood estimation) were compared for Weibull distribution and Type I censoring. Data obtained by Monte Carlo simulation were analyzed using two estimation methods and analysis results were compared by MSE(Mean Squared Error). Comparison results show that maximum likelihood estimator is better for censored data and complete data with more than 30 samples and least square estimator is better for small size complete data(less than and equal to 20 samples).

• 전기학회논문지
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• 제66권4호
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• Smart Structures and Systems
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• 제19권3호
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• pp.259-268
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• 2017

System identification of structures is one of the important aspects of structural health monitoring. The accuracy and efficiency of identification results is affected severely by measurement noises, especially when the structure system is large, such as bridge structures, and when online system identification is required. In this paper, the least square estimation (LSE) method is used combined with the substructure approach for identifying structural parameters of a cable-stay bridge with large degree of freedoms online. Numerical analysis is carried out by first dividing the bridge structure into smaller substructures and then estimates the parameters of each substructure online using LSE method. Simulation results demonstrate that the proposed approach is capable of identifying structural parameters, however, the accuracy and efficiency of identification results depend highly on the noise sensitivities of loading region, loading pattern as well as element size.

• 대한전자공학회논문지
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• 제26권1호
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• pp.78-87
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• 1989

본 논문에서는 백색 Gauss 잡음이 섞인 연속영상으로 부터 물체의 이동정보를 검출하기 위하여 최소자승추정의 시공간경사법을 제안하였다. 제안된 방법은 하나의 이동물체를 실시간으로 추적하도록 고속의 16-bit 마이크로 프로세서를 사용한 자동목표 추적장치에 적용되었다. 실험결과 제안된 방법은 기존의 최소자승추정의 시공간경사법에 비해서 매우 우수한 성능을 보였다.

• 대한전기학회논문지:전력기술부문A
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• 제51권8호
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• pp.371-378
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• 2002

This paper presents the rapid and accurate algorithm for fault detection and location estimation in the transmission line. This algorithm uses wavelet transform for fault detection and harmonics elimination and utilizes least square error method for fault impedance estimation. Wavelet transform decomposes fault signals into high frequence component Dl and low frequence component A3. The former is used for fault phase detection and fault types classification and the latter is used for harmonics elimination. After fault detection, an adaptive data window technique using LSE estimates fault impedance. It can find a optimal data window length and estimate fault impedance rapidly, because it changes the length according to the fault disturbance. To prove the performance of the algorithm, the authors test relaying signals obtained from EMTP simulation. Test results show that the proposed algorithm estimates fault location within a half cycle after fault irrelevant to fault types and various fault conditions.

• Communications for Statistical Applications and Methods
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• 제16권6호
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• pp.1055-1066
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• 2009

Many articles have considered a hybrid censoring scheme, which is a mixture of Type-I and Type-II censoring schemes. We introduce a double hybrid censoring scheme and derive some approximate maximum likelihood estimators(AMLEs) of the scale parameter for the half logistic distribution under the proposed double hybrid censored samples. The scale parameter is estimated by approximate maximum likelihood estimation method using two different Taylor series expansion types. We also obtain the maximum likelihood estimator(MLE) and the least square estimator(LSE) of the scale parameter under the proposed double hybrid censored samples. We compare the proposed estimators in the sense of the mean squared error. The simulation procedure is repeated 10,000 times for the sample size n = 20(10)40 and various censored samples. The performances of the AMLEs and MLE are very similar in all aspects but the MLE and LSE have not a closed-form expression, some numerical method must be employed.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권8호
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• pp.462-470
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• 2003

This paper presents an algorithm of identifying parametric uncertainty by way of an interval model. For a given set of frequency response data from an uncertain linear SISO system of which the upper and the lower bounds of both magnitude and phase responses are represented, the proposed algorithm consists of two main parts: first, the nominal model is identified by using Least Square Estimation (LSE), and then an interval model is constructed by expanding the extremal properties of interval systems, so that tightly enclose the given envelopes within those of interval model. Two numerical examples are given to demonstrate and verify the developed algorithm. The identified interval model can be used for evaluating the worst case performance and stability margins against parametric uncertainty by using some extremal properties on interval systems.

• 한국위성정보통신학회논문지
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• 제3권1호
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• pp.15-21
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• 2008

OFDM (직교 주파수 분할 다중화) 신호의 높은 PAPR은 시스템의 송신단에서 전력증폭기의 비선형적 특성으로 인해 비선형 왜곡이 불가피하게 발생한다. 이 현상은 대역 내 왜곡과 대역 외 방사를 초래한다. 본 논문에서는 다항식 (polynomial) 모델에 기반한 사전왜곡(pre-distortion) 기법으로 이러한 문제를 보상하는 기법을 제안한다. 비선형 및 역비선형 다항식 모델 추정은 LSE(Least Square Error) 알고리즘으로 수행한다. 또한 시스템의 성능 향상을 위해 피크제거와 클리핑 결합기법을 이용해 OFDM 신호가 전력증폭기의 포화 영역 근처에서 동작함으로써 발생하는 왜곡된 신호의 진폭을 제거한다.