• 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.

• 제어로봇시스템학회논문지
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• 제19권7호
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• pp.633-639
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• 2013

Several estimation methods used in the range measurement based wireless localization area have individual problems. These problems may not occur according to certain application areas. However, these problems may give rise to serious problems in particular applications. In this paper, three methods, ILS (Iterative Least Squares), DS (Direct Solution), and DSRM (Difference of Squared Range Measurements) methods are considered. Problems that can occur in these methods are defined and a simple hybrid solution is presented to solve them. The ILS method is the most frequently used method in wireless localization and has local minimum problems and a large computational burden compared with closed-form solutions. The DS method requires less processing time than the ILS method. However, a solution for this method may include a complex number caused by the relations between the location of reference nodes and range measurement errors. In the near-field region of the complex solution, large estimation errors occur. In the DSRM method, large measurement errors occur when the mobile node is far from the reference nodes due to the combination of range measurement error and range data. This creates the problem of large localization errors. In this paper, these problems are defined and a hybrid localization method is presented to avoid them by integrating the DS and DSRM methods. The defined problems are confirmed and the performance of the presented method is verified by a Monte-Carlo simulation.

• ETRI Journal
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• 제25권2호
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• pp.109-120
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• 2003

Though the airborne laser scanning (ALS) technique is becoming more popular in many applications, horizontal accuracy of points scanned by the ALS is not yet satisfactory when compared with the accuracy achieved for vertical positions. One of the major reasons is the drift that occurs in the inertial measurement unit (IMU) during the scanning. This paper presents an algorithm that adjusts for the error that is introduced mainly by the drift of the IMU that renders systematic differences between strips on the same area. For this, we set up an observation equation for strip-wise adjustments and completed it with tie point and control point coordinates derived from the scanned strips and information from aerial photos. To effectively capture the tie points, we developed a set of procedures that constructs a digital surface model (DSM) with breaklines and then performed feature-based matching on strips resulting in a set of reliable tie points. Solving the observation equations by the least squares method produced a set of affine transformation equations with 6 parameters that we used to transform the strips for adjusting the horizontal error. Experimental results after evaluation of the accuracy showed a root mean squared error (RMSE) of the adjusted strip points of 0.27 m, which is significant considering the RMSE before adjustment was 0.77 m.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.29-33
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• 2007

본 연구에서는 RLS(Rescursive Least Square) 알고리즘을 이용하여 정전용량형 습도센서를 사용한 원격 RF 센서 시스템의 시변 파라메타를 추정하고자 한다. IC 칩 형태의 원격 RF 센서 시스템이 가지는 구성의 복잡성 그리고 전력소모 문제를 해결하기 위해 보다 간단한 유도결합모델이 제안된다. 모델기반의 RLS 알고리즘을 수학적인 모델로 유도된 시스템에 적용시키기 위해 페이저법을 이용하여 모델의 파라메타를 재배열한다. 오차 제곱합의 수렴특성을 가진 RLS 알고리즘을 이용하여 시변 파라메타를 추정하며, 잡음을 내포한 측정 데이터에 대한 추정 성능을 확인함으로써 그 유효성을 검증하고자 한다.

• 한국수산과학회지
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• 제35권3호
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• pp.216-222
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• 2002

시계열자료가 가진 자기상관은 추정된 상관관계를 왜곡시키는 요인들 중의 하나로 작용한다. 회귀모형의 잔차항에 자기상관이 있는 지를 검정하기 위해 Durbin-Watson 통계량이 흔히 쓰인다. 잔차항에 자기상관을 가진 회귀모형의 효율성을 향상시키기 위해 yule-Walker 법, 비선형최소제곱법, 최우추정법 및 사전백색화법이 사용되어 왔다. 본 연구는 자기상관으로 인한 상관관계의 왜곡을 방지하기 위한 이들 방법들에 대해 고찰하였다. 사전백색화법을 제외한 앞의 3가지 방법을 20년간의 실제 시계열 자료에 적용하였으며 몬테카를로법을 이용하여 각 방법의 오차변이를 조사하였다. 각 방법의 평균잔차제곱분포의 경우, 최우추정법으로 추정된 평균잔차제곱이 가장 작았으며 분포 범위도 가장 작았으나 각 추정방법 사이에 유의한 차이가 발견되지는 않았다.

• 응용통계연구
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• 제26권3호
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• pp.431-440
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• 2013

우리는 두꺼운 꼬리를 갖는 분포의 모수를 추정하는 방법론을 연구하였다. 일반적으로 MLE(최대우도 추정량)가 모수추정 방법론중에 가장 많이 사용되는데, 이는 MLE가 점근적 일치성과 정규성 그리고 효율성을 가지고 있기 때문이다. 하지만 MLE가 늘 가장 좋은 추정법은 아니다. 어떤 경우에는 MLE가 존재하지 않을 수도 있고 계산이 안정적이지 않을 수도 있다. 본 논문에서는 비선형 최소제곱추정법을 이용한 모수추정 방법론을 제시하고 그 성능을 MLE와 비교하였다. NLS 추정량은 empirical CDF와 이론적 CDF의 차이의 제곱을 최소화 하는 방법론이다. 본 논문에서는 두꺼운 꼬리를 가지는 다양한 분포하에서 우리가 제안하는 NLS방법론과 MLE와의 성능을 비교하였다. 그 결과, Burr 분포에서 표본의 수가 적을 때 우리의 방법론이 MLE보다 좋은 성능을 보여주었고, Frechet 분포에서도 좋은 결과를 얻을 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.60-64
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• 2001

In this paper, a new learning methodology for Kernel Methods is suggested that results in a sparse representation of kernel space from the training patterns for classification problems. Among the traditional algorithms of linear discriminant function(perceptron, relaxation, LMS(least mean squared), pseudoinverse), this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epochs. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.351-353
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• 2006

In this paper, we present the traditional GPS Position- Velocity (PV) model to apply for both Discrete-Time Kalman Filter and Discrete-Time $H_{\infty}$ Filter. The positioning algorithms of both filters are proposed for a stand-alone low-cost GPS module to increase its accuracy. For disturbance cancellation, the Kalman Filter requires the statistical information about process and measurement noises while the $H_{\infty}$ Filter only requires that these noises are bounded. Experiments show that with the same measurement data, $H_{\infty}$ Filter gives us better positioning results compared with Least-Squared method and Kalman Filter.

• 품질경영학회지
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• 제25권1호
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• pp.69-79
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• 1997

We consider the semiparametric linear errors-in-variables model: yi=(${\alpha}+{\beta}ui+{\varepsilon}i$, xi=ui＋${\varepsilon}i$ i=1, …, n where (xi, yi) stands for an observation vector, (ui) denotes a set of incidental nuisance parameters, (${\alpha}$ , ${\beta}$) is a vector of regression parameters and (${\varepsilon}i$, ${\delta}i$) are mutually uncorrelated measurement errors with zero mean and finite variances but otherwise unknown distributions. On the basis of a simple small-sample low-noise a, pp.oximation, we propose a new method of comparing the mean squared errors(MSE) of the various competing estimators of the true regression parameters ((${\alpha}$ , ${\beta}$). Then we show that a class of estimators including the classical least squares estimator and the maximum likelihood estimator are consistent and first-order efficient within the class of all regular consistent estimators irrespective of type of measurement errors.

• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.250-255
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• 2016

Steganography is the art and science of secure communication. It focuses on both security and camouflage. Steganographic techniques must produce the resultant stego-image with less distortion and high resistance to steganalysis attack. This paper is mainly concerned with two steganographic techniques-least significant bit (LSB)++ and the reversible histogram transformation function (RHTF). LSB++ is likely to produce less distortion in the output image to avoid suspicion, but it is vulnerable to steganalysis attacks. RHTF using a mod function technique is capable of resisting the most popular and efficient steganalysis attacks, such as the regular-singular pair attack and chi-squared detection steganalysis, but it produces a lot of distortion in the output image. In this paper, we propose a new steganographic technique by combining both methods. The experimental results show that the proposed technique overcomes the respective drawbacks of each method.