• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.189-196
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• 2011

In this study, we perform a simulation study to compare frequently used standardization methods for interview scores based on trimmed mean, rank mean, and z-score mean. In this simulation study we assume that interviewer's score is influenced by a weighted average of true interviewee's true score and independent noise whose weight is determined by the professionality of the interviewer. In other words, as interviewer's professionality increases, the observed score becomes closer to the true score and if interviewer's professionality decreases, the observed score becomes closer to the noise instead of the true score. By adding interviewer's tendency bias to the weighed average, final interviewee's score is assumed to be observed. In this simulation, the interviewers's cores for each method are computed and then the method is considered best whose rank correlation between the method's scores and the true scores is highest. Simulation results show that when the true score is from normal distributions, z-score mean is best in general and when the true score is from Laplace distributions, z-score mean is better than rank mean in full interview system, where all interviewers meet all interviewees, and rank mean is better than z-score mean in half split interview system, where the interviewers meet only half of the interviewees. Trimmed mean is worst in general.

• Journal of the Korean Mathematical Society
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• v.35 no.3
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• pp.741-753
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• 1998

Let G(*) be a Borel function applied to a stationary long memory sequence {X$_{i}$} of standard Gaussian random variables. Focusing on the process {G(X$_{i}$)}, the present paper establishes the almost sure representation for the empirical quantile process, that is, Bahadur's representation, and for the empirical process with respect to sample mean. Statistical applications of the representations are also addressed.sed.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.33-36
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• 1992

In this paper we propose the impulse noise-robust Wiener filter based on a combination of Wiener and modified trimmed mean(MTM) filters. The robust Wiener filter uses the trimming operation of the MTM filter to replace the outliers with the median within the window and the new set of samples which can be considered as the random process with same mean are inputted into the following Wiener filter. We show that the robust Wiener filter is effective in frequency selective filtering of nonstationary signals while preserving signal edges with the rejection of impulse noise.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.42-47
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• 2007

Video Segmentation is an essential part in region-based video coding and any other fields of the video processing. Among lots of methods proposed so far, the watershed method in which the region growing is performed for the gradient image can produce well-partitioned regions globally without any influence on local noise and extracts accurate boundaries. But, it generates a great number of small regions, which we call over segmentation problem. Therefore we proposes that adaptive trimmed mean filter for resolving the over segmentation of image. Simulation result, we confirm that proposed ATM filter improves the performance to remove noise and reduces damage for the clear degree of image in case of the noise ratio of 20% and over.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.92-95
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• 1991

Constant false alarm rate(CFAR) processors are useful for detecting radar targets in background for which all parameters in the statistical distribution are not known and may be nonstationary. The well known "cell averging" (CA) CFAR processor is known to yield best performance in homogeneous case, but exhibits severe performance in the presence of an interfering target in the reference window or/and in the region of clutter edges. The "order statistics"(OS) CFAR processor is known to have a good performance above two nonhomogeneous cases. The modified OS-CFAR processor, known as "trimmed mean"(TM) CFAR processor performs somewhat better than the OS-CFAR processor by judiciously trimming the ordered samples. This paper proposes and analyzes the performance of a new CFAR processor called the "maximum trimmed mean"(MX-TM) CFAR processor combining the "greatest of"(GO) CFAR and TM-CFAR processors. The MAX operation is included to control false alarms at clutter edges. Our analyses show that the proposed CFAR processor has similar performance TM- and OS-CFAR processors in homogeneous case and in the precence of interfering targets, but can control the false rate in clutter edges. Simulation results are presented to demonstrate the qualitative effects of various CFAR processors in nonhomogeneous clutter environments.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.5 s.23
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• pp.75-80
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• 2002

Depending upon geographical features and surrounding errors in the survey field, inaccurate positioning is inevitable in a kinematic DGPs survey. Therefore, a data inaccuracy detection algorithm and an interpolation algorithm are essential to meet the requirement of a digital map. In this study, GPS characteristics are taken into account to develop the data inaccuracy detection algorithm. Then, the data interpolation algothim is obtained, based on the feature type of the survey. A digital map for 20km of a rural highway is produced by the kinematic DGPS survey and the features of interests are lines associated with the road. Since the vertical variation of GPS data is relatively higher, the trimmed mean of vertical variation is used as criteria of the inaccuracy detection. Four cases of 0.5%, 1%, 2.5% and 5% trimmings have been experimented. Criteria of four cases are 69cm, 65cm, 61cm and 42cm, respectively. For the feature of a curved line, cublic spine interpolation is used to correct the inaccurate data. When the feature is more or less a straight line, the interpolation has been done by a linear polynomial. Difference between the actual distance and the interpolated distance are few centimeters in RMS.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.3
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• pp.37-46
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• 2012

In RADAR and SONAR detection systems, noise environment can be classified into homogeneous and heterogeneous environment. Especially heterogeneous environments are modelled as target masking and clutter edge. Since the variability-index (VI) CFAR, a composed CFAR algorithm, dynamically selects one of the mean-level algorithms based on the VI and the MR (mean ratio) test, it is robust to various environments. However, the VI CFAR still suffers from lowered detection probabilities in heterogeneous environments. To overcome these problems, we propose an improved VI CFAR processor where TM (trimmed mean) CFAR and a sub-windowing technique are introduced to minimize the degradation of the detection probabilities appeared in heterogeneous environments. Computer simulation results show that the proposed method has the better performance in terms of detection probability and false alarm probability compared to the VI CFAR and single CFAR algorithms.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.495-504
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• 2012

In this study, we propose a statistical model based on projection that generates interview scores. In this model, each interviewee's true score and its related variable are viewed as X, Y values respectively in the two dimensional plane, and each interviewer's score is the projected score of true score X to the axis rotated by some angle, which reflects the interviewer's perspective. Each interviewer's observed interview score is obtained by adding personal bias and observed error to this projected score. We compared commonly used standardizing methods of interview scores such as trimmed mean method, rank method, and z-score method based on the proposed statistical model. In this simulation, two types of interview methods, two numbers of interviewers, two degrees of interviewers' expertise and two distributions and three correlations between actual score and its related variable are all considered.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.467-472
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• 2002

In this paper, we propose a Fuzzy Cellular Neural Network(FCNN) that is based on a-Least Trimmed Square Hausdorff distance(a-LTSHD) which applies Hausdorff distance(HD) to the FCNN structure in order to remove the impulse noise of images effectively and also improve the speed of operation. FCNN incorporates Fuzzy set theory to Cellular Neural Network(CNN) structure and HD is used as a scale which computes the distance between set or two pixels in binary images without confrontation of the feature object. This method has been widely used with the adjustment of the object. For performance evaluation, our proposed method is analyzed in comparison with the conventional FCNN, with the Opening-Closing(OC) method, and the LTSHD based FCNN by using Mean Square Error(MSE) and Signal to Noise Ratio(SNR). As a result, the performance of our proposed network structure is found to be superior to the other algorithms in the removal of impulse noise.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.793-802
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• 2012

One proposal is made to construct a nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of the idea of minimizing approximate variance of a proposed estimator using regression quantiles. This nonparametric estimator and some other L-estimators are studied and compared with well known M-estimators through a simulation study.