• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.15-20
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• 2015

Control charts are generally used for process control, but the role of traditional control charts have been limited in case of a non-contaminated process. Traditional ${\bar{x}}$-s control chart has not been activated well for such a problem because of trying to control processes as center line and control limits changed by the contaminated value. This paper suggests modified ${\bar{x}}$-s control chart based on robust estimation. In this paper, we consider the trimmed mean of the sample means and the trimmed mean of the sample standard deviations. By comparing with ARL value, the responding results are decided. The comparison resultant results of traditional control chart and modified control chart are contrasted.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.1-35
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• 2024

The assumption of homoscedasticity is one of the most crucial assumptions for many parametric tests used in the biological sciences. The aim of this paper is to compare the empirical probability of type I error and the power of ten parametric and two non-parametric tests for homoscedasticity with simulations under different types of distributions, number of groups, number of samples per group, variance ratio and significance levels, as well as through empirical data from an agricultural experiment. According to the findings of the simulation study, when there is no violation of the assumption of normality and the groups have equal variances and equal number of samples, the Bhandary-Dai, Cochran's C, Hartley's Fmax, Levene (trimmed mean) and Bartlett tests are considered robust. The Levene (absolute and square deviations) tests show a high probability of type I error in a small number of samples, which increases as the number of groups rises. When data groups display a nonnormal distribution, researchers should utilize the Levene (trimmed mean), O'Brien and Brown-Forsythe tests. On the other hand, if the assumption of normality is not violated but diagnostic plots indicate unequal variances between groups, researchers are advised to use the Bartlett, Z-variance, Bhandary-Dai and Levene (trimmed mean) tests. Assessing the tests being considered, the test that stands out as the most well-rounded choice is the Levene's test (trimmed mean), which provides satisfactory type I error control and relatively high power. According to the findings of the study and for the scenarios considered, the two non-parametric tests are not recommended. In conclusion, it is suggested to initially check for normality and consider the number of samples per group before choosing the most appropriate test for homoscedasticity.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.229-238
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis, data analysis, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters, because it is more primitive and explorative. In this paper we propose a new method of k-means clustering using the grid-based representative value(arithmetic and trimmed mean) for sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.93-101
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• 1997

A Hausdorff distance (HD) is one of commonly used measures for object matching. It calculates the distance between two point sets of edges in two-dimensional binary images without establishing correspondences. This paper proposes three object matching algorithm using robust HD measures based on M-estimation, least trimmed square (LTS), and .alpha.-trimmed mean methods, which are more efficient than the conventional HD measures. By computer simulation with synthetic and real images, the matching performance of the conventional HD smeasures and proposed' robust ones is compared.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.10
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• pp.1085-1093
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• 2010

This paper proposes a multiple filter for reduction of received signal fluctuation and enhancement of step altitude edge detection in FMCW radar altimeter. The proposed filter was composed of alpha-trimmed mean filter, frequency variation limiter, and 1/3 order static filter. Simulations by analysis of received signal show that the proposed filter provides better performance than moving average and standard median filters with error reduction. In particular, the proposed filter was improved in the ability of reducing fluctuation for ground hovering.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.375-382
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• 2010

In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.

• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.299-305
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• 1999

In thls paper, the representative beal textraction algorIthms for the diagnostic parameter extraction in noisy signal were compared. We used the avernge, median, mode, and trmmed mean to calculale the central tendency. In our experimenl, we have restricted to four kinds of noises -EMG noise, 60Hz powerline inlerference, ahrupl baseline shift, and baselme drift due to respimtion-which were commonly occurred in ECG mgnal, then we have calculated signal-to-noise ratios(SNRs) for the ECG corrupted with each noise and all noises together. As the result of this paper, we have proved that the average method has super lor performance than the others in the ECG corrupted wilh EMG noise. When the signal mcludes extreme value such as abrupt baseline shIft, the median, mode, trimmed mean methods have supenor performance in the SNR ratios. Especially when the ECG corrupted with baseline drift due to respirallon, the trimmed mean method was most efficient because ST level change was 0 V.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.1-11
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• 1996

As an estimate of a location parameter for a given data set, $\alpha$-trimmed mean has been studied for a long time by many statisticians because of its nice propoerties including robustness. However, its performance depends on the proportion of trimming say $\alpha$. In this paper, we suggest a data-driven choice of $\alpha$ and study its validity. Also, we suggest a new estimator and consider double-bootstrap to improve its performance. By using simulation study, the proposed method is compared with the exiting one in various cases. Real data sets are also analyzed by using the proposed method.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.231-234
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• 2001

In this paper, we present a robust watermarking technique based on a DCT-domain watermarking approach and an order statistic(OS) filter. The proposed technique inserts one watermark into each of four coefficients within a 2 ${\times}$ 2 block which is scanned on DCT coefficients in the zig-zag ordering from the medium frequency range. The detection algorithm uses an adaptive trimmed mean operator as a local estimator of the embedded watermark to obtain the desired robustness in the presence of additive Gaussian noise and JPEG compression attacks. The performance is analyzed through statistical analysis and numerical experiments. It is shown that the robustness properties against additive noise and JPEG compression attacks are more enhanced than the previous techniques.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.735-740
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• 2005

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.