• Journal of Korean Neurosurgical Society
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• v.65 no.2
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• pp.297-306
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• 2022

Objective : Intracranial atypical meningiomas have a poor prognosis and high rates of recurrence. Moreover, up to one-third of the recurrences undergo high-grade transformation into malignant meningiomas. We aimed to investigate the clinical factors that can predict the propensity of malignant transformation from atypical to anaplastic meningiomas. Methods : Between 2001 and 2018, all patients with atypical meningioma, in whom the tumors had undergone malignant transformation to anaplastic meningioma, were included. The patients' medical records documenting the diagnosis of atypical meningioma prior to malignant transformation were reviewed to identify the predictors of transformation. The control group comprised 56 patients with atypical meningiomas who were first diagnosed between January 2017 and December 2018 and had no malignant transformation. Results : Nine patients in whom the atypical meningiomas underwent malignant transformation were included. The median time interval from diagnosis of atypical meningioma to malignant transformation was 19 months (range, 7-78). The study group showed a significant difference in heterogeneous enhancement (77.8% vs. 33.9%), bone invasion (55.6% vs. 12.5%), mitotic index (MI; 14.8±4.9 vs. 3.5±3.9), and Ki-67 index (20.7±13.9 vs. 9.5±7.1) compared with the control group. In multivariate analysis, increased MI (odds ratio, 1.436; 95% confidence interval, 1.127-1.900; p=0.004) was the only significant factor for predicting malignant transformation. Conclusion : An increased MI within atypical meningiomas might be used as a predictor of malignant transformation. Tumors at high risk for malignant transformation might require more attentive surveillance and management than other atypical meningiomas.

• Spatial Information Research
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• v.11 no.4
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• pp.481-491
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• 2003

This study aims at generating a continuous map by coordinates transformation between cadastral maps with different surveying origins. The continuous cadastral map is useful in various fields of GIS. For this purpose, an experimental study was conducted at Osan-Si, Kyonggi Province in cooperation with related institutions. In this study, three control point zone, large, medium, and small zone were to!;ted. For each control point, the currently used data were compared with the data at the surveyed time. About coordinate transformation method, we tested Helmert, Affine, and Polynomial methods which are the most representative among 2-dimensional coordinate transformations. These three transformation methods were evaluated according to variation of transformed parcel shape and agreement with neighboring areas. As the result of the evaluation, Affine transformation in large zone is the most appropriate coordinate transformation method fer Osan-Si.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.18-27
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• 2019

The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.309-315
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• 2003

This study is aimed to research the precise and efficient method for coordinate transformation. In Korea, it is necessary to convert existing digital maps in TM coordinates to that in KTRF from 2007. In this study, coordinate transformation methods and conversion area are tested and analyzed. In the results of experiment, it shows that Affine method is preciser than Helmert method. But Affine method is have more distortion than Helmert method.

• Communications of the Korean Mathematical Society
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• v.15 no.4
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• pp.707-714
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• 2000

The authors aim at presenting a presumably new transformation formula involving generalized hypergeometric series by making use of series rearrangement technique which is one of the most effective methods for obtaining generating functions or other identities associated with (especially) the hypergeometric series. They also consider a couple of interesting special cases of their main result.

• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1599-1604
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• 2001

Axisymmetric compressible flow field induced by shock diffraction from a finite cone is investigated with experimental and computational methods. Double-exposure holographic interferograms show ima ges of the density field integrated along the light path. Using the sight-integrated density based on the Able transformation, the axisymmetric computational results are compared qualitatively with the experiment. In the present paper, we observed some distinguishing flow physics: the fault structure of vortex ring, the shock-vortex interaction, and the morphological transformation of shock waves.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.617-624
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• 2004

In this article the fuzzy number rank and the fuzzy rank transformation method are introduced in order to analyse the non-parametric fuzzy regression model which cannot be described as a specific functional form such as the crisp data and fuzzy data as a independent and dependent variables respectively. The effectiveness of fuzzy rank transformation methods is compared with other methods through the numerical examples.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.7-12
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• 2001

A large number of methods for the design optimization have been proposed in recent years. However, it is not easy to apply these methods to practical use because of many iterations. So, in the design optimization, physical and engineering investigation of the given model are very important, which results in an overall increase in the optimization speed. This paper describes a novel optimization procedure utilizing the conformal transformation method. This approach consists of two phases and has the advantage of grasping the physical phenomena of the model easily. Some numerical results that demonstrate the validity of the proposed method are also presented.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.574-574
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.575-583
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.