• Title/Summary/Keyword: High dimensional analysis

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Similarity Measure Design on High Dimensional Data

  • Nipon, Theera-Umpon;Lee, Sanghyuk
    • Journal of the Korea Convergence Society
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    • v.4 no.1
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    • pp.43-48
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    • 2013
  • Designing of similarity on high dimensional data was done. Similarity measure between high dimensional data was considered by analysing neighbor information with respect to data sets. Obtained result could be applied to big data, because big data has multiple characteristics compared to simple data set. Definitely, analysis of high dimensional data could be the pre-study of big data. High dimensional data analysis was also compared with the conventional similarity. Traditional similarity measure on overlapped data was illustrated, and application to non-overlapped data was carried out. Its usefulness was proved by way of mathematical proof, and verified by calculation of similarity for artificial data example.

A Study on the Development of a High Speed Feeding Type Three-Dimensional Bending Machine (초고속 이송 방식 3차원 Bending Machine 개발에 관한 연구)

  • Lim, Sang-Heon;Lee, Choon-Man
    • Journal of the Korean Society for Precision Engineering
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    • v.22 no.11 s.176
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    • pp.91-98
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    • 2005
  • This study has been focused on the development of a high speed feeding type three-dimensional bending machine. It is designed for manufacture of copper pipe for heat exchangers. For the purpose of design of the machine, analysis of bending process, structural analysis and reliability evaluation of the machine by a laser interferometer are carried out. The analysis is carried out by FEM simulation using commercial softwares, DEFORM, MARC and CATIA V5. In addition, the machine has attained high accuracy and repeatability. In order to improve the accuracy of this machine, the maximum speed, positioning accuracy and repeatability of feed are measured by the laser interferometer. The final results of analysis are applied to the design of a high speed feeding type three-dimensional bending machine and the machine is successfully developed.

Structural Analysis Library Development for Efficient High-Fidelity Composite Rotor Blade and Wing Design with High Aspect Ratio (높은 세장비를 가진 복합재 날개 구조의 효율적인 고기능 설계를 위한 구조해석 해석 라이브러리 개발)

  • Park, Jehong;Jang, Jun Hwan
    • Journal of the Korea Institute of Military Science and Technology
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    • v.22 no.4
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    • pp.460-466
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    • 2019
  • It takes a lot of time and human resources to build a detailed three-dimensional finite element analysis model that is almost similar to the actual structure for sophisticated analysis, and a lot of experience and know-how is required to form a reliable analytical model. In this paper, the one-dimensional beam model connected by stiffness matrix through blade analysis library was compared with the results of three-dimensional analysis with the stress calculated through the process of dimensional restoration analysis based on the principle of virtual work. By comparing the stress calculated through dimensional restoration analysis with the three-dimensional analytical model, We will introduce the development status and application case of the blade analysis library by comparing efficiency and accuracy.

A small review and further studies on the LASSO

  • Kwon, Sunghoon;Han, Sangmi;Lee, Sangin
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.5
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    • pp.1077-1088
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    • 2013
  • High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

Applications of response dimension reduction in large p-small n problems

  • Minjee Kim;Jae Keun Yoo
    • Communications for Statistical Applications and Methods
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    • v.31 no.2
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    • pp.191-202
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    • 2024
  • The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

Analysis of High Burnup Fuel Behavior Under Rod Ejection Accident in the Westinghouse-Designed 950 MWe PWR

  • Chan Bock Lee;Byung Oh Cho
    • Nuclear Engineering and Technology
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    • v.30 no.3
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    • pp.273-286
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    • 1998
  • As there has arisen a concern that failure of the high burnup fuel under the reactivity-insertion accident(RIA) may occur at the energy lower than the expected, fuel behavior under the rod ejection accident in a typical Westinghouse-designed 950 MWe PWR was analyzed by using the three dimensional nodal transient neutronics code, PANBOX2 and the transient fuel rod performance analysis code, FRAP-T6. Fuel failure criteria versus the burnup was conservatively derived taking into account available test data and the possible fuel failure mechanisms. The high burnup and longer cycle length fuel loading scheme of a peak rod turnup of 68 MWD/kgU was selected for the analysis. Except three dimensional core neutronics calculation, the analysis used the same core conditions and assumptions as the conventional zero dimensional analysis. Results of three dimensional analysis showed that the peak fuel enthalpy during the rod ejection accident is less than one third of that calculated by the conventional zero dimensional analysis methodology and the fraction of fuel failure in the core is less than 4 %. Therefore, it can be said that the current design limit of less than 10 percent fuel failure and maintaining the core coolable geometry would be adequately satisfied under the rod ejection accident, even though the conservative fuel failure criteria derived from the test data are applied.

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Demension reduction for high-dimensional data via mixtures of common factor analyzers-an application to tumor classification

  • Baek, Jang-Sun
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.3
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    • pp.751-759
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    • 2008
  • Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

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Numerical Analysis of the Three-Dimensional Wake Flow and Acoustic Field around a Circular Cylinder

  • Kim, Tae-Su;Kim, Jae-Soo
    • International Journal of Aeronautical and Space Sciences
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    • v.11 no.4
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    • pp.319-325
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    • 2010
  • For decades, researchers have rigorously studied the characteristics of flow traveling around blunt objects in order to gain greater understanding of the flow around aircraft, vehicles or vessels. Many different types of flow exist, such as boundary layer flow, flow separation, laminar and turbulent flow, vortex and vortex shedding; such types are especially observed around circular cylinders. Vortex shedding around a circular cylinder exhibits a two-dimensional flow structure possessing a Reynolds number within the range of 47 and 180. As the Reynolds number increases, the Karman vortex changes into a three-dimensional flow structure. In this paper, a numerical analysis was performed examining the flow and aero-acoustic field characteristics around a circular cylinder using an optimized high-order compact scheme, which is a high order scheme. The analysis was conducted with a Reynolds number ranging between 300 and 1,000, which belongs to B-mode flow around a circular cylinder. For a B-mode Reynolds number, a proper spanwise length is analyzed in order to obtain the characteristics of three-dimensional flow. The numerical results of the Strouhal number as well as the lift and drag coefficients according to Reynolds numbers are coincident with the other experimental results. Basic research has been conducted studying the effects an unstable three-dimensional wake flow on an aero-acoustic field.

INVITED PAPER MULTIVARIATE ANALYSIS FOR THE CASE WHEN THE DIMENSION IS LARGE COMPARED TO THE SAMPLE SIZE

  • Fujikoshi, Yasunori
    • Journal of the Korean Statistical Society
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    • v.33 no.1
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    • pp.1-24
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    • 2004
  • This paper is concerned with statistical methods for multivariate data when the number p of variables is large compared to the sample size n. Such data appear typically in analysis of DNA microarrays, curve data, financial data, etc. However, there is little statistical theory for high dimensional data. On the other hand, there are some asymptotic results under the assumption that both and p tend to $\infty$, in some ratio p/n ${\rightarrow}$c. The results suggest that the new asymptotic results are more useful and insightful than the classical large sample asymptotics. The main purpose of this paper is to review some asymptotic results for high dimensional statistics as well as classical statistics under a high dimensional asymptotic framework.

Three-Dimensional Borehole Radar Modeling (3차원 시추공 레이다 모델링)

  • 예병주
    • Economic and Environmental Geology
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    • v.33 no.1
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    • pp.41-50
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    • 2000
  • Geo-radar survey which has the advantage of high-resolution and relatively fast survey has been widely used for engineering and environmental problems. Three-dimensional effects have to be considered in the interpretation of geo-radar for high-resolution. However, there exists a trouble on the analysis of the three dimensional effects. To solve this problem an efficient three dimension numerical modeling algorithm is needed. Numerical radar modeling in three dimensional case requires large memory and long calculating time. In this paper, a finite difference method time domain solution to Maxwell's equations for simulating electromagnetic wave propagation in three dimensional media was developed to make economic algorithm which requires smaller memory and shorter calculating time. And in using boundary condition Liao absorption boundary. The numerical result of cross-hole radar survey for tunnel is compared with real data. The two results are well matched. To prove application to three dimensional analysis, the results with variation of tunnel's incident angle to survey cross-section and the result when the tunnel is parallel to the cross-section were examined. This algorithm is useful in various geo-radar survey and can give basic data to develop dat processing and inversion program.

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