• 제목/요약/키워드: Shape Data

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Noninformative Priors for the Common Shape Parameter in the Gamma Distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • 제18권1호
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    • pp.247-257
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    • 2007
  • In this paper, we develop the noninformative priors for the common shape parameter in the gamma distributions. We develop the matching priors and reveal that the second order matching prior does not exist. It turns out that the one-at-a-time reference prior and the two group reference prior satisfy a first order probability matching criterion. Some simulation study is peformed.

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Bayesian Model Selection in Weibull Populations

  • Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • 제18권4호
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    • pp.1123-1134
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    • 2007
  • This article addresses the problem of testing whether the shape parameters in k independent Weibull populations are equal. We propose a Bayesian model selection procedure for equality of the shape parameters. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian model selection procedure based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real example are provided.

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사두증 환자를 위한 맞춤형 헬멧 몰드 디자인 시스템 개발 (Development of a Customized Helmet Design System for Patients with Plagiocephaly)

  • 강영훈;박혜련;김성민
    • 한국의류산업학회지
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    • 제24권4호
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    • pp.443-450
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    • 2022
  • This study developed a three-dimensional helmet mold design software that can design helmets for treating the infant plagiocephaly (flat head syndrome) using three-dimensional head scan data. For this, the three-dimensional head data of sixth SizeKorea body measurement project as well as the data produced by a head modeling software were used to prepare the head shape data of plagiocephaly patients. A total of 14 landmarks and 10 dimensions of heads required for helmet mold shape design and plagiocephaly diagnosis were automatically measured using an anthropometric analysis software. Using the software developed in this study, plagiocephaly can be diagnosed not only visually by three-dimensional head data visualization but also quantitatively by calculating the medically defined indices such as cranial index, which measures the proportions of the head, and the cranial vault asymmetry index, which measures the asymmetry of the head. The basic shape of the helmet mold was automatically generated based on the head scan data. Additionally, it is possible to fine tune the shape of the mold to reflect individual characteristics by using a free form deformation technique. Finally, the designed helmet mold was converted into the data that can be printed on a three-dimensional printer for generating the actual prototype.

3D Building Reconstruction and Visualization by Clustering Airborne LiDAR Data and Roof Shape Analysis

  • Lee, Dong-Cheon;Jung, Hyung-Sup;Yom, Jae-Hong
    • 한국측량학회지
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    • 제25권6_1호
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    • pp.507-516
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    • 2007
  • Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

퍼지 신경망을 이용한 온라인 클러스터링 방법 (A On-Line Pattern Clustering Technique Using Fuzzy Neural Networks)

  • 김재현;서일홍
    • 전자공학회논문지B
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    • 제31B권7호
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    • pp.199-210
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    • 1994
  • Most of clustering methods usually employ a center or predefined shape of a cluster to assign the input data into the cluster. When there is no information about data set, it is impossible to predict how many clusters are to be or what shape clusters take. (the shape of clusters could not be easily represented by the center or predefined shape of clusters) Therefore, it is difficult to assign input data into a proper cluster using previous methods. In this paper, to overcome such a difficulty a cluster is to be represented as a collection of several subclusters representing boundary of the cluster. And membership functions are used to represent how much input data bllongs to subclusters. Then the position of the nearest subcluster is adaptively corrected for expansion of cluster, which the subcluster belongs to by use of a competitive learning neural network. To show the validity of the proposed method a numerical example is illustrated where FMMC(Fuzzy Min-Max Clustering) algorithm is compared with the proposed method.

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토크 컨버터의 형상 분석 (Geometrical Analysis of a Torque Converter)

  • 임원석
    • 한국자동차공학회논문집
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    • 제5권5호
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    • pp.197-212
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    • 1997
  • The performance of a torque converter can be expressed by the performance parameters such as flow radius and flow angle, on the mean flow path. The geometric analysis of the torque converter is required to determine these parameters for the modeling of the torque converter. In general, the blade shape is depicted by three dimensional data at the mid-surface of blade or those of the pressure and suction side. To generate three dimensional model of the blade using the data mentioned above, a consistent data format and a shape generation algorithm are required. This paper presents a useful consistent data format of the blades and an algorithm for the geometrical shape generation. By the geometric analysis program to which the shape generation algorithm is embedded, the variation of blade angles in rotating element analyzed. Then finally, the analyzed results of geometric profile of a blade are compared with those of the blade design principle, so called forced vortex theorem.

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Bayesian Hypothesis Testing for Homogeneity of the Shape Parameters in the Gamma Populations

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • 제18권4호
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    • pp.1191-1203
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    • 2007
  • In this paper, we consider the hypothesis testing for the homogeneity of the shape parameters in the gamma distributions. The noninformative priors such as Jeffreys# prior or reference prior are usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian testing procedure for the homogeneity of the shape parameters based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

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Comparison of Shape Variability in Principal Component Biplot with Missing Values

  • Shin, Sang-Min;Choi, Yong-Seok;Lee, Nae-Young
    • 응용통계연구
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    • 제21권6호
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    • pp.1109-1116
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    • 2008
  • Biplots are the multivariate analogue of scatter plots. They are useful for giving a graphical description of the data matrix, for detecting patterns and for displaying results found by more formal methods of analysis. Nevertheless, when some values are missing in data matrix, most biplots are not directly applicable. In particular, we are interested in the shape variability of principal component biplot which is the most popular in biplots with missing values. For this, we estimate the missing data using the EM algorithm and mean imputation according to missing rates. Even though we estimate missing values of biplot of incomplete data, we have different shapes of biplots according to the imputation methods and missing rates. Therefore we propose a RMS(root mean square) for measuring and comparing the shape variability between the original biplots and the estimated biplots.

VISUALIZATION OF 3D DATA PRESERVING CONVEXITY

  • Hussain Malik Zawwar;Hussain Maria
    • Journal of applied mathematics & informatics
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    • 제23권1_2호
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    • pp.397-410
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    • 2007
  • Visualization of 2D and 3D data, which arises from some scientific phenomena, physical model or mathematical formula, in the form of curve or surface view is one of the important topics in Computer Graphics. The problem gets critically important when data possesses some inherent shape feature. For example, it may have positive feature in one instance and monotone in the other. This paper is concerned with the solution of similar problems when data has convex shape and its visualization is required to have similar inherent features to that of data. A rational cubic function [5] has been used for the review of visualization of 2D data. After that it has been generalized for the visualization of 3D data. Moreover, simple sufficient constraints are made on the free parameters in the description of rational bicubic functions to visualize the 3D convex data in the view of convex surfaces.

COAG 특징과 센서 데이터 형상 기반의 후보지 선정을 이용한 위치추정 정확도 향상 (Improvement of Localization Accuracy with COAG Features and Candidate Selection based on Shape of Sensor Data)

  • 김동일;송재복;최지훈
    • 로봇학회논문지
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    • 제9권2호
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    • pp.117-123
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    • 2014
  • Localization is one of the essential tasks necessary to achieve autonomous navigation of a mobile robot. One such localization technique, Monte Carlo Localization (MCL) is often applied to a digital surface model. However, there are differences between range data from laser rangefinders and the data predicted using a map. In this study, commonly observed from air and ground (COAG) features and candidate selection based on the shape of sensor data are incorporated to improve localization accuracy. COAG features are used to classify points consistent with both the range sensor data and the predicted data, and the sample candidates are classified according to their shape constructed from sensor data. Comparisons of local tracking and global localization accuracy show the improved accuracy of the proposed method over conventional methods.