• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.533-536
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• 1997

In this paper, we visualized 3-dimensional volume of heart using volume method by thresholding in EBT slices data. Volume rendering is the method that acquire the color by casting a pixel ray to volume data. The gray level of heart region is so high that we decide heart region by thresholding method. When a pixel ray is cast to volume data, the region that is higher than threshold value becomes heart region. We effectively rendered the heart volume and showed the 3-dimensional heart volume.

• International Journal of CAD/CAM
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• v.9 no.1
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• pp.111-120
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• 2010

In this paper, we propose a volume rendering method using grid computing for large-scale volume data. Grid computing is attractive because medical institutions and research facilities often have a large number of idle computers. A large-scale volume data is divided into sub-volumes and the sub-volumes are rendered using grid computing. When using grid computing, different computers rarely have the same processor speeds. Thus the return order of results rarely matches the sending order. However order is vital when combining results to create a final image. Job-Scheduling is important in grid computing for volume rendering, so we use an obstacle-flag which changes priorities dynamically to manage sub-volume results. Obstacle-Flags manage visibility of each sub-volume when line of sight from the view point is obscured by other subvolumes. The proposed Dynamic Job-Scheduling based on visibility substantially increases efficiency. Our Dynamic Job-Scheduling method was implemented on our university's campus grid and we conducted comparative experiments, which showed that the proposed method provides significant improvements in efficiency for large-scale volume rendering.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.253-263
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• 2021

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

• Asian Journal of Business Environment
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• v.13 no.1
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• pp.31-41
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• 2023

Purpose: This study purposes to analyze the determinants of the volume of Indonesian tuna exports. Research design, data and methodology: The framework was developed from the gravity model for trade, which was expanded with additional variables of competitiveness, exchange rate, and industrial share of the destination country. The data sources used in this study are UN Comtrade and the World Bank. The data used is yearly data from 12 countries in 2001-2019. The scope of the study is limited to exports to the twelve main export destinations. Panel data regression analysis is used to determine the factors that affect the volume of Indonesian tuna exports. Results: The results show that according to the theory, Indonesia's GDP has a positive effect and economic distance has a negative effect on the volume of the exports. Meanwhile, the GDPs of the destination countries are not proven to have a positive effect. However, the higher the industrial share in the country, the higher the export volume tends to be. Conclusions: The conclusion obtained from this study is that Indonesia's GDP, economic distance, real exchange rate, industrial GDP share of the destination country, and the RCA index affect the volume of Indonesian tuna exports.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.7
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• pp.629-638
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• 2000

The main difference between mono-volume rendering and multi-volume rendering is data intermixing. In this paper, we first propose a selective rendering method for fast visualizing specific volume according to the surface level and then present data intermixing method for multiple volumes. The selective rendering method is to generate distance transformed volume using a distance transform to determine the minimum distance to the nearest interesting part and then render it. The data intermixing method for multiple volumes is to combine several volumes using intensity weighted intermixing method, opacity weighted intermixing method, opacity weighted intermixing method with depth information and then render it. We show the results of selective rendering of left ventricle and right ventricle generated from EBCT cardiac images and of data intermixing for combining original volume and left ventricular volume or right ventricular volume. Our method offers a visualization technique of specific volume according to the surface level and an acceleration technique using a distance transformed volume and the effective visual output and relation of multiple images using three different intermixing methods in three-dimensional space.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.19-27
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• 2021

In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle's location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner's sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver's privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1986-1993
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• 2003

The spatially resolved spray volume fractions from both line-of-sight data of direct measuring cells and a laser diffraction particle analyzer (LDPA) are tomographically reconstructed by the Convolution Fourier transformation, respectively. Asymmetric sprays generated from a twin-hole injector are tested with 12 equiangular projections of measurements. For each projection angle, a line-of-sight integrated injection rate was measured using a direct sampling method and also a liquid volume fraction from a set of line-of-sight Fraunhofer diffraction measurements was measured using a light extinction method. Interpolated data between the projection angles effectively increase the number of projections, significantly enhancing the signal-to-noise level in the reconstructed data. The reconstructed volume fractions from the direct sampling cells were used as reference data for evaluating the accuracy of the volume fractions from the LDPA.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.29-37
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• 2017

It has gradually become important to estimate a forest stand volume utilizing LiDAR data. Recently, various statistical models including a linear regression model has been introduced to estimate a forest stand volume using LiDAR data. One of limitations of the current approaches is in that the accuracy of observed forest stand volume data, which is used as a response variable, is questionable unstable. To overcome this limitation, we consider a spatial structure for a forest stand volume. In this research, we propose a hierarchical model for applying a spatial structure to a forest stand volume. The proposed model is applied to the LiDAR data and the forest stand volume for Bonghwa, Gyeongsangbuk-do.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.228-233
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• 2000

체적가시화(Volume Rendering)는 단면촬영기나 표면인식치 등을 이용해 읽어 들인 Data를 원래의 형태로 화면상에 보여 주는 것으로 일반적인 방법이 Sur face Rendering과 Volume Rendering이 있다. Volume Rendering은 Data 처리속도 문제와 한정적인 메모리 양으로 인해 지존의 알고리즘을 그대로 적용하는 경우 실시간 가시화가 힘들 뿐만 아니라 3차원 영상의 질이 저하되는 문제가 있었다 따라서, 본 연구는 3차원 영상의 질 저하 없이 실시간으로 MR Angio의 3차원 Volume 가시화를 구현한다 본 연구해서 사용되는 속도 개선 알고리즘은 Marc Levoy가 제안한 8진Tree(Octree) 자료구조를 이용하며, 또한 Volume Data 내에 존재하는 공기와 같이 가시화될 필요가 없는 부분에 대해 불필요한 계산을 피하고 가시화하고자 하는 부분만을 계산함으로써 Rendering에 소요되는 시간을 줄이는 방법을 사용한다.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.661-670
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• 2012

Urban forests provide great ecosystem services to population in metropolitan areas even though they occupy little green space in a huge gray landscape. Unfortunately, urbanization inherently results in threatening the green infrastructure, and the recent urbanization trends drew great attention of scientists and policy makers on how to preserve or restore green infrastructure in metropolitan area. For this reason, mapping the spatial distribution of the green infrastructure is important in urban environments since the resulting map helps us identify hot green spots and set up long term plan on how to preserve or restore green infrastructure in urban environments. As a preliminary step for mapping green infrastructure utilizing multi-source remote sensing data in urban environments, the objective of this study is to map vegetation volume by fusing LiDAR and multispectral data in urban environments. Multispectral imageries are used to identify the two dimensional distribution of green infrastructure, while LiDAR data are utilized to characterize the vertical structure of the identified green structure. Vegetation volume was calculated over the metropolitan Chicago city area, and the vegetation volume was summarized over 16 NLCD classes. The experimental results indicated that vegetation volume varies greatly even in the same land cover class, and traditional land cover map based above ground biomass estimation approach may introduce bias in the estimation results.