• Korean Journal of Medicinal Crop Science
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• v.16 no.2
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• pp.118-123
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• 2008

This study was executed to breed new cultivar of E. senticosus suitable for seed harvesting by selection method from 896 native plants collected from 35 regions. Basic selection points were C.V. values of characters such as filament length, blooming period, shoot length, no. of shoot and hundred fruits weight. By four steps of selection, two lines having high biomass yielding, diseases tolerant and fruiting capacities were finally selected and registered as new cultivar of Eleutherococcus senticosus at the Korea seed & variety service by regulation for seed production and marketing of plant variety.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.27-37
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• 2018

Streamline generation is one of the most representative visualization methods to analyze the flow stream of fluid dynamics dataset. It is a challenging problem, however, to determine the seed locations for effective streamline visualization. Meanwhile, it needs much time to compute effective seed locations and streamlines on the massive flow dataset. In this paper, we propose not only an importance based method to determine seed locations for the effective streamline placements but also a parallel streamline visualization method on the distributed visualization system. Moreover, we introduce case studies on the real fluid dynamics dataset using GLOVE visualization system to evaluate the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.627-636
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• 2010

Image segmentation technique is becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification to extract object regions of interest within images. This paper presents a new method for image segmentation to consider spectral and spatial information of high resolution satellite image. Firstly, the initial seeds were automatically selected using local variation of multi-spectral edge information. After automatic selection of significant seeds, a segmentation was achieved by applying MSRG which determines the priority of region growing using information drawn from similarity between the extracted each seed and its neighboring points. In order to evaluate the performance of the proposed method, the results obtained using the proposed method were compared with the results obtained using conventional region growing and watershed method. The quantitative comparison was done using the unsupervised objective evaluation method and the object-based classification result. Experimental results demonstrated that the proposed method has good potential for application in the object-based analysis of high resolution satellite images.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.417-422
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• 2004

It is the challenging task to predict seismic demand for structural design. In current seismic design provisions such as UBC, NEHRP, ATC 3-06, the seismic demand is calculated using the response spectrum with response modification factor (R). This paper investigates variability of seismic demand according to selecting the earthquake ground motion groups. Different Earthquake sets used by Miranda, Riddell and Seed selected were used in this study. Earthquake sets selected by authors include 62 sets of near field ground motion and 19 sets one pulse ground motion. Linear Elastic Response Spectrum (LERS), the variation of performance points of calculated by Capacity Spectrum Method (CSM) were considered with respect to the different sets of earthquake ground motions.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.111-121
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• 2006

The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.4
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• pp.394-399
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• 2011

This experiment was conducted to select suitable cultivars and evaluate growth characteristics to get basic information for sprouting peanut. On sprouting peanut, it showed a rapid increase in trans-resveratrol content that has effects on anti-cancer, anti-inflammatory, blood-sugar-lowering and other beneficial cardiovascular in mouse. For this experiment, characteristics of peanut sprouts were tested in 37 cultivars grown for 7 days at $26^{\circ}C$ temperature. There were a lots of variations in the growth characteristics among cultivars as followers : The range of 100 grain weight was 56 to 142 g, hypocotyl length was 4.3 cm to 5.8 cm, diameter of hypocotyl was 5.0 to 8.0 mm, epicotyl length was 0.8 cm to 4.6 cm, seedling ratio per seed number was 84% to 100%, weight per seedling was 4.9 g to 8.4 g, the rate of hypocotyl cleavage was 0% to 46%, the content of trans-resveratrol was $22.5\;{\mu}g/g$ to $88.2\;{\mu}g/g$ and sprout yield was 360% to 820%. The selection points considered were high sprout yield, high seedling rate, high resveratrol content, low brownish cotyledon, no hypocotyl cleavage, and fat hypocotyl etc. The best cultivar selected was 'Jokwang' that showed 7.8 mm diameter, clean cotyledon color, 100% seedling rate, 0% hypocotyl cleavage, $63.3\;{\mu}g/g$ resveratrol, and 820% sprouting yield. This cultivar was expected to be of use as a new food and nutraceutical material. Relationship between growth characteristics showed that root length had significant positive correlations with epicotyl length, resveratrol content and sprouting yield but negative correlations with hypocotyl diameter and cleavage. Hundred grain weight showed negative correlations with resveratrol content, seedling rate and sprouting yield but positively correlated with curved hypocotyl rate and hypocotyl cleavage positively. This result showed small grain seed will be more appropriate for sprouting peanut.