• Horticultural Science & Technology
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• v.33 no.4
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• pp.566-574
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• 2015

Yellow or transparent fruit peel color is caused by the accumulation or lack of naringenin chalcone (NG, C) in fruit peel and determines the red or pink appearance of tomato fruit, respectively. NGC biosynthesis is regulated by the SlMYB12 gene of the Y locus on chromosome 1, and DNA markers derived from SlMYB12 would be useful for marker-assisted selection (MAS) of tomato fruit color. To develop a gene-based marker, 4.9 kb of the SlMYB12 gene including a potential promoter region was sequenced from the red-fruited (YY) line 'FCR' and pink-fruited (yy) line 'FCP'. Sequence alignment of these SlMYB12 alleles revealed no sequence variations between 'FCR' and 'FCP'. To identify SlMYB12-linked single nucleotide polymorphisms (SNPs), 'FCR' and 'FCP' were genotyped using a SolCAP Tomato SNP array and CAPS markers (CAPS-456, 531, 13762, and 38123) were developed from the four SNPs (solcap_snp_sl_456, 531, 13762, and 38123) most closely flanking the SlMYB12. These CAPS markers were mapped using $F_2$ plants derived from 'FCR' ${\times}$ 'FCP'. The map positions of the fruit peel color locus (Y) were CAPS-13762 (0 cM) - 456 (11.09 cM) - Y (15.71 cM) - 38123 (17.82 cM) - 531 (30.86 cM), and the DNA sequence of SlMYB12 was physically anchored in the middle of CAPS-456 and CAPS-38123, indicating that fruit peel color in domesticated tomato is controlled by SlMYB12. A total of 64 SolCAP tomato germplasms were evaluated for their fruit peel color and SNPs located between solcap_snp_sl_456 and 38123. Seven SNPs that were detected in this interval were highly conserved for pink-fruited accessions and specific to transparent fruit peel traits, as depicted by a phenetic tree of 64 accessions based on the seven SNPs.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.4
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• pp.40-46
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• 2010

In this paper, we use the Raman spectra measured from platelet to the diagnosis of Alzheimer's disease(AD). The Raman spectra used in the experiments were preprocessed with the following method and then fed into the classifier. The first step of the preprocessing is a simple smoothing followed by background elimination to the original spectra to make it easy to measure the intensity of the peaks. The last step of the preprocessing was peak alignment with the reference peak. After the inspection of the preprocessed spectra, we found that proportion of two peak intensity at 743 and 757 $cm^{-1}$ and peak intensity at 1658 $cm^{-1}$ are the most discriminative features. Then we apply mapstd method for normalization. The method returned data with means to 0 and deviation to 1. With these two features, the classification result involving 278 spectra showed about 95.5% true classification in case of MLP(multi-layer perceptron). It means that the Raman spectra measured from platelet would be effectively used to the diagnosis of Alzheimer's disease.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.581-588
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• 2018

The aim of this study is to propose a fine co-registration approach for multi-temporal satellite images acquired from RapidEye, which has an advantage of availability for time-series analysis. To this end, we generate multitemporal ortho-rectified images using RPCs (Rational Polynomial Coefficients) provided with RapidEye images and then perform fine co-registration between the ortho-rectified images. A DEM (Digital Elevation Model) extracted from the digital map was used to generate the ortho-rectified images, and the RNCC (Registration Noise Cross Correlation) was applied to conduct the fine co-registration. Experiments were carried out using 4 RapidEye 1B images obtained from May 2015 to November 2016 over the Yeonggwang area. All 5 bands (blue, green, red, red edge, and near-infrared) that RapidEye provided were used to carry out the fine co-registration to show their possibility of being applicable for the co-registration. Experimental results showed that all the bands of RapidEye images could be co-registered with each other and the geometric alignment between images was qualitatively/quantitatively improved. Especially, it was confirmed that stable registration results were obtained by using the red and red edge bands, irrespective of the seasonal differences in the image acquisition.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.