• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.97-102
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• 2008

In this paper we propose an object-based image retrieval method using spatial color model and feature points registration method for an effective color query detection. The proposed method in other to overcome disadvantages of existing color histogram methods and then this method is use the HMMD model and rough set in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the database image and query image. Here, we select candidate regions in the similarity between the query image and database image. And we use SIFT registration methods in the selected region for object retrieving. The experimental results show that the proposed method is more satisfactory detection radio than conventional method.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.33-38
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• 2005

The goal of this study is to segment building points effectively utilizing scan-line characteristics of airborne laser scanner. Points are classified as to their altitude similarity and adjacency with other classified points, and point searching range for the classification is restricted within some number of scan-lines, preventing classification speed from lowering as the process goes on. Besides, we detected wrong discrimination of one object into more than two classes, then integrated them into a single class. Consequently we could discriminate points of each building from others, its annexes and none building points simultaneously.

• Asian Journal of Business Environment
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• v.7 no.1
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• pp.25-29
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• 2017

Purpose - In this paper, we categorize and segment the 28 national universities in South Korea and measure the degree of dissimilarity (or similarity) between pairs of ones by using dissimilarity distance matrix and cluster analysis, respectively, based on the seven quantitative evaluation of educational conditions (percentage of small-scale courses, percentage of lecture by the faculty, collection of books per student, material purchase per student, percentage of building capacity, percentage of real estate capacity and rate of accommodation) in 2015. In addition, multidimensional scaling (MDS) techniques can obtain visual representation for exploring patterns of proximities among 28 national universities based on seven attributes of educational conditions. Research design, data, and methodology - This work is carried out by the 2015 Announcement of University Information, which is provided by Ministry of Education in South Korea and utilized by multivariate analyses with CLUSTER, PROXIMITIES and ALSCAL modules in IBM SPSS 23.0. Results - We make certain that 28 national universities can be categorized into five clusters which have similar traits by applying two-stage cluster analysis. MDS is utilized to perform positioning of grouped places of cluster and 28 national universities joining every cluster. Conclusions - Both types and traits of each national university can be relatively assessed and practically utilized for each university competitiveness based on underlying results.

• The Plant Pathology Journal
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• v.30 no.2
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• pp.151-158
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• 2014

Three double-stranded RNAs (dsRNAs), approximately 1.85, 1.65 and 1.27 kb in size, were detected in an isolate of Cytospora sacchari from Iran. Partial nucleotide sequence revealed a 1,284 bp segment containing one ORF that potentially encodes a 405 aa protein. This protein contains conserved motifs related to RNA dependent RNA polymerases (RdRp) that showed similarity to RdRps of partitiviruses. The results indicate that these dsRNAs represent a novel Partitivirus that we tentatively designate Cytospora sacchari partitivirus (CsPV). Treatment of the fungal strain by cyclohexamide and also hyphal tip culture had no effect on removing the putative virus. Phylogenetic analysis of putative RdRp of CsPV and other partitiviruses places CsPV as a member of the genus Partitivirus in the family Partitiviridae, and clustering with Aspergillus ochraceous virus 1.

• Korean journal of food and cookery science
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• v.6 no.1
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• pp.13-19
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• 1990

Cowpea, mung bean, Kidney bean and red bean are simular properties. In order to elucidate the similarity among these four starches, some physicochemical properties of starches were compared. Water binding capacity of kidney bean and red bean (199%) starches are higher than mung bean and cowpea. The solubility, swlling power and optical transmittance of the four starches showed a similar pattern, but kidney bean and red bean starches had a lower swelling power than cowpea starches. Cowpea, mung bean, kidney bean and red bean starches had the blue value of 0.41, 0.47, 0.42 and 0.50, the alkali content of 8.4, 8.0, 4.13, 4.13, the amylose content of amylose of 30,000, 29,268, 52, 173 and 33, 611 and glucose unit per segment of amylopectin of 27.6, 26.8, 18.35 and 12.9 respectively. The results of X-ray diffraction studies showed A pattern for four starches.

• Journal of Ginseng Research
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• v.32 no.2
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• pp.114-119
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• 2008

Terpene synthase plays a key role in biosynthesis of triterpene saponins (ginsenosides) and is intermediate in the biosynthesis of a number of secondary metabolites. A terpene synthase (PgTPS) cDNA was isolated and characterized from the root of Panax ginseng c.A. Meyer. The deduced amino acid sequence of PgTPS showed a similarity with A. deliciosa (AAX16121) 61%, V. vinifera (AAS66357) 61%, L. hirsutum (AAG41891) 55%, M. truncatula (AAV36464) 52%. And the segment of a terpene synthase gene was amplified by reverse transcriptase-polymerase chain reaction (RTPCR). We studied expression of terpene synthase under stressful conditions like chilling, salt, UV, and heavy metal stress treatment. Expression of PgTPS was increased gradually after exposure to stresses such as chilling, salt, and UV illumination. But its transcription seems to be reduced by cadmium and copper treatment.

• Korean Journal of Medicinal Crop Science
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• v.16 no.4
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• pp.261-267
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• 2008

Malate dehydrogenase is a ubiquitous enzyme in plants, involving in a range of metabolic processes depending on its subcellular location. A malate dehydrogenase (PgMDH) cDNA was isolated and characterized from the root of Panax ginseng C. A. Meyer. The deduced amino acid sequence of PgMDH showed high similarity with the NAD-dependent mitochondrial malate dehydrogenase from Glycinemax (P17783), Eucalyptus gunnii (P46487), and Lycopersicon esculentum (AAU29198). And the segment of a malate dehydrogenase gene was amplified through RT-PCR. The expression of PgMDH was increased after treatments of chilling, salt, UV, cadmium or copper treatment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1819-1826
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• 2010

This paper proposes the image segmentation algorithm to extracts a fish object from a fish image for fish image retrieval. The conventional algorithm using gray level similarity causes wrong image segmentation result in the boundary area of the object and the background with similar gray level. The proposed algorithm uses the reinforced edge and the adaptive block-based threshold for the boundary area with weak contrast and the virtual object to improve the eroded or disconnected object in the boundary area without contrast. The simulation results show that the percentage of extracting the visual-fine object from the test images is under 90% in the conventional algorithm while it is 97.7% in the proposed algorithms.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.15-29
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• 2024

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat twin-to-twin transfusion syndrome. In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow field of view of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. This study proposes a method to solve the above problems. A novel encoder-decoder network with a dual-path structure is proposed to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the dice similarity coefficient, intersection over union, and pixel accuracy by 5.80%, 8.39% and 0.62%, respectively.