• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.129-141
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• 2006

The image data amount is increasing rapidly that used geography, sea information etc. with great development of a remote sensing technology using artificial satellite. Therefore, people need automatic method that use image processing description than macrography for analysis remote sensing image. In this paper, we propose that acquire texture information to use GLCM(Gray Level Co-occurrence Matrix) in red tide area of artificial satellite remote sensing image, and detects red tide area by PCA(principal component analysis) automatically from this data. Method by sea color that one feature of remote sensing image of existent red tide area detection was most. but in this paper, we changed into 2 principal component accumulation images using GLCM's texture feature information 8. Experiment result, 2 principal component accumulation image's variance percentage is 90.4%. We compared with red tide area that use only sea color and It is better result.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.49-55
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• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.83-103
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• 2015

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining-extracting meaningful information from unstructured text data-has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.255-265
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• 2015

Very high resolution (VHR) satellite imagery provide valuable information on urban change monitoring due to multi-temporal observation over large areas. Recently, there has been increased interest in the urban change detection technique using VHR Synthetic Aperture Radar (SAR) imaging system, because it can take images regardless of solar illumination and weather condition. In this paper, we proposed a texture-based urban change detection method using the VHR SAR texture features generated from Gray-Level Co-Occurrence Matrix (GLCM). In order to evaluate the efficiency of the proposed method, the result was compared, visually and quantitatively, with the result of Non-Coherent Change Detection (NCCD) which is widely used for the change detection of VHR SAR image. The experimental results showed the greater detection accuracy and the visually satisfactory result compared with the NCCD method. In conclusion, the proposed method has shown a great potential for the extraction of urban change information from VHR SAR imagery.

• Journal of the Korean Society for information Management
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• v.20 no.3
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• pp.277-297
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• 2003

This study was conducted upon the premise that index terms display the intellectual structure of a specific subject field. In this study, and attempt was made to grasp the intellectual structure of Library and Information. Science by clustering the index terms of the journals of the related academic societies at the Library of National Assembly - such as the Journal of the Korean Society for Information Management, the Journal of the Korean Library and Information Science Society, and the Journal of the Korean Society for Library and Information Science. Through the course of the study, index term clusters were generated based on the linkage of the index terms and the frequency of co-occurrence, and moreover, time periods analysis was conducted along with studies on first-appearing terms, in order to clarify the trend and development process of the Library and Information Science. This study also analysed the difference between two intellectual structure by comparing the structure generated by index term clusters with the existing structure of traditional classification systems.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Weed & Turfgrass Science
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• v.6 no.4
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• pp.345-349
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• 2017

Herbicides are important weed control tools for increasing crop yields and the efficiency of crop production. As the use of herbicides increases, the occurrrence of herbicide-resistant weeds has been an increaing problem. In Korea, since the first occurrence of acetolactate synthase (ALS) inhibitor resistant Monochoria korsakowii was reported in the Seosan reclaimed paddy field in 1998, resistance has been reported in 14 weed species, including Echinochola spp. and their populations are gradually increasing. The objective of this study is to investigate the nationwide occurrence of ALS and Acetyl-CoA Carboylase inhibitor resistant Echinochloa spp. in Korea. In 2013, 2014, and 2015, we collected 594 accessions of Echinochloa spp. in Korean rice fields except for Jeonnam and Chungbuk provinces. They were then treated with the recommended rates of penoxsulam and cyhalofop-butyl. We harvested seeds from 45 accessions of E. oryzicola in the case of cyhalofop-butyl treatment. Also, 44 and 46 accessions of E. oryzicola and E. crus-galli survived and their seeds were harvested after penoxsulam treatment. Twenty accessions of E. oryzicola survived from both herbicides inferring possible multiple resistance. Two accessions out of 20 inferred from possible multiple resistance survived after cyhalofop-butyl treatment at a dose of $500ga.i.ha^{-1}$. Seeds of herbicide resistant populations will be provided and utilized for further research.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.8 s.350
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• pp.35-43
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• 2006

In the paper, we propose a new method for power optimization that uses path balancing based on stochastic estimation of glitch in Statistical Static Timing Analysis (SSTA). The proposed method estimates the probability of glitch occurrence using tightness probability of each node in timing graph. In addition, we propose efficient gate sizing technique for glitch reduction using accurate calculation of sizing effect in delay considering probability of glitch occurrence. The efficiency of proposed method has been verified on ISCAS85 benchmark circuits with $0.16{\mu}m$ model parameters. Experimental results show up to 8.6% of accuracy improvement in glitch estimation and 9.5% of optimization improvement.

• Korean Journal of Social Welfare
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• v.63 no.2
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• pp.291-317
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• 2011

This study was designed to explore the experiences of female victims who had abused by alcohol-abusing spouse. The results were deducted from in-depth interview with 10 female victims by utilizing Giorgi's phenomenological method. The results of research showed that experiences of women could be classified in three stages. From the lack of understanding stage, "ignorance of drunken violence" had appeared. "Confinement of vicious circle of drunken violence" and "dealing with drunken violence in the community" had been found from the coping stage. Finally, from the resting stage "re-defining of the relationship" had been drawn. Based on these results, the practical and political implications were suggested.