• Journal of the Korean Society for Library and Information Science
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• v.36 no.4
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• pp.21-47
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• 2002

The present study, on the bass of a premise that reference lists are one of the social products, reflecting various social environments of their own society, was made as part of an attempt to explore the co-occurrence phenomenon of both Korean and Non-Korean Literatures occurred within the Korean references. 321 authors (articles) of 43 Korean journals on Social Sciences were surveyed on research channels and citation motivations and their 11358 references were analyzed. The findings are as follows : 1) The extent of the co-occurrence was that Non-Korean literatures were more 1.9 times (65.3%) cited than Korean ones and English (61.5%)-American (50.4%) predominancy was heavily found. 2) Research channel, worked as an indicator of the identity of researcher as well as the source of research ideas was most Non-Korean channel orientedness (55.8%). 3) Citation motivations were significantly depended on whether Korean or Non-Korean literatures and non-Korean literatures were cheifly cited to be conceptual motivations than other motivations. 4) Research channel among variables was worked as a main effect predicting major citation motivations on Non-Korean literatures. Finally, this study is very suggestive : 1) It might be a new approach and interpretation by adopting citation motivations to explore a process of knowledge producting of researchers 2) Partly, it proved empirically the relationship of knowledge producted by Korean researchers to Non-Korean knowledge through the analysis of citation motivations.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.197-206
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• 2010

High-quality and low-price digital printing devices are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use different manufactural systems, printed documents from different printers have little difference in visual. Analyzing this artifact, we can identify the color laser printers. First, high-frequency components of images are extracted from original images with discrete wavelet transform. After calculating the gray-level co-occurrence matrix of the components, we extract some statistical features. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, total 2,597 images of 7 printers (HP, Canon, Xerox DCC400, Xerox DCC450, Xerox DCC5560, Xerox DCC6540, Konica), are tested to classify the color laser printer. The results prove that the presented identification method performs well with 96.9% accuracy.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.121-133
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• 2005

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• 한국체육학회지인문사회과학편
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• v.56 no.2
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• pp.303-320
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• 2017

This paper seeks to better understand the focal issues and persons related to Rio Olympic soccer games through social data science and analytics. This study collected its data from online news articles and comments specific to KOR during the Olympic football games. In order to investigate the public interests for each game and target persons, this study performed the co-occurrence words analysis. Then after, the study applied the NodeXL software to perform its visualization of the results. Through this application and process, the study found several major issues during the Rio Olympic men's football game including the following: the match between KOR and PIJ, KOR player Heungmin Son, commentator Young-Pyo Lee, sportscaster Woo-Jong Jo. The study also showed the general public opinion expressed positive words towards the South Korean national football team during the Rio Olympics, though there existed negative words as well. Furthermore the study revealed positive attitude towards the commentators and casters. In conclusion, the way to increase the public's interest in big sporting events can be achieved by providing the following: contents that include various professional sports analysis, a capable domain expert with thorough preparation, a commentator and/or caster with artistic sense as well as well-spoken, explanatory power and so on. Multidisciplinary research combined with sports science, social science, information technology and media can contribute to a wide range of theoretical studies and practical developments within the sports industry.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.153-172
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• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• Journal of Korean Medicine Rehabilitation
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• v.24 no.2
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• pp.41-50
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• 2014

Objectives The purpose of this study was to evaluate the effects of Clematidis radix extract on $CoCl_2$-induced apoptosis in SH-SY5Y human neuroblastoma cells. Methods In order to investigate the protective effect of Clematidis radix on $CoCl_2$-induced cytotoxicity in neuronal cells, MTT(3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay, DAPI(4,6-diamidino-2-phenylindoleI) staining, TUNEL (terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling) assay, DNA fragmentation assay and western blotting were performed on SH-SY5Y human neuroblastoma cells. Results Cells treated with $CoCl_2$ exhibited several apoptotic features, while cells pre-treated with Clematidis radix prior to $CoCl_2$ exposure showed a decrease in the occurrence of apoptotic features. $CoCl_2$ increased HIF-$1{\alpha}$ expression, in contrast, Clematidis radix treatment decreased $CoCl_2$-induced HIF-$1{\alpha}$ expression. Pre-treatment with the extract of Clematidis radix suppressed Bax, cytochrome c, and caspase-3 expressions, and also increased Bcl-2 expression in SH-SY5Y human neuroblastoma cells. Conclusions These results suggest that Clematidis radix may exert a protective effect on $CoCl_2$-induced apoptosis in SH-SY5Y human neuroblastoma cells.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.51 no.1
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• pp.54-63
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• 2018

We investigated the effect of $CO_2$ exposure on the reproductive process of chameleon goby Tridentiger trigonocephalus. Rearing aquaria were exposed for 90 days to $CO_2$ gas through diffuser connected with pH controller maintaining at pH 7.2 ($156.31{\pm}7.90ppm$) in low treatment, and at pH 6.5 ($274.17{\pm}6.51ppm$) in high treatment. $CO_2$ treatment had no significant effects on survival rates although the value was decreased compared to the controls. In female fish, $CO_2$ treatment had no significant effects on gonadosomatic index (GSI), hepatosomatic index (HSI) and condition factor (CF). However, high $CO_2$ treatment decreased HSI and CF in males significantly compared to the controls (P<0.05). The spawning occurrence was 6 times in the low level $CO_2$ treatment, and 4 times in the high level $CO_2$ treatment although only once in the controls. For the histological observations, there was no significant difference in $CO_2$ treatments. However, in male fish, $CO_2$ treatment delayed the formation of sperm from spermatid compared to controls. These results suggest $CO_2$ may disrupt reproductive process by delaying gametogenesis in chameleon goby and it was more sensitive in males.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Journal of Mycology and Infection
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• v.23 no.4
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• pp.105-110
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• 2018

Background: Otomycosis is a fungal infection that comprises 7~10% of outer ear infections. Although the occurrence is higher in humid climates, relatively few studies have investigated otomycosis occurrences in humid environments. While recurrent chronic otitis media discharge in the ear creates a milieu in which otomycosis is likely to occur, investigations of otomycosis co-occurring with chronic otitis media have been rare. Objective: To examine the characteristics of patients with otomycosis co-occurring with chronic otitis media and identify causative fungi. Methods: The study included 60 patients with chronic otitis media who presented typical otomycosis findings in the outer ear canal and the presence of fungi. Patients were treated in the department of otolaryngology, Daegu Catholic University Medical Center, between July 2011 and June 2018. Results: The mean patient age was 57.77 years, and our study included 20 men and 40 women (p=0.010). The lesion was on the right in 39 patients and on the left in 21 (p=0.020). Ear discharge was the most common chief complaint at diagnosis. Of the 54 patients over age 19, 10 had diabetes (18.5%). Aspergillus was causative in 29 patients and Candida in 31. Aspergillus niger was identified in 15 patients, Aspergillus sp. in 14, Candida parapsilosis in 12, Candida sp. in six, and Candida albicans in five. Conclusion: Otomycosis and chronic otitis media co-occurrences increase with age. The Aspergillus and Candida genera were similar in proportion. A. niger was the most common Aspergillus species, while C. parapsilosis was the most common Candida.