• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.579-588
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• 2013

This paper proposes an effective watermarking approach for copyright protection of fire video images. The proposed watermarking approach efficiently utilizes the inherent characteristics of fire data with respect to color and texture by using a gray level co-occurrence matrix (GLCM) and fuzzy c-means (FCM) clustering. GLCM is used to generate a texture feature dataset by computing energy and homogeneity properties for each candidate fire image block. FCM is used to segment color of the fire image and to select fire texture blocks for embedding watermarks. Each selected block is then decomposed into a one-level wavelet structure with four subbands [LL, LH, HL, HH] using a discrete wavelet transform (DWT), and LH subband coefficients with a gain factor are selected for embedding watermark, where the visibility of the image does not affect. Experimental results show that the proposed watermarking approach achieves about 48 dB of high peak-signal-to-noise ratio (PSNR) and 1.6 to 2.0 of low M-singular value decomposition (M-SVD) values. In addition, the proposed approach outperforms conventional image watermarking approach in terms of normalized correlation (NC) values against several image processing attacks including noise addition, filtering, cropping, and JPEG compression.

• Journal of dental hygiene science
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• v.12 no.5
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• pp.469-476
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• 2012

The purpose of this study to grasp the effects of the stress coping method on the periodontal disease in order to provide raw data for the studies on the development of the training for effective stress copying method resulting in improvement in prevention, occurrence, and progression of periodontal disease. Data were collected from total of 326 subjects in their forties and fifties, during the period from March to October 2011. Using questionnaire and examination of periodontal health, and were analyzed using frequency, t-test, multivariate analysis of variance, multiple regression by SPSS ver. 19.0 (IBM Co., USA). The higher emotion-focused stress coping ($\beta$=0.341), the higher the gingival index, the higher the emotion-focused stress coping ($\beta$=0.239) the deeper the pocket depth, the higher the emotion-focused stress coping ($\beta$=0.158) the higher clinical attachment loss, showed statistically significant positive association. This study suggests that the dental clinic institute needs to implement education program including stress management and coping method improvement in order to prevent periodontal disease and make the occurrence and progression to be managed by professionals.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.187-196
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• 2020

In this study, collapsed building detection using UAV (Unmanned Aerial Vehicle) and PlanetScope satellite images was carried out, suggesting the possibility of utilization of heterogeneous sensors in object detection located on the surface. To this end, the area where about 20 buildings collapsed due to forest fire damage was selected as study site. First of all, the feature information of objects such as ExG (Excess Green), GLCM (Gray-Level Co-Occurrence Matrix), and DSM (Digital Surface Model) were generated using high-resolution UAV images performed object-based segmentation to detect collapsed buildings. The features were then used to detect candidates for collapsed buildings. In this process, a result of the change detection using PlanetScope were used together to improve detection accuracy. More specifically, the changed pixels acquired by the bitemporal PlanetScope images were used as seed pixels to correct the misdetected and overdetected areas in the candidate group of collapsed buildings. The accuracy of the detection results of collapse buildings using only UAV image and the accuracy of collapse building detection result when UAV and PlanetScope images were used together were analyzed through the manually dizitized reference image. As a result, the results using only UAV image had 0.4867 F1-score, and the results using UAV and PlanetScope images together showed that the value improved to 0.8064 F1-score. Moreover, the Kappa coefficiant value was also dramatically improved from 0.3674 to 0.8225.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.23-41
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• 2019

This study understand the subject domain of book which has been read by men and woman elderly by analizying the PFNET using library big data and confirm the difference between adult at age 30-40. This study extract co-occurrence matrix of book lending on the popular book list from library big data, for 4 group, men/woman elderly, men/woman adult. With these matrix, this study performs FP network analysis. And Pearson Correlation Analysis based on the Triangle Betweenness Centrality calculated on the loan book was performed to understand the correlation among the 4 clusters which has been created by PNNC algorithm. As a result, reading trend which has been focused on modern korean novel has been revealed in elderly regardless gender, among them, men elderly show extreme tendency concentrated on modern korean long series novel. In the correlation analysis, the male elderly showed a weak negative correlation with the adult male of r = -0.222, and the negative direction of all the other groups showed that the tendency of male elderly's loan book was opposite.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.92-104
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• 2021

This study aims to analyze online news articles to identify social issues related to patient safety and compare the changes in these issues before and after the implementation of the Patient Safety Act. This study performed text mining through the R program, wherein 7,600 online news articles were collected from January 1, 2010, to March 5, 2020, and examined using keyword analysis, topic modeling, and word co-occurrence network analysis. A total of 2,609 keywords were categorized into 8 topics: "medical practice", "medical personnel", "infection and facilities", "comprehensive nursing service", "medicine and medical supplies", "system development and establishment for improvement", "Patient Safety Act" and "healthcare accreditation". The study revealed that keywords such as "patient safety awareness", "infection control" and "healthcare accreditation" appeared before the implementation of the Patient Safety Act. Meanwhile, keywords such as "patient safety culture". and "administration and injection" appeared after the act's implementation with improved ranking of importance pertaining to nursing-related terminology. Interest in patient safety has increased in the medical community as well as among the public. In particular, nursing plays an important role in improving patient safety. Therefore, the recognition of patient safety as a core competency of nursing and the persistent education of the public are vital and inevitable.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.91-110
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• 2021

In the face of the prolonged Covid-19, this study conducted a network analysis to propose the policy direction for the Korean National Assembly to go forward. Using COVID-19 news articles, various types of networks were created and analyzed for the parliamentary activities of the Korean National Assembly related to Covid-19. Specifically, we utilize the co-occurrence and keyword information to generate two types of parliamentary networks: co-occurrence-based network and content-based network. In addition, a topic keyword-driven parliamentary network was constructed by using topic modeling. The results of the study are as follows. First, lawmakers in the ruling party had a wide range of topics regarding Covid-19, while lawmakers from other political parties had a limited number of issues covered. Next, a few representative legislators were identified as influential actors in most of the centrality indicators. Based on the research results, cooperation on diverse agendas related to Covid-19 should be promoted between lawmakers from various political parties. And representative legislators from both major parties should play a crucial role as intermediaries to increase communication between them.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.23-50
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• 2022

Source memory is a cognitive process that combines the representation of the origin of the episodic experience with an item. By studying this daily process, researchers have made fundamental discoveries that make up the foundation of brain and behavior research, such as executive function and binding. In this paper, we review and conduct a bibliometric analysis on source memory papers published from 1989 to 2020. This review is based on keyword co-occurrence networks and author citation networks, providing an in-depth overview of the development of source memory research and future directions. This bibliometric analysis discovers a change in the research trends: while research prior to 2010 focused on individuality of source memory as a cognitive function, more recent papers focus more on the implication of source memory as it pertains to connectivity between disparate brain regions and to social neuroscience. Keyword network analysis shows that aging and executive function are continued topics of interest, although frameworks in which they are viewed have shifted to include developmental psychology and meta memory. The use of theories and models provided by source memory research seem essential for the future development of cognitive enhancement tools within and outside of the field of Psychology.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Journal of the Korean Society for Library and Information Science
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• v.56 no.1
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• pp.253-275
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• 2022

Niklas Luhmann is one of the most contentious and difficult theorist in sociology but follow-up studies on his theory gradually increase for recent 10 years. The purpose of this study is to observe how follow-up studies use the difficult concepts of Luhmann. Unlike previous studies, this study adopted a keyword rather than an article as the unit of analysis because keywords are linguistic constructs that can make concepts observable. The study analyzed co-occurrence of keywords in 139 articles retrieved from social sciences category in Web of Science DB. The key findings were following: the most important keywords were the name of Luhmann(Niklas Luhmann) and theory(social systems); keywords were grouped into 4 clusters(social systems theory, systems theory, legal system and political system, the significant of Luhmann's theory from the viewpoint of the history of social theory); topic terms were systems theory, communication, Autopoiesis, risk, legal system, functional differentiation, environment, social theory, sociological theory, structural coupling, systems and evolution. The significance of the study is following: the study gives keywords as useful access point for beginners of Luhmann's theory; the study proves that content analysis by keywords network can be applied to trend analysis of difficult theoretical researches.