• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2021.05a
• /
• pp.53-54
• /
• 2021

Grenfell Tower was renovated in 2014 and 2016 at a high cost to replace the exterior materials, windows and co-heating facilities of the building. The exterior materials used during the repair work were sandwich panels filled with polyethylene and plastic, which were expanded on the aluminum metal surface. It is a product called Celotex RS 5000, a low-resolution but inexpensive repair material, and is currently an external material that cannot be used in high-rise buildings. Similar domestic fire cases began to focus social attention on the safety of high-rise buildings through the Busan Residential Complex Fire (2010), Uijeongbu Urban Living Housing Fire (2015), and Ulsan Residential Complex Fire (2020), and residents' safety concerns are increasing. In Korea, the occurrence and risk of similar fires are high, so setting up fire prevention measures through fire case investigation is considered the most basic measure in securing human safety. Therefore, the purpose of this study is to examine the status of fire damage caused by domestic and foreign eruptions, domestic and international research status and related regulations on external materials and windows starting from the Grenfell Tower fire in England.

• Progress in Medical Physics
• /
• v.33 no.4
• /
• pp.158-163
• /
• 2022

Purpose: We subjected scanning electron microscopic (SEM) images of the active layer of EBT3 film to texture analysis to determine the dose-response curve. Methods: Uncoated Gafchromic EBT3 films were prepared for direct surface SEM scanning. Absorbed doses of 0-20 Gy were delivered to the film's surface using a 6 MV TrueBeam STx photon beam. The film's surface was scanned using a SEM under 100× and 3,000× magnification. Four textural features (Homogeneity, Correlation, Contrast, and Energy) were calculated based on the gray level co-occurrence matrix (GLCM) using the SEM images corresponding to each dose. We used R-square to evaluate the linear relationship between delivered doses and textural features of the film's surface. Results: Correlation resulted in higher linearity and dose-response curve sensitivity than Homogeneity, Contrast, or Energy. The R-square value was 0.964 for correlation using 3,000× magnified SEM images with 9-pixel offsets. Dose verification was used to determine the difference between the prescribed and measured doses for 0, 5, 10, 15, and 20 Gy as 0.09, 1.96, -2.29, 0.17, and 0.08 Gy, respectively. Conclusions: Texture analysis can be used to accurately convert microscopic structural changes to the EBT3 film's surface into absorbed doses. Our proposed method is feasible and may improve the accuracy of film dosimetry used to protect patients from excess radiation exposure.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.275-275
• /
• 2022

Tsunamis triggered by undersea earthquakes have the characteristic of longer wavelengths and can propagate a very long distance. Although the occurrence frequency of tsunami is low, it can cause casualties and properties. Historically, tsunamis that occurred on the western coast of Japan attacked the eastern coast of the Korean Peninsula and damaged the property and the loss of human life in 1983 and 1993. By tsunami in 1983 especially, 2 people were killed, and more than 200 casualties occurred. In addition, it caused 2 million dollars in property damage at Imwon Port. In 2011, The eastern cities of Japan: Iwate, Miyagi, Ibaraki, and Fukushima were damaged by a tsunami that occurred near onshore along the Pacific ocean and caused more than 300 billion dollars in property damage, and 20,000 casualties occurred. Moreover, those provoked nuclear power plant meltdown at Fukushima. In this study, it was carried out a relationship between maximum tsunami heights and fault parameters of earthquake: strike angle, dip angle, and slip angle at Imwon port. Those fault parameters are known that it does not relate to the magnitude of earthquake directly. Virtual tsunamis, which could be triggered by probable undersea earthquakes in the future, were investigated and mutual information based on probability and information theory was introduced to figure out the relationship between maximum tsunami height and fault parameters. Fault parameters were evaluated according to the strong relationship with maximum tsunami heights finally.

• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.505-509
• /
• 2020

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.

• Journal of the Korean Society for information Management
• /
• v.40 no.4
• /
• pp.279-306
• /
• 2023

This study analyzed bibliographic attributes related to the selection and use of reading materials based on data on books borrowed or purchased together in order to understand the properties of reading materials that have complex user needs from various perspectives. As a result of creating co-occurrence matrices of bibliographic attribute terms by dividing them into 26 sub-attribute units related to KDC main class, target reader, and user age, and performing network analyses, the details and prominent mediating role of bibliographic attributes of reading materials were identified. The results of this study will be helpful in designing facets of reading information systems, including library OPAC, in the future.

• Annual Conference of KIPS
• /
• 2008.11a
• /
• pp.112-113
• /
• 2008

유방암은 유럽과 미국을 비롯해 전 세계적으로 증가하고 있으며 최근 우리나라에서도 가장 급속하게 늘고 있는 여성암중에 하나이다. 본 연구에서는 먼저 grey level co-occurrence matrix(GLCM)을 적용하여 유방영역을 분할한 후, median filter 를 적용하여 잡음을 제거하였다. 전처리 수행 후, 2차미분 행렬을 이용할여 미소석회화 부분을 강조한 후, 가우시안 정규분포도를 적용하여 미소석회화 후보군을 검출하였다. 검출된 후보군은 8 개의 feature 들을 적용하여 미소석회화를 최종 결정하였다. 본 연구를 통해서 조기 유방암 진단을 위한 발전된 미소석회화 검출 방법을 제안하였다.

• Journal of Wellbeing Management and Applied Psychology
• /
• v.6 no.4
• /
• pp.27-31
• /
• 2023

Purpose: This study analyzed papers identified by entering the two keywords 'unification education' and 'university' during research from 2013 to 2022 in order to identify trends and key concepts in unification education research at domestic universities. Research design, data, and methodology: The study analyzed 224 papers, excluding those on primary, middle, and high school unification education, as well as unrelated and duplicate papers. The analysis included developing a co-occurrence network of keywords, utilizing topic modeling to categorize research types, and confirming visualizations such as word clouds and sociograms. Results: In the final analysis, the research identified 1,500 keywords, with notable ones like 'Korea,' 'education,' 'unification.' Centrality analysis, measuring influence through connected keywords, revealed that 'Korea,' 'education,' 'north,' and 'unification' held significant positions. Keywords with high centrality compared to their frequency included 'learning,' 'development,' 'training,' 'peace,' and 'language,' in that order. Conclusions: This study investigated trends and structures in university-level unification education by analyzing papers identified with the keywords 'unification education' and 'university.' The use of keyword network analysis aimed to elucidate patterns and structures in university-level unification education. The significance of the study lies in offering foundational data for future research directions in the field of unification education at universities.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.47 no.5
• /
• pp.873-890
• /
• 2023

This study analyzed users' emotional responses to VI character design through YouTube comments. The researchers applied text-mining to analyze 116,375 comments, focusing on terms related to character design and characteristics of VI. Using the BERT model in sentiment analysis, we classified comments into extremely negative, negative, neutral, positive, or extremely positive sentiments. Next, we conducted a co-occurrence frequency analysis on comments with extremely negative and extremely positive responses to examine the semantic relationships between character design and emotional characteristic terms. We also performed a content analysis of comments about Miquela and Shudu to analyze the perception differences regarding the two character designs. The results indicate that form elements (e.g., voice, face, and skin) and behavioral elements (e.g., speaking, interviewing, and reacting) are vital in eliciting users' emotional responses. Notably, in the negative responses, users focused on the humanization aspect of voice and the authenticity aspect of behavior in speaking, interviewing, and reacting. Furthermore, we found differences in the character design elements and characteristics that users expect based on the VI's field of activity. As a result, this study suggests applications to character design to accommodate these variations.

• Asia pacific journal of information systems
• /
• v.29 no.4
• /
• pp.615-643
• /
• 2019

Now is the time for IS scholars to demonstrate the added value of academic theory through its integration with text mining, clearly outline how to implement this for text mining experts outside of the academic field, and move towards establishing this integration as a standard practice. Therefore, in this study we develop a systematic theory-based text-mining framework (TTMF), and illustrate the use and benefits of TTMF by conducting a text-mining project in an actual business case evaluating and improving hotel service quality using a large volume of actual user-generated reviews. A total of 61,304 sentences extracted from actual customer reviews were successfully allocated to SERVQUAL dimensions, and the pragmatic validity of our model was tested by the OLS regression analysis results between the sentiment scores of each SERVQUAL dimension and customer satisfaction (star rates), and showed significant relationships. As a post-hoc analysis, the results of the co-occurrence analysis to define the root causes of positive and negative service quality perceptions and provide action plans to implement improvements were reported.

• Journal of Forest and Environmental Science
• /
• v.40 no.1
• /
• pp.15-23
• /
• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.