• Korean Journal of Computational Design and Engineering
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• v.20 no.2
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• pp.145-158
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• 2015

ISO 15926 is an international standard for the sharing and integration of plant lifecycle information. Plant design data consist of logical configuration, equipment specifications, 2D piping and instrument diagrams (P&IDs), and 3D plant models (shape data). Although 3D computer-aided design (CAD) data is very important data across the plant lifecycle, few studies on the exchange of 3D CAD data using ISO 15926 have been conducted so far. For this, we analyze information requirements regarding plant 3D design in the process industry. Based on the analysis, ISO 15926 templates are defined for the representation of constructive solid geometry (CSG) - based 3D design data. Since system environments for 3D CAD modeling and Semantic Web technologies are different from each other, we present system architecture for processing and visualizing plant 3D design data in the Web Ontology Language (OWL) format. Through the visualization test of ISO 15926-based 3D design data for equipment with a prototype system, feasibility of the proposed method is verified.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.520-528
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• 2017

In this paper, we analyze semantics on mudras of Sahasra-bhuja Avalokitesvara and implement visual content as its application. The mudras are described in the Odaejineunjib that is a tangible cultural property. The semantics analysis on the mudras are essential for understanding the meaning of the 42 Hands(mudras) that present symbolic difference of hands holding on various items. So, in this paper, we analyze the semantics on the 42 Hands according to 5 parts which are basic classification of Honored Ones on Esoteric Buddhism. We implemented a visual contents showing Avalokitesvara according to semantics on the 42 Hands. And, in the process, we are able to provide the public with easy accessibility on mudras of Sahasra-bhuja Avalokitesvara. Applications of the mudras are as follows; game contents, traditional cultural contents, etc.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.668-677
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• 2021

In this paper, the characteristics and structural meaning of the Palyupsimryeon mandala, which represent the high spiritual world among Korean traditional cultural relics, are analyzed and visualized. Various studies on foreign mandalas are being introduced, but there is a lack of systematic analysis of the meaning of existing Korean mandalas. In this paper, we visualize the seeds of the Palyupsimryeon mandala in Korea by matching them with the image of the mandala, which is based on the shape of the mandala. Through this process, it shows that Korea's unique seed mandala has a form of mandala that combines the existing the Diamond World Mandala and the Matrix Mandala. Applications include Mandara's characterization, digital storytelling, and games.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• Fashion & Textile Research Journal
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• v.21 no.3
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• pp.277-287
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• 2019

This study analyzes the online issues of design innovation by a fashion creative director. The study selected fashion house Gucci as the main subject and analyzed social media posts. As for study methods, a social matrix program Textom 2.0 collected 13,014 nouns and adjectives using 'Gucci Collection' as a search keyword from Naver Blogs from March to August 2014 and from March to August 2016. Design related issues were derived through semantic network analysis using Ucinet6 and the NetDraw program. The results of the keyword frequency analysis showed that social media user interest for the Gucci collection increased based on the rapid increase in the number of posts from 1,064 to 2,126 after changing the fashion creative director. The results of visualization of semantic network analysis and content analysis also showed that the main issues related to the Gucci collection design changed after the replacement of the fashion creative director. The study found that issues formed around the product information worn by celebrities for promotion purposes during the 2014 period; however, during the 2016 period, issues were formed around 'vintage' and 'retro' runway concepts with design styles related to Alessandro Michele, the new creative director.

• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.537-544
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• 2022

As the penetration of electric vehicles and development of new models, user experience factors are getting more important in designing connectivity systems for car infotainment services. The primary object of this study is to identify commonalities and differences by comparing user experience factors in the Korean and US electric vehicle markets. This study derived connectivity keywords by text mining the vehicle introduction on the market in each country, and performed centrality, cluster analysis and visualization mapping using the semantic network analysis. As a result, the Korean new electric vehicle connectivity service mainly focused on driving functions such as driving, parking assistance, and charging, while US focused on device connection, convenience function control, app use, entertainment viewing. Based on the analysis, this study presented the practical implications in marketing, system design, and HMI design.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.155-162
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• 2022

In this paper, we examined the research trends and characteristics related to the metaverse in global journals published between 2000 and 2022 from the Web of Science database. The analysis included descriptive statistics, multidimensional scaling, keyword network analysis, and visualization. In addition, semantic network models were constructed, and centrality (betweenness and degree) analysis was performed using R and KH coder in two separate categories based on the trends and aspects of the publication: analysis period 1 (Jan 2000 to Dec 2020) and period 2 (Jan 2021 to Jun 2022). The results showed that the recent global research trends related to the metaverse could be quantitatively characterized using the semantic network analysis. Also, the results could be applied to suggest future research topics in the field of metaverse based on quantitative and empirical data.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.211-218
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• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.