• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1088-1099
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• 2003

The syntax-directed translation using semantic actions is frequently used in construction of compiler for text programming languages. it is very useful for the language designers to develop compiler back-end using a syntax structure of a source programming language. Due to the lack of the integrated representation method for a parse tree node and modeling method of syntax structures, it is very hard to construct compiler using syntax-directed translation in visual languages. In this Paper, we propose a visual language compiler generation method for constructing a visual languages compiler automatically, using syntax-directed translation. Our method uses the Picture Layout Grammar as a underlying grammar formalism. This grammar allows our approach to generate parser efficiently u sing And-Or-Waiting Graph and encapsulating syntax definition as one unit. Unlike other systems, we suggest separating the specification and the generation of semantic actions. Because of this, it provides a very efficient method for modification.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.340-345
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• 2009

기존 파일 시스템의 검색은 검색결과를 제목과 요약문의 텍스트 형태로 제공함으로써 검색 결과가 많은 경우에 한눈에 결과를 살펴보는데 불편할 뿐 아니라 사용자가 직접 수많은 검색결과의 표제나 저자, 목차, 요약문을 확인하여 적합한 정보를 일일이 판별해야 하는 불편이 있다. 이에 정보들간의 유사도를 계산하여 군집화하고, 키워드와 검색결과들 간의 적합도와 검색결과들 간의 연관성 정보를 3D 공간 상에 디스플레이 하는 'DocuSynth' 시스템을 개발하였다. 이 연관성 정보들은 실세계 상의 3 차원 메타포인 '거리'로 변환되어 디스플레이 된다. 즉, 사용자로 하여금 정보간의 거리가 가까울수록 연관도가 높다고 직관적으로 인지할 수 있는 화면으로 설계하였다. 또한 3D 환경의 사용성을 높이기 위해 네비게이션 컨트롤러와 컨트롤 변수에 대한 사용성 평가를 실시하여 시스템 변수로 적용하였다. 본 연구결과는 향후 도래할 3D Web 에 대한 아이디어 제시와 구현 가이드라인으로 활용될 것으로 예상된다.

• Convergence Security Journal
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• v.19 no.5
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• pp.75-84
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• 2019

Recently, the value of video as an important data of medical information technology is increasing due to the feature of rich clinical information. On the other hand, video is also required to be de-identified as a medical image, but the existing methods are mainly specialized in the stereotyped data and still images, which makes it difficult to apply the existing methods to the video data. In this paper, we propose an automated system to index candidate elements of personal identification information on a frame basis to solve this problem. The proposed system performs indexing process using text and person detection after preprocessing by scene segmentation and color knowledge based method. The generated index information is provided as metadata according to the purpose of use. In order to verify the effectiveness of the proposed system, the indexing speed was measured using prototype implementation and real surgical video. As a result, the work speed was more than twice as fast as the playing time of the input video, and it was confirmed that the decision making was possible through the case of the production of surgical education contents.

• Smart Media Journal
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• v.4 no.4
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• pp.70-79
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• 2015

The Construction Project Information System(CPIS), an information system constructed as part of the Construction Continuous Acquisition & Life-cycle Support(CALS) of the Ministry of Land, Infrastructure, and Transport(MOLIT), is designed to digitize construction projects across all stages, and enable sharing of information so as to enhance the productivity and efficiency of construction projects and secure their transparent administration. One of MOLIT's internal work systems, CPIS focuses on work-handling and data management. However, now over 10 years old after its construction, it focuses on text and document-based construction project information, but it cannot be interfaced with the visualization-based GIS, which limits the sharing and dissemination of information and the determination of the overall construction project status. To resolve the existing CPIS limitations and problems and to upgrade the system, this study examined domestic and overseas GIS technology trends and relevant information systems, and analyzed the CPIS status and problems. It thus proposed total GIS application measures to upgrade CPIS. Also, it identified detailed CPIS utilization measures and GIS application measures by unit system, and analyzed considerations for GIS application.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.19-28
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• 2004

This paper shows how to compensate the skew from the traffic sign included in the natural image and extract the text. The research deals with the Process related to the array image. Ail the process comprises four steps. In the first fart we Perform the preprocessing and Canny edge extraction for the edge in the natural image. In the second pan we perform preprocessing and postprocessing for Hough Transform in order to extract the skewed angle. In the third part we remove the noise images and the complex lines, and then extract the candidate region using the features of the text. In the last part after performing the local binarization in the extracted candidate region, we demonstrate the text extraction by using the differences of the features which appeared between the tett and the non-text in order to select the unnecessary non-text. After carrying out an experiment with the natural image of 100 Pieces that includes the traffic sign. The research indicates a 82.54 percent extraction of the text and a 79.69 percent accuracy of the extraction, and this improved more accurate text extraction in comparison with the existing works such as the method using RLS(Run Length Smoothing) or Fourier Transform. Also this research shows a 94.5 percent extraction in respect of the extraction on the skewed angle. That improved a 26 percent, compared with the way used only Hough Transform. The research is applied to giving the information of the location regarding the walking aid system for the blind or the operation of a driverless vehicle

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.773-786
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• 2017

This study was to analyze data of climate change impacts on social infrastructure using text-mining methodology, and to visualize the spatial information by integrating those with regional data layers. First of all, the study identified that the following social infrastructure; power, oil and resource management, transport and urban, environment, and water supply infrastructures, were affected by five kinds of climate factors (heat wave, cold wave, heavy rain, heavy snow, strong wind). Climate change impacts on social infrastructure were then analyzed and visualized by regions. The analysis resulted that transport and urban infrastructures among all kinds of infrastructure were highly impacted by climate change, and the most severe factors of the climate impacts on social infrastructure were heavy rain and heavy snow. In addition, it found out that social infrastructure located in Seoul and Gangwon-do region were relatively largely affected by climate change. This study has significance that atypical data in media was used to analyze climate change impacts on social infrastructure and the results were translated into spatial information data to analyze and visualize the climate change impacts by regions.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.189-198
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• 2019

This study aims to make meaning of the reactions of the Korean people through the text mining of comments on videos of the December 2018 MMA performance of intro on YouTube. For this, comments on 15 YouTube videos were collected over the past 10 months. With the collected data, a total of 5,135 comments were analyzed through crawling using the Python and BeautifulSoup programs, data was refined over a total of 3 sessions, and a final total of 5,080 comments were used as analysis material. A mining technique was used for data analysis and the process of refinement, analysis, and visualization was achieved using the Textom program. Research results showed that keyword analysis showed the keywords of 'performance', 'Korea', 'video', 'top', 'cool', 'dance', 'idol', 'legend', 'love', and 'gratitude' in that order and keywords such as 'patriotism' and 'Olympics' also appeared frequently. N-gram analysis showed that comments with contexts such as 'a top performance that will remain a legend among Korean idol performances', and 'an idol performance that displayed the traditional culture of Korea' were in higher ranks. Based on such keyword analysis results, topic modeling was applied and 5 top keywords were extracted from a total of 5 topics. Analysis results of topic contents and distribution showed that topics in the comments of this performance's videos largely consisted of the 3 reactions of 'high praise regarding the stage performance', 'affection towards the fusion and artistic sublimation of Korean traditional dance', and 'gratitude towards the uploading of cool dance videos'

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.61-76
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• 2015

In general, we have utilized the hierarchical concept tree as a crucial data structure for indexing huge amount of textual data. This paper proposes a generality rank-based method that can automatically develop hierarchical concept structures with the Wikipedia data. The goal of the method is to regard each of Wikipedia articles as a concept and to generate hierarchical relationships among concepts. In order to estimate the generality of concepts, we have devised a special ranking function that mainly uses the number of hyperlinks among Wikipedia articles. The ranking function is effectively used for computing the probabilistic subsumption among concepts, which allows to generate relatively more stable hierarchical structures. Eventually, a set of concept pairs with hierarchical relationship is visualized as a DAG (directed acyclic graph). Through the empirical analysis using the concept hierarchy of Open Directory Project, we proved that the proposed method outperforms a representative baseline method and it can automatically extract concept hierarchies with high accuracy.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.173-182
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• 2016

AI (Artificial Intelligence) has attracted interest as a key element for technological advancement in various fields. In Korea, internet companies are leading the development of AI business technology. Active government funding plans for AI technology has also drawn interest. But not everyone is optimistic about AI. Both positive and negative opinions coexist about AI. However, attempts on analyzing people's opinions about AI in a quantitative way was scarce. In this study, we used text mining on SNS (Social Networking Service) to collect opinions about AI. And then we performed a comparative analysis about whether people view it as a positive thing or a negative thing and performed a comparative analysis to recognize popular key-words. Based on the results, it was confirmed that the change of key-words and negative posts have increased through time. And through these results, we were able to predict trend about AI.