• International journal of advanced smart convergence
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• v.13 no.3
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• pp.170-175
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• 2024

The field of Generative AI(Artificial Intelligence) involves a technology that autonomously comprehends user intentions through commands and learns from provided data to generate new content, such as images or text. This capability, which allows autonomous creativity even with design keywords, is anticipated to play a significant role in the domain of visual communication design. This article delves into the tools of generative AI applicable to visual design and the methodology for design creation using these tools. Furthermore, it discusses how designers can interact visually with AI technology in the era of generative AI.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5005-5011
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• 2011

The purpose of this work is to know the validity of a new stimulation method in cognitive functional imaging using custom-designed images correspond to words or syllables improving the shortcomings of existing method using text. From March 2011 to May five Subjects in need of language related functional MRI scanning were selected and both of text stimulating method and image stimulating method sacanning were carried out three times each. Using 3.0T Philps MRI machine and Invivo Co's Eloquence system, data acquisition was performed with EPI-BOLD technique. Post processing was performed with SPM 99 while the activated signals were determined within 95 percent confidence level.The number of activation clusters and the activation ratio inside ROI were compared. As as result, all of the subject showed activation inside Broca area but it did not have statistical significance. In conclusion, the image sitimulation method has potential because image itself is a common means of recognition and it can be recognised easily even if there language barrier. This stimulation method can be applied to replacing the exising scanning method especially in the elderly, infants, foerigners who may not fully understand about the examination.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.401-414
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• 2021

The purpose of this study was to examine the associations of Korean older adults' use of Social Network Service (SNS) and text messages with frequency of contact with 1) non-coresident adult children, 2) siblings and relatives, or 3) friends, neighbors, and acquaintances. Data were drawn from the 2017 Survey of Living Conditions and Welfare Needs of Korean Older Persons 65+ (N=8,392), and older adults were categorized into 4 groups depending on their familiarity with use of SNS and text messages. Ordinary Least Squares regression models were estimated for analyses. Results revealed that older users of both types of communication media reported frequent exchanges of calls, text messages, etc. with both family and friends. However, using SNS and text messages was consistently related to more face-to-face contact with non-family members. To conclude, older adults' familiarity with communication media could be key to exchanges of emotional and instrumental support with informal social network members and quality of life in the community. Overall, our results highlight the importance of information communication education targeting older adults for continued involvement with their informal social network members.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.1-21
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• 2023

This study specifically reviewed the performance of the ranking schemes as an efficient feature selection method for text classification. Until now, feature ranking schemes are mostly based on document frequency, and relatively few cases have used the term frequency. Therefore, the performance of single ranking metrics using term frequency and document frequency individually was examined as a feature selection method for text classification, and then the performance of combination ranking schemes using both was reviewed. Specifically, a classification experiment was conducted in an environment using two data sets (Reuters-21578, 20NG) and five classifiers (SVM, NB, ROC, TRA, RNN), and to secure the reliability of the results, 5-Fold cross-validation and t-test were applied. As a result, as a single ranking scheme, the document frequency-based single ranking metric (chi) showed good performance overall. In addition, it was found that there was no significant difference between the highest-performance single ranking and the combination ranking schemes. Therefore, in an environment where sufficient learning documents can be secured in text classification, it is more efficient to use a single ranking metric (chi) based on document frequency as a feature selection method.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.131-139
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• 2016

This paper presents a workflow validation method for data-intensive graphical workflow models using real-time workflow tracing mode on data-intensive workflow designer. In order to model and validate workflows, we try to divide as modes have editable mode and tracing mode on data-intensive workflow designer. We could design data-intensive workflow using drag and drop in editable-mode, otherwise we could not design but view and trace workflow model in tracing mode. We would like to focus on tracing-mode for workflow validation, and describe how to use workflow tracing on data-intensive workflow model designer. Especially, it is support data centered operation about control logics and exchange variables on workflow runtime for workflow tracing.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.830-832
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• 2000

XML형식으로 표현된 RDF data를 VRML을 사용하여 시각적으로 나타내는 방법을 연구하였다. 현재 Web 환경은 동적으로 문서를 생성하고 Visual하게 표현하는 방향으로 발전하고 있으며 이러한 환경에서 XML은 실시간으로 data를 생성하기 쉬워 많이 사용되고 있다. 그러나 XML은 text 기반이기 때문에 data를 가시화하여 사용자한테 보여주기 힘들며 data를 표현하는데 너무 많은 융통성을 제공하고 있다는 단점이 있다. 이에 XML 표현에 제약을 둬 표준적인 방식으로 표현하도록 해주는 RDF가 유용하다고 할 수 있다. 본 논문에서는 VRML을 RDF와 결합하여 실시간으로 변하는 data를 시각화 도구를 사용하여 표현하는 방법에 대해 연구를 하였다 이를 위하여 Java Servlet을 사용하였으며 RDF 문서에서 data를 추출하여 VRML 펀드를 만들고. 그 코드를 사용자측에 전달하여 시각적으로 data를 볼 수 있도록 하는 시스템을 구현하였다.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.15-29
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• 2021

The study aims to grasp the perception and appraisal of urban park users through text analysis. This study used Google review data provided by Google Maps. Google Maps Review is an online review platform that provides information evaluating locations through social media and provides an understanding of locations from the perspective of general reviewers and regional guides who are registered as members of Google Maps. The study determined if the Google Maps Reviews were useful for extracting meaningful information about the user perceptions and appraisals for parks management plans. The study chose three urban parks in Seoul, South Korea; Seoul Forest, Boramae Park, and Olympic Park. Review data for each of these three parks were collected via web crawling using Python. Through text analysis, the keywords and network structure characteristics for each park were analyzed. The text was analyzed, as were park ratings, and the analysis compared the reviews of residents and foreign tourists. The common keywords found in the review comments for the three parks were "walking", "bicycle", "rest" and "picnic" for activities, "family", "child" and "dogs" for accompanying types, and "playground" and "walking trail" for park facilities. Looking at the characteristics of each park, Seoul Forest shows many outdoor activities based on nature, while the lack of parking spaces and congestion on weekends negatively impacted users. Boramae Park has the appearance of a city park, with various facilities providing numerous activities, but reviewers often cited the park's complexity and the negative aspects in terms of dog walking groups. At Olympic Park, large-scale complex facilities and cultural events were frequently mentioned, emphasizing its entertainment functions. Google Maps Review can function as useful data to identify parks' overall users' experiences and general feelings. Compared to data from other social media sites, Google Maps Review's data provides ratings and understanding factors, including user satisfaction and dissatisfaction.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1207-1212
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• 2009

Basic objective in cluster analysis is to discover natural groupings of items or variables. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input text data. Various measures of similarities (or dissimilarities) between objects (or variables) are developed. We introduce a real application problem of clustering procedure in SPSS when the distance matrix of the objects (or variables) is only given as an input data. It will be very helpful for the cluster analysis of huge data set which leads the size of the proximity matrix greater than 1000, particularly. Syntax command for matrix input data in SPSS for clustering is given with numerical examples.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.28-35
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• 2006

Global Flood Alert System (GFAS) is an attempt to make the best use of satellite rainfall data in flood forecasting. The project of GFAS is promoted both by Ministry of Land, Infrastructure and Transport-Japan (MLIT) and Japan Aerospace Exploration Agency (JAXA), under which Infrastructure Development Institute-Japan (IDI) has been working on the development of Internet-based information system and just launched trial run of GFAS in April 2006 on International Flood Network (IFNet) website. The function of GFAS is to connect space agencies and hydrological services/river authorities in charge of flood forecasting and warning by providing global rainfall information in maps, text data e-mails and so on which is produced from binary global rainfall data downloaded from National Aeronautics and Space Administration (NASA) website. Although the effectiveness of satellite rainfall data in flood forecasting and warning has yet to be verified, satellite rainfall is expected to play an important role to strengthen existing flood forecasting systems by diversifying hydrological data source.