• Korean Journal of Human Ecology
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• v.10 no.4
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• pp.399-409
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• 2001

The purpose of this study was to evaluate the preference of image for modern application of Korean traditional patterns. A survey was conducted using the random selection among female undergraduate students in Daegu city. The degree of interest and preference in Korean traditional style or something like that measured by 5 scale method. And then they were classified into two groups which were interest/non-interest group, and preference/non-preference group. The image of Korean traditional patterns consisted of semantic differential scales. Frequency, percentage and mean were analyzed, for difference of groups t-test was analyzed. The results were as fellows; 1. For the degree of interest for Korean traditional patterns, it was showed that 53.8% of total respondents took interest and about 40.4% of them had preference for traditional patterns. the correlation coefficient of the degree of interest and preference was 0.782(p<0.01) and showed that the positive correlation was high. 2. Among 20 kinds of Korean traditional patterns, the degree of preference for the patterns of plants and nature was quite high whereas that for the patterns of geometrical things was low relatively. 3. It was evaluated that pattern of nature was fresh, refined and womanly image generally. It was evaluated that pattern of plants was womanly, fresh, weak, light and soft image and that of animals was heavy, splendid, high-class, manly, strong and positive image. It was evaluated that pattern of geometrical things was the most refined image and high-class, rigid and strong. 4. The statistical significance of mean between interest/non-interest group was showed statistically in the patterns of clouds, mountains, lotus, apricot, orchid, dragon, phoenix and bogey. In case of pattern of orchids, the degree of preference was most different between interest/non-interest group. 5. The pattern of plants showed the most different evaluation for images between interest/non-interest group. For refined/old-fashioned polar adjective images, the interest group evaluated the pattern of plants more refined. 6. For pattern of orchids, the difference of degree of preference between preference/non-preference group was most remarkable in Korean traditional patterns. 7. The pattern of geometrical things showed the most different evaluation for images between preference/non-preference group. For warm/cool polar adjective images, the preference group evaluated the pattern of geometrical things cooler.

• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.129-155
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• 2010

In recent years, the changes in information technology have been so dramatic and the rate of changes has increased so much that information science research rigorously evolves with the passage of time and proliferates in diverging research directions dynamically. The aims of this study are to provide a global overview of research trends in information science and to trace its changes in the main topics over time. The study examined the topics of research articles published in JASIST between 1985 and 2009 and identified its changes during five 5 year periods. The study found that the most productive area has consistently been 'Information Retrieval', followed by 'Informetrics', 'Information Use and Users', 'Network and Technology', and 'Publishing and Services'. Information retrieval is a predominant core area in Information Science covering computer-based handling of multimedia information, employment of new semantic methods from other disciplines, and mass information handling on virtual environments. Currently Informetric studies shift from finding existing phenomena to seeking valuable descriptive results and researchers of information use have concentrated especially on information-seeking aspects, so adding greater sophistication to the relatively simple approach taken in information retrieval.

• Journal of Information Management
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• v.40 no.3
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• pp.79-97
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• 2009

Thanks to current development of many search engines and web technologies, a new semantic searching technology appears, featuring giving a relevant meaning to the keyword beyond the previous keyword search service. On the wave of advance of various search engines, the enhancement of OASIS offered by KIOM is needed as well. To do this, KIOM examined demographic and sociological analysis on their position, status, and career, the convenience of OASIS, and the value of papers offered in OASIS from members who have ever used it. Furthermore, the importance of each area involved in oriental medicine is also examined in terms of a new direction for OASIS improvement. Based on the result of the user survey, it turned out that not only an automatic search system that can find meaning of chinese character-centered key words but also a Authority-system which can distinguish homonym beyond simple keyword search system should be introduced quickly. Also, we reached the conclusion that it is necessary to interconnect a citation index information on references with laboratory information of the agencies concerned and interconnect major web sites around the world by using Open API. OASIS is the only domestic web site for offering papers that cover oriental medicine. Therefore, if requirements about the site in oriental medical circles are analyzed sufficiently and the problems of its information search system are improved, OASIS is expected to play a critical role in the development of oriental medicine.

• Horticultural Science & Technology
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• v.34 no.3
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• pp.472-487
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• 2016

This study aimed to determine the effects of floral fragrances on human brain waves and moods. A total of 44 subjects participated in this experiment. Group 1 consisted of 11 male and 14 female college students with a mean age of 24.5 years (${\pm}2.23$) and Group 2 consisted of 10 males and 9 females with a mean age of 54.3 years (${\pm}2.98$). Subjects were exposed to floral fragrances of Rosa hybrida, 'Hera' (hereafter referred to as "rose"), Cymbidium faberi (hereafter referred to as "orchid"), or odorless control flowers (hereafter referred to as "control"). Experiments took place in three rooms (rose, orchid, and control). Electroencephalographs (EEGs) were recorded during exposure to the odors and the data were processed using quantitative electroencephalographic (QEEG) techniques. The changing EEG patterns were analyzed by brain mapping and compressed spectral arrays, and the subjects' preferences (hedonic evaluations) were quantified with an A1 index. Increased activation of absolute alpha waves was verified on six of the eight EEG channels, with the right frontal and left occipital lobes exhibiting no changes and the left parietal region showing the greatest activation. According to the QEEG measurements in the electrode sites over the frontal, temporal, parietal, and occipital lobes, the strongest absolute alpha waves were induced in the parietal lobes, followed by the temporal lobes, with the other lobes showing no significant changes. On brain maps, the orchid fragrance induced greater absolute alpha and absolute mid-beta activities compared with the rose and control fragrances, and the rose fragrance induced high absolute mid-beta activation. To identify emotional responses to floral fragrances, the subjects were requested to fill in a questionnaire and the resulting odor-related emotional descriptors were analyzed using semantic differential and factor analysis. Principal component analysis identified "elegant" as the first principal component describing the floral fragrance, followed by "refreshing" and "aromatic." The subjects gave orchid higher scores for "elegant" and "refreshing," while finding rose more "aromatic." Differences in hedonic evaluation revealed by the A1 index appeared in the 65-115 sec range of scent exposure time. The subjects with ages of around 50 years showed olfactory preferences throughout the entire experimental time of 160 sec, most markedly in the later time segment (115-165 sec), showing an increasing preference with increasing exposure time. We conclude that rose fragrance can improve concentration by creating an aromatic environment conducive to a concentrated and calm state of mind, and orchid fragrance can make people feel pampered and relaxed by creating an elegant and refreshing environment.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.343-348
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• 2022

This study aims to analyze the user experience of unmanned checkout counters perceived by consumers using SNS big data. For this study, blogs, news, intellectuals, cafes, intellectuals (tips), and web documents were analyzed on Naver and Daum, and 'unmanned checkpoints' were used as keywords for data search. The data analysis period was selected as two years from January 1, 2020 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted through Textom, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, the perception of the checkout counter was clustered into accessibility, usability, continuous use intention, and others according to the definition of consumers' experience factors. From a supplier's point of view, if unmanned checkpoints spread indiscriminately to solve the problem of raising the minimum wage and shortening working hours, a bigger employment problem will arise from a social point of view. In addition, institutionalization is needed to supply easy and convenient unmanned checkout counters for the elderly and younger generations, children, and foreigners who are not familiar with unmanned calculation.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.627-635
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• 2022

As the non-face-to-face situation continues for a long time due to COVID-19, the underlying technologies of the 4th industrial revolution such as IOT, AR, VR, and big data are affecting the metaverse platform overall. Such changes in the external environment such as society and culture can affect the development of academics, and it is very important to systematically organize existing achievements in preparation for changes. The Korea Educational Research Information Service (RISS) collected data including the 'metaverse platform' in the keyword and used the text mining technique, one of the big data analysis. The collected data were analyzed for word cloud frequency, connection strength between keywords, and semantic network analysis to examine the trends of metaverse platform research. As a result of the study, keywords appeared in the order of 'use', 'digital', 'technology', and 'education' in word cloud analysis. As a result of analyzing the connection strength (N-gram) between keywords, 'Edue→Tech' showed the highest connection strength and a total of three clusters of word chain clusters were derived. Detailed research areas were classified into five areas, including 'digital technology'. Considering the analysis results comprehensively, It seems necessary to discover and discuss more active research topics from the long-term perspective of developing a metaverse platform.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.167-181
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• 2023

This study utilized TEXTOM, a social network analysis program to analyze changes in current tourism behavior after travel restrictions were eased after the outbreak of COVID-19. Data on the keywords 'domestic travel' and 'overseas travel' were collected from blogs, cafes, and news provided by Naver, Google, and Daum. The collection period was set from April to December 2022 when social distancing was lifted, and 2019 and 2020 were each set as one year and compared and analyzed with 2022. A total of 80 key words were extracted through text mining and centrality analysis was performed using NetDraw. Finally, through the CONCOR, the correlated keywords were clustered into 4. As a result of the study, tourism behavior in 2022 shows tourism recovery before the outbreak of COVID-19, segmentation of travel based on each person's preferred theme, prioritization of each country's corona mitigation policy, and then selecting a tourist destination. It is expected to provide basic data for the development of tourism marketing strategies and tourism products for the newly emerging tourism ecosystem after COVID-19.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.