• 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.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.2
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• pp.49-67
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• 2023

In recent years, the film industry in South Korea has experienced a period of prosperity, evidenced by the numerous awards won at major international film festivals. Furthermore, growing global interest in K-content and the expansion of the OTT industry following the COVID-19 pandemic are providing favorable conditions for the development of the domestic film industry. Sound effects play a crucial role in conveying the atmosphere and emotions of a film, making them an essential element of film production. In response, the Jeonju IT & CT Industry Promotion Agency has been promoting the development of Korean-style sound effects since 2013. Furthermore, the agency launched an online service called the "K-Sound Library," a sound effect archive, in 2021. However, the service has not been widely utilized because of issues with the database's construction and the system's problems. Therefore, this study aims to identify the K-Sound Library's problems through interviews with sound effects specialists about the online service of the first sound effect archive in South Korea. Based on the interviews and analyses of foreign cases, the study suggests ways to improve the search services' usability and the sound effects classification system.

• Information Systems Review
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• v.22 no.2
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• pp.77-99
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• 2020

Smart speaker is a device that provides an interactive voice-based service that can search and use various information and contents such as music, calendar, weather, and merchandise using artificial intelligence. Since AI technology provides more sophisticated and optimized services to users by accumulating data, early smart speaker manufacturers tried to build a platform through aggressive marketing. However, the frequency of using smart speakers is less than once a month, accounting for more than one third of the total, and user satisfaction is only 49%. Accordingly, the necessity of strengthening the user experience of smart speakers has emerged in order to acquire a large number of users and to enable continuous use. Therefore, this study analyzes the user experience of the smart speaker and proposes a method for enhancing the user experience of the smart speaker. Based on the analysis results in two stages, we propose ways to enhance the user experience of smart speakers by model. The existing research on the user experience of the smart speaker was mainly conducted by survey and interview-based research, whereas this study collected the actual review data written by the user. Also, this study interpreted the analysis result based on the smart speaker user experience dimension. There is an academic significance in interpreting the text mining results by developing the smart speaker user experience dimension. Based on the results of this study, we can suggest strategies for enhancing the user experience to smart speaker manufacturers.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.195-204
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• 2008

With the growth of airline management, as well as computer and IT security, the international trade in this modern society has been rapidly increasing, Along with the advancing, airplanes have become a universal means of communication. However, the complications associated with airplane safety have also been brought up as a result, the most concerning of which is terrorism. One of the main counterplans for preventing terrorism is Ground security activities the core of Ground security activities is absolute safety for passengers in both passenger terminal and freight terminal. Subastral security refers to physical protection, proximity control and 100% security search and freight guarding of the passengers' possessions, and the personnel's duties to perform such jobs are be! coming more crucial. On the other hand, Airport security check has bee n gradually developing since the 1960's, when hijacking began to take place. Although the airports have been providing more safe and comfortable services to their customers, terrorism is still happening today. When Ground security activities is minute, the users feel displeasure and discomfort, yet considering solely their convenience can brings problems in achieving safety. Since the 9.11 terror in 2001, the idea of improving and strengthening airport security was reinforced and a considerable amount of estate is being spent today for invention and application of new technology. Various nations, including the United States, have been improving their systems of security through public services; public police department is actively carrying out their duties in airports as well. In San Francisco International Airport, private police department is in charge of collection of data, national events, VIP protection, law enforcement, cooperation within facilities, daily-based patrol and traffic control. Under guidance and supervision of national organizations, such as TSA, general police department interprets X-Rays, operates metal detectors, checks passports or IDs and observes reactions to explosives. Under these circumstances, studies about advancement of cooperation and duties of general police department and private police department necessitated: especially about private police department and their training for searching equipments, decrease in number of turn over rate, invention of technology and prior settlement in estate for security. The privacy of the public, who make up the major population of airport passengers, must also be minimized. In the following research, the activities of police departments in San Francisco International Airport will be analyzed in order to understand recent actions of the United States on airport security.

• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.63-74
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• 2024

The fundamental basis for revitalizing cultural resources and developing content is national heritage(cultural property). In national heritage, cultural heritage is a tangible cultural heritage that represents the uniqueness of history and tradition, identity, and changes in life. In the case of museums, the collections (a museum-owned cultural heritage) represent the unique characteristics of the institution. In South Korea, it is recommended that museum collections be registered and used in the Cultural Heritage Standard Management System so that cultural heritage can be managed and utilized in connection with academics, industry, and administration. However, due to a lack of awareness of modern and contemporary heritage, the thematic classification chronology of the system was set mainly before the Joseon Dynasty, and a cultural heritage classification system suitable for national land information has not been established. Therefore, this study aims to propose a classification system for cadastral cultural heritage, based on the modern era when cadastral terminology was first used, using the cultural heritage owned by the LX Museum. Cadastral cultural heritage is characterized by the fact that although it is a field of specialized technology, the surveying or the production of it is not done by specific individuals only, and that while the production is professional, there are many educational aspects in its use. Therefore, unlike other specialized museum collections that are classified based on the functional aspects of their production methods, intended use, and creators, the classification method for cadastral cultural artifacts should be based on the characteristics of the cadastral tools and the outputs. This classification follows a three-tier stages with reference to the items in the Cultural Heritage Standard Management System. This classification aims at the effective use of knowledge by categorizing concepts and systematizing the subjects of data into a series of orders. A safe conservation and management environment for cadastral cultural heritage can be established, and academic and socio-cultural interpretation of the collection is possible by this classfication. Moreover, It is also expected to serve the basis for the national land information as well as searching for the national land information research, planning a exhibition, and the field of education in museum.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.