• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.17 no.2
• /
• pp.23-42
• /
• 2006

This is a study of the digital archives of Culturecontent.com where 'Cultural Archetype Contents' are currently in service. One of the major purposes of our study is to point out problems in the current system and eventually propose improvements to the digital archives. The government launched a four-year project for developing the cultural archetype content sources and establishing its related business with the hope of enhancing the nation's competitiveness. More specifically, the project focuses on the production of source materials of cultural archetype contents in the subjects of Korea's history. tradition, everyday life. arts and general geographical books. In addition, through this project, the government also intends to establish a proper distribution system of digitalized culture contents and to control copyright issues. This paper analyzes the digital archives system that stores the culture content data that have been produced from 2002 to 2005 and evaluates the current system's weaknesses and strengths. The summary of our findings is as follows. First. the digital archives system does not contain a semantic search engine and therefore its full function is 1agged. Second, similar data is not classified into the same categories but into the different ones, thereby confusing and inconveniencing users. Users who want to find source materials could be disappointed by the current distributive system. Our paper suggests a better system of digital archives with text mining technology which consists of five significant intelligent process-keyword searches, summarization, clustering, classification and topic tracking. Our paper endeavors to develop the best technical environment for preserving and using culture contents data. With the new digitalized upgraded settings, users of culture contents data will discover a world of new knowledge. The technology we introduce in this paper will lead to the highest achievable digital intelligence through a new framework.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.471-492
• /
• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of Korea Multimedia Society
• /
• v.7 no.1
• /
• pp.132-144
• /
• 2004

With the rapid development of internet technology, the number of internet users and the amount of multimedia information on the internet is ever increasing. Recently, the web sites, such as e-business sites and shopping mall sites, deal with lots of image information. As a result, it is required to support content- based image retrieval efficiently on such image data. This paper proposes an intelligent image retrieval system, which adopts XML, technology. To support object-based col)tent retrieval on product catalog images containing multiple objects, we describe a multi -level metadata structure which represents the local features, global features, and semantics of image data. To enable semantic-based and content-based retrieval on such image data, we design a XML-Schema for the proposed metadata and show how to represent such metadata using XML- documents. We also describe how to automatically transform the retrieval results into the forms suitable for the various user environments, such as web browser or mobile browser, using XSLT The proposed scheme can be easily implemented on any commercial platforms supporting XML technology. It can be utilized to enable efficient image metadata sharing between systems, and it will contribute in improving the retrieval correctness and the user's satisfaction on content-based e-catalog image retrieval.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.6
• /
• pp.513-524
• /
• 2022

Recently, detecting damages of civil infrastructures from digital images using deep learning technology became a very popular research topic. In order to adapt those methodologies to the field, it is essential to explain robustness of deep learning models. Our research points out that the existing pixel-based deep learning model evaluation metrics are not sufficient for detecting cracks since cracks have linear appearance, and proposes a new evaluation methodology to explain crack segmentation deep learning model more rationally. Specifically, we design, implement and validate a methodology to generate tolerance buffer alongside skeletonized ground truth data and prediction results to consider overall similarity of topology of the ground truth and the prediction rather than pixel-wise accuracy. We could overcome over-estimation or under-estimation problem of crack segmentation model evaluation through using our methodology, and we expect that our methodology can explain crack segmentation deep learning models better.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.2
• /
• pp.95-111
• /
• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.33-54
• /
• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.2
• /
• pp.143-157
• /
• 2008

Ubiquitous Web Services is able to be specified future Web Services technology for connecting with various application services in any device and network environments. The devices, in ubiquitous environment, have dynamic characteristic such as location and statuse. So, we must support methods of dynamic service discovery in ad-hoc network. There are many related works at transaction, security, QoS, semantic and Web Services composition with various fields. Recently, the studies are interested in the Ubiquitous by development of computing and network technology. However, they are an early stage. For this reason, in this paper, we propose a WSUN(Web Services on Universal Networks) for Ubiquitous Web Services. It is a SOA based framework. And this paper extracts necessity of WSUN environment from scenario. The framework is composed of US Broker(Universal Service Broker). It is designed for satisfying the conditions and supports dynamic service discovery using a US Registry (Universal Service Registry). Consequently. clients are able to discover and use Universal Service by protocol stack of the US Broker for Web Services. And it is a strong point which supports interoperability between heterogeneous networks.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.3
• /
• pp.34-42
• /
• 2008

The control points are important assets of countries which express the most accurate location information that is used in surveying land and other measurements. The location information has played an important role in our daily lives with the development of ubiquitous technology. While many researchers have recently applied new technology like RFID(Radio-Frequency Identification) to the effective management of control points, the research into data retrieval and the interoperability of control point data is still primitive step. Therefore, we construct a data modeling to effectively manage control points using ontology data structure and focus on semantic retrieval method. Our retrieval system can provide the inferred and associated information among data using Prot$\acute{e}$g$\acute{e}$-OWL tool. Our system has advantages in reducing the number of repeated queries by hierarchy searching and improving the searching time by association searching. Also, we propose an effective method to construct retrieval systems being able to edit items of categories and properties without editing the related codes.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.5
• /
• pp.49-58
• /
• 2005

There has been an increase in the interest of applications that use a combination of both pervasive computing technology and context-aware technology. This application based on the development environment along with the support of developing context-aware applications is now being researched thoroughly and by many. The service management system provides services that are needed for context-aware applications. This system is an integral part of the developmental environment of context-aware applications. But there is a restrictive matching based on ontology that uses simple syntactic matching or a plain type of service used in previous researches. And there is also no consideration for context-aware information. Also, if the user is unable to find a service that is satisfactory, or is a service which a user does not desire, they may use a service which is composed of other existing services. This paper proposes a service management system based on context-aware information. The proposed system enables the accurate finding of services by considering semantic matching methods based on ontology and context-aware information. If the user does not find a service that is helpful in the service registry, it can provide the service list to enable other existing service compositions, by providing the functionality of these service compositions. As a result, the experiment of the system proposed has shown that the system properly supported the service discovery based on context-aware information and service composition.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
• /
• v.23 no.3
• /
• pp.201-209
• /
• 2017

The Big Data, the element of the Fourth Industrial Revolution, is drawing attention as the 4th Industrial Revolution is mentioned in the 2016 World Economic Forum. Big Data is being used in various fields because it predicts the near future and can create new business. However, utilization and research in the field of packaging are lacking. Today packaging has been demanded marketing elements that effect on consumer choice. Big data is actively used in marketing. In the marketing field, big data can be used to analyze sales information and consumer reactions to produce meaningful results. Therefore, this study proposed a method of applying big data in the field of packaging focusing on marketing. In this study suggest that try to utilize the private data and community data to analyze interaction between consumers and products. Using social big data will enable to understand the preferred packaging and consumer perceptions and emotions in the same product line. It can also be used to analyze the effects of packaging among various components of the product. Packaging is one of the many components of the product. Therefore, it is not easy to understand the impact of a single packaging element. However, this study presents the possibility of using Big Data to analyze the perceptions and feelings of consumers about packaging.