• The Journal of Society for e-Business Studies
• /
• v.20 no.4
• /
• pp.209-225
• /
• 2015

Research and development projects in railroad domain are different from those in other domains in terms of their close relationship with laws. Some cases are reported that new technologies from R&D projects could not be industrialized because of relevant laws restricting them. This problem comes from the fact that researchers don't know exactly what laws can affect the result of R&D projects. To deal with this problem, we suggest a model for law retrieval system that can be used by researchers of railroad R&D projects to find related legislation. Input of this system is a research plan describing the main contents of projects. After laws related to the R&D project is provided with their rankings, which are assigned by scores we developed. A ranking of a law means its order of priority to be checked. By using this system, researchers can search the laws that may affect R&D projects throughout all the stages of project cycle. So, using our system model, researchers can get a list of laws to be considered before the project they participate ends. As a result, they can adjust their project direction by checking the law list, avoiding their elaborate projects being useless.

• Journal of Navigation and Port Research
• /
• v.26 no.1
• /
• pp.78-84
• /
• 2002

In this thesis a shipping broker agent system, which can be executed by a CORBA-based environment using the distributed object technique, CORBA is designed and implemented Nowadays, while the EC system is extensive on the web, legacy host systems have limitations and its capacity-upgrade os required. In order to solve these problems the method that distributes programs into several computer systems can reduce their loads, set low system capacity and minimize network bottleneck. In this thesis using Agent Communication Language(ACL), we propose an agent based prototype and implement an intelligent agent prototype for ships search using CORBA, which can exchange and shiare data between different systems.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.9
• /
• pp.120-125
• /
• 2016

For the purpose of protecting personal property and lives from incidents, accidents, and threats such as terrorism, video surveillance equipment has been installed and operates in many places. Video surveillance technology has gradually developed into high-quality, high-definition equipment, and a lot of products have been launched. However, closed circuit television (CCTV) equipment for security purposes can invade a person's privacy. In this paper, we propose a way to protect personal video images using meta-data in an intelligent Internet protocol (IP) camera. We designed the system to mask personal video information from meta-data, define the method of image-information access according to user privileges, and show how to utilize the meta-data during storage and recorded data searches. The suggested system complies with guidelines for CCTV installation and operation from Korea's Ministry of the Interior. Installed on only a single server so far, due to the limitations and technical difficulties of hardware performance, it has been difficult to find a method that can be applied to personal image information using real-time protection techniques. Applying the method proposed in this paper can satisfy the guidelines, reduce server costs, and reduce system complexity.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.59-74
• /
• 2011

As more users are willingly participating in the creation of web contents, flat folksonomy using simple tags has emerged as a powerful instrument to classify and share a huge amount of knowledge on the web. However, flat folksonomy has semantic problems, such as ambiguity and misunderstanding of tags. To alleviate such problems, many studies have built structured folksonomy with a hierarchical structure or relationships among tags. However, structured folksonomy also has some fundamental problems, such as limited tagging to pre-defined vocabulary for new tags and the timeconsuming manual effort required for selecting tags. To resolve these problems, we suggested a new method of attaching a categorized tag (CT), followed by its category, to web content. CTs are automatically integrated into collaboratively-built structured folksonomy (CSF) in real time, reflecting the tag-and-category relationships by majority users. Then, we developed a CT-based knowledge organization system (CTKOS), which builds the CSF to classify organizational knowledge and allows us to locate the appropriate knowledge.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.129-143
• /
• 2012

Recently, the rapid progress of a number of standardized web technologies and the proliferation of web users in the world bring an explosive increase of producing and consuming information documents on the web. In addition, most companies have produced, shared, and managed a huge number of information documents that are needed to perform their businesses. They also have discretionally raked, stored and managed a number of web documents published on the web for their business. Along with this increase of information documents that should be managed in the companies, the need of a solution to locate information documents more accurately among a huge number of information sources have increased. In order to satisfy the need of accurate search, the market size of search engine solution market is becoming increasingly expended. The most important functionality among much functionality provided by search engine is to locate accurate information documents from a huge information sources. The major metric to evaluate the accuracy of search engine is relevance that consists of two measures, precision and recall. Precision is thought of as a measure of exactness, that is, what percentage of information considered as true answer are actually such, whereas recall is a measure of completeness, that is, what percentage of true answer are retrieved as such. These two measures can be used differently according to the applied domain. If we need to exhaustively search information such as patent documents and research papers, it is better to increase the recall. On the other hand, when the amount of information is small scale, it is better to increase precision. Most of existing web search engines typically uses a keyword search method that returns web documents including keywords which correspond to search words entered by a user. This method has a virtue of locating all web documents quickly, even though many search words are inputted. However, this method has a fundamental imitation of not considering search intention of a user, thereby retrieving irrelevant results as well as relevant ones. Thus, it takes additional time and effort to set relevant ones out from all results returned by a search engine. That is, keyword search method can increase recall, while it is difficult to locate web documents which a user actually want to find because it does not provide a means of understanding the intention of a user and reflecting it to a progress of searching information. Thus, this research suggests a new method of combining ontology-based search solution with core search functionalities provided by existing search engine solutions. The method enables a search engine to provide optimal search results by inferenceing the search intention of a user. To that end, we build an ontology which contains concepts and relationships among them in a specific domain. The ontology is used to inference synonyms of a set of search keywords inputted by a user, thereby making the search intention of the user reflected into the progress of searching information more actively compared to existing search engines. Based on the proposed method we implement a prototype search system and test the system in the patent domain where we experiment on searching relevant documents associated with a patent. The experiment shows that our system increases the both recall and precision in accuracy and augments the search productivity by using improved user interface that enables a user to interact with our search system effectively. In the future research, we will study a means of validating the better performance of our prototype system by comparing other search engine solution and will extend the applied domain into other domains for searching information such as portal.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.6
• /
• pp.649-658
• /
• 2009

The recent emergence of the paradigm of new urban planning for building intelligent urban spaces, such as U-City and U-Eco City, of which the concept of ubiquitous technology is applied, requires high quality three-dimensional spatial information of the urban area. The aim of this study is to build a multi-dimensional spatio-visual information system that includes the solution for visualization, spatial information search, analysis, and evaluation by integrating various types of 3D-modeled spatial information concerning the large urban-size area based on the latest GIS application technology. The range of this study is the integration, visualization, and utilization of spatial information with the goal of building 3D virtual urban environment of high-quality and high-resolution by increasing the utilization of the systematic urban facilities in order to fully reflect the actual user's needs, using the aerial LiDAR data as the plan to overcome the limitations of the existing 3D urban modeling. By reproducing the virtual urban environment the most similar to the actual world through the mash-up of satellite images and aerial photos on the standard format of spatial information constituted of properties and signs, the system will be built with many analysis and utilization functions that support the view and sunlight analysis, various administrative tasks, as well as the decision making process of the city.

• Journal of KIISE:Computing Practices and Letters
• /
• v.11 no.1
• /
• pp.36-49
• /
• 2005

Recently, many researchers have given high attention to the semantic web services based on the semantic web technology While existing web services use the XML-based web service description language, WSDL, semantic web services are utilizing web service description languages such as DAML-S in ontology languages. The researchers of semantic web services are generally focused on web service discovery, web service invocation, web service selection and composition, and web service execution monitoring. Especially, the semantic web service discovery as the basis to accomplish the ultimate semantic web service environment has some different properties from previous information discovery areas. Hence, it is necessary to develop the storage system and discovery mechanism appropriate to the semantic well description languages. Even though some related systems have been developed, they are not appropriate for the embedded system environment, such as intelligent robotics, in which there are some limitations on memory disk space, and computing power In this regard, we in the embedded system environment have developed the document management system which efficiently manages the web service documents described by DAML-S for the purpose of the semantic web service discovery, In addition, we address the distinguishing characteristics of the system developed in this paper, compared with the related researches.

• Journal of Information Management
• /
• v.41 no.4
• /
• pp.227-249
• /
• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.211-227
• /
• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.93-111
• /
• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.