• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.363-364
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• 2021

Search technology is applied to various applications using artificial intelligence technology. It is used in many ways, from identifying customer preferences to personalized recommendation systems. The purpose of this study is to develop a system for detecting adult males mainly in children's living spaces. This will prevent dangerous situations of adult intruders in advance and can be used for outsider control system. In order to develop such a system, information about clothes is used, and adult detection system is developed using various factors such as color, pattern, fashion style, and size of clothes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.895-907
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• 2020

With the recent increasing influence of multimedia content other than the text-based content, services that help to process information in content brings us great convenience. These services' representative features are searching and masking the sensitive data. It is not difficult to find the solutions that provide searching and masking function for text information and image. However, even though we recognize the necessity of the technology for searching and masking a part of the audio data, it is not easy to find the solution because of the difficulty of the technology. In this study, we propose web application that provides searching and masking functions for audio data using audio partitioning method. While we are achieving the research goal, we evaluated several speech to text conversion APIs to choose a proper API for our purpose and developed regular expressions for searching sensitive information. Lastly we evaluated the accuracy of the developed searching and masking feature. The contribution of this work is in design and implementation of searching and masking a sensitive information from the audio data by the various functionality proving experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.88-95
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• 2009

This paper presents a traffic pattern matching hardware that can be used in high performance network applications. The presented hardware is designed for a contents security system which is to block various kinds of information drain or intrusion activities. The hardware consists of two parts: the header lookup and string pattern matching parts. For implementing the header lookup part in hardware, the TCAMs(ternary CAMs) are popularly used. Since the TCAM approach is inefficient in terms of the hardware and memory costs and the power consumption, however, we adopt and modify an alternative approach based on the comparator arrays and the HiCuts tree. Our implementation results, using Xilinx FPGA XC4VSX55, show that our design can reduce the usage of the FPGA slices by about 26%, and the Block RAM by about 58%. In the design of string pattern matching part, we design and use a hashing module based on cellular automata, which is hardware efficient and consumes less power by adaptively changing its configuration to reduce the collision rates.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.81-99
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• 2017

Technical documents are important research outputs generated by knowledge and information society. In order to properly use the technical documents properly, it is necessary to utilize advanced information processing techniques, such as summarization and information extraction. In this paper, to extract core information, we automatically extracted the terminologies and their definition based on definitional sentences patterns and the structure of technical documents. Based on this, we proposed the system to build a specialized terminology dictionary. And further we suggested the personalized services so that users can utilize the terminology dictionary in various ways as an knowledge memory. The results of this study will allow users to find up-to-date information faster and easier. In addition, providing a personalized terminology dictionary to users can maximize the value, usability, and retrieval efficiency of the dictionary.

• Journal of the Korean Society for information Management
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• v.32 no.1
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• pp.135-152
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• 2015

The goal of this paper is to explore the field of Computer and Information Science with the aid of text mining techniques by mining Computer and Information Science related conference data available in DBLP (Digital Bibliography & Library Project). Although studies based on bibliometric analysis are most prevalent in investigating dynamics of a research field, we attempt to understand dynamics of the field by utilizing Latent Dirichlet Allocation (LDA)-based multinomial topic modeling. For this study, we collect 236,170 documents from 353 conferences related to Computer and Information Science in DBLP. We aim to include conferences in the field of Computer and Information Science as broad as possible. We analyze topic modeling results along with datasets collected over the period of 2000 to 2011 including top authors per topic and top conferences per topic. We identify the following four different patterns in topic trends in the field of computer and information science during this period: growing (network related topics), shrinking (AI and data mining related topics), continuing (web, text mining information retrieval and database related topics), and fluctuating pattern (HCI, information system and multimedia system related topics).

• Journal of the Korean Society for information Management
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• v.14 no.1
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• pp.269-291
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• 1997

The study examines research themes, the relationship with other disciplines, and disciplinary organization of Information Science n order to analyze general research patterns in the field of Information Science. The methods used for this study is subject analysis of the journal articles and citation analysis. In result, it is found that most researchers in Information Science have studied on 'library-related information science'. But the study also finds the research pattern shift from library-related study to computer-related study.

• Journal of KIISE:Software and Applications
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• v.29 no.6
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• pp.367-376
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• 2002

XML allows users to define elements using arbitrary words and organize them in a nested structure. These features of XML offer both challenges and opportunities in information retrieval and document management. In this paper, we propose a new methodology for computing similarity considering XML semantics - meanings of the elements and nested structures of XML documents. We generate extended-element vectors, using thesaurus, to normalize synonyms, compound words, and abbreviations and build similarity matrix using them. And then we compute similarity between XML elements. We also discover and minimize XML structure using automata(NFA(Nondeterministic Finite Automata) and DFA(Deterministic Finite automata). We compute similarity between XML structures using similarity matrix between elements and minimized XML structures. Our methodology considering XML semantics shows 100% accuracy in identifying the category of real documents from on-line bookstore.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2725-2736
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• 1998

In this paper. we present a new pitch determination method for speech analysis. namely VFF(Variable Forgetting Factor) based. by using the WRLS-VFF-VT(Weighted Recursive Least Square-Variable Forgetting Factor-Variable Threshold) algorithm. A proposed method uses VFF to identify the glottal closure points which correspond to the instants of the main excitation pulses for voiced speech. The modified EGG

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.138-147
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• 2007

Metabolic pathway is a series of chemical reactions occuning within a cell and can be used for drug development and understanding of life phenomenon. Many biologists are trying to extract metabolic pathway information from huge literatures for their metabolic-circuit regulation study. We propose a text-mining technique based on the keyword and pattern. Proposed technique utilizes a web robot to collect huge papers and stores them into a local database. We use gene ontology to increase compound recognition rate and NCBI Tokenizer library to recognize useful information without compound destruction. Furthermore, we obtain useful sentence patterns representing metabolic pathway from papers and KEGG database. We have extracted 66 patterns in 20,000 documents for Glycosphingolipid species from KEGG, a representative metabolic database. We verify our system for nineteen compounds in Glycosphingolipid species. The result shows that the recall is 95.1%, the precision 96.3%, and the processing time 15 seconds. Proposed text mining system is expected to be used for metabolic pathway reconstruction.