• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1067-1078
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• 2020

With the development of internet technology, a lot of content is produced, and the demand for it is increasing. Accordingly, the number of contents in circulation is increasing, while the number of distributing illegal copies that infringe on copyright is also increasing. The Korea Copyright Protection Agency operates a illegal content obstruction program based on substring matching, and it is difficult to accurately search because a large number of noises are inserted to bypass this. Recently, researches using natural language processing and AI deep learning technologies to remove noise and various blockchain technologies for copyright protection are being studied, but there are limitations. In this paper, noise is removed from data collected online, and keyword-based illegal copies are searched. In addition, the same heavy uploader is estimated through profiling analysis for heavy uploaders. In the future, it is expected that copyright damage will be minimized if the illegal copy search technology and blocking and response technology are combined based on the results of profiling analysis for heavy uploaders.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.31-39
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• 2023

The evolution of Natural Language Processing (NLP) and the rise of large language models (LLM) like ChatGPT have paved the way for specialized question-answering (QA) systems tailored to specific domains. This study outlines a system harnessing the power of LLM in conjunction with document search algorithms to interpret and address user inquiries using documents from the Korea Atomic Energy Research Institute (KAERI). Initially, the system refines multiple documents for optimized search and analysis, breaking the content into managable paragraphs suitable for the language model's processing. Each paragraph's content is converted into a vector via an embedding model and archived in a database. Upon receiving a user query, the system matches the extracted vectors from the question with the stored vectors, pinpointing the most pertinent content. The chosen paragraphs, combined with the user's query, are then processed by the language generation model to formulate a response. Tests encompassing a spectrum of questions verified the system's proficiency in discerning question intent, understanding diverse documents, and delivering rapid and precise answers.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.1-9
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• 1998

A new parallel test algorithm and a built-in self test (BIST) architecture are developed to test various types of functional faults efficiently in content addressable memories (CAMs). In test mode, the read oepratin is replaced by one parallel content addressable search operation and the writing operating is performed parallely with small peripheral circuit modificatins. The results whow that an efficient and practical testing with very low complexity and area overhead can be achieved.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.3
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• pp.321-342
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• 2013

The purpose of this study was to compare and evaluate retrieval effectiveness of three types of image perception using different retrieval methods. Image types included specific, general, and abstract topics. The retrieval method included text only search, query by example (QBE) search, and a hybrid/hybrid search. Thirty-two college students were recruited for searching topics using Google image search system. The search results were compared with One-Way and Two-Way ANOVA. As a result, text search and hybrid search showed advantage when searching for specific and general topics. On the other hand, the QBE search performed better than both the text-only and hybrid search for abstract topics. The results have implications for the implementation of image retrieval systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.2
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• pp.152-157
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• 2010

The development of computer and internet has introduced various types of media - such as, image, audio, video, and voice - to the traditional text-based information. However, most of the information retrieval systems are based only on text, which results in the absence of ability to use available information. By utilizing the available media, one can improve the performance of search system, which is commonly called content-based retrieval and content-based image retrieval system specifically tries to incorporate the analysis of images into search systems. In this paper, a content-based image retrieval system using HSI color space, ART2 algorithm, and SOM algorithm is introduced. First, images are analyzed in the HSI color space to generate several sets of features describing the images and an SOM algorithm is used to provide candidates of training features to a user. The features that are selected by a user are fed to the training part of a search system, which uses an ART2 algorithm. The proposed system can handle the case in which an image belongs to several groups and showed better performance than other systems.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.49-56
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• 2014

This thesis proposes CBIRS/TB method that uses a tablet's color distribution information and form distinctive in content-based search. CBIRS/TB can avoid misuses and improper tablet uses by conducting content-based search in commonly prescribed tablets. The existing FE-CBIRS system is limited to recognizing only the image of color and shape of the tablet, that leads to applying insufficient form-specific information. While CBIRS/TB utilizes average, standard deviation, hue and saturation of each tablets in color, brightness, and contrast, FE-CBIRS has partial-sphere application problem; only applying the typical color of the tablet. Also, in case of the shape-specific-information, Invariant Moment is mainly used for the extracted partial-spheres. This causes delayed processing time and accuracy problems. Therefore, to improve this setback, this thesis indexed color-specific-information of the extracted images into categorized classification for improved search speed and accuracy.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.43-50
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• 2007

Delay in the process of Information Extraction, IE, is largely due to inability to correctly recognize the user's information requirement of particular search factors. Especially if the wrapper rules are used in a search engine, the search generally fails to classify internet documents properly and efficiently since the application of the same wrapper rules lacks extensibility throughout various types of existing internet document. In case of buying or selling a ship, if the price range, type. place of delivery, inspection site and other information relevant to the sales would be available through the internet for proper retrieval the sales could more readily succeed by using Ontology relating to sales or purchase information and by selectively searching for the desired information through the content based retrieval system. This system proposes to improve various wrapper systems existing throughout different internet sites and to eliminate unnecessary information tagged on the existing internet documents in order to create a more advanced information retrieval system.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.185-214
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• 2009

The purpose of this study is aimed to clarify the usefulness of meta search system using Open API of library online catalog by constructing OPAC-based search system using Open API of library online catalog and meta search system using Open API of library online catalog, and comparing the usability of the two experimental search systems. As for usability, on the whole, it was higher in meta search system using Open API of library online catalog than OPAC-based search system using Open API of library online catalog, and there was statistically significant difference. Therefore, if libraries share and use enriched content which is provided through Open API for book search, which is opened by Internet bookstores, search engines and Web portals, it is expected that it will be helpful in enhancing bibliographic data, expanding subject access point, empowering subject search ability, extending meta search service, improving book availability, and reducing catalog cost.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of the Semiconductor & Display Technology
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• v.5 no.2 s.15
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• pp.27-31
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• 2006

As more and more digital images are made by various applications, image retrieval becomes a primary concern in technology of multimedia. This paper presents color based descriptor that uses information of color distribution in color images which is the most basic element for image search and performance of proposed visual feature is evaluated through the simulation. In designing the image search descriptor used color histogram, HSV, Daubechies 9/7 and 2 level wavelet decomposition provide better results than other parameters in terms of computational time and performances. Also histogram quadratic matrix outperforms the sum of absolute difference in similarity measurements, but spends more than 60 computational times.