• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.185-185
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• 1997

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.283-286
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• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.185-190
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• 2010

In this paper we propose the CBIR algorithm which is based on a novel image block method that combined both color and edge feature. The main drawback of global histogram representation is dependent of the color without spatial or shape information, a new image block method that divided the image to 8 related blocks which contained more information of the image is utilized to extract image feature. Based on these 8 blocks, histogram equalization and edge detection techniques are also used for image retrieval. The experimental results show that the proposed image block method has better ability of characterizing the image contents than traditional block method and can perform the retrieval system efficiently.

• Journal of the Optical Society of Korea
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• v.14 no.2
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• pp.131-136
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• 2010

Modification of the phase gradient formulation is proposed in order to make phase retrieval less susceptible to noise. The modified formulation is derived from separation of the phase terms and the intensity modulation terms of interferograms, and subsequent differentiation to reduce the noise-induced error of the phase gradient vector. Its performance is evaluated and compared to that of the conventional formulation, and noise-robust nature is confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2721-2727
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• 2009

This paper is construction on ontology_based mobile Customer Relationship Management system for efficient customer information management and analysis of medicine center. As using ontology technique, we support medicine service at grade according to quality and healthy of the customer based customer information. Proposed CRM system provides medical information and seminars to whom are necessity. For this, priority retrieval and similarity retrieval are able to be in the personnel order and the regional.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.192-195
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• 2008

Radio occultation (RO) has been used in the planetary science since Microlab-1 was launched in 1995. With the RO technique, the profiles of atmosphere and the global atmospheric data can be obtained. In 2006, Taiwan launched six low Earth orbit (LEO) satellites as the RO constellation mission, known as FORMOSAT-3. In order to retrieve the RO data from original data, a retrieval algorithm, NCURO, is developed. The input of NCURO algorithm is mainly the excess phase of GPS signal, and the output is the dry pressure and dry temperature. Using temperature profiles retrieved by NCURO algorithm, temperature perturbation and potential energy of gravity wave have been evaluated. In this paper, the retrieval algorithm and the global distribution of energy of gravity waves are described and demonstrated.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10573-10576
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• 2015

Background: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrieval was tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.

• Journal of the Korean Society for Library and Information Science
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• v.6
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• pp.53-70
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• 1979

This is to report the objectives, planning and the process of the establishment of an on-line information retrieval system in Korea. The model is the case of the Library of the Korea Institute of Science and Technology (KIST) which is the multi-disciplinary contract research organization in science and technology. The report explains the objectives and the step by step process such as input materials, analysis of the materials, structure of the data base files, indexes, command language, and the process, technique and logic of information retrieval. A brief comparison is made with other systems such as OCLC and BALLOTS and the authors gave various illustrations and samples to clarify the processes. The authors regrets that they could not give any detailed information on the budget or the cost accrued so far.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.16-20
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• 2001

An important aspect of video program indexing and retrieval is the ability to segment video program into meaningful segments, in other words, the ability of content-based video program segmentation. In this paper, a new approach using speech recognition technology has been proposed for content-based video program segmentation. This approach uses speech recognition technique to synchronize closed caption with speech signal. Experimental results demonstrate that the proposed scheme is very promising for content-based video program segmentation.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.77-83
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• 2015

The storage and retrieval of multimedia data is becoming increasingly important in many application areas including record management, video(CCTV) management and Internet of Things (IoT). In these applications, the files containing multimedia that need to be stored and managed is tremendous and constantly scaling. In this paper, we propose a technique to retrieve a very large number of files, in multimedia format, using the Hadoop Framework. Our strategy is based on the management of metadata that describes the characteristic of files that are stored in Hadoop Distributed File System (HDFS). The metadata schema is represented in Hbase and looked up using SQL On Hadoop (Hive, Tajo). Both the Hbase, Hive and Tajo are part of the Hadoop Ecosystem. Preliminary experiment on multimedia data files stored in HDFS shows the viability of the proposed strategy.