• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.2
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• pp.396-406
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• 2009

In extension to our previous study, we develop metadata that specify elements' structural orders, to increase the efficiency level of XML document's retrieval process. Then, we proposed a structure-based indexing model. We expect the model to generate a more efficient retrieval process of horizontally and vertically related elements. To evaluate the model's performance level, we developed an experimental prototype and conducted an experiment on an XML corpus. On average, descendant, ancestor and sibling retrievals were approximately twelve percent faster than the ETID model. And retrievals specifying structural orders of particular element types were approximately twenty-five percent faster than the ETID model. In conclusion, metadata, such as Etype, Asso and Lsso, may make a meaningful contribution to retrieval processes that specify elements' order.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.243-253
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• 2009

For the last 14 years, space-borne satellite SAR system such as RADARSAT-1, ERS-2, and ENVISAT ASAR have provided a continuous observation over the ocean. However, the data acquired from those systems were limited to C-band frequency until the advent of the first spacebome German X-band SAR system TerraSAR-X in 2007. Korea is also planning to launch the nation's first X-band SAR satellite (KOMPSAT-5) in 2010. It is timely and necessary to develop X-band models for estimating geophysical parameters from these X-band SAR systems. In this study, X-band wind retrieval model was investigated and developed based on numerical ocean scattering model (radar backscattering model and hydrodynamic interaction model). Although these models have not yet been tested and validated for broad ranges of wind conditions, the estimated wind speeds from TerraSAR-X data show generally good agreement with in-situ measurements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.193-205
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• 2007

Increasing amount of digital images requires more accurate and faster way of image retrieval. So far, image retrieval method includes content-based retrieval and keyword based retrieval, the former utilizing visual features such as color and brightness and the latter utilizing keywords which describe the image. However, the effectiveness of these methods as to providing the exact images the user wanted has been under question. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as a feedback during the retrieval session in order to define user’s need and provide improved result. Yet, the methods which have employed relevance feedback also have drawbacks since several feedbacks are necessary to have appropriate result and the feedback information can not be reused. In this paper, a novel retrieval model has been proposed which annotates an image with a keyword and modifies the confidence level of the keyword in response to the user’s feedback. In the proposed model, not only the images which have received positive feedback but also the other images with the visual features similar to the features used to distinguish the positive image are subjected to confidence modification. This enables modifying large amount of images with only a few feedbacks ultimately leading to faster and more accurate retrieval result. An experiment has been performed to verify the effectiveness of the proposed model and the result has demonstrated rapid increase in recall and precision while receiving the same number of feedbacks.

• Journal of the Korean Society for information Management
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• v.10 no.1
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• pp.53-63
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• 1993

The conventional fuzzy set model has been criticized as a retrieval model because the MIN and MAX operators have the properties adverse to effective calculation of document values. Since the first introduction of fuzzy set theory a variety of fuzzy operators have been developed, which can replace the MIN and MAX operators. We analyze their behavioral aspects of generating document values, and propose the enhanced fuzzy set model based on a class of fuzzy operators called positively compensatory operators. We also show through performance experiments that the proposed fuzzy set model provides higher retrieval effectiveness.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.23-37
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• 1994

In this paper, we design and implement the prototype system, and propose the Extended Faceted Classification. Scheme and the Hybrid Retrieval Method that support classifying the software components, storing in library, and efficient retrieval according to user's request. In order to designs the classification scheme, we identify several necessary items by analyzing basic classes of software components that are to be classified. Then, we classify the items by their characteristics, decide the facets, and compose the component descriptors. According to their basic characteristics, we store software components in the library by clustering their application domains and are assign weights to the facets and its items to describe the component characteristics. In order to retrieve the software components, we use the retrieval-by-query model, and the weights and similarity for easy retrieval of similar software components. As the result of applying proposed classification scheme and retrieval model, we can easily identify similar components and the process of classification become simple. Also, the construction of queries becomes simple, the control of the size and order of the components to be retrieved possible, and the retrieval effectiveness is improved.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.789-796
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• 2005

Because existing information retrieval systems, in particular library retrieval systems, use 'exact keyword matching' with user's query, they present user with massive results including irrelevant information. So, a user spends extra effort and time to get the relevant information from the results. Thus, this paper will propose SULRM a Retrieval Model using Subject Classification Table, User profile, and LSI(Latent Semantic Indexing), to provide more relevant results. SULRM uses document filtering technique for classified data and document ranking technique for non-classified data in the results of keyword-based retrieval. Filtering technique uses Subject Classification Table, and ranking technique uses user profile and LSI. And, we have performed experiments on the performance of filtering technique, user profile updating method, and document ranking technique using the results of information retrieval system of our university' digital library system. In case that many documents are retrieved proposed techniques are able to provide user with filtered data and ranked data according to user's subject and preference.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.1-11
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• 2002

This paper discusses problems of the Poisson Mixture model which Is widely used to decide the effective words in judging relevant document. Gamma Distribution model and Gauss Patterns model as an alternative of the Poisson Mixture model are studied. Classification experiments by using TREC sub-collection, WSJ[1,2] with MGQUERY and AidSearch3.0 system are discussed.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.421-431
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• 2023

Nuclear power plants (NPPs) have technical specifications (Tech Specs) to ensure that the equipment and key operating parameters necessary for the safe operation of the power plant are maintained within limiting conditions for operation (LCO) determined by a safety analysis. The LCO of Tech Specs that identify the lowest functional capability of equipment required for safe operation for a facility must be complied for the safe operation of NPP. There have been previous studies to aid in compliance with LCO relevant to rule-based expert systems; however, there is an obvious limit to expert systems for implementing the rules for many situations related to LCO. Therefore, in this study, we present a retrieval methodology for similar LCO cases in determining whether LCO is met or not met. To reflect the natural language processing of NPP features, a domain dictionary was built, and the optimal term frequency-inverse document frequency variant was selected. The retrieval performance was improved by adding a Boolean retrieval model based on terms related to the LCO in addition to the vector space model. The developed domain dictionary and retrieval methodology are expected to be exceedingly useful in determining whether LCO is met.

• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.103-120
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• 2007

Node is the semantic unit and a part of structured document. Information retrieval from structured documents offers an opportunity to go subdivided below the document level in search of relevant information, making any element in an structured document a retrievable unit. The node-based document retrieval constitutes several similarity calculating methods and the extended node retrieval method using structure information. Retrieval performance is hardly influenced by the methods for determining document similarity The extended node method outperformed the others as a whole.