• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1085-1092
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• 2002

Studies on image information processing have been performed since long time ago because in daily life most of information are acquired by the since of sight. Since there should be a lot of data to describe image as a digital form, data compression is required in order to store or transmit digital image. Lately among most of image compression methods adopted on image compression standards, transform coding methods have been primarily used which transforms the correlations between pixels of image on frequency domain before image compression. It is blown that the standard methods using especially DCT features blocking effect which is the major cause of degrading the quality of image at high compression rate. Fractal encoding using quadtree partition is applied after reducing original image, and we are to find a optimal encoding for the number of scaling bit and offset bit.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.557-562
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• 2004

A surface-piercing barrier model is presented for understanding morphological development in the sheltered region and investigating the main factors causing the severe accumulation. Surface-piercing structures like vertical barriers, surface docks and floating breakwaters are recently favored from the point of view of a marine scenario since they do not in general partition the natural sea. The numerical solutions are compared with experimental data on wave profiles and morphological change rates within a rectangular harbor of a constant depth protected by surface-piercing thin breakwaters as a simplified problem. Our numerical study involves several modules: 1) wave dynamics analyzed by a plane-wave approximation, 2) suspended sediment transport combined with sediment erosion-deposition model, and 3) concurrent morphological changes. Scattering waves are solved by using a plane wave method without inclusion of evanescent modes. Evanescent modes are only considered in predicting the reflection ratio against the vertical barrier and energy losses due to vortex shedding from the lower edge of plate are taken into account. A new relationship to relate the near-bed concentration to the depth-mean concentration is presented by analyzing the vertical structure of concentration. The numerical solutions were also compared with experimental data on morphological changes within a rectangular harbor of constant water depth. Through the numerical experiments, the vortex-induced flow appears to be not ignorable in predicting the morphological changes although the immersion depth of a plate is not deep.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.2
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• pp.181-192
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• 2014

In this paper, we propose a robust real-time myocardial border tracking algorithm for echocardiography. Commonly, after an initial contour of LV border is traced at one or two frame from the entire cardiac cycle, LV contour tracking is performed over the remaining frames. Among a variety of tracking techniques, optical flow method is the most widely used for motion estimation of moving objects. However, when echocardiography data is heavily corrupted in some local regions, the errors bring the tracking point out of the endocardial border, resulting in distorted LV contours. This shape distortion often occurs in practice since the data acquisition is affected by ultrasound artifacts, dropout or shadowing phenomena of cardiac walls. The proposed method deals with this shape distortion problem and reflects the motion realistic LV shape by applying global deformation modeled as affine transform partitively to the contour. We partition the tracking points on the contour into a few groups and determine each affine transform governing the motion of the partitioned contour points. To compute the coefficients of each affine transform, we use the least squares method with equality constraints that are given by the relationship between the coefficients and a few contour points showing good tracking results. Many real experiments show that the proposed method supports better performance than existing methods.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.157-164
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• 2015

This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.179-188
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• 2005

Much research for spatial join has been extensively studied over the last decade. In this paper, we focus on the filtering step of candidate objects for spatial join operations on the input tables that none of the inputs is indexed. In this case, many algorithms has presented and showed excellent performance over most spatial data. However, if data sets of input table for the spatial join ale skewed, the join performance is dramatically degraded. Also, little research on solving the problem in the presence of skewed data has been attempted. Therefore, we propose a spatial hash strip join (SHSJ) algorithm that combines properties of the existing spatial hash join (SHJ) algorithm based on spatial partition for input data set's distribution and SSSJ algorithm. Finally, in order to show SHSJ the outperform in uniform/skew cases, we experiment SHSJ using the Tiger/line data sets and compare it with the SHJ algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.3
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• pp.19-32
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• 2002

Topographic information is indispensable in the applications that require elevational data. These applications are exemplified by watershed partition, extraction of drainage networks, viewshed analysis, derivation of geomorphologic features, quantification of landslide-terrain, and identification of topographic settings susceptible to landsliding. Therefore, we study the accuracy of data on topographic parameters derived from digital elevation models(DEMs). This research wished to analyze the effect that data source and grid size get in topography parameter using gridded DEM. An analysis of topography parameter extract and compared drainage basin, watershed slope, stream network using DEM is constructed by digital map and DTED DEM. Especially, when extract stream network from gridded DEM, received much effects according to threshold value of flowaccumulation regardless of DEM grid size. Therefore, this study applied equal threshold value of flowaccumulation for two data sources, and compare and analyzed stream network.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.128-137
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• 2002

In this paper, we propose the LPC+-file for efficient indexing of high-dimensional image data. With the proliferation of multimedia data, there Is an increasing need to support the indexing and retrieval of high-dimensional image data. Recently, the LPC-file (5) that based on vector approximation has been developed for indexing high-dimensional data. The LPC-file gives good performance especially when the dataset is uniformly distributed. However, compared with for the uniformly distributed dataset, its performance degrades when the dataset is clustered. We improve the performance of the LPC-file for the strongly clustered image dataset. The basic idea is to adaptively partition the data space to find subspaces with high-density clusters and to assign more bits to them than others to increase the discriminatory power of the approximation of vectors. The total number of bits used to represent vector approximations is rather less than that of the LPC-file since the partitioned cells in the LPC+-file share the bits. An empirical evaluation shows that the LPC+-file results in significant performance improvements for real image data sets which are strongly clustered.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.177-188
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• 2018

The scale of graph data has been increased rapidly because of the growth of mobile Internet applications and the proliferation of social network services. This brings upon the imminent necessity of efficient distributed and parallel graph processing approach since the size of these large-scale graphs are easily over a capacity of a single machine. Currently, there are two popular parallel graph processing approaches, vertex-centric graph processing and block centric processing. While a vertex-centric graph processing approach can easily be applied to the parallel processing system, a block-centric graph processing approach is proposed to compensate the drawbacks of the vertex-centric approach. In these systems, the initial quality of graph partition affects to the overall performance significantly. However, it is a very difficult problem to divide the graph into optimal states at the initial phase. Thus, several dynamic load balancing techniques have been studied that suggest the progressive partitioning during the graph processing time. In this paper, we present a load balancing algorithms for the block-centric graph processing approach where most of dynamic load balancing techniques are focused on vertex-centric systems. Our proposed algorithm focus on an improvement of the graph partition quality by dynamically reassigning blocks in runtime, and suggests block split strategy for escaping local optimum solution.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.1-16
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• 2003

XML(Extensible Markup Language) is an emerging standard for data representation and exchange in e-commerce and internet-based information. However, to realize this potential, it is necessary to be able to extract structured data from XML documents and store it in a database, as well as to generate XML documents from data extracted from a database. Although many DBMS vendors are scrambling to extend their products to handle XML, there is a need for a lightweight, DBMS and platform-independent XML middleware as well. In this paper we describe such a XML2RDB middleware, that solves the following problems . generating relational schema from XML DTDs for storage of XML documents, importing data from XML documents into relational tables, creating XML documents according to a XMLQL(XML Query Language) from data extracted from a database.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.175-180
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• 2001

This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set\ulcorner Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.