• Journal of the Korean Institute of Navigation
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• v.10 no.1
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• pp.129-138
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• 1986

A matrix partitioning method is proposed for the 2-D motion analysis of floating bodies. For the numerical solution, the boundary of a floating body is approximated with a series of line segments and the governing integral equation is transformed into a system of linear equations. A new solution procedure of resulting linear equation with complex coefficients is formulated and programmed using a matrix partitioning scheme and the Choleski decomposition. From the case study, it is found that the proposed method is efficient in the motion analysis of floating bodies, especially in the calculation of hydrodynamic coefficients. Also, it requires smaller memory size and less computing time compared with conventional methods.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1188-1202
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• 2012

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5244-5259
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• 2019

With the continuous development of LBS (Location Based Service) applications, privacy protection has become an urgent problem to be solved. Differential privacy technology is based on strict mathematical theory that provides strong privacy guarantees where it supposes that the attacker has the worst-case background knowledge and that knowledge has been applied to different research directions such as data query, release, and mining. The difficulty of this research is how to ensure data availability while protecting privacy. Spatial multidimensional data are usually released by partitioning the domain into disjointed subsets, then generating a hierarchical index. The traditional data-dependent partition methods need to allocate a part of the privacy budgets for the partitioning process and split the budget among all the steps, which is inefficient. To address such issues, a novel two-step partition algorithm is proposed. First, we partition the original dataset into fixed grids, inject noise and synthesize a dataset according to the noisy count. Second, we perform IH-Tree (Improved H-Tree) partition on the synthetic dataset and use the resulting partition keys to split the original dataset. The algorithm can save the privacy budget allocated to the partitioning process and obtain a more accurate release. The algorithm has been tested on three real-world datasets and compares the accuracy with the state-of-the-art algorithms. The experimental results show that the relative errors of the range query are considerably reduced, especially on the large scale dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.2080-2089
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• 1997

Recent evolution in compute technology makesthe multimedia processing widely availiable. Conventional storage systems do not meet the requirements of multimedia data. Several approaches were suggested to improve disk storing methods for them. Bocheck proposed a disk partitioning technique for multiple steams assuming that all steams have same retrieval intervals with the same amount data for each access. While Bocheck's one provides a good method for same period, it does not consider the case of different periods of continous media streams. This paper proposes a new partitioning technique in which a fixed number of blocks are assigned for stresms with different retrieval periodicity. The analysis shows this problem is the same as the one scheduling the steams into a given sequence. The simulation was done to compare the proposed m-sequence merge method with the conventional Scan-EDF and Partitioning methods.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.903-911
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• 2008

Association rule mining searches for interesting relationships among items in a given large database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. Many data is most quantitative data. There is a need for partitioning techniques to quantitative data. The partitioning process is referred to as binning. We introduce several binning methods ; parameter mean binning, equi-width binning, equi-depth binning, clustering-based binning. So we apply these binning methods to several distribution types of quantitative data and present the best binning method for association rule discovery.

• Spatial Information Research
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• v.9 no.1
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• pp.15-30
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• 2001

Solid waste collection service is viewed as one of the most important public services in urban area. The purpose of this study is to apply the GIS based regional partitioning and arc routing methods for solid waste collection districting and vehicle routing-scheduling in order to provide waste collection service more efficiently. In this study, solid waste deposit sites are derived from the centroid of each building and the amount of solid waste is deduced based on the number of households and establishments. The regional partitioning procedure is performed based on waste collection zones which are constructed from waste deposit sites. The result of this study shows that solid waste collection districts which are delineated by regional partitioning method are able to increase efficiencies and cut costs in performing solid waste collection services. Also the output of vehicle-scheduling from the analysis of arc routing may provide more efficiently and quickly manage the scheduling of the residential solid waste collection routes, reducing costs with minimal deadheading costs. Therefore, the application of GIS based on regional partitioning and arc routing methods would be very useful to construct a solid waste management system by supplying the important and flexible informations for solid waste collection districts and vehicle routing-scheduling for waste collection.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.564-568
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• 2011

Microblogs became popular information delivery ways due to the spread of smart phones. They have the characteristic of reflecting the interests of users more quickly than other medium. Particularly, in case of the subject which attracts many users, microblogs can supply rich information originated from various information sources. Nevertheless, it has been considered as a hard problem to obtain useful information from microblogs because too much noises are in them. So far, various methods are proposed to extract and track some subjects from particular documents, yet these methods do not work effectively in case of microblogs which consist of short phrases. In this paper, we propose a graph-based topic extraction and partitioning method to understand interests of users about a certain keyword. The proposed method contains the process of generating a keyword graph using the co-occurrences of terms in the microblogs, and the process of splitting the graph by using a network partitioning method. When we applied the proposed method on some keywords. our method shows good performance for finding a topic about the keyword and partitioning the topic into sub-topics.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.6
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• pp.585-590
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• 2002

In the typical data partitioning for error-resilient video coding, motion and macroblock header information is separated from the texture information. It can be an effective tool for the transmission of video over the error prone environment. For Intra-coded frames, however, the loss of DCT (discrete cosine transform) coefficients is fatal because there is no ther information to reconstruct the corrupted macroblocks by errors. For Inter-coded frames, when error occurs in DCT coefficients, the picture quality is degraded because all DCT coefficients are discarded in those packets. In this paper, we propose an efficient data partitioning and coding method for DCT-based error-resilient video. The quantized DCT coefficients are partitioned into the even-value approximation and the remainder parts. It is shown that the proposed algorithm provides a better quality of the high priority part than the conventional methods.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.37-48
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• 2010

In this paper, we analyze the shortcomings of the previous declustering methods, which are based on grid-like partitioning and a mapping function from a cell to a disk number, for high-dimensional space and propose a solution. The problems arise from the fact that the number of splitting is small(for the most part, binary-partitioning is sufficient), and the side length of a range query whose selectivity is small is quite large. To solve this problem, we propose a mathematical model to estimate the performance of a grid-like partitioning method. With the proposed estimation model, we can choose a good grid-like partitioning method among the possible schemes and this results in overall improvement in declustering performance. Several experimental results show that we can improve the performance of a previous declustering method up to 2.7 times.