• 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.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1995.11a
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• pp.79-88
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• 1995

Most works done so far have concentrated on developing data modeling techniques such as multi-level relation for data protection. These techniques, however, cannot be applied to practical area. This is because they require new queries or new architectures. In this paper, we propose a query processing technique for multi-level databases using horizontal partitioning and views, which does not need any change in database architecture and query language.

• Journal of Computing Science and Engineering
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• v.4 no.3
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• pp.207-224
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• 2010

This paper presents a compression scheme for mesh geometry, which is suitable for mobile graphics. The main focus is to enable real-time decoding of compressed vertex positions while providing reasonable compression ratios. Our scheme is based on local quantization of vertex positions with mesh partitioning. To prevent visual seams along the partitioning boundaries, we constrain the locally quantized cells of all mesh partitions to have the same size and aligned local axes. We propose a mesh partitioning algorithm to minimize the size of locally quantized cells, which relates to the distortion of a restored mesh. Vertex coordinates are stored in main memory and transmitted to graphics hardware for rendering in the quantized form, saving memory space and system bus bandwidth. Decoding operation is combined with model geometry transformation, and the only overhead to restore vertex positions is one matrix multiplication for each mesh partition. In our experiments, a 32-bit floating point vertex coordinate is quantized into an 8-bit integer, which is the smallest data size supported in a mobile graphics library. With this setting, the distortions of the restored meshes are comparable to 11-bit global quantization of vertex coordinates. We also apply the proposed approach to compression of vertex attributes, such as vertex normals and texture coordinates, and show that gains similar to vertex geometry can be obtained through local quantization with mesh partitioning.

• International Journal of Internet, Broadcasting and Communication
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• v.5 no.1
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• pp.14-17
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• 2013

RDF data is widely used for exchanging data nowadays to enable semantic web era. This leads to the need for storing and retrieving these data efficiently and effectively. Recently, the structure index in graph-based perspective is considered as a promising approach to deal with issues of complex query graphs. However, even though there are many researches based on structure indexing, there can be a better architectural approach instead of addressing the issue as a part. In this research, we propose architecture for storing, query processing and retrieving RDF data in efficient manner using structure indexing. Our research utilizes research results from iStore and 2 relation-based approaches and we focus on improving query processing to reduce the time of loading data and I/O cost.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.11-18
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• 2023

This study explores a new energy partitioning approach to determine the fracture toughness of 3-D printed pristine/recycled high density polyethylene (HDPE) blends employing the essential work of fracture (EWF) concept. The traditional EWF approach conducts a uniaxial tensile test with double-edge notched tensile (DENT) specimens and measures the total energy defined by the area under a load-displacement curve until failure. The approach assumes that the entire total energy contributes to the fracture process only. This assumption is generally true for extruded polymers that fracture occurs in a material body. In contrast to the traditional extrusion manufacturing process, the current 3-D printing technique employs fused deposition modeling (FDM) that produces layer-by-layer structured specimens. This type of specimen tends to include separation energy even after the complete failure of specimens when the fracture test is conducted. The separation is not relevant to the fracture process, and the raw experimental data are likely to possess random variation or noise during fracture testing. Therefore, the current EWF approach may not be suitable for the fracture characterization of 3-D printed specimens. This paper proposed a new energy partitioning approach to exclude the irrelevant energy of the specimens caused by their intrinsic structural issues. The approach determined the energy partitioning location based on experimental data and observations. Results prove that the new approach provided more consistent results with a higher coefficient of correlation.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.179-182
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• 2001

This paper has been studied a wavelet based still image transmission over the wireless channel. EZW(Embedded Zerotree Wavelet) is an efficient and scalable wavelet based image coding technique, which provides progressive transfer of signal resulted in multi-resolution representation. It reduces therefore the reduce cost of storage media. Although EZW has many advantages, it is very sensitive on error. Because coding are performed in subband by subband, and it uses arithmetic coding which is a kind of variable length coding. Therefore only 1∼2bit error may degrade quality of the entire image. So study of error localization and recovery are required. This paper investigates the use of reversible variable length codes(RVLC) and data partitioning. RVLC are known to have a superior error recovery property due to their two-way decoding capability and data partitioning is essential to applying RVLC. In this work, we show that appropriate data partitioning length for each SNR(Signal-to-Noise Power Ratio) and error localization in wireless channel.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.275-278
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• 2003

Terrestrial DMB(Digital Multimedia Broadcasting) system is the standard that offers multimedia broadcasting services at mobile environment and is based on Eureka-147 DAB(Digital Audio Broadcasting) for transmission method. Also DMB provides the error protection method of convolution coding. In this paper, we study on the effective error resilience coding of MPEG-4 video stream over DMB system. To accomplish error resilience, we first partition one data into several data using the data partitioning, and we control the coding rate of the convolution coding according to the importance of the partitioned data. In this algorithm, we suggest and analyze the efficient rate control algorithm considering convolution code rate.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.279-282
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• 2003

Terrestrial DMB(Digital Multimedia Broadcasting) system is the standard that offers multimedia broadcasting services at mobile environment and is based on Eureka-147 DAB(Digital Audio Broadcasting) for transmission method. Also DMB provides the error protection method of convolution coding. In this paper, we study on the effective error resilience coding of MPEG-4 video stream over DMB system. To accomplish error resilience, we first partition one data into several data using the data partitioning, and we control the coding rate of the convolution coding according to the importance of the partitioned data. In this algorithm, we suggest and analyze the efficient rate control algorithm considering convolution code rate.