• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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• pp.107-114
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• 2006

Material selection is one of the important activities in design and manufacturing. A selected material at the conceptual design stage affects functionality of the designed part as well as manufacturability and cost of the final product. Unfortunately there are not many accessible material databases that can be used for design. In this research, a web-based material database was constructed. In order to assist designers to compare different materials, two-dimensional and three-dimensional graphs were provided via the web browser Using these graphical tools, multi-dimensional comparison was available in more intuitive manner. As a case study, this system was applied for material selection of an automotive engine pulley.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.724-727
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• 2005

Material selection is one of the important activities in design and manufacturing. A selected material at the conceptual design stage affects material properties of the designed part as well as manufacturability and cost of the final product. Unfortunately there are not many accessible material databases that can be used for design. In this research, a web-based material database was constructed. In order to assist designers to compare different materials, two-dimensional and three-dimensional graphs were provided. Using these graphical tools, multi-dimensional comparison was available in more intuitive manner. To provide environmental safety of materials, the database included National Fire Protection Association publication Standard No.704. The web-based tool is available at http://fab.snu.ac.kr/matdb.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.44-49
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• 2010

In this paper, we first address the problems associated with indexing and searching for reduced-dimensional vectors, which are reduced by using a combination of angle approximation and dimension grouping. Then, we propose a novel method to solve the problems. We also show the superiority of the proposed method by performing extensive experiments with synthetic and real-life data sets.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.939-947
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• 2016

As the number of legacy database systems and the size of data to manipulate have been vastly increased, it has become more difficult and complex to analyze characteristics of data. To improve the efficiency of data analysis and help administrators to make decisions in business life, BI(Business Intelligence) system is used. To construct data warehouse and cube from legacy database systems makes it easy and fast to transform raw data into integrated and categorized meaningful information. In this paper, we built a BI system for an University administration. Several source system databases were integrated to data warehouse to build data cubes. The implemented BI system shows much faster data analysis and reporting ability than the manipulation in legacy systems. It is especially efficient in multi dimensional data analysis, nonetheless in single dimensional analysis.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.1-11
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• 2000

Up to the present, many GIS logical data models have been proposed to incorporate time dimension into traditional 2-dimensional (2D space) GIS databases. However, these models are difficult to implementation of multi-dimensional GIS and have problems of significant data redundancy and search performance. This paper proposes a new storage strategy and a separate storage structure, that is composed of current database and past database, in order to reduce data redundancy and improve search performance of temporal query. Also we design moving algorithm for migration from current database to past database, and design efficient search algorithm for temporal query.

• Journal of the Optical Society of Korea
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• v.20 no.2
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• pp.239-244
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• 2016

Mugshot face images, routinely collected by police, usually contain both frontal and profile views. Existing automated face recognition methods exploited mugshot databases by enlarging the gallery with synthetic multi-view face images generated from the mugshot face images. This paper, instead, proposes to match the query arbitrary view face image directly to the enrolled frontal and profile face images. During matching, the 3D face shape model reconstructed from the mugshot face images is used to establish corresponding semantic parts between query and gallery face images, based on which comparison is done. The final recognition result is obtained by fusing the matching results with frontal and profile face images. Compared with previous methods, the proposed method better utilizes mugshot databases without using synthetic face images that may have artifacts. Its effectiveness has been demonstrated on the Color FERET and CMU PIE databases.

• Journal of KIISE
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• v.42 no.2
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• pp.242-254
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• 2015

In this paper, we address the problem of improving the performance of multi-step k-NN search using multi-dimensional indexes. Due to information loss by lower-dimensional transformations, existing multi-step k-NN search solutions produce a large tolerance (i.e., a large search range), and thus, incur a large number of candidates, which are retrieved by a range query. Those many candidates lead to overwhelming I/O and CPU overheads in the postprocessing step. To overcome this problem, we propose two efficient solutions that improve the search performance by reducing the tolerance of a range query, and accordingly, reducing the number of candidates. First, we propose a tolerance reduction-based (approximate) solution that forcibly decreases the tolerance, which is determined by a k-NN query on the index, by the average ratio of high- and low-dimensional distances. Second, we propose a coefficient control-based (exact) solution that uses c k instead of k in a k-NN query to obtain a tigher tolerance and performs a range query using this tigher tolerance. Experimental results show that the proposed solutions significantly reduce the number of candidates, and accordingly, improve the search performance in comparison with the existing multi-step k-NN solution.

• Journal of KIISE:Databases
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• v.30 no.1
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• pp.80-94
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• 2003

In this paper. we propose an enhanced concurrency control algorithm that minimizes the query delay efficiently. The factors that delay search operations and deteriorate the concurrency of index structures are node splits and MBR updates in multi dimensional index structures. In our algorithm, to reduce the query delay by split operations, we optimize exclusive latching time on a split node. It holds exclusive latches not during whole split time but only during physical node split time that occupies small part of whole split time. Also to avoid the query delay by MBR updates we introduce partial lock coupling(PLC) technique. The PLC technique increases concurrency by using lock coupling only in case of MBR shrinking operations that are less frequent than MBR expansion operations. For performance evaluation, we implement the proposed algorithm and one of the existing link technique-based algorithms on MIDAS-III that is a storage system of a BADA-III DBMS. We show through various experiments that our proposed algorithm outperforms the existing algorithm In terms of throughput and response time.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.509-525
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• 2005

In this paper, we propose the index structure based on a vector approximation for efficiently supporting the similarity search of multi-dimensional data. The proposed index structure splits a region with the space partition method and allocates to the split region dynamic bits according to the distribution of data. Therefore, the index structure splits a region to the unoverlapped regions and can reduce the depth of the tree by storing the much region information of child nodes in a internal node. Our index structure represents the child node more exactly and provide the efficient search by representing the region information of the child node relatively using the region information of the parent node. We show that our proposed index structure is better than the existing index structure in various experiments. Experimental results show that our proposed index structure achieves about $40\%$ performance improvements on search performance over the existing method.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.