• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.1
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• pp.63-74
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• 2005

The telematics device, which is a typical embedded system on the transportation or vehicle, requires the embedded spatial DBMS based on RTOS (Real Time Operating System) for processing the huge spatial data in real time. This spatial DBMS can be developed very easily by SQL3 functions of the ORDBMS such as UDT (user-defined type) and UDF (user-defined function). However, developing index suitable for the embedded spatial DBMS is very difficult. This is due to the fact that there is no built-in SQL3 functions to construct spatial indexes. In this study, we compare and analyze both Generalized Search Tree and Relational Indexing methods which are suggested as common ways of developing User-Defined Indexes nowadays. Two implementations of R-Tree based on each method were done and region query performance test results were evaluated for suggesting a suitable indexing method of an embedded spatial DBMS, especially for telematics devices.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.1-11
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• 2007

Recently, with the development of collecting methods of spatial data, the spatial data is produced, circulated, and used in various fields of industry and research. To manage the mass spatial data efficiently, the researches on extension of the existing commercial DBMS, such as ESRI's ArcSDE or Oracle's Oracle Spatial, is making progress actively. However, the usage of the extension of the commercial DBMS Incurs an additional expense and causes an interoperability problem due to differences in spatial data types and spatial operators. Therefore, in this paper, we developed an OpenGIS Spatial Interface for Oracle, which supports a standard interface by fellowing the "Simple Features Specification for SQL" proposed by OGC(Open Geospatial Consortium). Since the OpenGIS Spatial Interface provides all spatial data types and spatial operators proposed in "Simple Features Specification for SQL", users can manage mass spatial data of Oracle efficiently by using the standard interface without additional expense. In addition, we proved that the OpenGIS Spatial Interface is superior to the Oracle Spatial in the response time through the performance evaluation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.5 s.335
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• pp.23-30
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• 2005

In the construction of an extension field, there is a connection between the polynomial multiplication method and the degree of polynomial. The existing methods, KO and MSK methods, efficiently reduce the complexity of coefficient-multiplication. However, when we construct the multiplication of an extension field using KO and MSK methods, the polynomials are padded with necessary number of zero coefficients in general. In this paper, we propose basic properties of KO and MSK methods and algorithm that can reduce coefficient-multiplications. The proposed algorithm is more reducible than the original KO and MSK methods. This characteristic makes the employment of this multiplier particularly suitable for applications characterized by specific space constrains, such as those based on smart cards, token hardware, mobile phone or other devices.

• The Journal of the Korea Contents Association
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• v.9 no.3
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• pp.1-8
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• 2009

This paper proposes a new document clustering system using fuzzy logic-based genetic algorithm (GA) and semantic vector expansion technology. It has been known in many GA papers that the success depends on two factors, the diversity of the population and the capability to convergence. We use the fuzzy logic-based operators to adaptively adjust the influence between these two factors. In traditional document clustering, the most popular and straightforward approach to represent the document is vector space model (VSM). However, this approach not only leads to a high dimensional feature space, but also ignores the semantic relationships between some important words, which would affect the accuracy of clustering. In this paper we use latent semantic analysis (LSA)to expand the documents to corresponding semantic vectors conceptually, rather than the individual terms. Meanwhile, the sizes of the vectors can be reduced drastically. We test our clustering algorithm on 20 news groups and Reuter collection data sets. The results show that our method outperforms the conventional GA in various document representation environments.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1083-1097
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• 1998

The conventional geographic information systems(GIS) is a software which handles spatial and aspatial information of objects in the real world. The system can not support users time-varying information because it manipulates their snapshot data in the spatial database. Also even though it supports time-varying information, it is very limited and hs many difficulties in presenting and processing queries. This paper therefore describes an integrated spatiotemporal data model using loosely-coupled approach which is extended a time dimension for the previous spatial database and which handles time-varying historical information of spatial objects. Conclusionally this paper not only designed a data structure for spatiotemporal database, but also implemented spatial comparison operations varying over time.

• Journal of KIISE
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• v.42 no.1
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• pp.93-96
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• 2015

The SIFT method is well-known for robustness against various image transformations, and is widely used for image retrieval and matching. The SIFT method extracts keypoints using scale space analysis, which is different from conventional keypoint detection methods that depend only on the image space. The SIFT method has also been extended to use higher-order scale space derivatives for increasing the number of keypoints detected. Such detection of additional keypoints detected was shown to provide performance gain in image retrieval experiments. Herein, a sigma based normalization method for keypoint detection is introduced using higher-order scale space derivatives.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.105-119
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• 2015

The overseas expansion is essential to expand domestic geospatial industries in a state of saturation. But current overseas expansion method has be limited to expand global market. Inter-industry convergence strategies may be the most resonable alternative to expand global market through raising the expansion possibility to developing countries with ODA funds and to developed countries with converging global competitive industries. This research investigates various foreign developed and developing countries to draw each demand. As a result, easiness of convergence, confidentiality of information, complementarity of poor infrastructure, responsiveness of various demands and sustainability of system are needed to successful convergence on multiple industries. This research seeks convergence framework to meet this demands, and suggests each component. This convergence framework is consisted of geospatial convergence common framework, inter-industry convergence model and institutional supporting system for overseas expansion.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.5-14
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• 2002

The generalization of geospatial data is an important way in deriving a new database from an original one. The generalization of a geospatial object changes not only its geometric and aspatial attributes but also results in propagation to other objects along their relationship. We call it generalization propagation of geospatial databases. Without proper handling of the propagation, it brings about an inconsistent database or loss of semantics. Nevertheless, previous studies in the generalization have focused on the derivation of an object by isolating it from others. And they have proposed a set of generalization operators, which were intended to change the geometric and aspatial attributes of an object. In this paper we extend the definition of generalization operators to cover the propagation from an object to others. In order to capture the propagation, we discover a set of rules or constraints that must be taken into account during generalization procedure. Each generalization operator with constraints is expressed in relational algebra and it can be converted to SQL statements with ease. A prototype system was developed to verify the correctness of extended operators.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.73-75
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• 2006

생물체 내에서 중요 생물학적 기능을 수행하는 기본 단위인 단백질 및 이들의 상호작용 대한 많은 연구가 이루어져 다양한 생물체에 대한 단백질 상호작용 데이터베이스가 구축되었다. 본 논문에서는 효모에 대해 공개되어있는 단백질 상호작용 데이터를 이용하여 새로운 단백질 상호작용을 예측하는 방법을 제안한다. 논문에서는 문헌에서 연관 정보를 효율적으로 찾아내기 위하여 제안된 연관개념공간 탐색 방법을 확장하여 단백질 상호작용 예측에 사용한다. 단백질들은 각각이 가지는 다양한 속성들의 벡터로 간주되며, 상호작용은 해당 단백질들의 연관성을 통해 이루어지는 것으로 표현된다. 상호작용하는 두 단백질들의 속성은 단어의 공동 출현과 같이 고려되어 단백질 상호작용은 두 단백질 벡터의 요소로 표현되고 벡터의 요소 속성들 간의 연관성을 표현하기 위해 연관속성개념공간으로 사상되어 공간상의 거리 기반으로 연관속성을 추출한다. 추출된 연관속성을 최대로 포함하는 단백질들 간의 상호작용을 예측하는 방식으로 단백질 상호작용을 예측한다. 논문에서 제안한 방법은 효모의 단백질 상호작용 예측에 대해 평균 약 91.8%의 예측 정확도를 보여, 연관속성개념공간을 이용한 방법이 단백질 상호작용을 예측하는 또 다른 대안으로 사용 될 수 있음을 확인하였다.