• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.57-63
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• 2009

Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in a image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the k-nearest neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images in-putted on a continual input image. However, the robustness of recognition strategies using PCA depends on several factors, including illumination. When scene constancy is not secured due to varying illumination conditions, the learning performance the feature detector can be compromised, undermining the recognition quality. This paper proposes a new PCA recognition in which database of objects can be detected under different illuminations between input images and the model images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2919-2925
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• 2014

Because of the rapid growth of mobile communication and wireless communication, Location-based services are handled in many applications. So, the management and analysis of spatio-temporal data are a hot issue in database research. Index structure and query processing of such contents are very important for these applications. This paper addressees algorithms that make index structure by using Douglas-Peucker Algorithm and process nearest neighbor search query efficiently on moving objects trajectories. We compare and analyze our algorithms by experiments. Our algorithms make small size of index structure and process the query more efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.126-132
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• 2011

In the application areas of LBS(Location Based Service) and ITS(Intelligent Transportation System), continuous k-nearest neighbor query(CkNN) which is defined as a query to find the nearest points of interest to all the points on a given path is widely used. It is necessary to acquire results quickly in the above applications and be applicable to spatial network databases. It is also able to cope successfully with frequent updates of POI objects. This paper proposes a new method to search nearest POIs for moving query objects on the spatial networks. The method produces a set of split points and their corresponding k-POIs as results with partial order among k-POIs. The results obtained from experiments with real dataset show that the proposed method outperforms the existing methods. The proposed method achieves very short processing time(15%) compared with the existing method.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.3
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• pp.319-331
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• 2007

Nowadays, urbanized area expands its boundary, and distribution of urbanized area is gradually transformed into more complicated pattern. In Korea, SMA(Seoul Metropolitan Area) has outstanding urbanized area since 1960. But it is ambiguous whether urban distribution is clustered or dispersed. That is to say, it is difficult to understand spatial distribution pattern of urbanized area, although urbanized area has grown gradually. This study aims to show the way in which expansions of urbanized area impact on spatial distribution pattern of urbanized area. We use GIS analysis based on raster dataset, quadrat analysis, and nearest neighbor analysis to know distribution pattern of urbanized area in time-series urban growth. Experiments show that cohesion of SMA's urbanized area had increased to the early 1980s, but has decreased from the middle 1980s. Also, urban growth of SMA has been characterized not by spillover growth but by leapfrogging growth and road-influenced growth since the middle 1980s.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.52.2-52.2
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• 2013

We explore the relative importance of the role of small-scale environment and large-scale environment in triggering nuclear activity of the local galaxies using a volume-limited sample with $M_r$ < -19.5 and 0.02 < z < 0.0685 selected from the Sloan Digital Sky Survey Data Release 7. The active galactic nuclei (AGN) host sample is composed of Type II AGNs identified with flux ratios of narrow emission lines with S/N > 6 and the central velocity dispersion of the sample galaxies is limited to have a narrow range between 130 < ${\sigma}$ < 200($km\;s^{-1}$), corresponding to 7.4 < $log(M_{BH}/M_{\odot})$ < 8.1 in order to fix the mass of the supermassive black hole at the center of its host galaxy. In this study, we find that the AGN fraction ($f_{AGN}$) of late-type galaxies are larger than of early-type galaxies and that for target galaxy with late-type nearest neighbor, $f_{AGN}$ starts to increase as the target galaxy approaches the virial radius of the nearest neighbor (about a few hundred kpc scale). The latter result may support the idea that the hydrodynamic interaction with the nearest neighbor as well as tidal interaction and merger also plays an important role in triggering the nuclear activity of galaxy. We also find that early-type cluster galaxies show decline of AGN activity compared to ones in lower density regions, whereas the direction of dependence of AGN activity for late-type galaxies is opposite.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• Journal of Wetlands Research
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• v.16 no.1
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• pp.103-112
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• 2014

Area average rainfall estimation is important to determine the exact amount of the available water resources and the essential input data for rainfall-runoff analysis. Like that, the necessary criterion for accurate area average rainfall estimate is the uniform spatial distribution of raingauge network. In this study, we suggest the spatial distribution evaluation methodology of raingauge network to estimate better area average rainfall and after the suggested method is applied to Han River and Geum River basin. The spatial distribution of rainfall network can be quantified by the nearest neighbor index. In order to evaluate the effects of the spatial distribution of rainfall network by each basin, area average rainfall was estimated by arithmetic mean method, the Thiessen's weighting method and estimation theory for 2013's rainfall event, and evaluated the involved errors by each cases. As a result, it can be found that the estimation error at the best basin of spatial distribution was lower than the worst basin of spatial distribution.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.17-27
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• 2010

Feature selection and feature weighting are useful techniques for improving the classification accuracy of k-Nearest Neighbor (k-NN) classifier. The main propose of feature selection and feature weighting is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. In this paper, a novel hybrid approach is proposed for simultaneous feature selection, feature weighting and choice of k in k-NN classifier based on Genetic Algorithm. The results have indicated that the proposed algorithm is quite comparable with and superior to existing classifiers with or without feature selection and feature weighting capability.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.75.2-75.2
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• 2010

We investigate the dependence of occurrence of bar in galaxies on galaxy properties and environment. The environmental conditions considered include the large-scale background density and distance to the nearest neighbor galaxy. We use a volume-limited sample of 33,296 galaxies brighter than $M_r$=-19.5+5logh at $0.02{\leqq}z{\leqq}0.05489$, drawn from the Sloan Digital Sky Survey Data Release 7. We classify the galaxies into early and late types, and identify bars by visual inspection. We find that the fraction of barred galaxies ($f_{bar}$) is 18.2% on average in the case of late-type galaxies, and depends on both u-r color and central velocity dispersion $(\sigma);f_{bar}$ is a monotonically increasing function of u-r color, and has a maximum value at intermediate velocity dispersion (${\sigma}{\simeq}170km\;s^{-1}$). This trend suggests that bars are dominantly hosted by systems having intermediate-mass with no recent interaction or merger history. We also find that $f_{bar}$ does not directly depend on the large-scale background density as its dependence disappears when other physical parameters are fixed. We discover the bar fraction decreases as the separation to the nearest neighbor galaxy becomes smaller than 0.1 times the virial radius of the neighbor regardless of neighbor's morphology. These results imply that it is difficult for bars to be maintained during strong tidal interactions, and that the source for this phenomenon is gravitational and not hydrodynamical.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.223-224
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• 2021

Recently, k-farthest neighbor (kFN) queries have not as much attention as k-nearest neighbor (kNN) queries. Therefore, this study considers moving k-farthest neighbor (MkFN) queries for spatial network databases. Given a positive integer k, a moving query point q, and a set of data points P, MkFN queries can constantly retrieve k data points that are farthest from the query point q. The challenge with processing MkFN queries in spatial networks is to avoid unnecessary or superfluous distance calculations between the query and associated data points. This study proposes a batch processing algorithm, called MOFA, to enable efficient processing of MkFN queries in spatial networks. MOFA aims to avoid dispensable distance computations based on the clustering of both query and data points. Moreover, a time complexity analysis is presented to clarify the effect of the clustering method on the query processing time. Extensive experiments using real-world roadmaps demonstrated the efficiency and scalability of the MOFA when compared with a conventional solution.