• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.85-88
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• 2009

In multimedia database systems, the nearest neighbor Query occurs frequently and requires the processing cost higher than other spatial Queries do. It needs the measurement of search distance that the number of searched nodes and the computation time in an index can be minimized for optimizing the cost of processing the nearest neighbor query. The circular location property of objects is considered to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we propose the processing method of nearest neighbor queries be considered a circular location property of object where the search space consists of a circular domain and show its characteristics. The proposed method uses the circular minimum distance and the circular optimal distance, the search measurement for optimizing the processing cost of nearest neighbor queries.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.83-90
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• 2010

The number of searched nodes and the computation time in an index should be minimized for optimizing the processing cost of the nearest neighbor query. The Measurement of search distance considered a circular location property of objects is required to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we propose the processing method of the nearest neighbor query be considered a circular location property of object where the search space consists of a circular domain and show its performance by experiments. The proposed method uses the circular minimum distance and the circular optimal distance which are the search measurements for optimizing the processing cost of the nearest neighbor query.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2173-2184
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• 1997

Among spatial queries handled in spatial database systems, nearest neighbor queries to find the nearest spatial object from the given locaion occur frequently. The number of searched nodes in an index must be minimized in order to increase the performance of nearest neighbor queries. An Existing approach considered only the processing of an nearest neighbor query in a two-dimensional search space and could not optimize the number of searched nodes accurately. In this paper, we propose the optimal search distance and prove its properties. The proposed optimal search distance is the measurement of a new search distance for accurately selecting the nodes which will be searched in processing nearest neighbor queries. We present an algorithm for processing the nearest neighbor query by applying the optimal search distance to R-trees and prove that the result of query processing is correcter than the existing approach.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.406-420
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• 2020

In order to make up the deficiencies of the existing research results which cannot effectively deal with the nearest neighbor query based on the line segments in obstacle space, the k nearest neighbor query method of line segment in obstacle space is proposed and the STA_OLkNN algorithm under the circumstance of static obstacle data set is put forward. The query process is divided into two stages, including the filtering process and refining process. In the filtration process, according to the properties of the line segment Voronoi diagram, the corresponding pruning rules are proposed and the filtering algorithm is presented. In the refining process, according to the relationship of the position between the line segments, the corresponding distance expression method is put forward and the final result is obtained by comparing the distance. Theoretical research and experimental results show that the proposed algorithm can effectively deal with the problem of k nearest neighbor query of the line segment in the obstacle environment.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.32-41
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• 1999

In spatial database system, the nearest neighbor query occurs frequently and requires the processing cost higher than other spatial queries do. The number of nodes to be searched in the index can be minimized for optimizing the cost of processing the nearest neighbor query. The optimal search distance is pr9posed for the measurement of a search distance to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we prove properties of the optimal search distance in N-dimensional. We show through experiments that the performance of query processing of our method is superior to other method using maximum search distance.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.872-878
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• 2000

In this paper a computer programming for the expression of nearest neighbor atoms in face-centered cubic (FCC) and body-centered cubic (BCC) crystals was suggested as one of the approaches to understand each of the crystal structure. By using this computer programming the distance values between a reference atom and the nearest neighbor atoms, and the numbers of the nearest neighbor atoms were calculated ane compared for the FCC and BCC crystals. In this algorithm, the positions of the atoms in a crystal were defined as two categories: the corner atoms and face- or body-centered atoms, and considered respectively. For the same order of nearest neighbor atoms except the second order ones the distance values form the reference atom were smaller in the FCC crystals than those in the BCC. Also, the numbers of he first and third nearest neighbor atoms n the FCC crystals were larger than those in the BCC. This difference was explained by the comparison of each atomic packing ratio of the FCC and BCC crystals. The algorithm used in this programming can also be expanded to the analysis of other crystal structures.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.2 s.332
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• pp.83-88
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• 2005

We propose the design method for the VLSI architecture of FNNPDS combined PDS(partial distance search) and FNNS(fast nearest neighbor search), which are used to fast encoding in vector quantization, and obtain the results that FNNPDS(fast nearest neighbor partial distance search) is faster method than the conventional methods by simulation. In simulations, we investigate timing diagrams described searching time of the nearest codevector for an input vector, and compare the average clock cycles per input vector for Lena and Peppers images. According to the result of simulations, the number of the clock cycle of FNNPDS was reduced to $79.2\%\~11.7\%$ as compared with the number using the conventional techniques.

• Journal of IKEEE
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• v.26 no.2
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• pp.169-175
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• 2022

This paper introduces an interference elimination method using k-nearest neighbor (KNN) algorithm for precise distance estimation by reducing interference between ultrasonic sensors. Conventional methods compare current distance measurement result with previous distance measurement results. If the difference exceeds some thresholds, conventional methods recognize them as interference and exclude them, but they often suffer from imprecise distance prediction. KNN algorithm classifies input values measured by multiple ultrasonic sensors and predicts high accuracy outputs. Experiments of distance measurements are conducted where interference frequently occurs by multiple ultrasound sensors of same type, and the results show that KNN algorithm significantly reduce distance prediction errors. Also the results show that the prediction performance of KNN algorithm is superior to conventional voting methods.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.1025-1030
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• 2004

In multimedia database systems, the k nearest neighbor query occurs frerluently and requires the processing cost higher than other spatial queries do. The numberof searched nodes and the computation time in an index can be minimized for optimizing the cost of processing the k nearest neighbor query. In this paper, we propose the search distance which can reduce the computation time of the optimal search distance.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.181-191
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• 2005

The classification error of 'ㅁ', 'ㅇ' is one of the main causes of incorrect recognition in Korean characters, but there haven't been enough researches to solve this problem. In this paper, a new feature extraction method from Korean grapheme is proposed to recognize 'ㅁ', 'ㅇ'effectively. First, we defined an optimal neighbor-distance selection measure using modified Hausdorff distance, which we determined the optimal neighbor-distance by. And we extracted neighbor-angle feature which was used as the effective feature to classify the two graphemes 'ㅁ', 'ㅇ'. Experimental results show that the proposed feature extraction method worked efficiently with the small number of features and could recognize the untrained patterns better than the conventional methods. It proves that the proposed method has a generality and stability for pattern recognition.