• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.1-8
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• 2010

For three and more rapidly moving vehicles, they want to search the nearest location for meeting. Each vehicle has a different velocity and a efficient method is needed for shifting a short distance. It is observed that the existing group nearest-neighbor query has been investigated for static query points; however these studies do not extend to highly dynamic vehicle environments. In this paper, we propose a novel Dynamic Nearest-Neighbor query processing for Multiple Vehicles (DNN_MV). Our method retrieves the nearest neighbor for a group of moving query points with a given vector and takes the direction of moving query points with a given vector into consideration for DNN_MV. Our method efficiently calculates a group nearest neighbor through a centroid point that represents the group of moving query points. The experimental results show that the proposed method operates efficiently in a dynamic group nearest neighbor search.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.374-376
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• 2022

항로표지 배치의 적합성 평가 및 검증에 활용하기 위해 기계학습 (Machine Learning)을 통해 대표항적선을 결정한다. 이 연구에서는 대표항적선과 항로표지와의 최근접 거리를 계산하고 시인가능 거리 및 거리율 등을 통해 항로표지의 배치 적합성을 평가하고 검증한다.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.37-43
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• 2020

The paper proposed the method of calculating the latest approach angle that can reliably recognize multiple laser images even with the change in separation distance between screen and laser launch device. This method recognizes the angle of the laser pattern angle by using the distance of the laser pattern angle, and the angle extraction of the laser detects the laser image from the acquired image using the labeling algorithm, and performs the huff conversion to extract the angle of the straight line. The distance of the reference angle and angle of the laser image extracted using Euclidean distance among similarity scales is calculated, and the furnace is recognized using the calculated distance result value. Experiments with changing the separation distance to "200 cm to 400 cm" showed 100% recognition of individual strands at all separation distances. The experiment confirmed the reliability of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.153-156
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• 2011

최근 많은 관심을 받고 있는 증강현실 위치기반 서비스와 같이 사용자의 한정된 시야각이 존재하는 상황에서 사용자가 원하는 데이터를 효과적으로 제공하기 위하여 본 논문에서는 새로운 위치기반 질의인 시야 최근접 질의 (VFNN: View Field Nearest Neighbor Queries)를 소개한다. VFNN 질의는 사용자의 시야각내에 위치하는 가장 가까운 데이터를 검색한다. 본 논문에서 제안하는 VFNN 질의 처리 알고리즘은 가장 널리 활용되고 있는 공간 데이터 색인 구조인 $R^*$-tree를 사용한다. 특히, 질의 점과 MBR 사이의 최소거리인 MINDIST뿐만 아니라, 질의 점과 MBR 사이의 최대 각, 최소 각을 정의한다. 이를 활용하여 $R^*$-tree 탐색 시 질의 결과 값을 포함하지 않는 노드들을 연산에서 제외함으로써 질의 처리의 효율성을 향상시킨다. 마지막으로 실험을 통하여 VFNN 질의 처리 알고리즘의 성능을 평가한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1410-1411
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• 2012

최근 모바일 사용자의 안전한 위치기반 서비스의 사용을 위한 아웃소싱 데이터베이스에서 객체 및 사용자의 위치 정보를 보호하는 연구가 위치 데이터를 보호하기 위한 연구가 활발히 진행되고 있다. 그러나 기존 연구는 불필요한 객체 정보를 요구하기 때문에, 높은 질의 처리 시간을 지니는 단점을 지닌다. 이러한 문제점을 해결하기 위해, 본 논문에서는 기준 POI를 중심으로 객체의 방향성 정보와 변환된 거리를 이용하여, 사용자와 객체의 정보를 보호하는 k-최근접 질의처리 알고리즘을 제안한다.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.11-19
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• 2008

Recently, advances in mobile devices and wireless communication technologies require research on various location-based services. As a result, many studies on processing k-nearest neighbor query, which is most im portant one in location-based services, have been done. Most of existing studies use pre-computation technique to improve retrieval performance by computing network distance between POIs and nodes beforehand in spatial networks. However, they have a drawback that they can not deal with effectively the update of POIs to be searched. In this paper, we propose a distributed grid scheme using S-GRID to overcome the disadvantage of the existing work as well as to manage the location information of a large number of moving objects in efficient way. In addition, we describe a k-nearest neighbor(k-NN) query processing algorithm for the proposed distributed grid scheme. Finally, we show the efficiency of our distributed grid scheme by making a performance comparison between the k-NN query processing algorithm of our scheme and that of S-GRID.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.673-682
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• 2005

Distance browsing queries, namely k-nearest neighbor queries, are the most important queries in spatial database applications, e.g., Geographic Information Systems(GISs). Recently, GIS applications trends to extend themselves toward wide multi-user environments such as the Web. Since many techniques for such queries, where Hjaltason and Samet's algorithm is the most efficient one, were optimized for only one query, we need to complement them suitable for multi-user environments. It can be a good approach that we store many individual query results in a cache, i.e., query result caching and reuse them in evaluating incoming queries, j.e., query result matching. In this paper, we propose a complementary Hjaltason and Samet's algerian capable of reusing previous query results in a cache for answering distance browsing queries in multi-user GIS environments. Our experimental results conform the efficiency of our approach.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.125-131
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• 2001

Various kind of disasters happens in our society. Most of them require immediate treatment to save life or to protect valuable products. When an accident happens in a place, it is reported to the headquarter of emergency measures system. According to the nature of accident several treatments orders are transmitted to the related authorities. In this paper, we introduce an intelligent emergency measures system that uses trigger mechanism of active databases. The system responds to various events spontaneously without intervention of mankind by triggering proper rules. The most important part of an action in the system is the capability of searching places to apply adequate treatments quickly. We have developed a new method for range queries and nearest neighbor queries which utilize the z-ordering technique to get fast responses. Those new methods are further extended to handle more realistic actual distance of road among positions.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.405-410
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• 2009

This paper proposes an efficient path finding scheme capable of searching the paths to k static objects from a given query point, aiming at both improving the legacy k-nearest neighbor search and making it easily applicable to the road network environment. To the end of improving the speed of finding one-to-many paths, the modified A* obviates the duplicated part of node scans involved in the multiple executions of a one-to-one path finding algorithm. Additionally, the cost to the each object found in this step makes it possible to finalize the k objects according to the network distance from the candidate set as well as to order them by the path cost. Experiment results show that the proposed scheme has the accuracy of around 100% and improves the search speed by $1.3{\sim}3.0$ times of k-nearest neighbor searches, compared with INE, post-Dijkstra, and $na{\ddot{i}}ve$ method.