• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.82.1-82
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• 2002

$\textbullet$ Nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter incorporating numbers of validated measurements $\textbullet$ Probability density function of the NDS $\textbullet$ Simulation results in a clutter environment to verify the performances $\textbullet$ Sensitivity analysis for the unknown spatial clutter density

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.271-274
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• 2002

본 논문은 (1)에 기술된 퍼지 K-nearest neighbor(NN) 알고리즘의 확장인 interval 제2종 퍼지 K-NN을 제안한다. 제안된 방법에서는, 각 패턴벡터의 멤버쉽 값들에 불확실성(Uncertainty)을 할당하는 것에 의해 interval 제2종 퍼지 멤버쉽으로의 확장을 시도한다. 이러한 확장은, K의 결정에 존재하는 불확실성은 다루고, 조정할 수 있게 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.17-22
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• 2008

최근접 객체 질의(Nearest Neighbor Query)는 질의가 요청된 지점으로부터 가장 가까운 객체를 찾는 질 의로 위치기반 서비스 분야에서 가장 널리 사용되고 있는 질의의 형태이다. 이를 기반으로 한 지역 최근접 객체 질의 (Range Nearest Neighbor), 연속 최근접 객체 질의(Continuos Nearest Neighbor)등의 확장 된 개념으로 다양한 최근접 객체 질의가 제안되어 왔다. 그러나 지금까지의 최근접 객체 질의를 기반으로 한 연구들은 점으로 표현된 질의를 기준으로 하여 최근접 객체를 찾는 기준점 최근접 객체(Point Nearest Neighbor) 질의를 기반으로 하고 있어, 점으로 표현이 불가능한 1 차원 형태의 질의에 대하여 효과적인 최근접 객체를 검색하는 연구는 연구된 바 없다. 본 논문에서는 한 개 이상의 1 차원 형태의 선분으로 이루어진 질의에 대하여 질의 주변의 객체 중 최근접 객체를 찾는 다중선 최근접 객체 질의 (Polyline Nearest Neighbor)를 정의하고 효과적인 질의 처리 알고리즘을 제안하였다. 제안된 기법의 성능 분석을 위한 실험은 객체와 질의가 다양한 형태로 분포되어 있는 환경아래 진행되었으며, 실험 결과는 기대 값과 근접한 결과 값을 얻었다.

• 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.

• Journal of Digital Convergence
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• v.3 no.1
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• pp.149-163
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• 2005

Researches for NN(nearest neighbor) query which is often used in LBS system, have been worked. However. Conventional NN query processing techniques are usually meaningless in moving object management system for LBS since their results may be invalidated as soon as the query and data objects move. To solve these problems, in this paper we propose a new nearest neighbor query processing technique, called CTNN, which is possible to meet continuous trajectory nearest neighbor query processing. The proposed technique consists of Approximate CTNN technique which has quick response time, and Exact CTNN technique which makes it possible to search accurately nearest neighbor objects. Experimental results using GSTD datasets shows that the Exact CTNN technique has high accuracy, but has a little low performance for response time. They also shows that the Approximate CTNN technique has low accuracy comparing with the Exact CTNN, but has high response time.

• 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 Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.59-71
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• 2004

Nearest neighbor query retrieves nearest located target objects, and is very frequently used in mobile environment. In this paper we propose a novel neatest neighbor query processing technique that is able to retrieve nearest located target object from the user who is continuously moving with a direction. The proposed method retrieves objects using the direction property of moving object as well as euclidean distance to target object. The proposed method is applicable to traffic information system, travel information system, and location-based recommendation system which require retrieving nearest located object.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.29-36
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• 2019

Unmanned Autonomous Vehicle's driving road in the national defense includes not only paved roads, but also unpaved roads which have rough and unexpected changes. This Unmanned Autonomous Vehicles monitor and recon rugged or remote areas, and defend own position, they frequently encounter environments roads of various and unpredictable. Thus, they need additional learning to drive in this environment, we propose a Additional Learning Nearest Neighbor (ALNN) which is modified from Approximate Nearest Neighbor to allow for quick learning while avoiding the 'Forgetting' problem. In addition, since the Execution speed of the ALNN algorithm decreases as the learning data accumulates, we also propose a solution to this problem using GPU parallel processing based on OpenGL Shader Language. The ALNN based on GPU algorithm can be used in the field of national defense and other similar fields, which require frequent and quick application of additional learning in real-time without affecting the existing learning data.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.11-20
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• 2008

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1429-1432
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• 2003

This paper addresses the problem of finding a constrained nearest neighbor for moving query point(we call it CNNMP) The Nearest neighbor problem is classified by existence of a constrained region, the number of query result and movement of query point and target. The problem assumes that the query point is not static, as 1-nearest neighbor problem, but varies its position over time to the constrained region. The parameters as NC, NCMBR, CQR and QL for the algorithm are also presented. We suggest the query optimization algorithm in consideration of topological relationship among them