• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.23-30
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• 2015

A shoreline mapping is essential for describing coastal areas, estimating coastal erosions and managing coastal properties. This study has planned to map the 3D shorelines with the airborne LiDAR(Light Detection and Ranging) data and the KOMPSAT-2 imagery, acquired in Uljin, Korea. Following to the study, the DSM(Digital Surface Model) is generated firstly with the given LiDAR data, while the NDWI(Normalized Difference Water Index) imagery is generated by the given KOMPSAT-2 imagery. The classification method is employed to generate water and land clusters from the NDWI imagery, as the 2D shorelines are selected from the boundaries between the two clusters. Lastly, the 3D shorelines are constructed by adding the elevation information obtained from the DSM into the generated 2D shorelines. As a result, the constructed 3D shorelines have had 0.90m horizontal accuracy and 0.10m vertical accuracy. This statistical results could be concluded in that the generated 3D shorelines shows the relatively high accuracy on classified water and land surfaces, but relatively low accuracies on unclassified water and land surfaces.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.49-57
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• 2005

In this paper. a CLB-based CPLD low power technology mapping algorithm for trade-off is proposed. To perform low power technology mapping for CPLD, a given Boolean network has to be represented to DAG. The proposed algorithm consists of three step. In the first step, TD(Transition Density) calculation have to be Performed. Total power consumption is obtained by calculating switching activity of each nodes in a DAG. In the second step, the feasible clusters are generated by considering the following conditions : the number of output. the number of input and the number of OR-terms for CLB within a CPLD. The common node cluster merging method, the node separation method, and the node duplication method are used to produce the feasible clusters. The proposed algorithm is examined by using benchmarks in SIS. In the case that the number of OR-terms is 5, the experiments results show reduction in the power consumption by 30.73$\%$ comparing with that of TEMPLA, and 17.11$\%$ comparing with that of PLAmap respectively

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.235-238
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• 2007

본 논문에서는 기존의 클러스터 기반 퍼지 모델트리에서 트리의 깊이에 따른 over-fitting으로 인한 훈련 및 검증데이터의 일관성 문제점을 해결하기 위해 상호 노드간의 정보를 고려하는 방법을 제안하고자 한다. 제안된 방법은 우선 입력과 출력변수의 속성을 고려한 퍼지 클러스터링에 의해 중심벡터를 계산한 후, 중심벡터들과 입력 속성간의 소속도를 이용하여 구간 분할된 영역별로 각각의 선형모델을 구축한다. 예측 단계에서는 입력된 데이터가 잎노드에 도달하는 노드간의 중심벡터와 입력 데이터간의 거리값에 따른 소속도를 계산한 후 최종적으로 무게 중심법을 이용하여 출력값을 예측하게 된다. 제안된 방법의 우수성을 보이기 위해 다양한 벤치마크 데이터를 대상을 실험한 결과, 기존의 클러스터 기반 퍼지 모델트리보다 향상된 성능을 보임을 알 수 있었다.

• Annual Conference of KIPS
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• 2015.04a
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• pp.139-142
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• 2015

데이터 센터에서는 물리적인 규모 증가와는 달리 별도의 처리 없이는 분산처리 프레임워크가 동일한 클러스터 내에서 복수로 동작할 수 없어 전체 환경을 정적으로 분할하여 이들을 배치하는 것이 일반적이다. 그러나 최근 연구에서는 복수의 프레임워크를 한 클러스터 내에서 동작시킴으로써 클러스터의 활용률을 높이는 방향으로 이루어지고 있다. Mesos는 복수의 분산처리 프레임워크를 한 클러스터에서 동작시키기 위한 시스템 중 하나로 각 프레임워크 스케줄러의 스케줄링을 지원하는 단일 Allocation Module을 가진다. Allocation Module은 모든 Slave와 프레임워크 스케줄러들의 요청을 처리하는데, 시스템 규모가 커질수록 Allocation Module으로 집중되는 부하가 증가하여 이에 따른 할당 속도 저하로 정상적인 동작이 불가능해진다. 이 문제를 해결하기 위해 본 논문에서는 Mesos 시스템의 Allocation Module 병렬화를 제안한다. 제안 방식을 통해 Allocation Module의 부하를 분산함과 동시에 Head-of-line Blocking으로 인한 스케줄링 지연 문제를 해결할 수 있을 것이다.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.37-41
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• 2012

In this paper, we proposed FPGA algorithm for consider the power consumption. Proposed algorithm generated a feasible cluster by circuit partition considering the CLB condition within FPGA. Separated the feasible cluster reduced power consumption using glitch removal method. Glitch removal appled delay buffer insertion method by signal process within the feasible cluster. Also, removal glitch between the feasible clusters by signal process for circuit. The experiments results show reduction in the power consumption by 7.14% comparing with that of [9].

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.816-820
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• 2004

Clustering is a process of dividing similar data objects in data set into clusters and acquiring meaningful information in the data. The main issues related to clustering are the effective clustering of high dimensional data and optimization. This study proposed a method of measuring similarity based on SVM and a new method of calculating the number of clusters in an efficient way. The high dimensional data are mapped to Feature Space ones using kernel functions and then similarity between neighboring clusters is measured. As for created clusters, the desired number of clusters can be got using the value of similarity measured and the value of Δd. In order to verify the proposed methods, the author used data of six UCI Machine Learning Repositories and obtained the presented number of clusters as well as improved cohesiveness compared to the results of previous researches.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.309-315
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• 2016

The node mobility in the wireless mobile network environment increases the energy consumption. This paper proposes a CNMA (cluster-based node management algorithm) to reduce the energy consumption caused by node mobility, and to prolong the life cycle for cluster member nodes. The proposed CNMA traces the mobility for nodes between cluster header and member, and it analyses the energy capacity as monitoring periodically their relationship. So, it makes a division and merges by analysing the state transition for nodes. This paper is to reduce the energy consumption due to the node mobility. The simulation results show that the proposed CNMA can efficiently control the energy consumption caused by mobility, and it can improve the energy cycle.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.37-39
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• 2003

이동체 데이터베이스를 위한 과거 궤적 색인으로 R-tree계열이 많이 사용된다. 그러나 R-tree계열의 색인은 공간 근접성만을 고려하였기 때문에 동일 궤적을 검색하기에는 많은 노드 접근이 필요하다. 이동체 색인의 검색에서 영역 질의와 궤적 질의는 공간 근접성과 궤적 연결성과 같이 상반된 특징으로 인하여 함께 고려되지 않았다. 이동체 색인에서 영역 질의의 성능개선을 위해서는 노드 간의 심한 중복과 사장 공간(Dead Space)을 줄여야 하고, 궤적 질의의 성능 개선을 위해서는 이동체의 궤적 보존이 이루어져야 한다. 이와 같은 요구 조건을 만족하기 위해, 이 논문에서는 R-tree 기반의 색인 구조에서 새로운 분할 정책을 제안한다. 제안하는 색인 구조의 노드 분할 정책은 궤적 클러스터링을 위한 동일 궤적을 그룹화해서 분할하는 공간 축 분할 정책과 공간 활용도를 높이는 시간 축 분할 정책을 제안한다. 본 논문에서는 R-tree기반의 색인 구조에서 변경된 분할 정책을 구현하고, 실험 평가를 수행한다. 이 성능 평가를 통해서 검색성능이 우수함을 보인다.

• Journal of Internet Computing and Services
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• v.13 no.6
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• pp.1-8
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• 2012

Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering and is adequate to cluster so many documents rapidly and easily. It has disadvantage that the random initial centers cause different result. So, the better choice is to place them as far away as possible from each other. We propose a new method of selecting initial centers in K-Means clustering. This method uses triangle height for initial centers of clusters. After that, the centers are distributed evenly and that result is more accurate than initial cluster centers selected random. It is time-consuming, but can reduce total clustering time by minimizing the number of allocation and recalculation. We can reduce the time spent on total clustering. Compared with the standard algorithm, average consuming time is reduced 38.4%.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.215-218
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• 2014

In hashing-based methods for approximate nearest neighbors(ANN) search, by mapping data points to k-bit binary codes, nearest neighbors are searched in a binary embedding space. In this paper, we present a hashing method using a PCA-based clustering method, Principal Direction Divisive Partitioning(PDDP). PDDP is a clustering method which repeatedly partitions the cluster with the largest variance into two clusters by using the first principal direction. The proposed hashing method utilizes the first principal direction as a projective direction for binary coding. Experimental results demonstrate that the proposed method is competitive compared with other hashing methods.