• 한국주거학회논문집
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• 제24권4호
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• pp.39-52
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• 2013

This study examined the key factors that significantly improved the demand of multi-habitation. It determined the factors at the macroscopic level (or push factors) and the microscopic level (or pull factors). Focusing on a microscopic viewpoint, this study looked at the process of settlement through investigating 78 MH residents in the Seoul metropolitan area. The survey included the questions, such as who they are, how they prepared for moving, and how much they enjoyed their rural lives. In addition, any differences in this process were analyzed depending on respondents' characteristics. Major findings are as follows: First, general macro-level circumstances seemed supportive for the MH lifestyles. Second, six keywords were determined to represent the recent MH trends. They are "semi-sedentism, clustering, young people, female, money, and policy". Third, the distances between the original towns for native residents and new second-home towns for MH residents affected the interactions among them. However, these two groups had better relationships when the second-home towns were apart from the original towns. I then considered the need of a buffer zone between the two residential areas for MH residents. The conceptual difference between MH residents (i.e., semi-sedentism) and original rural residents (i.e., sedentism) might require certain types of buffer zones to continue good relationships among them.

• ETRI Journal
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• 제29권5호
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.116-119
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• 2005

As to the synthetical estimation of land covering parameters or the compounded land covering classification for multi-resolution satellite data, former researches mainly adopted linear or nonlinear regression models to describe the regression relationship of land covering parameters caused by the degradation of spatial resolution, in order to improve the retrieval accuracy of global land covering parameters based on 1;he lower resolution satellite data. However, these methods can't authentically represent the complementary characteristics of spatial resolutions among different satellite data at arithmetic level. To resolve the problem above, a new compounded land covering classification method at arithmetic level for multi-resolution satellite data is proposed in this .paper. Firstly, on the basis of unsupervised clustering analysis of the higher resolution satellite data, the likelihood distribution scatterplot of each cover type is obtained according to multiple-to-single spatial correspondence between the higher and lower resolution satellite data in some local test regions, then Parzen window approach is adopted to derive the real likelihood functions from the scatterplots, and finally the likelihood functions are extended from the local test regions to the full covering area of the lower resolution satellite data and the global covering area of the lower resolution satellite is classified under the maximum likelihood rule. Some experimental results indicate that this proposed compounded method can improve the classification accuracy of large-scale lower resolution satellite data with the support of some local-area higher resolution satellite data.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권7호
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• pp.657-668
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• 2008

기존의 상용 저장 시스템은 데이타를 저장할 때 몇 가지 문제점을 가지고 있다. 먼저, 데이타를 저장함에 있어서 실용적인 중복제거 기법이 널리 활용되고 있지 못하기 때문에 저장 장치 낭비를 초래하고 있다. 또한 대규모 데이타 입출력을 처리하기 위해서 고사양의 시스템을 요구한다는 부분도 문제점으로 지적할 수 있다. 이와 같은 문제를 해결하기 위해서 본 논문에서는 블록 수준에서의 중복을 제거하기 위한 방안으로 파일 지문을 이용한 클러스터링 기반 저장 시스템을 제안하고 있다. 본 연구는 기존의 저장 시스템과 몇 가지 부분에서 차이를 보인다. 먼저, 파일 블록의 지문을 이용한 다단계 중복 제거 기법을 통하여 불필요한 데이타에 대한 저장 용량을 효과적으로 줄일 수 있었다. 또한 입출력 시스템 부분에서는 클러스터링 기법을 적용함으로써 데이타 전송 및 입출력 시간을 효과적으로 감소시켰다. 본 논문에서는 제안된 방법을 검증하기 위해서 몇 가지 실험을 수행하였으며, 실험 결과 저장 공간과 입출력 성능이 크게 개선되었음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4272-4286
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• 2016

In resource constrained sensor networks, usage of efficient routing protocols can have significant impact on energy dissipation. To save energy, we propose an energy efficient routing protocol. In our approach, which integrates clustering and routing in sensor networks, we perform network coding during data routing in order to achieve additional power savings in the cluster head nodes. Efficacy of the proposed method in terms of the throughput and end-to-end delay is demonstrated through simulation results. Significant network lifetime is also achieved as compared with other techniques.

• 전자공학회논문지C
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• 제34C권2호
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• pp.38-49
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• 1997

In this paper, w describe partitioning large circuits into multiple chips on the programmable FPCB for rapid prototyping. FPCBs consists of areas for FPGAs for logic and interconnect components, and the routing topology among them are predetermined. In the partition problem for FPCBs, the number of wires ofr routing among chips is fixed, which is an additonal constraints to the conventional partition problem. In order to deal with such aconstraint properly we first define a new partition problem, so called the topologybased partition problem, and then propose a heuristic method. The heuristic method is based on the simulated annealing and clustering technique. The multi-level tree clustering technique is used to obtain faster and better prtition results. In the experimental results for several test circuits, the restrictions for FPCB were all satisfied and the needed execution time was about twice the modified K-way partition method for large circuits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.190-207
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• 2015

In the combustion engineering, to decrease pollution and increase production efficiency, and to optimally keep solid burning material amount constant in a burner online, it needs a smart method to detect the amount variation of the burning materials in a high temperature environment. This paper presents an online machine vision system for automatically measuring and detecting the burning material amount inside a burner or a boiler. In the camera-protecting box of the system, a sub-system for cooling is constructed by using the cooling water circulation techqique. In addition, the key and intelligent step in the system is to detect the pile profile of the variable burning material, and the algorithm for the pile profile tracing was studied based on the combination of the gey level (color) discontinuity and similarity based image segmentation methods, the discontinuity based sub-algorithm is made on the quaternion convolution, and the similarity based sub-algorithm is designed according to the region growing with multi-scale clustering. The results of the two sub-algoritms are fused to delineate the final pile profile, and the algorithm has been tested and applied in different industrial burners and boilers. The experiements show that the proposed algorithm works satisfactorily.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (C)
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• pp.397-401
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• 2008

추천시스템은 일반적으로 협동적 필터링이라는 정보 필터링 기술을 사용한다. 협동적 필터링은 유사한 성향을 갖는 다른 고객들이 상품에 대해서 매긴 평가에 기반하기 때문에 고객에게 가장 적합한 유사 이웃들을 적절히 선정해 내는 것이 추천시스템의 예측의 질 향상을 위해서 필요하다. 본 논문에서는 다중 속성 정보를 기반으로 한 다단계 클러스터링을 통한 이웃선정 방법을 제안한다. 이 방법은 대규모 데이터 셋에서 탐색 공간을 줄이기 위해 클러스터링을 수행하여 적절한 이웃 고객들의 집합을 검색하여 추출한다. 이 때, 다중 속성 정보에 따라 단계적으로 클러스터링을 수행함으로써 보다 정제된 고객 집합을 구성할 수 있도록 한다. 본 논문에서는 고객 선호도와 위치 정보 및 아이템의 선호도와 위치 정보를 대표적인 속성 정보로 사용함으로써 모바일 환경에서 보다 정확한 추천이 이루어질 수 있도록 한다.

• 한국음향학회지
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• 제33권5호
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• pp.341-350
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• 2014

본 논문에서는 수중음향 센서 네트워크에서 자기 조직화 기법을 활용하는 에너지 효율적 클러스터링 기법을 제안한다. 제안 기법은 클러스터 헤드 선출에 각 노드의 배터리 잔여량 정보와 이웃 노드의 수를 고려하며, 클러스터 헤드의 배터리 잔여량이 특정 수준 이하로 떨어졌을 경우에만 클러스터 재구성을 수행함으로써 노드의 에너지 소모를 줄이고 네트워크 전체에 에너지 소모를 분산시켜 네트워크의 수명을 연장시킬 수 있다. 또한, 클러스터 헤드는 클러스터 멤버 노드로부터 수집한 데이터를 다중 홉 중계 방식으로 싱크 노드에 전송하여 에너지 소모를 줄인다. 컴퓨터 모의실험을 통해, 일정 시간 경과 후 전체 노드의 배터리 잔여량의 합, 생존 노드의 수, 네트워크 구성 단계에서의 에너지 소모량, 전체 노드의 에너지 소모 편차 등을 구하고 대표적 클러스터링 기법 중의 하나인 LEACH 기법과 비교 및 분석한다. 모의실험 결과, 제안 기법이 LEACH 기법에 비해 네트워크 운용 시간을 두 배 향상시킬 수 있으며, 전체 노드의 에너지 소모 편차 또한 감소시킴을 알 수 있다.

• Journal of Information Processing Systems
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• 제9권4호
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.