• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1424-1441
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• 2014

Two-tier femtocell networks, consisting of a conventional cellular network underlaid with femtocell hotspots, play an important role in the indoor coverage and capacity of cellular networks. However, the cross- and co-tier interference will cause an unacceptable quality of service (QoS) for users with universal frequency reuse. In this paper, we propose a novel downlink interference mitigation strategy for spectrum-shared two-tier femtocell networks. The proposed solution is composed of three parts. The first is femtocells clustering, which maximizes the distance between femtocells using the same slot resource to mitigate co-tier interference. The second is to assign macrocell users (MUEs) to clusters by max-min criterion, by which each MUE can avoid using the same resource as the nearest femtocell. The third is a novel adaptive power control scheme with femtocells downlink transmit power adjusted adaptively based on the signal to interference plus noise ratio (SINR) level of neighboring users. Simulation results show that the proposed scheme can effectively increase the successful transmission ratio and ergodic capacity of femtocells, while guaranteeing QoS of the macrocell.

• 산업경영시스템학회지
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• 제19권39호
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

• 한국컴퓨터그래픽스학회논문지
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• 제15권3호
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• pp.19-26
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• 2009

본 논문에서는 분할 축과 평면의 위치를 동적으로 결정하는 적응형 KD 트리 구조를 이용한 정점 군집화(Adaptive Vertex Clustering) 알고리즘과 이를 이용한 새로운 메쉬 분할 방법을 소개하고자 한다. 정점 군집화는 주로 한 개의 거대한 3차원 메쉬를 여러 개의 파티션(Partition)으로 분할하여 효율적으로 처리하고자 할 때 사용되는 기법으로, 옥트리 구조를 이용한 공간 분할 기법과 K-평균 군집화(K-Means Clustering) 방법 등이 있다. 그러나 옥트리 방식은 공간 분할 축과 이에 따른 분할된 공간의 크기가 고정되어 있어서 파티션 메쉬 면의 정렬 상태가 고르지 못하고 포함된 정점의 개수가 균등하지 못한 단점이 있다. 또한, K-평균군집화는 균등한 파티션을 얻을 수 있는 반면 반복처리와 최적화를 위해 많은 시간이 소요된다는 단점이 있다. 본 논문에서는 적응형 정점 군집화를 통해 빠른 시간에 균등한 메쉬 분할을 생성하는 알고리즘을 제안하고자 한다. 본 적응형 KD 트리는 메쉬가 포함된 경계상자(Bounding Box) 공간을 정점의 개수와 분할 축의 크기를 기준으로 계층적으로 분할한다. 그 결과 각 파티션 메쉬는 컴팩트성(compactness)의 특성을 유지하며 균등한 수의 정점을 포함하게 되어 각 파티션의 균등한 처리시간 및 메모리 소요량 등의 장점을 살려 향후 메쉬 간소화 및 압축 등의 다양한 메쉬 처리에 활용될 수 있기를 기대한다. 본 방법을 적용한 3차원 모델의 실험 통계와 분할된 파티션 메쉬의 시각적인 결과도 함께 제시하였다.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.233-236
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• 2001

In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

• ETRI Journal
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• 제34권6호
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• pp.922-931
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• 2012

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy-efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near-optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near-optimal energy-efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy-efficient routing technique produces a longer network lifetime and achieves better energy efficiency.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.457-458
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• 2007

Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer poor performance of learning. To archive good clustering performance, we develop a hybrid neural network model. It is the combination of Multi-Layer Perceptron (MLP) and Adaptive Resonance Theory 2 (ART2). It inherits two distinct advantages of stability and plasticity from ART2. Meanwhile, by combining the merits of MLP, it improves the performance for clustering. Experiment results show that our model can be used for clustering with promising performance.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.464-469
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• 2001

CRM(Customer Relationship Management : CRM) is an advanced marketing supporting system which analyze customers\` transaction data and classify or target customer groups to effectively increase market share and profit. Many engines were developed to implements the function and those for classification and clustering are considered core ones. In this study, an improved clustering method based on SOM(Self-Organizing Maps : SOM) is proposed. The proposed clustering method finds the optimal number of clusters so that the effectiveness of clustering is increased. It considers all the data types existing in CRM data warehouses. In particular, and adaptive algorithm where the concepts of degeneration and fusion are applied to find optimal number of clusters. The feasibility and efficiency of the proposed method are demonstrated through simulation with simplified data of customers.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2000년도 추계 학술대회논문집
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• pp.86-91
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• 2000

Automatic method for generation of mesh and three dimension natural convection flow result adapted by this method are presented in this paper. It lake long time to meshing com plex 3-D geometries, and It's difficult to clustering grid at surface boundary. Octree structure resolve this difficulty.

• 한국정보통신학회논문지
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• 제17권2호
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• pp.495-502
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• 2013

MANET(Mobile Ad-hoc Network)에서는 에너지 효율을 높이기 위한 다양한 클러스터링 기법과 라우팅 알고리즘이 연구되고 있다. 일반적으로 무선 Ad-hoc 네트워크에서는 LEACH와 같은 클러스터 기반의 동적 라우팅 알고리즘이 많이 사용된다. 본 논문에서는 클러스터내의 각 노드가 가지는 속성을 고려하여 클러스터를 생성하고 노드를 관리하는 ATICC(Adaptive Time Interval Clustering Control) 알고리즘 기법을 제안한다. 제안한 ATICC은 노드의 속성 중의 하나인 잔여에너지 값으로 노드의 에너지 레벨을 분류한다. 그리고 분류된 에너지 레벨에 대응하는 시간차 컨트롤 기법을 이용하여 클러스터링 과정을 수행하거나 노드들을 관리한다. 특히 제안한 ATICC 알고리즘은 MANET에서 클러스터의 생성, 재생성, 진입 노드 및 이탈 노드의 검출과 관리를 통해 노드의 에너지 관리 효율을 향상시키고 클러스터의 Lifetime을 증가시키는 결과를 보여주었다.