• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.783-790
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• 2009

The necessity of wireless sensor networks is increasing in the recent years. So many researches are studied in wireless sensor networks. The clustering algorithm provides an effective way to prolong the lifetime of the wireless sensor networks. The one-hop routing of LEACH algorithm is an inefficient way in the energy consumption of cluster-head, because it transmits a data to the BS(Base Station) with one-hop. On the other hand, other clustering algorithms transmit data to the BS with multi-hop, because the multi-hop transmission is an effective way. But the multi-hop routing of other clustering algorithms which transmits data to BS with multi-hop have a data bottleneck state problem. The unequal clustering algorithm solved a data bottleneck state problem by increasing the routing path. Most of the unequal clustering algorithms partition the nodes into clusters of unequal size, and clusters closer to the BS have small-size the those farther away from the BS. However, the energy consumption of cluster-head in unequal clustering algorithm is more increased than other clustering algorithms. In the thesis, I propose an energy efficient unequal clustering algorithm which decreases the energy consumption of cluster-head and solves the data bottleneck state problem. The basic idea is divided a three part. First of all I provide that the election of appropriate cluster-head. Next, I offer that the decision of cluster-size which consider the distance from the BS, the energy state of node and the number of neighborhood node. Finally, I provide that the election of assistant node which the transmit function substituted for cluster-head. As a result, the energy consumption of cluster-head is minimized, and the energy consumption of total network is minimized.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.57-68
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• 2017

Clustering algorithms attempt to find a partition of a finite set of objects in to a potentially predetermined number of nonempty subsets. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet prior distribution calculates posterior probabilities when the number of clusters was known. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. Examples are given to show how these models perform on real data.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.373-375
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• 2005

본 논문은 무선 센서 네트워크상에서 클러스터 2중 분할 알고리즘을 제안한다. 본 알고리즘은 센서 네트워크에서 클러스터 방식 프로토콜이 데이터를 헤드에서 수집하고 집약하여 전송한다는 특성과 이웃한 노드간 유사한 데이터를 가진다는 특성을 이용한다. 인접한 이웃노드가 쌍을 형성하여 교대로 센싱하는 논리적인 클러스터 2중 분할을 하고 헤드도 2개가 존재하여 교대로 데이터 전송을 함으로써 에너지 효율을 높인다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.102-110
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• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.384-394
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• 2014

The femtocell overlaid cellular network (FOCN) has been used to enhance the capacity of existing cellular systems. To obtain the desired system performance, both cross-tier interference and co-tier interference in an FOCN need to be managed. This paper proposes an interference management scheme that adaptively constructs a femtocell cluster, which is a group of femtocell base stations that share the same frequency band. The performance evaluation shows that the proposed scheme can enhance the performance of the macrocell-tier and maintain a greater signal to interference-plus-noise ratio than the outage level can for about 99% of femtocell users.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.507-511
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• 2008

Supporting energy efficiency and load balancing in wireless sensor network is the most important issue in devising the hierarchical routing protocols. Recently, the dual layered clustering scheme with GPS was proposed for the supporting of load balancing for cluster heads but there would be many collided messages in the overlapped area between two layers. Thereby, the purpose of this paper is to reduce the collision rate in the overlapped layer by concisely distinguish them with the same number of nodes in them. For the layer partition, this paper uses an equation $x^2+ y^2{\le}(\frac{R}{\sqrt{2\pi}})^2$ to distinguish layers. By using it, the scheme could efficiently distinguish two layers and gets the balanced number of elements in them. Therefore, the proposed routing scheme could prolong the overall network life cycle about 10% compared to the previous two layered clustering scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.262-267
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• 2008

The focus of this paper is on devising an efficient clustering task for arbitrary color data. In order to tackle this problem, the inherent uncertainty and vagueness of color are represented by a fuzzy color model. By taking a fuzzy approach to color representation, the proposed model makes a soft decision for the vague regions between neighboring colors. A definition on a three-dimensional fuzzy color ball is introduced, and the degree of membership of color is computed by employing a distance measure between a fuzzy color and color data. With the fuzzy color model, a novel fuzzy clustering algorithm for efficient partition of color data is developed.

• Korean Journal of Remote Sensing
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• v.16 no.2
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• pp.145-156
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• 2000

Clustering is the process of partitioning a data set into meaningful clusters. As the data to process increase, a laster algorithm is required than ever. In this paper, we propose a clustering algorithm to partition a multispectral remotely sensed image data set into several clusters using a hash search algorithm. The processing time of our algorithm is compared with that of clusters algorithm using other speed-up concepts. The experiment results are compared with respect to the number of bands, the number of clusters and the size of data. It is also showed that the processing time of our algorithm is shorter than that of cluster algorithms using other speed-up concepts when the size of data is relatively large.

• Bulletin of the Korean Chemical Society
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• v.19 no.6
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• pp.625-628
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• 1998

The dynamic mechanical properties of the unclustered cesium neutralized poly(methyl methacrylate-co-methacrylic acid) ionomers plasticized with three different plasticizers of low molecular weight were investigated. It was found that the effectiveness of the plasticization followed the order: glycerol (Gly) 4-decylaniline (4DA) >dioctyl phthalate (DOP). For the ionomer plasticized with Gly, the only effect was a significant decrease in the Tg. Thus it is concluded that the polar plasticizer not only increases the mobility of the ionomer but also dissolves the ionic groups. In the case of the 4DA-plasticized ionomer, both a drastic decrease in the Tg and the appearance of a second glass transition were observed. Therefore, it is suggested that the nonpolar 4DA molecules partition evenly in the poly(methyl methacrylate) matrix and cluster phases via hydrogen bonding between the aniline group of the plasticizer and the carbonyl groups of the ionomer. As a result, the Tg is lowered, multiplets can form, and the material behaves like a clustered ionomer.