• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.141-150
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• 2007

This paper proposes how to reduce the amount of data transmitted in each sensor and cluster head in order to lengthen the lifetime of sensor network. The most important factor of reducing the sensor's energy dissipation is to reduce the amount of messages transmitted. This paper proposed is to classify the node's cluster state into 6 categories in order to reduce both the number and amount of data transmission: Initial, Cluster Head, Cluster Member, Non-transmission Cluster Head, Non-transmission Cluster Member, and Sleep. This should increase the efficiency of filtering and decrease the inaccuracy of the data compared to the methods which enlarge the filter width to do more filtering. This method is much more efficient and effective than the previous work. We show through various experiments that our scheme reduces the network traffic significantly and increases the network's lifetime than existing methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.330-333
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• 2003

Initial cluster size for clustering of partitioning methods is very important to the clustering result. In K-means algorithm, the result of cluster analysis becomes different with optimal cluster size K. Usually, the initial cluster size is determined by prior and subjective information. Sometimes this may not be optimal. Now, more objective method is needed to solve this problem. In our research, we propose a hybrid genetic algorithm, a tree induction based evolution algorithm, for determination of optimal cluster size. Initial population of this algorithm is determined by the number of terminal nodes of tree induction. From the initial population based on decision tree, our optimal cluster size is generated. The fitness function of ours is defined an inverse of dissimilarity measure. And the bagging approach is used for saying computational time cost.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.792-796
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• 2009

Determining the optimal number of cluster is an important issue in research area of data clustering. It is choosing the cluster validity method and finding the cluster number where it optimizes the cluster validity. In this paper, an efficient heuristic for determining optimal number of cluster using clustering balance is proposed. The experimental results using k-means at artificial and real-life data set show that proposed algorithm is excellent in aspect of time efficiency.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.314-317
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• 2000

Cluster system provides attractive scalability in terms of computation power and memory size. With the advances in high speed computer network technology, cluster systems are becoming increasingly competitive compared to expensive parallel machines. In parallel processing program, each task load is difficult to predict before running the program and each task is interdependent each other in many ways. Load imbalancing induces an obstacle to system performance. Most of researches in load balancing were concerned with distributed system but researches in cluster system are few. In cluster system, the dynamic load balancing algorithm which evaluates each processor's load in runtime is purpose that the load of each node are evenly distributed. But, if communication cost or node complexity becomes high, it is not effective method for all nodes to attend load balancing process. In that circumstances, it is good to reduce the number of node which attend to load balancing process. We have modeled cluster systems and proposed marginal dynamic load balancing algorithms suitable for that circumstances.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.29.4-29.4
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• 2020

Structure in the universe forms hierarchically with the small scales forming first and merging into larger scales. Galaxy clusters are at the pinnacle of the formation process. Peering far into the universe, we can observe galaxy clusters early in their evolution. SpARCSJ1049+56 is a galaxy cluster located at a redshift of 1.71. It has been shown to be rich in cluster galaxies, to have intense star formation, and to have a significant amount of molecular gas. Through careful control of systematics, we detected the weak-lensing signal from this distant galaxy cluster. I will present our HST infrared weak-lensing detection of the cluster with a focus on the method. Our lensing analysis found that the cluster is massive and is rare in a LambdaCDM universe. I will also present the Chandra X-ray discovery of cold gas coincident with the intense star formation and discuss the implications of the detection.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.491-493
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• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.579-583
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• 2001

In the area of data clustering in high dimensional space, one of the difficulties is the time-consuming process for computing vector similarities. It becomes worse in the case of the agglomerative algorithm with the group-average link and mean centroid method, because the cluster similarity must be recomputed whenever the cluster center moves after the merging step. As a solution of this problem, we present an incremental method of similarity computation, which substitutes the scalar calculation for the time-consuming calculation of vector similarity with several measures such as the squared distance, inner product, cosine, and minimum variance. Experimental results show that it makes clustering speed significantly fast for very high dimensional data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.43-51
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• 2017

MANET consists of only wireless nodes having limited processing capability. It processes routing and data transmission through cooperation among each other. And it is exposed to many attack threats due to the dynamic topology by movement of nodes and multi-hop communication. Therefore, the reliability of transmitted data between nodes must be improved and security of integrity must be high. In this paper, we propose a method to increase the reliability of transmitted data by providing a secure cryptography protocol. The proposed method used a hierarchical structure to provide smooth cryptographic services. The cluster authentication node issues the cluster authentication key pair and unique key to the nodes. The nodes performs the encryption through two steps of encryption using cluster public key and block encryption using unique key. Because of this, the robustness against data forgery attacks was heightened. The superior performance of the proposed method can be confirmed through comparative experiment with the existing security routing method.

• Journal of The Korean Astronomical Society
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• v.37 no.3
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• pp.119-129
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• 2004

A new method has been developed to solve the star cluster membership problem. It is based on synthetic photometry employing the Black Body concept as stellar radiation simulator. Synthetic color-magnitude diagram is constructed showing the main sequence band and the positions of binary star systems of combinations of various components through different photometric tracks. The method has been applied to the Hyades. The cluster membership problem has been re-appraised for the cluster (both single and binary) stars. For the binary members, the components' spectral types have been derived by the method. The results obtained agree very well with those found in literature, The method is simpler than the others and can be developed to undertake other cases as multiple star systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.397-402
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• 2007

In this paper, we present a method for clustering incomplete Microarray data using alternating optimization in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster Information such as cluster centroids and all available non-missing values in each iteration. The clustering results of the proposed method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.