• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.517-520
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• 2008

Wireless sensor network supporting healthcare environment should provide customized service in accordance with context information such as continuous location change and status information for people or movable object. In addition, we should consider data transmission guarantees a person's bio information and privacy security provided through sensor network. In this paper analyzes LEACH protocol which guarantees the dynamic self-configuration, energy efficiency through configuration of inter-node hierarchical cluster between nodes and key distribution protocol used for security for data transmission between nodes. Based on this analysis result, we suggested self-configuration routing protocol supporting node mobility which is weakness of the existing LEACH protocol and data transmission method by applying key-pool pre-distribution method whose memory consumption is low, cluster unit public key method to sensor node.

• The KIPS Transactions:PartC
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• v.10C no.2
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• pp.163-170
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• 2003

In this paper we propose a robust clustered croup Multicast in Ad-hoc network. The proposed scheme applies to weighted clustered Algorithm. Ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration or reliable support services such as wired network and base station. In ad hoc network routing protocol because of limited bandwidth and high mobility robust, simple and energy consume minimal. WCGM method uses a base structure founded on combination weighted value and applies combination weight value to cluster header keeping data transmission by scoped flooding, which is the advantage of the exiting FGMP method. Because this method has safe and reliable data transmission, it shows the effect to decrease both overhead to preserve transmission structure and overhead for data transmission.

• Journal of Broadcast Engineering
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• v.22 no.4
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• pp.509-518
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• 2017

High-resolution digital holograms can be quickly generated by using a PC cluster that is based on server-client architecture and is composed of several GPU-equipped PCs. However, the data transmission time between PCs becomes a large obstacle for fast generation of video holograms because it linearly increases in proportion to the number of frames. To resolve the problem with the increase of data transmission time, this paper proposes a multi-threading-based method. Hologram generation in each client PC basically consists of three processes: acquisition of light sources, CGH operation using GPUs, and transmission of the result to the server PC. Unlike the previous method that sequentially executes the processes, the proposed method executes in parallel them by multi-threading and thus can significantly reduce the proportion of the data transmission time to the total hologram generation time. Through experiments, it was confirmed that the total generation time of a high-resolution video hologram with 150 frames can be reduced by about 30%.

• Journal of KIISE:Information Networking
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• v.31 no.4
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• pp.353-362
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• 2004

Mobile Ad-Hoc network tends to expose scarce computing resources and various security threats because all traffics are carried in air along with no central management authority. To provide secure communication and save communication overhead, a scheme is inevitable to serurely establish session keys. However, most of key establishment methods for Ad-Hoc network focus on the distribution of a group key to all hosts and/or the efficient public key management. In this paper, a secure and efficient scheme is proposed to establish a session key between two Ad-Hoc nodes. The proposed scheme makes use of the secret sharing mechanism and the Diffie-Hellman key exchange method. For secure intra-cluster communication, each member node establishes session keys with its clusterhead, after mutual authentication using the secret shares. For inter-cluster communication, each node establishes session keys with its correspondent node using the public key and Diffie-Hellman key exchange method. The simulation results prove that the proposed scheme is more secure and efficient than that of the Clusterhead Authentication Based Method(1).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.479-485
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• 2011

A number of case studies that find distribution of longevity population and influencing factors through the spatial data fusion using GIS techniques are growing. The majority cases of these studies are adopt census administrative boundary data for the spatial analysis. However, these methods cannot fully explain the phenomenon of longevity because there are a variety of spatial characteristics within the census administrative boundaries. Therefore, studies of spatial unit are required that realistically reflect the phenomenon of human longevity. The dasymetric mapping method enables to product of spatial unit more realistic than census administrative boundary map and statistic estimates of small area utilizing diversity spatial information. In this study, elderly population of small area has been estimated within statistically significant level that applied the vector based dasymetric mapping method. Also, the cluster analysis confirmed that the variation of local spatial relationship within census administrative boundary. The result of this study implied that the need for local-level studies of the human longevity and the validity of the dashmetric mapping techniques.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.423-430
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• 2011

The purpose of this study was to examine if influence parents on soccer player role socialization. The participants of the study are 149 who are university soccer players. The stratified cluster random sampling method has been used in this study. The material collection device was the brochure named [Influence parents on soccer player role socialization]. The result of reliability check up was Cronbach's ${\alpha}$. 8847~.7306. To analyze materials, the "ANOVA" and "regression analysis" were used as statistic analysis techniques. The conclusion based on above study method and the result of material analysis are here below. 1. Participation of parents influence on status of team internal. 2. Participation of parents influence on position of team. 3. Participation of parents influence on career of get a prize.

• Journal of Korea Multimedia Society
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• v.10 no.4
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• pp.441-449
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• 2007

In clustering for image segmentation, large amount of computation and typical segmentation errors have been important problems. In the paper, we suggest a new method for minimizing these problems. Markers in marker-controlled watershed transform represent segmented areas because they are starting-points of extending areas. Thus, clustering restricted by marker pixels can reduce computational complexity. In our proposed method, the markers are selected by Gabor texture energy, and cluster information of them are generated by FCM (fuzzy c-mean) clustering. Generated areas from watershed transform are merged by using cluster information of markers. In the test of Brodatz' texture images, we improved typical partition-errors obviously and obtained less computational complexity compared with previous FCM clustering algorithms. Overall, it also took regular computational time.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1138-1151
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• 2011

Sensor network clustering scheme is an efficient method that prolongs network lifetime. However, when it is applied to an environment in which collected data of the sensor nodes easily overlap, sensor node unnecessarily consumes energy. Accordingly, we proposed a data prediction scheme that sensor node can predict current data to exclude redundant data transmission and to minimize data transmission among the cluster head node and member nodes. Our scheme excludes redundant data collection by neighbor nodes. Thus it is possible that energy efficient data transmission. Moreover, to alleviate unnecessary data transmission, we introduce data prediction graph whether transmit or not through analyze between prediction and current data. According to the result of performance analysis, our method consume less energy than the existing clustering method. Nevertheless, transmission efficiency and data accuracy is increased. Consequently, network lifetime is prolonged.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.265-271
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• 2004

The interaction network of protein -protein plays an important role to understand the various biological functions of cells. Currently, the high -throughput experimental techniques (two -dimensional gel electrophoresis, mass spectroscopy, yeast two -hybrid assay) provide us with the vast amount of data for protein-protein interaction at the proteome scale. In order to recognize the role of each protein in their network, the efficient bioinformatical and computational analysis methods are required. We propose a systematic and mathematical method which can analyze the protein -protein interaction network rigorously and enable us to capture the biological and physical essence of a topological character and stability of protein -protein network, and sensitivity of each protein along the biological pathway of their network. We set up a Laplacian matrix of spectral graph theory based on the protein-protein network of yeast proteome, and perform an eigenvalue analysis and apply a perturbation method on a Laplacian matrix, which result in recognizing the center of protein cluster, the identity of hub proteins around it and their relative sensitivities. Identifying the topology of protein -protein network via a Laplacian matrix, we can recognize the important relation between the biological pathway of yeast proteome and the formalism of master equation. The results of our systematic and mathematical analysis agree well with the experimental findings of yeast proteome. The biological function and meaning of each protein cluster can be explained easily. Our rigorous analysis method is robust for understanding various kinds of networks whether they are biological, social, economical...etc

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1769-1778
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• 2018

As the value of using Big Data is increasing, various researches are being carried out utilizing big data analysis technology in the field of education as well as corporations. In this paper, we propose a method to predict learning achievement using big data cluster analysis. In the proposed method, students in Korea Children and Youth Panel Survey(KCYPS) are classified into groups with similar learning habits using the Kmeans algorithm based on the learning habits of students of the first year at middle school, and group features are extracted. Next, using the extracted features of groups, the first grade students at the middle school in the test group were classified into groups having similar learning habits using the cosine similarity, and then the neighbors were selected and the learning achievement was predicted. The method proposed in this paper has proved that the learning habits at middle school are closely related to at the university, and they make it possible to predict the learning achievement at high school and the satisfaction with university and major.