• Journal of Communications and Networks
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• v.10 no.2
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• pp.221-227
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• 2008

The proposed zone-based self-organized clustering broadcasts neighbor information to only a zone with the same ID. Besides, the zone-based self-organized clustering with unique IDs can communicate securely even if the state transition of nodes in zone-based self-organized clustering is threatened by corrupted nodes. For this security, the Byzantine agreement protocol with proactive asynchronous verifiable secret sharing (AVSS) is considered. As a result of simulation, an efficiency and a security of the proposed clustering are better than those of a traditional clustering. Therefore, this paper describes a new and extended self-organized clustering that securely seeks to minimize the interference in mobile ad hoc networks (MANETs).

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.180-190
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• 2011

Efficient energy consumption is a critical factor for deployment and operation of wireless sensor networks (WSNs). To achieve energy efficiency there have been several hierarchical routing protocols that organize sensors into clusters where one sensor is a cluster-head to forward messages received from its cluster-member sensors to the base station of the WSN. In this paper, we propose a self-organized clustering method for cluster-head selection and cluster based routing for a WSN. To select cluster-heads and organize clustermembers for each cluster, every sensor uses only local information and simple decision mechanisms which are aimed at configuring a self-organized system. By these self-organized interactions among sensors and selforganized selection of cluster-heads, the suggested method can form clusters for a WSN and decide routing paths energy efficiently. We compare our clustering method with a clustering method that is a well known routing protocol for the WSNs. In our computational experiments, we show that the energy consumptions and the lifetimes of our method are better than those of the compared method. The experiments also shows that the suggested method demonstrate properly some self-organized properties such as robustness and adaptability against uncertainty for WSN's.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.151-162
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• 1994

A new neural network structure called Self-organized Distributed Networks (SODN) is proposed for developing the neural network-based multidimensional system models. The learning with the proposed networks is fast and precise. Such properties are caused from the local learning mechanism. The structure of the networks is combination of dual networks such as self-organized networks and multilayered local networks. Each local networks learns only data in a sub-region. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the proposed networks. The simulation results of the proposed networks show better performance than the standard multilayer neural networks and the Radial Basis function(RBF) networks.

• Journal of the Korean institute of surface engineering
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• v.34 no.6
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• pp.531-536
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• 2001

Co-22 %Cr alloy films are promising for high-density perpendicular magnetic recording media with their perpendicular anisotropy and large coercivity of 3000 Oe. We observed that a self organized nano structure (SONS) of fine ferromagnetic Co-enriched phase and paramagnetic Cr-enriched phase appears inside the grain of Co-Cr magnetic alloy thin films at the elevated substrate temperature after do-sputtering. The periodic fine Co-enriched phase and Cr-enriched phase is the plate shape of 80 (equation omitted)-wide and 1000 (equation omitted)-long. Cr-enriched phases are located at the center of grains. We prepared 5000 (equation omitted) -thick Co-22 %Cr films on polyimide substrate with varying substrate temperature of $ 30^{\circ}C$, $ 150^{\circ}C$ ,200 $^{\circ}C$, $300^{\circ}C$, and $400^{\circ}C$, respectively. A transmission electron microscope equipped with energy dispersive X-ray analyzer is employed to observe the microstructure of each samples after Co-enri-ched phase are etched selectively. The self organized nano structure of Co-enriched and Cr-enriched lamellar is observed above the substrate temperture of $150^{\circ}C$. No compositional change is observed with substrate temperature. The compositional phase separation in self organized structure becomes clear as the substrate temperature increases. Our results implies that the self organized nano structure in Co-22 %Cr film is ideal for ultra high density recording media by recording selectively on Co-enri-ohed phase.

• Journal of Pharmaceutical Investigation
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• v.40 no.4
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• pp.201-212
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• 2010

Self-organized nanogels from polysaccharide derivatives offer a promising approach in treatment of cancer due to their flexibility in chemistry and their ability to improve the therapeutic index of a drug by modifying biodistribution by their preferential localization at target sites and lower distribution in normal healthy tissues. These properties have promoted studies of active cancer targeting by self-organized nanogels for even better accumulation in solid tumors. However although many researchers have reported their potential by using cell culture systems and small animal tumor models in cancer therapy, these nanogels need more decoration such as conjugation with targeting moiety and endowment of stimuli-sensitivity for precise targeting of the cancer site. In this review, we summarize the recent efforts in developing novel targeting approaches via active endocytosis and stimuli-sensitive systems responding to hyperthermic or acidic tumor pH conditions.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.170-179
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• 1993

In this study, self-organized neural network is used to solve the vorrespondence problem of the axial stereo image. Edge points are extracted from a pair of stereo images and then the edge points of rear image are assined to the output nodes of neural network. In the matching process, the two input nodes of neural networks are supplied with the coordi- nates of the edge point selected randomly from the front image. This input data activate optimal output node and its neighbor nodes whose coordinates are thought to be correspondence point for the present input data, and then their weights are allowed to updated. After several iterations of updating, the weights whose coordinates represent rear edge point are converged to the coordinates of the correspondence points in the front image. Because of the feature map properties of self-organized neural network, noise-free and smoothed depth data can be achieved.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.2
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• pp.99-103
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• 2001

We present a detailed of the interband transitions of InAs/GaAs self-organized quantum dots(QDs) based on surface photovoltage(SPV), photoreflactance(PR) and photoluminescence(PL) spectroscopies. At room temperature, interband absorption transitions of QDs have been observed by using SPV spectrum, which clearly exhibits three well-resolved absorption transitions of QDs have been observed by using SPV spectrum, which clearly exhibits three well-resolved absorption peaks. The absorption line shape is Gaussian-like. Furthermore, the corresponding interband transitions are also observed in PR and PL experiments at 77K.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1894-1899
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• 1994

In this paper, a new identification method is proposed for unknown digitally modulated input signals. The proposed identification method is implemented using a self-organized neural network which is based on the characteristic features of the symbol magnitude; the number of symbol magnitude levels, amplitude probability density and adjacent symbol magnitude ratio. The proposed method was performed for 5 QAM signals. The simulation results show that the self-organized neural network can accurately recognize all kinds of patterns even at SNR 8dB. The proposed method can be applied to the intelligent communication system on ISDN and multi-point polling networks.

• The Journal of Information Technology
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• v.5 no.2
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• pp.17-26
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• 2002

On this paper, we propose SOSL(Self-Organized Supervised Learning) and it's architecture SOSL is hybrid type neural network. It consists of several CBP (Component Back Propagation) neural networks, and a modified PCA neural networks. CBP neural networks perform supervised learning procedure in parallel to clustered and complex input patterns. Modified PCA networks perform it's learning in order to transform dimensions of original input patterns to lower dimensions by clustering and local projection. Proposed SOSL can effectively apply to neural network learning with large input patterns results in huge networks size.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.574-581
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.