• Journal of Communications and Networks
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• 제10권2호
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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).

• 대한산업공학회지
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• 제37권3호
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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.

• 전자공학회논문지B
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• 제31B권11호
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• pp.151-162
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• 1994

다차원 시스템(multidimensional system)에 대한 정확한 모델링을 위해 “자율 분산 신경망(Self-organized Distirbuted Networks, SODN)”을 제안하였다. 제안한 신경망은 자율 신경망(Self-organized Networks)과 다수의 소규모 다층 신경망(Multilayer Neural Networks)이 조합되어 지역적 병렬 학습을 수행하는 부분 학습망으로서 학습 속도가 빠르고 학습의 정밀도를 높일 수 있으며 타 부분망 학습에서 문제가 되는 과다한 학습 메모리 소요와 학습되니 않은 영역에 대한 낮은 일반화능력 등의 문제가 보완된 새로운 신경망이다. 학습 실험 결과, 제안한 신경망은 기존의 다층 신경망과 RBF(Radial Basis Function) 신경망에 비해서 우수한 성능을 보였다.

• 한국표면공학회지
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• 제34권6호
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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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• 제40권4호
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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.

• 한국정밀공학회지
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• 제10권4호
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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.

• 한국전기전자재료학회논문지
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• 제14권2호
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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.

• 한국통신학회논문지
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• 제19권10호
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• pp.1894-1899
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• 1994

본 논문에서는 미상 디지털 변조 신호가 입력되는 경우에 변조형식을 식별하는 방법을 제안하였다. 제안된 방법에서는 수신된 신호의 진폭 레벨수, 진폭 확률 분포 및 인접 신호간 진폭비를 특징벡터로 이용하여 자율조직 신경망으로 구현하였다. 제안된 방법의 타당성을 검증하기 위하여 5개의 QAM 신호에 적용하고, 모의실험을 통하여 인식도를 확인한 결과, 8dB에서도 변조형식을 식별하여 그 효용성이 입증되었다. 본 논문에서 제안한 방법은 종합정보통신망. 다중 접속 통신망에서 지능형 통신시스템 구현에 활용할 수 있다.

• 정보학연구
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• 제5권2호
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• pp.17-26
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• 2002

본 연구에서는 자기조직화 교사학습 신경망인 SOSL(Self-Organized Superised Learning)과 이 신경망의 구조를 제안한다. SOSL신경망은 하이브리드 형태의 신경망으로써 다수 개의 컴포넌트 에러 역전파 신경망들과 수정된 PCA신경망으로 구성된다. CBP신경망은 군집화되고 복잡한 입력패턴에 대하여 교사학습을 병렬적으로 수행한다. 수정된 PCA신경망은 군집화 및 지역투영에 의하여 원 입력패턴을 보다 작은 차원으로 변환시키기 위하여 사용된다. 제안된 SOSL은 많은 입력패턴을 가짐으로써 큰 네트워크 크기를 가지게 되는 신경망에 효과적으로 적용이 가능하다.

• 대한전기학회논문지
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• 제45권4호
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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.