• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.799-802
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• 2005

In this paper, we analyses of Key Management Protocol for Wireless Sensor Networks in Wireless Sensor Networks. Wireless sensor networks have a wide spectrum of civil military application that call for security, target surveillance in hostile environments. Typical sensors possess limited computation, energy, and memory resources; therefore the use of vastly resource consuming security mechanism is not possible. In this paper, we propose a cryptography key management protocol, which is based on identity based symmetric keying.

• 충청수학회지
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• 제35권1호
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• pp.39-52
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• 2022

We introduce Clifford-valued neural networks with leakage delays. Furthermore, we study the uniqueness and existence of Clifford-valued Hopfield artificial neural networks having the Stepanov weighted pseudo almost periodic forcing terms on leakage delay terms. However the noncommutativity of the Clifford numbers' multiplication made our investigation diffcult, so our results are obtained by decomposing Clifford-valued neural networks into real-valued neural networks. Our analysis is based on the differential inequality techniques and the Banach contraction mapping principle.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.1297-1300
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• 1998

In this paper, we discuss the identification of a chaotic system using chaotic neural networks. Because of selfconnections in neuron itself and interconnections between neurons, chaotic neural networks identifiers show good performance in highly nonlinear dynamics such as chaotic system. Simulation results are presented to demonstrate robustness of chaotic neural networks identifier.

• Computers and Concrete
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• 제30권4호
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• pp.237-242
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• 2022

This paper is concerned with exponential stability in mean square for stochastic static neutral neural networks with varying delays. By using Lyapunov functional method and with the help of stochastic analysis technique, the sufficient conditions to guarantee the exponential stability in mean square for the neural networks are obtained and some results of related literature are extended.

• 한국수자원학회논문집
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• 제34권4호
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• pp.303-316
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• 2001

본 연구에서는 중소하천수계에서 수문학적 예측을 위하여 Hybrid Neural Networks의 일종인 반경기초함수(RBF) 신경망모형이 적용되었다. RBF 신경망모형은 4종류의 매개변수로 구성되어 있으며, 지율 및 지도훈련과정으로 이루어져있다. 반경기초함수로서 가우스핵함수(GKF)가 이용되었으며, GKF의 매개변수인 중심과 폭은 K-Means 군집알고리즘에 의해 최적화 된다. 그리고 RBF 신경망모형의 매개변수인 중심, 폭, 연결강도와 편차벡터는 훈련을 통하여 최적 매개변수의 값이 결정되며, 이 매개변수들을 이용하여 모형의 검증과정이 이루어진다. RBF 신경망모형은 한국의 IHP 대표유역중 하나인 위천유역에 적용하였으며, 모형의 훈련과 검증을 위하여 10개의 강우사상을 선택하였다. 또한 RBF 신경망모형과 비교검토하기 위하여 엘만 신경망(ENN)모형을 이용하였으며, ENN 모형은 일단게 할선역전파(OSSBP) 및 탄성역전파(RBP)알고리즘으로 이루어져 있다. 모형의 훈련과 검증과정을 통하여 RBF 신경망모형이 ENN 모형보다 양호한 결과를 나타내는 것으로 분석되었다. RBF 신경망모형은 훈련시키는데 시간이 적게 들고, 이론적 배경이 부족한 수문학자들도 쉽게 사용할 수 있는 신경망모형이다.

• International Journal of Computer Science & Network Security
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• 제24권9호
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• pp.111-118
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• 2024

The concept of social stratification and hierarchy among human dates back to the origin of human race. Presently, the growing reputation of social networks has given us with an opportunity to analyze these well-studied phenomena over different networks at different scales. Generally, a social network could be defined as a collection of actors and their interactions. In this work, we concern ourselves with a particular type of social networks, known as trust networks. In this type of networks, there is an explicit show of trust (positive interaction) or distrust (negative interaction) among the actors. In other words, an actor can designate others as friends or foes. Trust networks are typically modeled as signed networks. A signed network is a directed graph in which the edges carry an edge weight of +1 (indicating trust) or -1 (indicating distrust). Examples of signed networks include the Slashdot Zoo network, the Epinions network and the Wikipedia adminship election network. In a social network, actors tend to connect with each other on the basis of their perceived social hierarchy. The emergence of such a hierarchy within a social community shows the manner in which authority manifests in the community. In the case of signed networks, the concept of social hierarchy can be interpreted as the emergence of a tree-like structure comprising of actors in a top-down fashion in the order of their ranks, describing a specific parent-child relationship, viz. child trusts parent. However, owing to the presence of positive as well as negative interactions in signed networks, deriving such "trust hierarchies" is a non-trivial challenge. We argue that traditional notions (of unsigned networks) are insufficient to derive hierarchies that are latent within signed networks In order to build hierarchies in signed networks, we look at two interpretations of trust namely presence of trust (or "good") and lack of distrust (or "not bad"). In order to develop a hierarchy signifying both trust and distrust effectively, the above interpretations are combined together for calculating the overall trustworthiness (termed as deserve) of actors. The actors are then arranged in a hierarchical fashion based on these aggregate deserve values, according to the following hypothesis: actor v is assigned as a child of actor u if: (i) v trusts u, and (ii) u has a higher deserve value than v. We describe this hypothesis with additional qualifiers in this thesis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• Computers and Concrete
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• 제12권3호
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• pp.285-301
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• 2013

This paper concentrates on the results of experimental work on tensile strength of self-compacting concrete (SCC) caused by flexure, which is called rupture modulus. The work focused on concrete mixes having water/binder ratios of 0.35 and 0.45, which contained constant total binder contents of 500 $kg/m^3$ and 400 $kg/m^3$, respectively. The concrete mixes had four different dosages of a superplasticizer based on polycarboxylic with and without silica fume. The percentage of silica fume that replaced cement in this research was 10%. Based upon the experimental results, the existing equations for anticipating the rupture modulus of SCC according to its compressive strength were not exact enough. Therefore, it is decided to use artificial neural networks (ANN) for anticipating the rupture modulus of SCC from its compressive strength and workability. The conclusion was that the multi layer perceptron (MLP) networks could predict the tensile strength in all conditions, but radial basis (RB) networks were not exact enough in some circumstances. On the other hand, RB networks were more users friendly and they converged to the final networks quicker.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• 한국보건간호학회지
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• 제22권2호
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• pp.211-223
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• 2008

Purpose: The principal objective of this study was to assess correlations among the self-efficacy, social support networks, and health behavior of undergraduate students. Methods: The data were collected via questionnaires that investigated self- efficacy, social support networks, health behaviors, health-related factors, and general characteristics. A total of 310 subjects were selected and evaluated for a 3-week period. The data of 300 subjects were analyzed using descriptive analysis, t-test, ANOVA, and correlation, after 10 questionnaires had been excluded due to incomplete data. Results: We noted significant differences and impacts on self-efficacy according to the grade, perceived health status, and BMI. Social support networks differed significantly according to dwelling type and pocket money. Health behavior differed depending on the gender, major, dwelling type, religion, health status, and BMI. We noted a significant positive correlation between self-efficacy & social support networks, and between social support networks & health behavior, but we noted no significant correlation between self-efficacy & health behavior. Conclusion: Health care providers should focus on self-efficacy and social support networks in order to prevent bad health behavior among undergraduates.