• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2951-2954
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• 2007

Tumor hemodynamics in vascular state is numerically simulated using pressure node solution. The tumor angiogenesis pattern in our previous study is used for the geometry of vessel networks. For tumor angiogenesis, the equation that governed angiogenesis comprises a tumor angiogenesis factor (TAF) conservation equation in time and space, which is solved numerically using the Galerkin finite element method. A stochastic process model is used to simulate vessel formation and vessel. In this study, we use a two-dimensional model with planar vessel structure. Hemodynamics in vessel is assumed as incompressible steady flow with Newtonian fluid properties. In parent vessel, arterial pressure is assigned as a boundary condition whereas a constant terminal pressure is specified in tumor inside. Kirchhoff's law is applied to each pressure node to simulate the pressure distribution in vessel networks. Transient pressure distribution along with angiogenesis pattern is presented to investigate the effect of tumor growth in tumor hemodynamics.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1982-1984
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• 2004

Stator coil of rotating machinery has shown different characteristics according to impregnated with coil or not. And this is major determinant of equipment's life. In this paper, PD characteristics is studied as a classification scheme between two specimens. Processing of the coil impregnation is very important thing because that influences on thermal and electrical characteristics of the coil. And then PD is occurring at the coil and causing insulation degradation. For processing statistical processing, PD data acquired from PD detector using PDASDA(partial discharge acquisition, storage and display system). And also these statistical distribution and parameter are applied to classify PD sources by neural networks. As a result of, Neural Networks have a good discrimination rate for classification PD sources.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.1
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• pp.70-80
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• 1998

Many algorithms to find a minimum cost design of water distribution network (WDN) have been developed during the last decades. Most of them have tried to optimize cost only while satisfying other constraining conditions. For this, a certain degree of simplification is required in their calculation process which inevitably limits the real application of the algorithms, especially, to large networks. In this paper, an optimum design method using the Genetic Algorithms (GA) is developed which is designed to increase the applicability, especially for the real world large WDN. The increased to applicability is due to the inherent characteristics of GA consisting of selection, reproduction, crossover and mutation. Just for illustration, the GA method is applied to find an optimal solution of the New York City water supply tunnel. For the calculation, the parameter of population size and generation number is fixed to 100 and the probability of crossover is 0.7, the probability of mutation is 0.01. The yielded optimal design is found to be superior to the least cost design obtained from the Linear Program method by $4.276 million.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.135-140
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• 2000

베이지안 신경망 모형(Bayesian Neural Networks Models)에서 주어진 입력값(input)은 블랙 박스(Black-Box)와 같은 신경망 구조의 각 층(layer)을 거쳐서 출력값(output)으로 계산된다. 새로운 입력 데이터에 대한 예측값은 사후분포(posterior distribution)의 기대값(mean)에 의해 계산된다. 주어진 사전분포(prior distribution)와 학습데이터에 의한 가능도함수(likelihood functions)를 통해 계산되어진 사후분포는 매우 복잡한 구조를 갖게 됨으로서 기대값의 적분계산에 대한 어려움이 발생한다. 이때 확률적 추정에 의한 근사 방법인 몬테칼로 적분을 이용한다. 이러한 방법으로서 Hybrid Monte Carlo 알고리즘은 우수한 결과를 제공하여준다(Neal 1996). 본 논문에서는 Hybrid Monte Carlo 알고리즘과 기존에 많이 사용되고 있는 Gibbs sampling, Metropolis algorithm, 그리고 Slice Sampling등의 몬테칼로 방법들을 비교한다.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.1
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• pp.149-157
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• 2008

The reduction of leakage is a major issue of the South Korea water industry. The inclusion of pressure dependent leakage terms in network analysis allows the application of optimization techniques to identify the most effective means of reducing water leakage in distribution networks. This paper proposes a method to find optimal setting and location of control valve for reducing leakage efficiently. The proposed search method differs from previous methods for addressing optimal valve location problem and improves the GA simulation time with guaranteeing for getting the global optimal solution. The strength of this method has been demonstrated by means of case studies. This allows the procedure of optimization to be more robust and computational efficient.

• Convergence Security Journal
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• v.8 no.1
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• pp.143-152
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• 2008

Sensor networks have a wide spectrum of military and civil applications, particularly with respect to security and secure keys for encryption and authentication. This thesis presents a new centralized approach which focuses on the group key distribution with revocation capability for Wireless Sensor Networks. We propose a new personal key share distribution. When utilized, this approach proves to be secure against k-number of illegitimate colluding nodes. In contrast to related approaches, our scheme can overcome the security shortcomings while keeping the small overhead requirements per node. It will be shown that our scheme is unconditionally secure and achieves both forward secrecy and backward secrecy. The analysis is demonstrated in terms of communication and storage overheads.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.9
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• pp.1006-1012
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• 2004

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. Two learning schemes are used to classification; BP(Back propagation algorithm), SOM(self organized map - kohonen network). As a PD source, using treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And a]so these distribution characteristics are applied to classify PD sources by two scheme of the neural networks. In conclusion, recognition efficiency of BP is superior to SOM.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.441-451
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• 2014

This article addresses an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using the artificial bee colony algorithm. The objective function is adapted to enhance the overall system static voltage stability index and to achieve maximum net yearly savings. Load variations have been considered to optimally scope the fixed and switched capacitors required. The numerical results are compared with those obtained using recent heuristic methods and show that the proposed approach is capable of generating high-grade solutions and validated viability.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.194-198
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• 2023

The current electricity networks will undergo profound changes in the years to come to be able to meet the growing demand for electricity, while minimizing the costs of consumers and producers, etc. The electricity network of tomorrow or even the intelligent « Smart Grids » network will be the convergence of two networks: the electricity network and the telecommunications network. In this context falls our work which aims to study the impact of the integration of energy decentralization into the electricity network. In this sense, we have implemented a new smart grid model where several coexisting suppliers can exchange information with consumers in real time. In addition, a new approach to energy distribution optimization has been developed. The simulation results prove the effectiveness of this approach in improving energy exchange and minimizing consumer purchase costs and line losses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.4
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• pp.376-384
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• 2009

In this article, we demonstrate that the asymmetric key-based architecture for securing wireless sensor networks recently introduced by Haque et al. is insecure under impersonation attack, since it does not provide authentication semantics. In addition, we show that, for the scheme to work correctly, the resulting key distribution construction should be symmetric and group-wise.