• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.257-261
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• 2005

The helicopter system is non-linear and complex. Futhermore, because of absence of accurate mathematical model, it is difficult accurately to control its attitude. therefore, we propose a WAVENET control technique to control efficiently its elevation angle and azimuth one. Wavelet neural network(WAVENET) can construct systematically initial neural network as applying wavelet theory to feedforward network. It is proved through computer simulation that WAVENET has more excellent approximation capability than existing neural network. The simulation results using MATLAB are introduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.147-147
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• 2021

A partition of water distribution network (WDN) into district metered areas (DMAs) brings the efficiency and efficacy for water network operation and management (O&M), especially in monitoring pressure and leakage. Traditionally, the DMA configurations (i.e., number, shape, and size of DMAs) are permanent and cannot be changed occasionally. This leads to changes in water quality and reduced network redundancy lowering network resilience against abnormal conditions such as water demand variability and mechanical failures. This study proposes a framework to automatically divide a WDN into dynamic DMA configurations, in which the DMA layouts can self-adapt in response to abnormal scenarios. To that aim, a complex graph theory is adopted to sectorize a WDN into multiscale DMA layouts. Then, different failure-based scenarios are investigated on the existing DMA layouts. Here, an optimization-based model is proposed to convert existing DMA layouts into dynamic layouts by considering existing valves and possibly placing new valves. The objective is to minimize the alteration of flow paths (i.e., flow direction and velocity in the pipes) while preserving the hydraulic performance of the network. The proposed method is tested on a real complex WDN for demonstration and validation of the approach.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.383-388
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• 2005

In the construction industry, complex works are carried out with significant resources under non-linear circumstances where clear concepts of project management could be of benefit to all parties and personnel involved. In this paper, we define the optimum project management configuration for construction management by using DSM (Design Structure Matrix). Furthermore DSM can be visualized as a network model, and then Graph Theory provides us the numerical results.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1183-1188
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• 2004

The inverse heat conduction problem (IHCP) is a problem of estimating boundary condition from temperature measurement at one or more interior points. Neural networks are general information processing systems inspired by the connectionist theory of human brain. By properly training the network by the learning rule, the neural network method can handle many non-linear or other complex problems. In this work, neural network is applied to complicated inverse heat conduction problems. Efficiency of the procedure is enhanced by incorporating the radial basis functions (RBF). The RBF is trained faster than other neural network and can find smooth solution. In order to demonstrate the effectiveness of the current scheme, a typical one-dimensional IHCP is considered. At one surface, the temperature as well as the heat flux is known. The unknown temperature of interest is estimated on the other side of the slab. The results from the proposed method based on RBF neural network are compared with the conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5116-5134
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• 2015

In this paper, due to the network security defense is mainly static defense, a dynamic classification network security defense strategy model is proposed by analyzing the security situation of complex computer network. According to the network security impact parameters, eight security elements and classification standard are obtained. At the same time, the dynamic classification algorithm based on fuzzy theory is also presented. The experimental analysis results show that the proposed model and algorithm are feasible and effective. The model is a good way to solve a safety problem that the static defense cannot cope with tactics and lack of dynamic change.

• Composites Research
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• v.22 no.3
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• pp.44-54
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• 2009

In this paper, we proposed a numerical model the complex permittivity for the E-glass fabric/epoxy composite laminate containing electrical conductive carbon black. The model is based on the percolation theory and for the composites over than the percolation threshold and in higher frequency band in that the AC conductivity is fully proportional to the frequency. The measurement for the complex permittivity wasperformed at the frequency band of 0.5 GHz $\sim$ 18.0 GHz using a vector network analyzer with a 7 mm coaxial air line. The proposed model is composed of the numerical equations of the scaling law used in percolation theory and constants obtained from experiments to quantify the model itself. The model describes the complex permittivity as the function of frequency and filler concentration. The model was verified by being compared with the measurements.

• Steel and Composite Structures
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• v.50 no.4
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• pp.375-382
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• 2024

The network theory studies interconnection between discrete objects to find about the behavior of a collection of objects. Also, nanomaterials are a collection of discrete atoms interconnected together to perform a specific task of mechanical or/and electrical type. Therefore, it is reasonable to use the network theory in the study of behavior of super-molecule in nano-scale. In the current study, we aim to examine vibrational behavior of spherical nanostructured composite with different geometrical and materials properties. In this regard, a specific shear deformation displacement theory, classical elasticity theory and analytical solution to find the natural frequency of the spherical nano-composite structure. The analytical results are validated by comparison to finite element (FE). Further, a detail comprehensive results of frequency variations are presented in terms of different parameters. It is revealed that the current methodology provides accurate results in comparison to FE results. On the other hand, different geometrical and weight fraction have influential role in determining frequency of the structure.

• Kyungpook Mathematical Journal
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• v.56 no.4
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• pp.1191-1205
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• 2016

The emergence of various Internet worms, including the stand-alone Code Red worm that caused a distributed denial of service (DDoS), has prompted many studies on their propagation speed to minimize potential damages. Many studies, however, assume the same probabilities for initially infected nodes to infect each node during their propagation, which do not reflect accurate Internet worm propagation modelling. Thus, this paper analyzes how Internet worm propagation speed varies according to the number of vulnerable hosts directly connected to infected hosts as well as the link costs between infected and vulnerable hosts. A mathematical model based on centrality theory is proposed to analyze and simulate the effects of degree centrality values and closeness centrality values representing the connectivity of nodes in a large-scale network environment on Internet worm propagation speed.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.