• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.247-254
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• 2003

While the interests in the environmental problem during the construction are increasing, the use of low noise-vibration auger-drilled pilling is increasing to solve noise and vibration problem in pilling. Therefore, in Korea, SIP (Soil-Cement Injected Precast Pile) method is mainly used as auger-drilled pilling. However, there is no proper design criteria compatible with the ground condition of Korea, so which is most wanted. To improve and supplement this situation, direct shear tests for the friction between SIP pile skin interface and soil were executed on various conditions. Through the analysis of test results, skin friction characteristics of SIP were investigated thoroughly Also, hyperbolic model parameter fomulas which describe the friction behavior and the new non-linear unit skin friction capacity model with SM, SC soil were suggested.

• Convergence Security Journal
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• v.11 no.1
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• pp.25-32
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The Weibull distribution applied to distribution was based on finite failure NHPP. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and $R_{sq}$.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.5
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• pp.952-964
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• 1992

The interaction of natural convection and radiation heat transfer in a two dimensional square enclosure containing absorbing, emitting and linear anisotropically scattering gray medium is numerically analyzed. P-1 and P-3 approximation is introduced to calculate radiation heat transfer. The effects of scattering albedo, wall emissivity, scattering anisotropy, and optical thickness on the characteristics of the flow and temperature field and heat transfer are investigated. Temperature and velocity profiles depend a great deal on the scattering albedo, and the importance of this effect increases with decrease in albelo. Planck number is another important parameter in radiation heat transfer. The increase in scattering albedo increases convection heat transfer and decreases radiation heat transfer at hot wall. However, the increase in scattering albedo decreases both convection and radiation heat transfer at cold wall. The increase in optical thickness decreases radiation heat transfer. The scattering anisotropy has important effects on the radiation heat transfer only. The highly forward scattering leads to an increase of radiation heat transfer whereas the highly backward scattering leads to an decrease of radiation heat transfer. The effect of scattering anisotropy decreases when reducing the wall emissivity.

• Smart Structures and Systems
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• v.20 no.6
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• pp.709-728
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• 2017

This disquisition proposes a nonlocal strain gradient beam theory for thermo-mechanical dynamic characteristics of embedded smart shear deformable curved piezoelectric nanobeams made of porous electro-elastic functionally graded materials by using an analytical method. Electro-elastic properties of embedded curved porous FG nanobeam are assumed to be temperature-dependent and vary through the thickness direction of beam according to the power-law which is modified to approximate material properties for even distributions of porosities. It is perceived that during manufacturing of functionally graded materials (FGMs) porosities and micro-voids can be occurred inside the material. Since variation of pores along the thickness direction influences the mechanical and physical properties, so in this study thermo-mechanical vibration analysis of curve FG piezoelectric nanobeam by considering the effect of these imperfections is performed. Nonlocal strain gradient elasticity theory is utilized to consider the size effects in which the stress for not only the nonlocal stress field but also the strain gradients stress field. The governing equations and related boundary condition of embedded smart curved porous FG nanobeam subjected to thermal and electric field are derived via the energy method based on Timoshenko beam theory. An analytical Navier solution procedure is utilized to achieve the natural frequencies of porous FG curved piezoelectric nanobeam resting on Winkler and Pasternak foundation. The results for simpler states are confirmed with known data in the literature. The effects of various parameters such as nonlocality parameter, electric voltage, coefficient of porosity, elastic foundation parameters, thermal effect, gradient index, strain gradient, elastic opening angle and slenderness ratio on the natural frequency of embedded curved FG porous piezoelectric nanobeam are successfully discussed. It is concluded that these parameters play important roles on the dynamic behavior of porous FG curved nanobeam. Presented numerical results can serve as benchmarks for future analyses of curve FG nanobeam with porosity phases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.847-851
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• 2011

Photovoltaic System is increasing install capacity based on environmental-friendly characteristics. It have been actively studied to improve the efficiency. In order to design highly efficient system, it is important to understand the output characteristics of solar panels. The single diode model can represent the physical characteristics of solar panel. But it needs complex process such as mutli-step measurement and numerical analysis to get the exact parameters. In this paper, The method for extracting characteristic parameters of the single diode model based on the I-V characteristic curves in the panel manufacturer's data-sheet is presented. To verify the proposed method, solar cell model constructed in simulink. Simulink model output compared with output graph in datasheet.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2786-2800
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• 1997

In this paper, we investigate how well the channel memory (statistical dependence in the occurrence of transmission errors) can be used in the evaluation of widely used error control schemes. For this we assume a special case named as the simplest Markovian block-error pattern with two states, in which each block is classified into two classes of whether the block transmission is in error or not. We apply the derived pattern to the performance evaluation of the practical link-level procedures, LAPB/D/M with multi-reject options, and investigate both throughput and user-perceived response time behaviors on the discrete-time domain to determine how much the performance of error recovery action is improved under burst error condition. Through numerical examples, we show that the simplest Markovian block-error pattern tends to be superior in throughput and delay characteristics to the random error case. Also, instead of mean alone, we propose a new measure of the response time specified as mean plus two standard deviations 50 as to consider user-perceived worst cases, and show that it results in much greater sensitivity to parameter variations than does mean alone.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.389-398
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• 2002

Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process(NHPP) and performs Bayesian inference using prior information. The failure process is analyzed to develop a suitable mean value function for the NHPP ; expressions are given for several performance measure. Actual software failure data are compared with several model on the constant reflecting the quality of testing. The performance measures and parametric inferences of the suggested models using Rayleigh distribution and Laplace distribution are discussed. The results of the suggested models are applied to real software failure data and compared with Goel model. Tools of parameter point inference and 95% credible intereval was used method of Gibbs sampling. In this paper, model selection using the sum of the squared errors was employed. The numerical example by NTDS data was illustrated.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.97-106
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• 2003

When a route choice is done under uncertainty, a driver has some expectation of traffic conditions that will occur according to the route chosen. This study tries to build a framework in which we can observe the learning behavior of the drivers' expectations of the travel time under nonstationary environment. In order to investigate how drivers have their subjective expectations on traffic conditions in response to public information, a numerical experiment is carried out. We found that rational expectations(RE) formation about the route travel time can be expressed by the adaptive expectation model when the travel time changes in accordance with the nonstationary process which consists of permanent shock and transient shock. Also, we found that the adaptive parameter of the model converges to the fixed value corresponding to the route conditions.