• Structural Engineering and Mechanics
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• v.7 no.3
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• pp.259-276
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• 1999

An analytical solution for the shape functions of a beam segment supported on a generalized two-parameter elastic foundation is derived. The solution is general, and is not restricted to a particular range of magnitudes of the foundation parameters. The exact shape functions can be utilized to derive exact analytic expressions for the coefficients of the element stiffness matrix, work equivalent nodal forces for arbitrary transverse loads and coefficients of the consistent mass and geometrical stiffness matrices. As illustration, each distinct coefficient of the element stiffness matrix is compared with its conventional counterpart for a beam segment supported by no foundation at all for the entire range of foundation parameters.

• Journal of Multimedia Information System
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• v.3 no.4
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.3 s.43
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• pp.33-42
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• 2005

Frequency domain Volterra model is applied to nonlinear parameter identification procedure for dynamic systems modeled by nonlinear function. The frequency domain Volterra kernels, which correspond io linear, quadratic, and cubic transfer functions in lime domain, are incorporated in nonlinear parametric identification procedure. The nonlinear transfer functions, which can be derived from the Volterra series representation of the nonlinear differential equation of the system by Schetzen's method(1980), are directly used for modeling input output relation. The error is defined by the difference between the observed output and the estimated output which is calculated by substituting the observed input to nonlinear frequency domain model. The system parameters are searched by minimizing the error. Volterra model guarantees enough accuracy and convergence and the estimated coefficients have a good agreement with their actual values not only in the linear frequency region but also in the legion where the $2^{nd}\;or\;3^{rd}$ order nonlinearity is dominant.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.313-320
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• 2008

A typical software development life cycle consists of a series of phases, each of which has some ability to insert and detect defects. To achieve desired quality, we should progress the defect removal with the all phases of the software development. The well-known model of phase-based defect profile is Gaffney model. This model assumes that the defect removal profile follows Rayleigh curve and uses the parameters as the phase index number. However, these is a problem that the location parameter cannot present the peak point of removed defects when you apply Gaffney model to the actual situation. Therefore, Gaffney model failed to represent the actual defect profile. This paper suggests two different models: One is modified Gaffney model that introduce the parameter of Putnam's SLIM model to replace of the location parameter, the other is the growth function model because the cumulative defect profile shows S-shaped. Suggested model is analyzed and verified by the defect profile sets that are obtained from 5 different software projects. We could see from the experiment, the suggested model performed better result than Gaffney model.

• Journal of Environmental Science International
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• v.14 no.12
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• pp.1203-1209
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• 2005

Basic dyes, Rhodamine 6G(R6G), Rhodamine B(RB), and Methylene Blue(MB), dissolved in water were used to investigate single-component adsorption affinity to the pearl layer fractionated according to the size. Unfractionated pearl layers were also used as adsorbents for the R6G and RB. The Langmuir and the Redlich-Peterson(RP) models were used to fit the adsorption data, and the goodness of fit was examined by using determination coefficient($R^2$) and standard deviation(SSE). The 3-parameter RP model was found to be better in describing the dye adsorption data than the 2 parameter Langmuir model, as can be expected from the number of parameters involved in the model. The adsorption affinity to the fractionated pearl layer was higher than that to the unfractionated layer The affinity order to the fractionated Conchiolin layer was found to be R6G > MB > RB. Furthermore, the dye adsorption capacity of the various types of pearl layer was found to be in the order, the fractionated pearl > powdered pearl > unfractionated pearl, exhibiting different adsorption isotherms according to the types of layer used in the study.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.423-436
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• 2003

The objective of this study is to propose an automatic parameter estimation scheme considering runoff components of Tank model. It estimates model parameters by Powell's automatic algorithm based on the runoff component separation of the observed hydrograph by using digital filter method. The selected study areas are the 4 main dam sites on the Han River. The simulated flows are compared with the observed flows depending on whether runoff component consideration or not. As a result, the estimated model parameters from classical Powell's method only can relatively well simulate the time variation of total runoff, but gives poor runoff component simulations. Therefore, it can be concluded that the proposed automatic parameter estimation scheme in this study Is more reliable and objective.

• Honam Mathematical Journal
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• v.38 no.3
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• pp.435-454
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• 2016

This paper investigates the effect of dual-phase-lags on a thermoviscoelastic orthotropic solid with a cylindrical cavity. The cylindrical cavity is subjected to a thermal shock varying heat and its material is taken to be of Kelvin-Voigt type. The phase-lag thermoelastic model, Lord and Shulman's model and the coupled thermoelasticity model are employed to study the thermomechanical coupling, thermal and mechanical relaxation (viscous) effects. Numerical solutions for temperature, displacement and thermal stresses are obtained by using the method of Laplace transforms. Numerical results are plotted to illustrate the effect phase-lags, viscoelasticity, and the variability thermal conductivity parameter on the studied fields. The variations of all field quantities in the context of dual-phase-lags and coupled thermoelasticity models follow similar trends while the Lord and Shulman's model may be different. The influence of viscosity parameter and variability of thermal conductivity is very pronounced on temperature and thermal stresses of the thermoviscoelastic solids.

• Korean Journal of Artificial Intelligence
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• v.12 no.3
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• pp.33-39
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• 2024

In this paper, we explore the application of Kodaly hand signs in enhancing children's music education, performances, and auditory assistance technologies. This research focuses on improving the recognition rate of Multilayer Perceptron (MLP) models in identifying Kodaly hand sign scales through the integration of Artificial Neural Networks (ANN). We developed an enhanced MLP model by augmenting it with additional parameters and optimizing the number of hidden layers, aiming to substantially increase the model's accuracy and efficiency. The augmented model demonstrated a significant improvement in recognizing complex hand sign sequences, achieving a higher accuracy compared to previous methods. These advancements suggest that our approach can greatly benefit music education and the development of auditory assistance technologies by providing more reliable and precise recognition of Kodaly hand signs. This study confirms the potential of parameter augmentation and hidden layers optimization in refining the capabilities of neural network models for practical applications.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.729-748
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• 1999

In this paper we present combined estimator of capture-recapture and line transect model using bivariate detection function and detection probability according to objects being in cluster population. Here bivariate detection function use distance and cluster size. The simulation shows that combined estimator approaches the more true value the larger size parameter. Therefore this estimator using the bivariate detection function is more efficient in estimate the population size and density by size parameter.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.133-140
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• 2008

In this paper, we analyzed the accuracy of orbit modeling with various control point configurations and adjustment unknown parameter sets. We used 152 GCP points acquired from GPS surveying, which were distributed from Choon-chun to Nha-ju along 420km in distance. For orbit modeling, seven adjustment parameter sets were chosen to include parameters for satellite position, velocity and attitude angles at different degree of freedom. Firstly we determined the location of model point in seven configurations. Secondly we estimated model parameters for each parameter set and for each GCP configurations. Finally we applied the model to reference check points and analyzed its accuracy. We were able to find the unknown parameter set that produce best orbit modeling performance regardless of the configuration of model points.