• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.314-319
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• 2015

Purpose: The ratio of straw to grain mass as a function of cutting height affects combine efficiency and power consumption and is an important input parameter to combine simulation models. An equation was developed to predict straw to grain ratios for wheat as a function of cutting height. Methods: Two mass functions, one for straw and one for grain, were developed using regression techniques and measured data collected in west Texas during the summer, and used to predict the straw to grain ratio. Results: Three equations were developed to facilitate the simulation of a combine during wheat harvest. Two mass functions, one for straw and one for grain, were also developed; a quadratic equation describes the straw mass with an $R^2$ of 0.992. An S-shaped curve describes the mass function for grain with an $R^2$ of 0.957. An equation for straw to grain ratio of wheat was developed as a function of cutting height. The straw to grain ratio has an $R^2$ value of 0.947. Conclusions: In all cases, the equations had $R^2$ values above 0.94 and were significant at the 99.9 percent probability level (alpha = 0.001). Although all three equations are useful, the grain mass and straw to grain ratio equations will have direct application in combine simulation models.

• Journal of the Korea Society for Simulation
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• v.7 no.2
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• pp.137-152
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• 1998

One of the components that constitute the simulation models is the state variables whose values are determined by the time related simulation process. Embedding rule-based expert systems into the simulation models should provide a systematic way of handling these time-dependent variables without distracting the essential problem solving capabilities of the expert systems which are well suited for expressing the decision making function of complex cases. The expert system, however, is inefficient in dealing with the time elapsing characteristics of target system compare to the simulation models. To solve the problem, this paper provides an interruptible inference engine whose inferencing process can be interrupted when the variables' value, which are used as the parameters of the rules, are not yet determined due to the time dependent nature of the state variables. The process is resumed when the variables are ready. The elapse of time is calculated by time-advance function of the simulation model to which the expert system has been embedded. The example modeling shown exploits the embedded interruptible inferencing capability for the controlling and monitoring of metal grating process.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.1
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• pp.107-119
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• 2018

PURPOSE: This research was intended to investigate the influence to function recovery at the early stage after surgery, by conducting Multipath Electrical Simulation and isometric exercise treatment as early stage medical treatment method for Total knee arthroplasty patients. METHODS: The subject of 30 patients having Unilateral Total knee arthroplasty over age 65, Multipath Electrical Simulation and isometric exercise (experiment group I), Conventional Electrical Simulation and isometric exercise (experiment group II) and isometric exercise (control group). The intervention was performed in 5 times per a week and 60 minutes per a day during 4 weeks. We performed research by conducting Neuromuscular Electrical Stimulation and isometric exercise together and measured pain, range of motion, muscle strength and gait ability before and after intervention. RESULTS: The result showed therapeutic improvement in experiment group I, experiment group II and control group, but Multipath Electrical Simulation and isometric exercise showed significant improvement in function recovery of early stage compared to Conventional Electrical Simulation and isometric exercise, only isometric exercise. CONCLUSION: Based on research result, in order for early state function recovery of Total knee arthroplasty patients, when conducting neuromuscular electrical stimulation and isometric exercise together, especially when applying Multipath Electrical Stimulation, we could know that it showed more significant improvement to function recovery after surgery. Also, we suggest that Multipath Electrical Simulation may become a useful tool as a method for intervention and performing in various diseases for weakening of Quadriceps muscle.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.437-447
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• 2010

Spatial estimation of environmental variables has been regarded as an important preliminary procedure for decision-making. A minimum variance criterion, which has often been adopted in traditional kriging algorithms, does not always guarantee the optimal estimates for subsequent decision-making processes. In this paper, a geostatistical framework is illustrated that consists of uncertainty modeling via stochastic simulation and risk modeling based on loss functions for the selection of optimal estimates. Loss functions that quantify the impact of choosing any estimate different from the unknown true value are linked to geostatistical simulation. A hybrid loss function is especially presented to account for the different impact of over- and underestimation of different land-use types. The loss function-specific estimates that minimize the expected loss are chosen as optimal estimates. The applicability of the geostatistical framework is demonstrated and discussed through a case study of copper mapping.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05c
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• pp.194-198
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• 2002

Energy Distribution Function in pure $CH_4$, $CF_4$ and mixtures of $CF_4$ and Ar, have been analyzed over a range of the reduced electric field strength between 0.1 and 350[Td] by the two-tenn approximation of the Boltzmann equation (BEq.) method and the Monte Carlo simulation (MCS). The results of the Boltzmann equation and the Monte Carlo simulation have been compared with the data presented by several workers. The deduced transport coefficients for electrons agree reasonably well with the experimental and simulation data obtained by Nakamura and Hayashi. The energy distribution function of electrons in $CF_4-Ar$ mixtures shows the Maxwellian distribution for energy. That is, f(${\varepsilon}$) has the symmetrical shape whose axis of symmetry is a most probably energy

• Journal of the Korean Society of Combustion
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• v.22 no.3
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• pp.29-34
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• 2017

The multi-environment probability density function model has been applied to simulate a turbulent premixed flame in a swirl combustor. To realistically account for the unsteady flow motion inside the combustor, the formulations are derived for the large eddy simulation. The Flamelet generated manifolds is utilized to simplify a multi-dimensional composition space with reasonable accuracy. The sub grid scale mixing is modeled by the interaction by exchange with the mean mixing model. To validate the present approach, the simulation results are compared with experimental data in terms of mean velocity, temperature, and species mass fractions.

• Journal of the Korea Society for Simulation
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• v.10 no.4
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• pp.41-50
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• 2001

When optimizing a complex system by determining the optimum condition of the system parameters of interest, we often employ the process of estimating the unknown objective function, which is assumed to be a second order spline function. In doing so, we normally use common random numbers for different set of the controllable factors resulting in more accurate parameter estimation for the objective function. In this paper, we will show some mathematical result for the analysis of variance when using common random numbers in terms of the regression error, the residual error and the pure error terms. In fact, if we can realize the special structure of the covariance matrix of the error terms, we can use the result of analysis of variance for the uncorrelated experiments only by applying minor changes.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.397-398
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• 2007