• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1774-1786
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• 2018

Phenotypic variation among clones (individuals with identical genes, i.e. isogenic individuals) has been recognized both theoretically and experimentally. We investigate the effects of phenotypic variation on evolutionary dynamics of a population. In a population, the individuals are assumed to be haploid with two genotypes : one genotype shows phenotypic variation and the other does not. We use an individual-based Moran model in which the individuals reproduce according to their fitness values and die at random. The evolutionary dynamics of an individual-based model is formulated in terms of a master equation and is approximated as the Fokker-Planck equation (FPE) and the coupled non-linear stochastic differential equations (SDEs) with multiplicative noise. We first analyze the deterministic part of the SDEs to obtain the fixed points and determine the stability of each fixed point. We find that there is a discrete phase transition in the population distribution when the probability of reproducing the fitter individual is equal to the critical value determined by the stability of the fixed points. Next, we take demographic stochasticity into account and analyze the FPE by eliminating the fast variable to reduce the coupled two-variable FPE to the single-variable FPE. We derive a quasi-stationary distribution of the reduced FPE and predict the fixation probabilities and the mean fixation times to absorbing states. We also carry out numerical simulations in the form of the Gillespie algorithm and find that the results of simulations are consistent with the analytic predictions.

• Korean Management Science Review
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• v.34 no.3
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• pp.1-14
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• 2017

This study empirically analyzes the impact of inventory management performance on the energy efficiency in Korean petrochemical companies. The concept of the distance function is used to define the energy efficiency and the estimation of the distance function is performed using the stochastic frontier analysis. The inventory turnover is selected as the variable indicating the inventory management performance of the company. The main results of this study are as follows. First, the inventory turnover has a positive impact on energy efficiency. Second, during the period over 2011~2015, while the gap in energy efficiency among the companies expanded, the average energy efficiency decreased. Third, the average energy efficiency in upstream process companies was greater than downstream process companies and the gap in energy efficiency among downstream process companies was greater than upstream process companies. Fourth, the average marginal effect of inventory turnover on energy efficiency increased gradually from 2011 to 2015. Finally, the average marginal effect of inventory turnover in downstream process companies was greater than upstream process companies, and the gap in the marginal effect of inventory turnover among downstream process companies was greater than upstream process companies. These results together imply the importance of inventory management in terms of energy efficiency.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.4
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• pp.249-257
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• 2000

In this paper, we propose the use of stochastic finite element method, that is popularly employed in mechanical structure analysis, for more practical designing purpose of RF device. The proposed method is formulated based on the vector finite element method cooperated by pertubation analysis. The method utilizes sensitivity analysis algorithm with covariance matrix of the random variables that represent for uncertain physical quantities such as length or various electrical constants to compute the probabilities of the measure of performance of the structure. For this computation one need to know the variance and covariance of the random variables that might be determined by practical experiences. The presenting algorithm has been verified by analyzing several device with different be determined by practical experiences. The presenting algorithm has been verified by analysis several device with different measure of performanes. For the convenience of formulation, two dimensional analysis has been performed to apply it into waveguide with dielectric slab. In the problem the dielectric constant of the dielectric slab is considered as random variable. Another example is matched waveguide and cavity problem. In the problem, the dimension of them are assumed to be as random variables and the expectations and variances of quality factor have been computed.

• Structural Engineering and Mechanics
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• v.68 no.4
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• pp.423-442
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• 2018

In this paper, masonry infilled reinforced concrete (RC) frames are analyzed through a probabilistic approach. A macro-modeling technique, based on an equivalent diagonal pin-jointed strut, has been resorted to for modelling the stiffening contribution of the masonry panels. Since it is quite difficult to decide which mechanical characteristics to assume for the diagonal struts in such simplified model, the strut width is here considered as a random variable, whose stochastic characterization stems from a wide set of empirical expressions proposed in the literature. The stochastic analysis of the masonry infilled RC frame is conducted via the Probabilistic Transformation Method by employing a set of space transformation laws of random vectors to determine the probability density function (PDF) of the system response in a direct manner. The knowledge of the PDF of a set of response indicators, including displacements, bending moments, shear forces, interstory drifts, opens an interesting discussion about the influence of the uncertainty of the masonry infills and the resulting implications in a design process.

• Structural Engineering and Mechanics
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• v.20 no.3
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• pp.293-312
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• 2005

In this paper, free vibration of rotating beam with random properties is studied. The cross-sectional area, elasticity modulus, moment of inertia, shear modulus and density are modeled as random fields and the rotational speed as a random variable. To study uncertainty, stochastic finite element method based on second order perturbation method is applied. To discretize random fields, the three methods of midpoint, interpolation and local average are applied and compared. The effects of rotational speed, setting angle, random property variances, discretization scheme, number of elements, correlation of random fields, correlation function form and correlation length on "Coefficient of Variation" (C.O.V.) of first mode eigenvalue are investigated completely. To determine the significant random properties on the variation of first mode eigenvalue the sensitivity analysis is performed. The results are studied for both Timoshenko and Bernoulli-Euler rotating beam. It is shown that the C.O.V. of first mode eigenvalue of Timoshenko and Bernoulli-Euler rotating beams are approximately identical. Also, compared to uncorrelated random fields, the correlated case has larger C.O.V. value. Another important result is, where correlation length is small, the convergence rate is lower and more number of elements are necessary for convergence of final response.

• Water for future
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• v.25 no.4
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• pp.109-119
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• 1992

The purpose of this study is to develop real-time streamflow forecasting models in order to manage effectively the flood warning system and water resources during the storm. The stochastic system models of the rainfall-runoff process using in this study are constituted and applied the Recursive Least Square and the Instrumental Variable-Approximate Maximum Likelihood algorithm which can estimate recursively the optimal parameters of the model. Also, in order to improve the performance of streamflow forecasting, initial values of the model parameter and covariance matrix of parameter estimate errors were evaluated by using the observed historical data of the hourly rainfall-runoff, and the accuracy and applicability of the models developed in this study were examined by the analysis of the I-step ahead streamflow forecasts.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.887-896
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• 1994

It is common practice in the case of optimum reservoir operation model that the reservoir inflow series are generated by stochastic model with keeping other variable such as water demands from the reservoir constant. However, when the input and output of the water distribution system have close relationship the output variables can be stochastically generated in relation with the input variables. In the present study the reservoir inflow series, the input of the system, is generated by periodic autoregressive model with constant parameter, and the agricultural water demand series, the output, is generated using periodic multivariate autoregressive model with constant parameter. The time period of the data series generated is taken as 10-day which is the common period used for agricultural water uses. The results of data generation by two different models showed that the periodic stochastic models well represent the characteristics of the historical time series, and that in the case of generating model for agricultural demand series it has closer relation with reservoir inflow than with the series itself.

• Journal of the Korean Society of Groundwater Environment
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• v.1 no.2
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• pp.90-99
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• 1994

Heterogeneous hydraulic conductivity in a flow domain is generated under the assumption that it is a random variable with a lognormal, spatially-correlated distribution. The hydraulic head and the conductivity in a groundwater flow system are represented as a stochastic process. The method of Monte Carlo Simulation (MCS) and the finite element method (FEM) are used to determine the statistics of the head and the logconductivity. The second moments of the head and the logconductivity indicate that the cross-covariance of the logconductivity with the head has characteristic distribution patterns depending on the properties of sources, boundary conditions, head gradients, and correlation scales. The negative cross-correlation outlines a weak-response zone where the flow system is weakly responding to a stress change in the flow domain. The stochastic approach has a potential to quantitatively delineate the zone of influence through computations of the cross-covariance distribution.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.6
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• pp.37-46
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• 2001

In this research, a controller design method based on optimization is proposed that can satisfy constraints on maximum responses of building structures subject to around excitation modeled by partially stochastic process. The class of controllers to be optimized is restricted to LQR. Weighting matrix on controlled outputs is used as design variable. Objective function, constraint functions and their gradients are computed by the parameterization of control gain with Riccati matrix. Full state feedback controllers designed by proposed optimization method satisfy various design objectives and their necessary maximum control forces are computed for the production of actuator. LQG controllers composed of Kalman filter and LQR designed by proposed method perform well with little deterioration. So it is possible to design output feedback controllers satisfying constraints on various maximum responses of structures.