• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.686-691
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• 1994

For evaluating the response fluctuation of the actual environmental acoustic system excited by arbitrary random inputs, it is important to predict a whole probability distribution form closely connected with evaluation indexes Lx, Leq and so on. In this paper, a new type evaluation method is proposed by introducing three functional models matched to the prediction of the response probability distribution from a problem-oriented viewpoint. Because of the positive variable of the sound intensity, the response probability density function can be reasonably expressed theoretically by a statistical Laguerre expansion series form. The relationship between input and output is described by the regression relationship between the distribution parameters(containing expansion coefficients of this expression) and the stochastic input. These regression functions are expressed in terms of the orthogonal series expansion and their parameters are determined based on the least-squares error criterion and the measure of statistical independency.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.1
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• pp.1-7
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• 2020

In general, an inspection schedule is established based on the long-term fatigue life during the design stage. However, in the design stage, it is difficult to clearly identify the uncertainty factors affecting long-term fatigue life. In this study, the probabilistic fatigue life assessment was conducted in accordance with the methodology of DNV-GL. Firstly, The initial crack distribution estimated through the initial crack propagation analysis was updated by reflecting the results of crack inspection. Secondly, the updated crack distribution was compared with the initial crack distribution, and the probability of failure was updated with the effect of crack inspection.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.220-226
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• 2021

In supply chain, there are a variety of different uncertainties including demand, service time, lead time, and so forth. The uncertainty of demand has been commonly studied by researchers or practitioners in the field of supply chain. However, the uncertainty of upstream supply chain has also increased. A problem of uncertainty in the upstream supply chain is the fluctuation of the lead time. The stochastic lead time sometimes causes to happen so called the order crossover which is not the same sequences of the order placed and the order arrived. When the order crossover happens, ordinary inventory policies have difficult to find the optimal inventory solutions. In this research, we investigate the lead time distribution in case of the order crossover and explore the resolutions of the inventory solution with the order crossover.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.854-867
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• 2014

This paper deals with the performance comparison of a PSO algorithm inspired in the process of simulating the behavior pattern of the organisms. The PSO algorithm finds the optimal solution (fitness value) of the objective function based on a stochastic process. Generally, the stochastic process, a random function, is used with the expression related to the velocity included in the PSO algorithm. In this case, the random function of the normal distribution (Gaussian) or uniform distribution are mainly used as the random function in a PSO algorithm. However, in this paper, because the probability distribution which is various with 2 shape parameters can be expressed, the performance comparison of a PSO algorithm using the beta probability distribution function, that is a random function which has a high degree of freedom, is introduced. For performance comparison, 3 functions (Rastrigin, Rosenbrock, Schwefel) were selected among the benchmark Set. And the convergence property was compared and analyzed using PSO-FIW to find the optimal solution.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.38-49
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• 2012

In this study, we analyze statistical characteristics of influent water quality in Daejeon waste water treatment plant and apply a stochastic model for data generation. In the analysis, the influent water quality data from year 2003 to 2008, except for year 2006, are used. Among water quality variables, we find strong correlations between BOD and T-N; T-N and T-P; BOD and T-P; $COD_{Mn}$ and T-P; and BOD and $COD_{Mn}$. We also find that different water quality variables follow different theoretical probability distribution functions, which also depends on whether the seasonal cycle is removed. Finally, we generate the influent water quality data using the multi-season 1st Markov model (Thomas-Fiering model). With model parameters calibrated for the period 2003~2005, the generated data for 2007~2008 are well compared with observed data showing good agreement in general. BOD and T-N are underestimated by the stochastic model. This is mainly due to the statistical difference in observed data itself between two periods of 2003~2005 and 2007~2008. Therefore, we expect the stochastic model can be applied with more confidence in the case that the data follows stationary pattern.

• Computers and Concrete
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• v.26 no.2
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• pp.127-136
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• 2020

There is an inherent randomness for concrete microstructure even with the same manufacturing process. Meanwhile, the concrete material under the aqueous environment is usually not fully saturated by water. This study aimed to develop a stochastic micromechanical framework to investigate the probabilistic behavior of the unsaturated concrete from microscale level. The material is represented as a multiphase composite composed of the water, the pores and the intrinsic concrete (made up by the mortar, the coarse aggregates and their interfaces). The differential scheme based two-level micromechanical homogenization scheme is presented to quantitatively predict the concrete's effective properties. By modeling the volume fractions and properties of the constituents as stochastic, we extend the deterministic framework to stochastic to incorporate the material's inherent randomness. Monte Carlo simulations are adopted to reach the different order moments of the effective properties. A distribution-free method is employed to get the unbiased probability density function based on the maximum entropy principle. Numerical examples including limited experimental validations, comparisons with existing micromechanical models, commonly used probability density functions and the direct Monte Carlo simulations indicate that the proposed models provide an accurate and computationally efficient framework in characterizing the material's effective properties. Finally, the effects of the saturation degrees and the pore shapes on the concrete macroscopic probabilistic behaviors are investigated based on our proposed stochastic micromechanical framework.

• Journal of applied mathematics & informatics
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• v.32 no.1_2
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• pp.7-16
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• 2014

Erlang-truncated exponential distribution is widely used in the field of queuing system and stochastic processes. This family of distribution include exponential distribution. In this paper we establish some exact expression and recurrence relations satisfied by the quotient moments and conditional quotient moments of the upper record values from the Erlang-truncated exponential distribution. Further a characterization of this distribution based on recurrence relations of quotient moments of record values is presented.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.2
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• pp.63-72
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• 2016

An expected-discounted cost model based on RRP(Renewal Reward Process), referred to as a stochastic decision model, has been developed to estimate the optimal period of in-depth inspection which is one of critical issues in the life-cycle maintenance management of harbor structures such as rubble-mound breakwaters. A mathematical model, which is a function of the probability distribution of the service-life, has been formulated by simultaneously adopting PIM(Periodic Inspection and Maintenance) and CBIM(Condition-Based Inspection and Maintenance) policies so as to resolve limitations of other models, also all the costs in the model associated with monitoring and repair have been discounted with time. From both an analytical solution derived in this paper under the condition in which a failure rate function is a constant and the sensitivity analyses for the variety of different distribution functions and conditions, it has been confirmed that the present solution is more versatile than the existing solution suggested in a very simplified setting. Additionally, even in that case which the probability distribution of the service-life is estimated through the stochastic process, the present model is of course also well suited to interpret the nonlinearity of deterioration process. In particular, a MCS(Monte-Carlo Simulation)-based sample path method has been used to evaluate the parameters of a damage intensity function in stochastic process. Finally, the present stochastic decision model can satisfactorily be applied to armor units of rubble mound breakwaters. The optimal periods of in-depth inspection of rubble-mound breakwaters can be determined by minimizing the expected total cost rate with respect to the behavioral feature of damage process, the level of serviceability limit, and the consequence of that structure.

• Earthquakes and Structures
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• v.5 no.4
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• pp.395-416
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• 2013

The magneto-rheological (MR) damper contributes to the new technology of structural vibration control. Its developments and applications have been paid significant attentions in earthquake engineering in recent years. Due to the shortages, however, inherent in deterministic control schemes where only several observed seismic accelerations are used as the trivial input and in classical stochastic optimal control theory with assumption of white noise process, the derived control policy cannot effectively accommodate the performance of randomly base-excited engineering structures. In this paper, the experimental and analytical studies on stochastic seismic response control of structures with specifically designed MR dampers are carried out. The random ground motion, as the base excitation posing upon the shaking table and the design load used for structural control system, is represented by the physically based stochastic ground motion model. Stochastic response analysis and reliability assessment of the tested structure are performed using the probability density evolution method and the theory of extreme value distribution. It is shown that the seismic response of the controlled structure with MR dampers gain a significant reduction compared with that of the uncontrolled structure, and the structural reliability is obviously strengthened as well.