• Clean Technology
• /
• v.7 no.2
• /
• pp.119-126
• /
• 2001

The prediction of ozone formation was studied using the neural network and the stochastic method. Parameter estimation method and artificial neural network(ANN) method were employed in the stochastic scheme. In the parameter estimation method, extended least squares(ELS) method and recursive maximum likelihood(RML) were used to achieve the real time parameter estimation. Autoregressive moving average model with external input(ARMAX) was used as the ozone formation model for the parameter estimation method. ANN with 3 layers was also tested to predict the ozone formation. To demonstrate the performance of the ozone formation prediction schemes used in this work, the prediction results of ozone formation were compared with the real data. From the comparison it was found that the prediction schemes based on the parameter estimation method and ANN method show an acceptable accuracy with limited prediction horizon.

• Journal of Korean Society for Quality Management
• /
• v.35 no.4
• /
• pp.61-66
• /
• 2007

The performance of a building material degrades as time goes by and the failure of the material is often defined as the point at which the performance of the material reaches a pre-specified degraded level. Based on a stochastic deterioration model, a performance based service life prediction method for building materials and components is developed. As a stochastic degradation model, a gamma process is considered and lifetime distribution and service life of a material are predicted using the degradation model. A numerical example is provided to illustrate the use of the proposed service life prediction method.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.5 no.4
• /
• pp.17-26
• /
• 2001

In order to estimate peak ground motions and frequency characteristics of strong ground motions in southern korea, we employed the stochastic simulation method with the moment magnitude(M$_{w}$) and the hypocentral distance(R). We estimated same input parameters that account for specific properties of source and propagation processes, and applied them to the stochastic simulation method. The stress drop($\Delta$$\sigma$) of 100-bar was estimated considering results of research in ENA, China, and southern korea. The attenuation parameter x was calculated by analyzing 57 seismograms recorded from September 1996 to October 1997 and the estimation result of the attenuation parameter x is 0.00112+0.000224 R where R is hypocenter distance. We estimated strong ground motion relations using the stochastic simulation method with suitable input parameters(e.g. $\Delta$$\sigma$, x, and so on). At last, we derived relations between hypocentral distances and ground motions(seismic attenuation equation) using results of the stochastic prediction.esults of the stochastic prediction.n.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.3
• /
• pp.1053-1063
• /
• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.12 no.3
• /
• pp.265-274
• /
• 2010

In many tunnels, falling or sliding of rock blocks often occur, which cannot be predicted because of the complexity of rock discontinuities and it has brought an exponential increase in costs and time to manage. It is difficult to estimate the properties of rock masses before the tunnel excavation. The observational design and construction method in tunnels has been becoming important recently. In this study, a new hybrid stochastic-deterministic rock block analysis method for the prediction of the unstable rock blocks before the tunnel excavation is proposed, and then applied to the tunnel construction based on actual rock discontinuity information observed in the field. The comparisons and investigations with the analytical results in the tunnel construction have confirmed the validity and applicability of this new hybrid stochastic-deterministic rock block analysis method in tunnels.

• Proceedings of the Korean Reliability Society Conference
• /
• 2000.04a
• /
• pp.213-222
• /
• 2000

In present study, a stochastic model is developed for the low cycle fatigue life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. In the proposed model, fatigue phenomenon is considered as a Markov process, and damage vector and reliability are defined on every plane. Any low cycle fatigue damage evaluating method can be included in the proposed model. The model enables calculation of statistical reliability and crack initiation direction under variable multiaxial loading, which are generally not available. In present study, a critical plane method proposed by Kandil et al., maximum tensile strain range, and von Mises equivalent strain range are used to calculate fatigue damage. When the critical plane method is chosen, the effect of multiple critical planes is also included in the proposed model. Maximum tensile strain and von Mises strain methods are used for the demonstration of the generality of the proposed model. The material properties and the stochastic model parameters are obtained from uniaxial tests only. The stochastic model made of the parameters obtained from the uniaxial tests is applied to the life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. The predicted results show good accordance with experimental results.

• Journal of the Korean Mathematical Society
• /
• v.38 no.3
• /
• pp.657-667
• /
• 2001

We propose a statistical interpolation approximate solution for a nonlinear stochastic integral equation of a stock price process. The proposed method has the order O(h$^2$) of local error under the weaker conditions of $\mu$ and $\sigma$ than those of Milstein' scheme.

• Structural Engineering and Mechanics
• /
• v.55 no.3
• /
• pp.525-534
• /
• 2015

A new structural dynamic fuzzy reliability analysis under stochastic loads which are applied several times is proposed in this paper. The fuzzy reliability prediction models based on time responses with and without strength degeneration are established using the stress-strength interference theory. The random loads are applied several times and fuzzy structural strength is analyzed. The efficiency of the proposed method is demonstrated numerically through an example. The results have shown that the proposed method is practicable, feasible and gives a reasonably accurate prediction. The analysis shows that the probabilistic reliability is a special case of fuzzy reliability and fuzzy reliability of structural strength without degeneration is also a special case of fuzzy reliability with structural strength degeneration.

• Korean Journal of Construction Engineering and Management
• /
• v.6 no.1 s.23
• /
• pp.73-79
• /
• 2005

This paper, introduces a Stochastic Project Scheduling Simulation system (SPSS III) developed by the author to predict a project completion probability in a certain time. The system integrates deterministic CPM, probabilistic PERT, and stochastic Discrete Event Simulation (DES) scheduling methods into one system. It implements automated statistical analysis methods for computing the minimum number of simulation runs, the significance of the difference between independent simulations, and the confidence interval for the mean project duration as well as sensitivity analysis method in What-if analyzer component. The SPSS 111 gives the several benefits to researchers in that it (1) complements PERT and Monte Carlo simulation by using stochastic activity durations via a web based JAVA simulation over the Internet, (2) provides a way to model a project network having different probability distribution functions, (3) implements statistical analyses method which enable to produce a reliable prediction of the probability of completing a project in a specified time, and (4) allows researchers to compare the outcome of CPM, PERT and DES under different variability or skewness in the activity duration data.

• Proceedings of the KSME Conference
• /
• 2003.04a
• /
• pp.1851-1856
• /
• 2003

A new Lagrangian stochastic dispersion model is developed by combining the GLM(generalized Langevin model) and the elliptic relaxation method. Under the physically plausible assumptions a simple analytical solution of elliptic relaxation is obtained. To compare the performance of our model with other model, the statistics of particle velocity as well as concentration are investigated. Numerical simulation results show good agreement with available experimental data.