• Proceedings of the Korean Society for Rock Mechanics Conference
• /
• 2011.09a
• /
• pp.89-97
• /
• 2011

Brittle failure of rock is a classical rock mechanics problem. Rock failure not only involves initiation and propagation of single crack, but also is a complex problem associated with initiation, propagation and coalescence of many cracks. As the most important feature of rock material properties is the heterogeneity, the Weibull statistical distribution is employed in the rock failure process analysis (RFPA) method to describe the heterogeneity in rock properties. In this paper, the applications of the RFPA method in geotechnical engineering and rockburst modeling are introduced with emphasis, which can provide some references for relevant researches.

• The Korean Journal of Applied Statistics
• /
• v.22 no.1
• /
• pp.205-220
• /
• 2009

For modeling skewed semicircular data, we derive new family of the exponential distributions. We extend it to the l-axial exponential distribution by a transformation for modeling any arc of arbitrary length. It is straightforward to generate samples from the f-axial exponential distribution. Asymptotic result reveals two things. The first is that linear exponential distribution can be used to approximate the l-axial exponential distribution. The second is that the l-axial exponential distribution has the asymptotic memoryless property though it doesn't have strict memoryless property. Some trigonometric moments are also derived in closed forms. Maximum likelihood estimation is adopted to estimate model parameters. Some hypotheses tests and confidence intervals are also developed. The Kolmogorov-Smirnov test is adopted for goodness of fit test of the l-axial exponential distribution. We finally obtain a bivariate version of two kinds of the l-axial exponential distributions.

• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1125-1145
• /
• 2010

For modeling(skewed) semicircular data, we derive a new exponential family of distributions. We extend it to the l-axial exponential family of distributions by a projection for modeling any arc of arbitrary length. It is straightforward to generate samples from the l-axial exponential family of distributions. Asymptotic result reveals that the linear exponential family of distributions can be used to approximate the l-axial exponential family of distributions. Some trigonometric moments are also derived in closed forms. The maximum likelihood estimation is adopted to estimate model parameters. Some hypotheses tests and confidence intervals are also developed. The Kolmogorov-Smirnov test is adopted for a goodness of t test of the l-axial exponential family of distributions. Samples of orientations are used to demonstrate the proposed model.

• Environmental Engineering Research
• /
• v.14 no.1
• /
• pp.13-18
• /
• 2009

In this study, specific pollutant releases during the Asian monsoon season were estimated and the information was applied to the non-point pollutant sources management from two forested watersheds of the Soyang Lake. The two watersheds are part of the 2,703 km2 Soyang Lake watershed in the northern region of the Han River. The outlets of the two watersheds were respectively analyzed for continuous water quality concentration and for discharge during various single rainfall events. Statistical power function methods are utilized to compare stream discharge and pollutant flux release during the study period. Based on the monitoring data during the study period, the specific load flux method using simulated discharge was conducted and validated in the two watersheds. The model predictions corresponded well with the measured and calculated pollutant releases. The modeling approach taken in this study was found to be applicable for the two forested watersheds.

• Advances in Computational Design
• /
• v.5 no.3
• /
• pp.323-347
• /
• 2020

The enhancements of bioenergy production effectiveness require the comprehensively experimental study of several parameters affecting these bioprocesses. The interpretation of the obtained experimental results and the estimation of optimum yield are extremely complicated such as misinterpreting the results of an experiment. The use of mathematical modeling and statistical experimental designs can consistently supply the predictions of the potential yield and the identification of defining parameters and also the understanding of key relationships between factors and responses. This paper summarizes several mathematical models used to achieve an adequate overall and maximal production yield and rate, to screen, to optimize, to identify, to describe and to provide useful information for the effect of several factors on bioenergy production processes. The usefulness, the validity and, the feasibility of each strategy for studying and optimizing the bioenergy-producing processes were discussed and confirmed by the good correlation between predicted and measured values.

• Journal of Mechanical Science and Technology
• /
• v.20 no.3
• /
• pp.391-398
• /
• 2006

This work involves a method for modeling the flow distribution in the stack of a solid oxide fuel cell. Towards this end, a three dimensional modeling of the flow through a Solid Oxide Fuel Cell (SOFC) stack was carried out using the CFD analysis. This paper examines the efficacy of using cold flow analysis to describe the flow through a SOFC stack. It brings out the relative importance of temperature effect and the mass transfer effect on the SOFC manifold design. Another feature of this study is to utilize statistical tools to ascertain the extent of uniform flow through a stack. The results showed that the cold flow analysis of flow through SOFC might not lead to correct manifold designs. The results of the numerical calculations also indicated that the mass transfer across membrane was essential to correctly describe the cathode flow, while only temperature effects were sufficient to describe the anode flow in a SOFC.

• Journal of Korea Society of Industrial Information Systems
• /
• v.12 no.3
• /
• pp.10-18
• /
• 2007

According to the recent growth of the internet commercialization and differentiated QoS(quality of service), statistical traffic modeling is necessary for forecasting and controlling future network capacity. This paper reviews tile essential components in web workloads. And I propose adjusted internet traffic modeling using heavy-tailed distributions and intervention techniques.

• Bulletin of the Korean Chemical Society
• /
• v.20 no.5
• /
• pp.525-527
• /
• 1999

Many types of chemical data tend to be characterized by many measured variables on each of a few observations. In this situation, target concentration can be predicted using multivariate statistical modeling. However, it is necessary to use a few variables considering size and cost of instrumentation, for an example, for development of a portable biomedical instrument. This study presents, with a spectral data set of total hemoglobin in whole blood, the possibility that modeling using only a few variables can improve predictability compared to modeling using all of the variables. Predictability from the model using three wavelengths selected from all possible regression method was improved, compared to the model using whole spectra (whole spectra: SEP = 0.4 g/dL, 3-wavelengths: SEP=0.3 g/dL). It appears that the proper selection of variables can be more effective than using whole spectra for determining the hemoglobin concentration in whole blood.

• Journal of Sensor Science and Technology
• /
• v.32 no.6
• /
• pp.475-480
• /
• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Proceedings of the Korean Nuclear Society Conference
• /
• 2005.05a
• /
• pp.47-48
• /
• 2005