• Geomechanics and Engineering
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• v.36 no.6
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• pp.631-640
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• 2024

In the traditional slope stability analysis, ignoring the spatial variability of slope soil will lead to inaccurate analysis. In this paper, the K-L series expansion method is adopted to simulate random field of soil strength parameters. Based on Random Limit Equilibrium Method (RLEM), the influence of variation coefficient and fluctuation range on reliability of soil slope supported by micro-pile is investigated. The results show that the fluctuation ranges and the variation coefficients significantly influence the failure probability of soil slope supported by micro-pile. With the increase of fluctuation range of soil strength parameters, the mean safety factor of the slope increases slightly. The failure probability of the soil slope increases with the increase of fluctuation range when the mean safety factor of the slope is greater than 1. The failure probability of the slope increases by nearly 8.5% when the fluctuation range is increased from δ_v=2 m to δ_v =8 m. With the increase of the variation coefficient of soil strength parameters, the mean safety factor of the slope decreases slightly, and the probability of failure of soil slope increases accordingly. The failure probability of the slope increases by nearly 31% when the variation coefficient increases from COV_c=0.2, COV_φ=0.05 to COV_c=0.5, COV_φ=0.2.

• Geophysics and Geophysical Exploration
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• v.21 no.1
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• pp.54-60
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• 2018

The data acquired from seismic exploration can be used to detect the existence of oil and gas resources through appropriate processing and interpretation. The seismic attributes indicating the existence of resources are extracted from amplitude information, where the Q-factor representing intrinsic attenuation plays an useful role of hydrocarbon indicator. So, the accuracy of Q-factor estimation is very important to investigate the existence of resources. In this study, we calculated the Q-factor and analyzed the error rate through a numerical example. To mimic real data, random noise was added to the synthetic data. With the noise-added data, the Q-factor was estimated and the error rate was analyzed by using the spectral ratio method (SRM) and peak frequency shift method (PFSM). Both methods provided a relatively accurate Q-factor when the signal-to-noise ratio was 90 dB. However, the peak frequency shift method (PFSM) produced better results than the spectral ratio method (SRM) as the level of random noise increased.

• Journal of Astronomy and Space Sciences
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• v.29 no.2
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• pp.97-101
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• 2012

Monitoring sunspots consistently is the most basic step required to study various aspects of solar activity. To achieve this goal, the observers must regularly calculate their own correction factor $k$ and keep it stable. Relatively recently, two observing teams in South Korea have presented interesting papers which claim that revisions that take the yearly-basis $k$ into account lead to a better agreement with the international relative sunspot number $R_i$, and that yearly $k$ apparently varies with the solar cycle. In this paper, using artificial data sets we have modeled the sunspot numbers as a superposition of random noise and a slowly varying background function, and attempted to investigate whether the variation in the correction factor is coupled with the solar cycle. Regardless of the statistical distributions of the random noise, we have found the correction factor increases as sunspot numbers increase, as claimed in the reports mentioned above. The degree of dependence of correction factor $k$ on the sunspot number is subject to the signal-to-noise ratio. Therefore, we conclude that apparent dependence of the value of the correction factor $k$ on the phase of the solar cycle is not due to a physical property, but a statistical property of the data.

• Journal of Mechanical Science and Technology
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• v.17 no.7
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• pp.966-977
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• 2003

This paper investigates how intentional mistuning of bladed disks reduces their sensitivity to unintentional random mistuning. The class of intentionally mistuned disks considered here is limited, for cost reasons, to arrangements of two types of blades (A and B, say). A two-step procedure is then described to optimize the arrangement of these blades around the disk to reduce the effects of unintentional random mistuning. First, a pure optimization effort is undertaken to obtain the pattern (s) of the A and B blades that yields small/the smallest value of the largest amplitude of response to a given excitation in the absence of unintentional random mistuning using Genetic Algorithm. Then, in the second step, a qualitative/quantitative estimate of the sensitivity for the optimized intentionally mistuned bladed disks with respect to unintentional random mistuning is performed by analyzing their amplification factor, probability density function and passband/stopband structures. Examples of application with simple bladed disk models demonstrate the significant benefits of using this class of intentionally mistuned disks.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1993.10a
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• pp.55-59
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• 1993

Interaction between system and disturbance results in system with time-dependent parameter. Parameter variation due to interaction has random characteristics. Most of the randomly varying parameters in control problem is regarded as white noise random process, which is not a realistic model. In real situation those random variation is colored noise random process. Modified F-P-K equation is proposed to get the response of the random parametric system using some correction factor. Proposed technique is employed to obtain the colored noise parametric system response and confirmed via Monte-Carlo Simulation.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.115-123
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• 1994

Under variable amplitude loading conditions, retardation or accelerated condition of fatigue crack growth occurs with every cycle, Because fatigue crack growth behavior varied depend on load time history. The modeling of stress amplitude with storm loading acted to ships and offshore structures applied this paper. The crack closure behavior examine by recording the variation in load-strain relationship. By taking process mentioned above, fatigue crack growth rate, crack length, stress intensity factor, and crack closure stress intensity factor were obtained from the stress cycles of each type of storm ; A(6m), B(7m), C(8m), D(9m), E(11m) and F(15m) which was wave height. It showed that the good agreement with between the experiment results and simulation of storm loads. So this estimated method of crack propagtion rate gives a good criterion for the safe design of vessels and marine structure.

• Journal of The Korean Astronomical Society
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• v.25 no.2
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• pp.79-90
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• 1992

The physical properties of the flux factor K and its application are discussed in connection to the high amplification events. The effects due to random motions of stars in a foreground galaxy are examined in the frame work of moving caustics of gravitational microlenses.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.425-431
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• 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River's branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, $NH_3-N$, T-N, $PO_4-P$, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, $R^2$, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.05a
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• pp.211-216
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• 2002

This paper focuses on the measurement of increased work efficiency expected from the information system through random interactions of the organizational behavioral factors whose attributes can be changed with the implementation of the information systems. Specifically the work reported here is concerned with modeling and analyzing the random interrelationships among the organizational behavioral factors which an information system will have impact on throughout the time horizon of its implementation in terms of office productivity. In addition, it is also concerned with developing a multi-factor analysis model based on random interactions to be used to assess the impact of information systems.