• The KSFM Journal of Fluid Machinery
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• v.16 no.2
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• pp.42-47
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• 2013

Ship stability prediction is very complex in reality. In this paper, risk based approach is applied to predict the probability of a certified ship, which is effected by the forces of sea especially the wave loading. Safety assessment and risk analysis process are also applied for the probabilistic prediction of ship stability. The survival probability of ships encountering with different waves at sea is calculated by the existed statistics data and risk based models. Finally, ship capsizing probability is calculated according to single degree of freedom(SDF) rolling differential equation and basin erosion theory of nonlinear dynamics. Calculation results show that the survival probabilities of ship excited by the forces of the seas, especially in the beam seas status, can be predicted by the risk based method.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.272-287
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• 2012

The optimal use of sonar systems for detection is a practical problem in a given ocean environment. In order to quantify the mission achievability in general, measure of effectiveness(MOE) is defined for specific missions. In this paper, using the specific MOE for detection, which is represented as cumulative detection probability(CDP), an integrated software package named as Optimal Acoustic Search Path Planning(OASPP) is developed. For a given ocean environment and sonar systems, the discrete observations for detection probability(PD) are used to calculate CDP incorporating sonar and environmental parameters. Also, counter-detection probability is considered for vulnerability analysis for a given scenario. Through modeling and simulation for a simple case for which an intuitive solution is known, the developed code is verified.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.39-44
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• 2012

Water supply capacity and operational capability in agricultural reservoirs are expressed differently in the limited storage due to seasonal and local variation of precipitation. Since agricultural water supply and demand basically assumes the uncertainty of hydrological phenomena, it is necessary to improve probabilistic approach for potential risk assessment of water supply capacity in reservoir for enhanced operational storage management. Here, it was introduced the irrigation vulnerability characteristic curves to represent the water supply capacity corresponding to probability distribution of the water demand from the paddy field and water supply in agricultural reservoir. Irrigation vulnerability probability was formulated using reliability analysis method based on water supply and demand probability distribution. The lower duration of irrigation vulnerability probability defined as the time period requiring intensive water management, and it will be considered to assessment tools as a risk mitigated water supply planning in decision making with a limited reservoir storage.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.28-34
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• 2006

A procedure of estimating failure probability is demonstrated for a pressurized pipe of CrMo steel used at $538^{\circ}C$. Probabilistic fracture mechanics were employed considering variations of pressure loading, material properties and geometry. Probability density functions of major material variables were determined by statistical analyses of implemented data obtained by previous experiments. Distributions of the major variables were reflected in Monte Carlo simulation and failure probability as a function of operating time was determined. The creep crack growth life assessed by conventional deterministic approach was shown to be conservative compared with those obtained by probabilistic one. Sensitivity analysis for each input variable was also conducted to understand the most influencing variables to the residual life analysis. Internal pressure, creep crack growth coefficient and creep coefficient were more sensitive to failure probability than other variables.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.536-538
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• 2022

Since the invention of a novel diversity technique, namely a space-time line code (STLC), though the previous studies have theoretically analyzed the error rate and ergodic capacity, the outage probability has not been revealed yet. In this paper, we characterize the probability density function of the instantaneous signal-to-noise ratio, and mathematically derive the closed-form expression of the outage probability. Based on numerical simulations, furthermore, we validate the accuracy of the mathematical analysis, and present the insight into the system design and implementation.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.395-403
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• 2017

An efficient evaluation method for the probability of a tornado missile strike without using the Monte Carlo method is proposed in this paper. A major part of the proposed probability evaluation is based on numerical results computed using an in-house code, Tornado-borne missile analysis code, which enables us to evaluate the liftoff and flight behaviors of unconstrained objects on the ground driven by a tornado. Using the Tornado-borne missile analysis code, we can obtain a stochastic correlation between local wind speed and flight distance of each object, and this stochastic correlation is used to evaluate the conditional strike probability, $Q_V(r)$, of a missile located at position r, where the local wind speed is V. In contrast, the annual exceedance probability of local wind speed, which can be computed using a tornado hazard analysis code, is used to derive the probability density function, p(V). Then, we finally obtain the annual probability of tornado missile strike on a structure with the convolutional integration of product of $Q_V(r)$ and p(V) over V. The evaluation method is applied to a simple problem to qualitatively confirm the validity, and to quantitatively verify the results for two extreme cases in which an object is located just in the vicinity of or far away from the structure.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• Economic and Environmental Geology
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• v.33 no.4
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• pp.295-307
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• 2000

Probabilistic analysis is a powerful method to quantify variability and uncertainty common in engineering geology fields. In rock slope engineering, the uncertainty and variation may be in the form of scatter in orientations and geometries of discontinuities, and also test results. However, in the deterministic analysis, the factor of safety which is used to ensure stability of rock slopes, is based on the fixed representative values for each parameter without a consideration of the scattering in data. For comparison, in the probabilistic analysis, these discontinuity parameters are considered as random variables, and therefore, the reliability and probability theories are utilized to evaluate the possibility of slope failure. Therefore, in the probabilistic analysis, the factor of safety is considered as a random variable and replaced by the probability of failure to measure the level of slope stability. In this study, the stochastic properties of discontinuity parameters are evaluated and the stability of rock slope is analyzed based on the random properties of discontinuity parameters. Then, the results between the deterministic analysis and the probabilistic analysis are compared and the differences between the two analysis methods are explained.

• Journal of the Korean Society for Railway
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• v.14 no.1
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• pp.24-30
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• 2011

Conventional slope stability analysis is focused on calculating minimum factor of safety or maximum probability of failure. To minimize inherent uncertainty of soil properties and analytical model and to reflect various analytical models and its failure shape in slope stability analysis, slope stability analysis method considering simultaneous failure probability for multi failure mode was proposed. Linear programming recently introduced in system reliability analysis was used for calculation of simultaneous failure probability. System reliability analysis for various analytical models could be executed by this method. For application analysis for embankment, the results of this method shows that system stability of embankment calculate quantitatively.

• Structural Engineering and Mechanics
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• v.75 no.6
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• pp.771-784
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• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.