• Title, Summary, Keyword: probability model

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Probability-Based Context-Generation Model with Situation Propagation Network (상황 전파 네트워크를 이용한 확률기반 상황생성 모델)

  • Cheon, Seong-Pyo;Kim, Sung-Shin
    • The Journal of Korea Robotics Society
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    • v.4 no.1
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    • pp.56-61
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    • 2009
  • A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

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A 3D analytical model for the probabilistic characteristics of self-healing model for concrete using spherical microcapsule

  • Zhu, Hehua;Zhou, Shuai;Yan, Zhiguo;Ju, Woody;Chen, Qing
    • Computers and Concrete
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    • v.15 no.1
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    • pp.37-54
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    • 2015
  • In general, cracks significantly deteriorate the in-situ performance of concrete members and structures, especially in urban metro tunnels that have been embedded in saturated soft soils. The microcapsule self-healing method is a newly developed healing method for repairing cracked concrete. To investigate the optimal microcapsule parameters that will have the best healing effect in concrete, a 3D analytical probability healing model is proposed; it is based on the microcapsule self-healing method's healing mechanism, and its purpose is to predict the healing efficiency and healing probability of given cracks. The proposed model comprehensively considers the radius and the volume fraction of microcapsules, the expected healing efficiency, the parameters of cracks, the broken ratio and the healing probability. Furthermore, a simplified probability healing model is proposed to facilitate the calculation. Then, a Monte Carlo test is conducted to verify the proposed 3D analytical probability healing model. Finally, the influences of microcapsules' parameters on the healing efficiency and the healing probability of the microcapsule self-healing method are examined in light of the proposed probability model.

Effect of Boundary Conditions of Failure Pressure Models on Reliability Estimation of Buried Pipelines

  • Lee, Ouk-Sub;Pyun, Jang-Sik;Kim, Dong-Hyeok
    • International Journal of Precision Engineering and Manufacturing
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    • v.4 no.6
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    • pp.12-19
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    • 2003
  • This paper presents the effect of boundary conditions in various failure pressure models published for the estimation of failure pressure. Furthermore, this approach is extended to the failure prediction with the aid of a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with each corrosion defect in buried pipelines for long exposure period with unit of years. A failure probability model based on the von-Mises failure criterion is adapted. The log-normal and standard normal probability functions for varying random variables are adapted. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically investigated for the corrosion pipeline by using an adapted failure probability model and varying failure pressure model.

Estimation of Failure Probability Using Boundary Conditions of Failure Pressure Model for Buried Pipelines (파손압력모델의 경계조건을 이용한 매설배관의 파손확률 평가)

  • Lee, Ouk-Sub;Kim, Eui-Sang;Kim, Dong-Hyeok
    • Proceedings of the KSME Conference
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    • pp.310-315
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    • 2003
  • This paper presents the effect of boundary condition of failure pressure model for buried pipelines on failure prediction by using a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with various corrosion defects for long exposure periods in years. A failure pressure model based on a failure function composed of failure pressure and operation pressure is adopted for the assessment of pipeline failure. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically studied by using a failure probability model for the corrosion pipeline.

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Ruin Probability in a Compound Poisson Risk Model with a Two-Step Premium Rule (이단계 보험요율의 복합 포아송 위험 모형의 파산 확률)

  • Song, Mi-Jung;Lee, Ji-Yeon
    • Communications for Statistical Applications and Methods
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    • v.18 no.4
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    • pp.433-443
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    • 2011
  • We consider a compound Poisson risk model in which the premiums may depend on the state of the surplus process. By using the overflow probability of the workload process in the corresponding M/G/1 queueing model, we obtain the probability that the ruin occurs before the surplus reaches a given large value in the risk model. We also examplify the ruin probability in case of exponential claims.

ON THE PROBABILITY OF RUIN IN A CONTINUOUS RISK MODEL WITH DELAYED CLAIMS

  • Zou, Wei;Xie, Jie-Hua
    • Journal of the Korean Mathematical Society
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    • v.50 no.1
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    • pp.111-125
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    • 2013
  • In this paper, we consider a continuous time risk model involving two types of dependent claims, namely main claims and by-claims. The by-claim is induced by the main claim and the occurrence of by-claim may be delayed depending on associated main claim amount. Using Rouch$\acute{e}$'s theorem, we first derive the closed-form solution for the Laplace transform of the survival probability in the dependent risk model from an integro-differential equations system. Then, using the Laplace transform, we derive a defective renewal equation satisfied by the survival probability. For the exponential claim sizes, we present the explicit formula for the survival probability. We also illustrate the influence of the model parameters in the dependent risk model on the survival probability by numerical examples.

The Effect Analysis of Missile Warning Radar Using Probability Model (확률 모델을 이용한 미사일 경고 레이다의 효과도 분석)

  • Park, Gyu-Churl;Hong, Sung-Yong
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.20 no.6
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    • pp.544-550
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    • 2009
  • To analyze the threat decision performance of MWR(Missile Warning Radar) give analysis on condition that we decide the effective threat using the POC(Probability of Over Countermeasure)/PUC(Probability of Under Countermeasure). Thus, we execute the simulation using the Monte-Carlo method to analyze effect, but the execution time of simulation took longer than we expected. In this paper, the effect analysis is proposed using the probability model to reduce the execution time of simulation. We present the setting method of parameter for probability model and the effect analysis result of MWR using the simulation. Also, we present the comparison result of simulation execution time for Monte-Carlo and probability model.

A Compound Poisson Risk Model with a Two-Step Premium Rule

  • Song, Mi Jung;Lee, Jiyeon
    • Communications for Statistical Applications and Methods
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    • v.20 no.5
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    • pp.377-385
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    • 2013
  • We consider a compound Poisson risk model in which the premium rate changes when the surplus exceeds a threshold. The explicit form of the ruin probability for the risk model is obtained by deriving and using the overflow probability of the workload process in the corresponding M/G/1 queueing model.

Estimating Suitable Probability Distribution Function for Multimodal Traffic Distribution Function

  • Yoo, Sang-Lok;Jeong, Jae-Yong;Yim, Jeong-Bin
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.21 no.3
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    • pp.253-258
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    • 2015
  • The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

Reliability Estimation of Buried Gas Pipelines in terms of Various Types of Random Variable Distribution

  • Lee Ouk Sub;Kim Dong Hyeok
    • Journal of Mechanical Science and Technology
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    • v.19 no.6
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    • pp.1280-1289
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    • 2005
  • This paper presents the effects of corrosion environments of failure pressure model for buried pipelines on failure prediction by using a failure probability. The FORM (first order reliability method) is used in order to estimate the failure probability in the buried pipelines with corrosion defects. The effects of varying distribution types of random variables such as normal, lognormal and Weibull distributions on the failure probability of buried pipelines are systematically investigated. It is found that the failure probability for the MB31G model is larger than that for the B31G model. And the failure probability is estimated as the largest for the Weibull distribution and the smallest for the normal distribution. The effect of data scattering in corrosion environments on failure probability is also investigated and it is recognized that the scattering of wall thickness and yield strength of pipeline affects the failure probability significantly. The normalized margin is defined and estimated. Furthermore, the normalized margin is used to predict the failure probability using the fitting lines between failure probability and normalized margin.