• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.387-389
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• 2013

In order to determine the collision risk, the probability estimation is very important part for accurate risk estimation. Recently, the collision risk at the Busan North Port is studied for making the risk map by authors. The result has been found some connections with previous collision places. For more precise estimation, the probability calculation is necessary. Recently the Bayesian matrix is mainly used for calculating the probabilities. Also considering the oil spill risk with tankers, ships' speed, relative angle and ships' size are key aspect whether breaking the double hull or not. This research presents the way of estimating the probabilities not her research and also the collision risk probability considerations for small divided areas.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.321-327
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• 2011

Bootstrap methods is the computer-based resampling method that estimates the standard errors and confidence intervals of summary statistics using the plug-in principle for assessing the accuracy or uncertainty of statistical estimates, and the BCa method among the Bootstrap methods is known much superior to other Bootstrap methods in respect of the standards of statistical validation. Therefore this study suggests the method of the representation and treatment of uncertainty in flood risk assessment and water resources planning from the construction and application of rainfall frequency analysis model considersing the uncertainty based on the nonparametric BCa method among the Bootstrap methods for the assessement of the estimation of probability rainfall and the effect of uncertainty considering the uncertainty of the parameter estimation of probability in the rainfall frequency analysis that is the most fundamental in flood risk assessement and water resources planning.

• ETRI Journal
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• v.41 no.5
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• pp.658-669
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• 2019

For dual-channel time-frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single-source points (TF-SSP) is used to improve the TF sparsity of each source. For more distinguishability, as the ratios of the coefficients from different columns of the mixing matrix are close, a local peak-detection mechanism based on interval probability (LPIP) is proposed. LPIP utilizes uniform subintervals to optimize and classify the TF coefficient ratios of the detected TF-SSP effectively in the case of a high level of TF overlap among sources and reduces the TF interference points and redundant signal features greatly to enhance the estimation accuracy. The simulation results show that under both noiseless and noisy cases, the proposed method performs better than the selected mainstream traditional methods, has good robustness, and has low algorithm complexity.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1762-1766
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• 2006

In order to protect properties and human lives from disasters such as heavy rainfall, rational Probability Maximum Flood(PMF) estimation procedures for existing dam basins are recently required. This study analyzes the Probable Maximum Flood(PMF) as a part of a counterplan for disaster preventions of hydraulic structures such as dams, according to recent unfavorable weather conditions. In this study, an improvement method of parameter estimation was proposed, being estimated as an appropriate method for application to the unit hydrograph, the time of concentration and storage constant corresponding to the discharge of flood were considered differently when estimating PMF in Hoengseong dam basin.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.109-118
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• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

• Proceedings of the KSME Conference
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• 2003.04a
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.232-239
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• 2006

In this paper, we analyze the preamble model for Wireless PAN(WPAN) in proposed Ultra WideBand(UWB) Multi-Band OFDM(MB-OFDM) system of IEEE 802.15.3a standard. Besides, we propose effective Carrier Frequency Offset and Symbol Timing Offset Estimation algorithm which offers enhanced performance, and analyze its performance using Detection Probability, False Alarm Probability, Missing Probability, Mean Acquisition Time and MSE(Mean Square Error) through simulation in AWGN and UWB channel environments.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.4
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• pp.163-169
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• 1984

We propose a new parameter estimation algorithm that converges with probability one and in mean square, if the mean is the function of parameter of the probability density function. This recursive algorithm is applicable also even though the parameters we estimate are multiparameter case. And the results are shown by the computer simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.589-595
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• 2011

This paper proposes an improved multi-stage timing offset estimation scheme for orthogonal frequency division multiplexing (OFDM) systems in multipath fading channel environment. The conventional multi-stage timing offset estimation scheme is very sensitive to the random multipath components. By exploiting the sample standard deviation of the cross-correlation values, the proposed scheme achieves a robustness to the random multipath components. Simulation results demonstrate that the proposed scheme has a higher correct estimation probability and has a better mean square error (MSE) performance than the conventional scheme in multipath fading channels.