• Journal of Korean Society on Water Environment
• /
• v.25 no.3
• /
• pp.425-431
• /
• 2009

It is very important to know the probability distribution of water-quality constituents for water-quality control and management of rivers and reservoirs effectively. The probability distribution of BOD in Anseong Stream was analyzed in this paper using Kolmogorov-Smirnov test which is widely used goodness-of-fit method. It was known that the distribution of BOD in Anseong Stream is closer to Log-normal, Gamma and Weibull distributions than Normal distribution. Normal distribution can be partially applied depending on significance level, but Log-normal, Gamma and Weibull distributions can be used in any significance level. Also the estimated Log-normal distribution of BOD at Jinwi3 station was to be compared with the measured in 2001, 2002 and 2003 years. It was revealed that the estimated probability distribution of BOD at Jinwi3 follows a theoretical distribution very well. The applicable probability distribution of BOD can be used to explain more rigorously and scientifically the achievement or violation of target concentration in TMDL(Total Maximum Daily Load).

• Geomechanics and Engineering
• /
• v.21 no.3
• /
• pp.289-300
• /
• 2020

Estimating the damage induced by an explosion around a blast hole has always been a challenging issue in geotechnical engineering. It is difficult to determine an exact dimension for damage zone since many parameters are involved in the formation of failures, and there are some uncertainties lying in these parameters. Thus, the present study adopted a probabilistic approach towards this problem. First, a reliability model of the problem was established and the failure probability of induced damage was calculated. Then, the corresponding exceedance risk curve was developed indicating the relation between the failure probability and the cracked zone radius. The obtained risk curve indicated that the failure probability drops dramatically by increasing the cracked zone radius so that the probability of exceedance for any crack length greater than 4.5 m is less than 5%. Moreover, the effect of each parameter involved in the probability of failure, including blast hole radius, explosive density, detonation velocity, and tensile strength of the rock, was evaluated by using a sensitivity analysis. Finally, the impact of the decoupling ratio on the reduction of failures was investigated and the location of its maximum influence was demonstrated around the blast point.

• Journal of Environmental Science International
• /
• v.28 no.1
• /
• pp.19-35
• /
• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.6
• /
• pp.1122-1129
• /
• 2007

For a system with multiple real-time tasks of different deadlines, it is very difficult to find the optimal checkpoint interval because of the complexity in considering the scheduling of tasks. In this paper, we determine the optimal checkpoint interval for multiple real-time tasks that are scheduled by RM(Rate Monotonic) algorithm. Faults are assumed to occur with Poisson distribution. Checkpoints are inserted in the execution of task with equal distance in the same task, but different distances in other tasks. When faults occur, rollback to the latest checkpoint and re-execute task after the checkpoint. We derive the equation of maximum slack time for each task, and determine the number of re-executable checkpoint intervals for fault recovery. The equation to check the schedulibility of tasks is also derived. Based on these equations, we find the probability of all tasks executed within their deadlines successfully. Checkpoint intervals which make the probability maximum is the optimal.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.19 no.3
• /
• pp.93-102
• /
• 2015

This study evaluates collapse probabilities of structures which are designed according to a domestic seismic design code, KBC2009. In evaluating their collapse probabilities, to do this, probabilistic distribution models for seismic hazard and structural capacity are required. In this paper, eight major cities in Korea are selected and the demand probabilistic distribution of each city is obtained from the uniform seismic hazard. The probabilistic distribution for the structural capacity is assumed to follow a underlying design philosophy implicitly defined in ASCE 7-10. With the assumptions, the structural collapse probability in 50 years is evaluated based on the concept of a risk integral. This paper then defines an mean value of the collapse probabilities in 50 years of the selected major cities as the target risk. Risk-targeted spectral accelerations are finally suggested by modifying a current mapped spectral acceleration to meet the target risk.

• Wind and Structures
• /
• v.23 no.4
• /
• pp.351-366
• /
• 2016

Nowadays, wind energy is the most rapidly developing technology and energy source and it is reusable. Due to its cleanliness and reusability, there have been rapid developments made on transferring the wind energy systems to electric energy systems. Converting the wind energy to electrical energy can be done only with the wind turbines. So installing a wind turbine depends on the wind speed at that location. The expected wind power can be estimated using a perfect probability distribution. In this paper Weibull and Weibull distribution with multiple parameters has been used in deriving the mathematical expression for estimating the wind power. Statistically the parameters of Weibull and Weibull distribution are estimated using the maximum likelihood techniques. We derive a probability distribution for the power output of a wind turbine with given rated wind speeds for the regions where the wind speed histograms present a bimodal pdf and compute the first order moment of this distribution.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1988.10a
• /
• pp.7-12
• /
• 1988

This study presents rational methods for probability-based estimates of basic design wind speeds in Korea and develops a risk-bases nation-wide map of design wind speeds. The paper examines the fitting of the Type-I extreme model to maximum yearly non-typhoon wind data from long-term records based on the conventional method and to maximum monthly nod-typhoon wind data from short-term records following Grigorin's approach. The paper also reviews the applicability of the method using short records of about 5 years. The basic design wind speeds for typhoon and non-typhoon wind at a station are made to be obtained from a mixed model which is given as a product of typhoon and non-typhoon extreme wind distributions. A practical method which is based on the fitting of the Type I model to records or typhoon and non-typhoon mixed wind data at a station is also preposed in this study.

• Economic and Environmental Geology
• /
• v.28 no.4
• /
• pp.433-438
• /
• 1995

The most popular minimization method is based on the least-squares criterion, which uses the $L_2$ norm to quantify the misfit between observed and synthetic data. The solution of the least-squares problem is the maximum likelihood point of a probability density containing data with Gaussian uncertainties. The distribution of errors in the geophysical data is, however, seldom Gaussian. Using the $L_2$ norm, large and sparsely distributed errors adversely affect the solution, and the estimated model parameters may even be completely unphysical. On the other hand, the least-absolute-deviation optimization, which is based on the $L_1$ norm, has much more robust statistical properties in the presence of noise. The solution of the $L_1$ problem is the maximum likelihood point of a probability density containing data with longer-tailed errors than the Gaussian distribution. Thus, the $L_1$ norm gives more reliable estimates when a small number of large errors contaminate the data. The effect of outliers is further reduced by M-fitting method with Cauchy error criterion, which can be performed by iteratively reweighted least-squares method.

• Journal of the Korean Statistical Society
• /
• v.22 no.1
• /
• pp.71-81
• /
• 1993

Among k independent normal populations with unknown means and a common unknown variance, the problem of detecting the population with the largest absolute value of mean is considered. This problem is formulated in a manner close to the framework of testing hypotheses, and the maximum error probability and the minimum power are considered. The power charts necessary to determine the sample size are provided. The problem of detecting the population with the smallest absolute value of mean is also considered.

• Speech Sciences
• /
• v.11 no.4
• /
• pp.43-52
• /
• 2004

Model parameters in HMM based speech recognition systems are normally estimated using Maximum Likelihood Estimation(MLE). The MLE method is based mainly on the principle of statistical data fitting in terms of increasing the HMM likelihood. The optimality of this training criterion is conditioned on the availability of infinite amount of training data and the correct choice of model. However, in practice, neither of these conditions is satisfied. In this paper, we propose a training algorithm, MCE(Minimum Classification Error), to improve the performance of a speech recognizer detecting mispronunciation of a foreign language. During the conventional MLE(Maximum Likelihood Estimation) training, the model parameters are adjusted to increase the likelihood of the word strings corresponding to the training utterances without taking account of the probability of other possible word strings. In contrast to MLE, the MCE training scheme takes account of possible competing word hypotheses and tries to reduce the probability of incorrect hypotheses. The discriminant training method using MCE shows better recognition results than the MLE method does.