• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.147-150
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• 1996

A homing torpedo's performance can be expressed a function of many variables, i.e. technical and tactical variables. When designing a homing torpedo, these variables have to be decided upon. The system effectiveness of a homing torpedo can be determined by analyzing of these variables. This paper describes a procedure of simulation metamodelling using a Factor Analysis methodology. A simulation model was used in order to obtain the data base for analyzing detection probability of torpedo. By analyzing the main and interaction effects these variables on the analysis of detection probability, we will show the importance of certain variables, of a homing torpedo.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.10-15
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• 2009

In this paper, we propose new methods which is to determine level of noise factor. Even Taguchi give level of noise factor which is best(or maximum) and worst(or minimum) condition, we give level of noise factor which is representative value by observing noise factor frequency. Sometimes level of noise factor is given one, two and three. We know this method is more fit in real fields.

• Journal of the Korean Society of Safety
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• v.22 no.1 s.79
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• pp.61-66
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• 2007

Car crashes are the leading cause of death for persons of every age. Specially, human-related factor has been known to be the primary causal factor of such crashes than vehicle-and environmental-related factors. There are various studies to analyze driver's behavior and characteristics in driving for reducing the car crashes in many areas of car engineering, psychology, human factor, etc. However, there are almost no studies which analyze mainly the human errors in driving and estimate their probabilities in terms of human reliability analysis. This study estimates the probability of human error in driving, i.e. driver error probability. First, fifty driver errors are investigated through DBQ (Driver Behavior Questionnaire) revision and the error likelihoods in driving are collected which are judged by skillful drivers using revised DBQ. Next, these likelihoods are converted into driver error probabilities using the results that verbal probabilistic expressions are changed into quantitative probabilities. Using these probabilities we can improve the warning effects on drivers by indicating their driving error likelihoods quantitatively. We can also expect the reduction effects of car accident through controlling especially dangerous error groups which have higher probabilities. Like these, the results of this study can be used as the primary materials of safety education on drivers.

• Geotechnical Engineering
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• v.1 no.1
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• pp.47-58
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• 1985

A new approach is develped to analyze the reliability of the shallow foundation. The measure of the safety of the structhure is expressed In terms of the probability of failure, instead of the conventional factor of safety. Many uncertainties involved in the deterministic stability anaitsis can be reasouably treated by using the probabilistic approach. Both the soil properties and loads are assumed to be random variables. Accordingly, the capacity and demand are considered to be normal, log-normal, and beta variated. Use is made of Error Propagation Method to investigate the probability of failure. And the relationship is investigated between the probability of failure and the central factor of safety. The results are computer programed and several case studies are performed using developed program.

• Geomechanics and Engineering
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• v.15 no.3
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• pp.879-888
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• 2018

Probability-based design codes have been developed to sufficiently confirm the safety level of structures. One of the most acceptable probability-based approaches is Load Resistance Factor Design (LRFD), which measures the safety level of the structures in terms of the reliability index. The main contribution of this paper is to calibrate the load and resistance factors of the design code for tunnels. The load and resistance factors are calculated using the available statistical models and probability-based procedures. The major steps include selection of representative structures, consideration of the limit state functions, calculation of reliability for the selected structures, selection of the target reliability index and calculation of load factors and resistance factors. The load and resistance models are reviewed. Statistical models of resistance (load carrying capacity) are summarized for strength limit state in bending, shear and compression. The reliability indices are calculated for several segments of a selected circular tunnel designed according to the tunnel manual report (Tunnel Manual). The novelty of this paper is the selection of the target reliability. In doing so, the uniform spectrum of reliability indices is proposed based on the probability paper. The final recommendation is proposed based on the closeness to the target reliability index.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.453-461
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• 1999

The estimation of fatigue life at the design stage is very important in order to arrive at feasible and cost effective solutions considering the total lifetime of the structure and machinery compo-nents. In this study the practical procedure of prediction of fatigue life by use of cumulative damage factors based on Miner-Palmgren hypothesis and probability density function is shown with a $135,000m^3$ LNG tank being used as an example. In particular the parameters of Weibull distribution taht determine the stress spectrum are dis-cussed. At the end some of uncertainties associated with fatigue life prediction are discussed. The main results obtained from this study are as follows: 1. The practical procedure of prediction of fatigue life by use of cumulative damage factors expressed in combination of probability density function and S-N data is proposed. 2. The calculated fatigue life is influenced by the shape parameter and stress block. The conser-vative fatigue design can be achieved when using higher value of shape parameter and the stress blocks divded into more stress blocks.

• Geomechanics and Engineering
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• v.5 no.5
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• pp.485-497
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• 2013

The factor of safety is the most common measure of the safety margin for slopes. When the traditionally defined factor is used in kinematic approach of limit analysis, calculations can become elaborate, and iterative methods have to be used. To avoid this inconvenience, the safety factor was defined in terms of the work rates that are part of the work balance equation used in limit analysis. It was demonstrated for two simple slopes that the safety factors calculated according to the new definition fall close to those calculated using the traditional definition. Statistical analysis was carried out to find out whether, given normal distribution of the strength parameters, the distribution of the safety factor can be approximated with a well-defined probability density function. Knowing this function would make it convenient to calculate the probability of failure. The results indicated that the normal distribution could be used for low internal friction angle (up to about $16^{\circ}$) and the Johnson distribution could be used for larger angles ${\phi}$. The data limited to two simple slopes, however, does not allow assuming these distributions a priori for other slopes.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.427-436
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• 2000

In this paper, we consider a Bayesian model selection problem of lifetime distributions using fractional Bayes factor with noninformative prior when type II censored data are given. For a given type II censored data, we calculate the posterior probability of exponential, Weibull and lognormal distributions and select the model which gives the highest posterior probability. Our proposed methodology is explained and applied to real data and simulated data.

• The Journal of Engineering Geology
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• v.20 no.2
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• pp.155-167
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• 2010

In analysis of slope stability, deterministic analysis which yields a factor of safety has been used until recently. However, probability of failure is considered as a more efficient method because it deals with the uncertainty and variability of rock mass. In both methods, a factor of safety or a probability of failure is calculated for a slope although characteristics of rock mass, such as characteristics of joints, weathering degree of rock and so on, are not uniform throughout the slope. In this paper, we divided a model slope into several zones depending on conditions of rock mass and joints, and probabilities of failure in each zone are calculated and compared with that calculated in whole slope. The persistence of joint was also used as a parameter in calculation of probability of failure. A rock slope located in Hongcheon, Gangwondo was selected and the probability of failure using zoning and persistence as parameter was calculated to confirm the applicability of model analysis.

• Journal of Korean Society of Transportation
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• v.24 no.6 s.92
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• pp.33-43
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• 2006

This research is to the selection of optimal probability distribution as well as the estimation for design hourly factor in consideration of traffic characteristic, such as road function, lane number and AADT. To accomplish the objectives, we are applied to various probability distribution using traffic data that observed at permanent traffic count points in 2005. The parameters or the selected 14 probability distribution were estimated based on the method of maximum likelihood and the validity condition of the estimated parameter The goodness-of-fit test, such as chi-square test. was performed as well as the estimation of design hourly factor. As a result, An appropriate distributions of each case were selected : Pearson V for two lane of rural roads, LogLogistic for the four lane of rural roads, LogLogistic for the urban roads, Extreme value for recreation roads. And optimal K factor are as following : $0.1{\sim}0.2 $ for two lane of rural roads, $0.09{\sim}0.14$ for the four lane of rural roads. $0.07{\sim}0.13$ for the urban roads, $0.1{\sim}0.2$ for recreation roads.