• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.555-564
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• 1997

Uncertainty, which arises when little information is revealed, can be represented by a non-additive probability, while risk is described by an additive one. This paper demonstrates that in the presence of uncertainty a steady state probability exists, which implies that we can estimate an average over a long period even under uncertainty. It is also shown that the steady state probability may not be unique in the presence of uncertainty. This implies that the estimated average under uncertainty is less accurate than under risk.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.147-156
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• 2013

Each field of modern society, industrialization and the development of science and technology are rapidly changing. However, as a side effect of rapid social change has caused various problems. Crime of the side effects of rapid social change is a big problem. In this paper, a model for predicting crime and Markov chains applied to the crime, predictive modeling is proposed. Markov chain modeling of the existing one with the overall status of the case determined the probability of predicting the future, but this paper predict the events to increase the probability of occurrence probability of the prediction and the recent state of the entire state was divided by the probability of the prediction. And the whole state and the probability of the prediction and the recent state by applying the average of the prediction probability and the probability of the prediction model were implemented. Data was applied to the incidence of crime. As a result, the entire state applies only when the probability of the prediction than the entire state and the last state is calculated by dividing the probability value. And that means when applied to predict the probability, close to the crime was concluded that prediction.

• Journal of Korean Society for Quality Management
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• v.27 no.4
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• pp.143-152
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• 1999

In Shewhart control chart, the average run length(ARL) is calculated using the mean of a conventional geometric distribution(CGD) assuming a sequence of identical and independent Bernoulli trials. In this, the success probability of CGB is the probability that any point exceeds the control limits. When the process is in-control state, there is no problem in the above assumption since the probability that any point exceeds the control limits does not change if the in-control state continues. However, if the out-of-control state begins and continues during the process, the probability of exceeding the control limits may take two forms. First, once the out-of-control state begins with exceeding probability p, it continues with the same exceeding probability p. Second, after the out-of-control state begins, the exceeding probabilities may very according to some pattern. In the first case, ARL is the mean of CGD with success probability p as usual. But in the second case, the assumption of a sequence of identical and independent Bernoulli trials is invalid and we can not use the mean of CGD as ARL. This paper concentrate on that point. By adopting one generalized binomial distribution(GBD) model that allows correlated Bernoulli trials, generalized geometric distribution(GGD) is defined and its mean is derived to find an alternative ARL when the process is in out-of-control state and the exceeding probabilities take the second form mentioned in the above. Small-scale simulation is performed to show how an alternative ARL works.

• Korean Journal of Child Studies
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• v.6 no.1
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• pp.3-16
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• 1985

The purpose of this study was the application of sequential analysis to mother-infant interaction data, with particular reference to goodness of fit. The subjects of this study were 22 7- to 16-month-old infants(12 girls and 10 boys) and their mothers. Each mother-infant dyad was videotaped in a 5-min free-play session in the playroom. The videotaped data was transcribed on the behavioral checklist every 3 seconds. The recorded raw data were lagged by one time interval (3 sec.). Transitional probabilities from behavior at time t-1 to behavior at time t were gathered. The statistical analysis of frequency data and transitional probabilities consisted of Z test, t test, and sign test. It was found that regarding 1) direction of effect: the transitional probability of infant vocalization following maternal vocalization was significantly higher than the reverse; the transitional probability of a 'Coacting State' following a 'Mother Active State' was significantly higher than the reverse; the probability of a 'Mother Active State' following 'Quiescent State' was significantly higher than that of a 'Coacting State' following an 'Infant Active State'; 2) sex differences: male infants' transitional probability from an 'Infant Active State' to a 'Quiescent State' was significantly higher than that of female infants; 3) age differences: more than younger infants older infants had higher transitional probabilities from a 'Mother Active State' to a 'Coacting State', from a 'Parallel State' to a 'Coacting State', and from a 'Quiescent State' to a 'Parallel State'. These showed goodness of fit for sex and age differences, particularily for direction of effect.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.2
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• pp.131-139
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• 1999

An easy and efficient method is proposed for a computation of reliability of preprocessing filters in the fire control system when the sensor data are frequently unreliable depending on the operation environment. It computes state transition probability matrix after modeling filter states as a Markov process, and computing false alarm and detection probability of each filter state under the given sensor failure probability. It shows that two important indices such as distributed state probability and error variance can be derived easily for a reliability assessment of the given sensor fusion system.

• Steel and Composite Structures
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• v.52 no.4
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• pp.461-473
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• 2024

This study focuses on the reliability of a transmission line under wind excitation and evaluates the failure probability using explicit data resources. The data-driven framework for calculating the failure probability of a transmission line subjected to wind loading is presented, and a probabilistic method for estimating the yearly extreme wind speeds in each wind direction is provided to compensate for the incompleteness of meteorological data. Meteorological data from the Xuwen National Weather Station are used to analyze the distribution characteristics of wind speed and wind direction, fitted with the generalized extreme value distribution. Then, the most vulnerable tower is identified to obtain the fragility curves in all wind directions based on uncertainty analysis. Finally, the failure probabilities are calculated based on the presented method. The simulation results reveal that the failure probability of the employed tower increases over time and that the joint probability distribution of the wind speed and wind direction must be considered to avoid overestimating the failure probability. Additionally, the mixed wind climates (synoptic wind and typhoon) have great influence on the estimation of structural failure probability and should be considered.

• Structural Engineering and Mechanics
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• v.47 no.1
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• pp.45-58
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• 2013

In structural reliability analysis, the response surface method is a powerful method to evaluate the probability of failure. However, the location of experimental points used to form a response surface function must be selected in a judicious way. It is necessary for the highly nonlinear limit state functions to consider the design point and the nonlinear trend of the limit state, because both of them influence the probability of failure. In this paper, in order to approximate the actual limit state more accurately, experimental points are selected close to the design point and the actual limit state, and consider the nonlinear trend of the limit state. Linear, quadratic and cubic polynomials without mixed terms are utilized to approximate the actual limit state. The direct Monte Carlo simulation on the approximated limit state is carried out to determine the probability of failure. Four examples are given to demonstrate the efficiency and the accuracy of the proposed method for both numerical and implicit limit states.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.28-37
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• 2006

State-dependent call admission control (SDCAC) is proposed to make efficient use of scarce wireless resource in a hierarchical wireless network with heterogeneous traffic. With SDCAC, new calls are accepted according to an acceptance probability taking account of not only cell dwell time but also call holding time and system state (i.e., occupied bandwidth). An analytical method is developed to calculate performance measures of interest, e.g., new call blocking probability, forced termination probability, over. all weighted blocking probability. Numerical results with not only stationary but nonstationary traffic loads are presented to show the robustness of SDCAC. It is shown that SDCAC performs much better than the other considered schemes under nonstationary traffic load.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.12
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• pp.1-8
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• 1997

In this paper, a new method of state code assignment for reduction of switching activities of state transition in asynchronous circuits is proposed. The algorithm is based on a on-hot code and modifies it to reduce switching activities. To estimate switching activities as a cost functions we introduce state transition probability (STP). AS a results, the proposed algorithm has an advantage of 60% over with the conventional code assignment in terms of switching and code length of state assignment.

• Water Engineering Research
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• v.4 no.3
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• pp.155-173
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• 2003

Parameters and quantiles of the 2-parameter generalized Pareto distribution were estimated using the methods of regular moments, modified moments, probability weighted moments, linear moments, maximum likelihood, and entropy for Monte Carlo-generated samples. The performance of these seven estimators was statistically compared, with the objective of identifying the most robust estimator. It was found that in general the methods of probability-weighted moments and L-moments performed better than the methods of maximum likelihood estimation, moments and entropy, especially for smaller values of the coefficient of variation and probability of exceedance.