• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.29-34
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• 1997

The prediction of detection range of a passive sonar system is essential to estimate the performance and to optimize the operation of a developed sonar system. In this paper, a model for the prediction of detection range in a range-dependent ocean environment based on the sonar equation is developed and tested. The prediction model calculates the transmission loss using PE propagation model, signal excess, and the detection probability at each target depth and range. The detection probability is integrated to give the estimated detection range. In order to validate the developed model, two cases are considered. One is the case when target depth is known. The other is the case when the target depth is unknown. The computational results agree well with the previously published results for the range-independent environment. Also,the developed model is applied to the range-dependent ocean environment where the warm eddy exists. The computational results are shown and discussed. The developed model can be used to find the optimal frequency of detection, as well as the optimal search depth for the given range-dependent ocean environment.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.454-464
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• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

• Journal of Soil and Groundwater Environment
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• v.21 no.3
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• pp.21-34
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• 2016

In the present study, we applied various machine learning techniques comparatively for prediction of subsurface structures based on multiple secondary information (i.e., well-logging data). The machine learning techniques employed in this study are Naive Bayes classification (NB), artificial neural network (ANN), support vector machine (SVM) and logistic regression classification (LR). As an alternative model, conventional hidden Markov model (HMM) and modified hidden Markov model (mHMM) are used where additional information of transition probability between primary properties is incorporated in the predictions. In the comparisons, 16 boreholes consisted with four different materials are synthesized, which show directional non-stationarity in upward and downward directions. Futhermore, two types of the secondary information that is statistically related to each material are generated. From the comparative analysis with various case studies, the accuracies of the techniques become degenerated with inclusion of additive errors and small amount of the training data. For HMM predictions, the conventional HMM shows the similar accuracies with the models that does not relies on transition probability. However, the mHMM consistently shows the highest prediction accuracy among the test cases, which can be attributed to the consideration of geological nature in the training of the model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.11
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• pp.18-24
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• 2009

This paper proposes two novel prediction-based adaptive selection cooperation schemes combined with a new relay selection strategy. In the proposed schemes, the destination predicts whether the transmission will be successful or not before a single relay is selected to transmit source's decoded data. Depending on the prediction, the destination feeds back a command to the whole network. Numerical results show that the proposed schemes combined with the relay selection strategy successfully reduce its outage probability, improve its throughput, save transmitted power, and prolong the lifetime of the network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.389-391
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• 2021

Traffic regulations are expanding to prevent traffic accidents for people's safety, but traffic accidents are not decreasing. In this study, the probability of traffic accidents occurring at a specific time and place is estimated by analyzing various factors such as weather forecast data from the Meteorological Agency, day of the week, time of day, location data, and location information. This study combines objective data on the occurrence of numerous previous traffic accidents with various additional elements not considered in previous studies to derive a more improved traffic accident probability prediction model. The results of this study can be effectively used for various transportation-related services for the safety of people.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.69-70
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• 2020

Accidents in the construction industry are very high compared to other industries, and the number is also increasing steeply every year. Relevant studies were limited for solving the problems. The purpose of this study is to develop a comprehensive risk prediction process for personnel deployed at construction sites on safety management. First of all, the variables were divided into fixed, real-time and working types variables, and the relevant comprehensive data were collected. Second, the probability of a disaster was derived based on the collected data, and weights for each variable were calculated using the dummy regression analysis method using statistical methodology. Lastly, the resulting weighting and disaster probability equation was constructed, and The Final Risk Calculation Formula was developed. The Final Risk Calculation Formula presented in this study is expected to have a significant impact on the establishment of effective safety management measures to prevent possible safety accidents at construction sites

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.266-266
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• 2019

Marine accidents are increasing year by year, and various accidents occur such as engine failure, collision, stranding, and fire. These marine accidents present a risk of large casualties. It is important to prevent accidents beforehand. In this study, we propose a modeling to predict the occurrence of marine accidents by applying the Markov Chain Process that can predict the future based on past data. Applying the proposed modeling, the probability of future marine accidents was calculated and compared with the actual frequency. Through this, a probabilistic model was proposed to prepare a prediction system for marine accidents, and it is expected to contribute to predicting various marine accidents.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.160-165
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• 2004

At this research, we examined probability of light bulb's life span value and prediction on purpose to inquire out the span of repeat velocity as fracture probability by executing the fatigue test, which is considered property of Tungsten filament's thermal fatigue used as an automobile bulb. As a result we can confirm what the most suitable solution is weibull distribution and log normal distribution. Tungsten filament's span gets longer as the fatigue repeat velocity gets shorter And, repeat span is about 15%~40% shorter than sequence life span.

• Bulletin of the Korean Chemical Society
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• v.21 no.1
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• pp.93-96
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• 2000

A diffusion-influenced pseudo-first order reversible reaction A + B ⇔C + B is investigated by the molecular dynamics (MD) simulation method. Theoretical finding that the temporal evolution of reactants [conditional probabilities] in the reversible system can be expressed by the irreversible survival probability with an effective rate parameter is confirmed even in the presence of solvent particles. We carry out molecular dynamics simulations for both the irreversible and the reversible cases to evaluate the survival and the conditional probabilities for each cases. When the resultant irreversible survival probability is inserted into the proposed relation, the conditional probabilities given by the simulation are exactly reproduced.

• Journal of the Korean Statistical Society
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• v.14 no.2
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• pp.63-75
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• 1985

This paper considers a statistical calibration problem in which the standard as wel as the nonstandard measurement is subject to error. Since the classicla approach cannot handle this situation properly, a functional relationship model with additional feature of prediction is proposed. For the analysis of the problem four different approaches-two estimation techniques (ordinary and grouping least squares) combined with two prediction methods (classical and inverse prediction)-are considered. By Monte Carlo simulation the perromance of each approach is assessed in term of the probability of concentration. The simulation results indicate that the ordinary least squares with inverse prediction is generally preferred in interpolation while the grouping least squares with classical prediction turns out to be better in extrapolation.