• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.682-687
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• 2000

In this research the remaining service life of the concrete due to the steel corrosion was predicted by three cases; causing carbonation, using sea sand, using deicing salts. In case of deterioration by carbonation, effective carbonation depth, effective coverage depth and relative humidity are considered for predicting method. In case of using sea sand, predicting method is made of rust growth equation from polarization resistance method. In case of using deicing salts, predicting method is made of transformation of Fick's law. Three methods are very useful in predicting service life of concrete.

• Journal of Welding and Joining
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• v.13 no.3
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• pp.77-88
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• 1995

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO$_{2}$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a backpropagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the ailure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.947-955
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• 2020

Predicting the sloshing motion of a coolant during a seismic assessment of a rectangular spent fuel pool is of critical concern. Linear theory, which provides a simple analytical method, has been used to predict the sloshing motion in rectangular pools and tanks. However, this theory is not suitable for the high-frequency excitation problem. In this study, the authors developed a simple analytical method for predicting the sloshing motion in a rectangular pool for a wide range of excitation frequencies. The correlation among the linear theory parameters, influencing on excitation and convective waves, and the excitation frequency is investigated. Sloshing waves in a rectangular pool with several liquid heights are predicted using the original linear theory, a modified linear theory and computational fluid dynamics analysis. The results demonstrate that the developed method can predict sloshing motion over a wide range of excitation frequencies. However, the developed method has the limitations of linear solutions since it neglects the nonlinear features of sloshing motion. Despite these limitations, the authors believe that the developed method can be useful as a simple analytical method for predicting the sloshing motion in a rectangular pool under various external excitations.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.51-60
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• 2001

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermo-mechanical analysis has been performed for the $CO_2$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a back propagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the failure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Geomechanics and Engineering
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• v.20 no.2
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.12
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• pp.1090-1097
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• 2000

Predicting building energy use can be useful to evaluate its energy performance. This study proposed empirical approach for predicting building energy use with regression analysis. For the empirical analysis, simple regression models were developed based on the historical energy consumption data as a function of daily outside temperature, the predicting equations were derived for different operational modes and day types, then the equations were applied for predicting energy use in a building. BY selecting a real building as a case study, the feasibilities of the empirical approach for predicting building energy use were examined. The results showed that empirical approach with regression analysis was fairly reliable by demonstrating prediction accuracy of $pm10%$ compared with the actual energy consumption data. It was also verified that the prediction by regression models could be simple and fairly accurate. Thus, it is anticipated that the empirical approach will be useful and reliable tool for many purposes: retrofit savings analysis by estimating energy usage in an existing building or the diagnosis of the building operational problems with real time analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1252-1254
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• 2004

BMI(brain machine interface) has been recently applied to give a disabled person mobility. This study is to determine the effective EEG parameters for predicting the movement moment of body limbs thought analysis of moving averaged ERD. The results showed that the proposed method for classifying EEG for predicting the movement seemed to be better than the classical method of determining ERD.

• Speech Sciences
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• v.10 no.2
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• pp.7-25
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• 2003

Predicting the right prosodic elements is a key factor in improving the quality of synthesized speech. Prosodic elements include break, pitch, duration and loudness. Pitch, which is realized by Fundamental Frequency (F0), is the most important element relating to the quality of the synthesized speech. However, the previous method for predicting the F0 appears to reveal some problems. If voiced and unvoiced sounds are not correctly classified, it results in wrong prediction of pitch, wrong unit of triphone in synthesizing the voiced and unvoiced sounds, and the sound of click or vibration. This kind of feature is usual in the case of the transformation from the voiced sound to the unvoiced sound or from the unvoiced sound to the voiced sound. Such problem is not resolved by the method of grammar, and it much influences the synthesized sound. Therefore, to steadily acquire the correct value of pitch, in this paper we propose a new model for predicting and classifying the voiced and unvoiced sounds using the CART tool.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.273-290
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• 2006

This study suggests a frequency matrix technique to predict personal credit rate more efficiently using incomplete data sets. At first this study test on multiple discriminant analysis and logistic regression analysis for predicting personal credit rate with incomplete data sets. Missing values are predicted with mean imputation method and regression imputation method here. An artificial neural network and frequency matrix technique are also tested on their performance in predicting personal credit rating. A data set of 8,234 customers in 2004 on personal credit information of Bank A are collected for the test. The performance of frequency matrix technique is compared with that of other methods. The results from the experiments show that the performance of frequency matrix technique is superior to that of all other models such as MDA-mean, Logit-mean, MDA-regression, Logit-regression, and artificial neural networks.