• Journal of the Ergonomics Society of Korea
• /
• v.28 no.3
• /
• pp.55-62
• /
• 2009

This study is concerned with predicting human search performance using a visual lobe. The most previous studies on human performance in visual search have been limited to a single-target search. This study extended the visual search research to multiple-target search including targets of different types as well as targets of same types. A model for predicting visual search performance was proposed and the model was validated by human search data. Additionally, this study found that human subjects always did not use a constant ratio of the whole visual lobe size for each type of targets in visual search process. The more conspicuous the target is, the more ratio of the whole visual lobe size human subjects use. The model that can predict human performance in multiple-target search may facilitate visual inspection plan in manufacturing.

• Applied Chemistry for Engineering
• /
• v.31 no.5
• /
• pp.520-525
• /
• 2020

In this study, we developed a machine learning-based model for predicting the production stage temperature of distillation process. It is necessary to predict an accurate temperature for control because the control of the distillation process is done through the production stage temperature. The temperature in distillation process has a nonlinear complex relationship with other variables and time series data, so we used the recurrent neural network algorithms to predict temperature. In the model development process, by adjusting three recurrent neural network based algorithms, and batch size, we selected the most appropriate model for predicting the production stage temperature. LSTM₁₂₈ was selected as the most appropriate model for predicting the production stage temperature. The prediction performance of selected model for the actual temperature is RMSE of 0.0791 and R² of 0.924.

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

• Atmosphere
• /
• v.17 no.2
• /
• pp.135-145
• /
• 2007

We developed a simple regression model for predicting the intesity change (central pressure) of major tropical cyclones (TCs) for 24 hours after landfall using 51 TC cases which landed over the Korean Peninsula for 1951-2004. Clusters 1 and 4 with a relatively strong intensity of TC after landfall classified by Choi and Kim (2007) are used to develop a statistical model for the prediction of TC intensity change. Predicting parameters (falling constants) in the regression models $(P_t=P_0+alnt)$ are 6.46 and 10.11 for clusters 1 and 4, respectively. It might be mentioned that there is some feasibility in employing a simple regression model developed in this study for TC intensity change after landfall for operational purpose of TC forecasting compared with RSMC-Tokyo best-track in both TC cases of Clusters 1 and 4 and Ewiniar (0603) case, but the room for improvement of model still remains for further study.

• Journal of Environmental Science International
• /
• v.26 no.4
• /
• pp.421-432
• /
• 2017

In this study, we examined the suitability of ten disinfection models for predicting the inactivation of Artemia sp. via single or combined physical and chemical treatments. The effect of Hydraulic Retention Time (HRT) on the inactivation of Artemia sp. was examined experimentally. Disinfection models were fitted to the experimental data by using the GInaFiT plug-in for Microsoft Excel. The inactivation model were evaluated on the basis of RMSE (Root Mean Square Error), SSE (mean Sum Square Error) and $r^2$. An inactivation model with the lowest RMSE, SSE and $r^2$ close to 1 was considered the best. The Weibull+Tail model was found to be the most appropriate for predicting the inactivation of Artemia sp. via electrolytic treatment and electrolytic-ultrasonic combined treatment. The Log-linear+Tail model was the most appropriate for modeling inactivation via homogenization and combined electrolytic-homogenization treatment. The double Weibull disinfection model was the most suitable for the predicting inactivation via ultrasonic treatment.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.34 no.3
• /
• pp.29-41
• /
• 2009

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2008.10a
• /
• pp.1-7
• /
• 2008

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss its methodological characteristics in comparison with other existing classification methods. Also, to assess the prediction power of the model, we conduct a series of experiments employing survey data of consumer choices of MP3 players. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Journal of the Korean Home Economics Association
• /
• v.43 no.11 s.213
• /
• pp.225-234
• /
• 2005

The objective of this study was to develop a model for predicting Korean adult consumers who frequently eat food-away-from-home. A total of 7,032 adults aged 19 years and older from the 2001 National Health and Nutrition Survey in Korea were used as subjects. The data were analyzed using a data mining procedure including logistic regression and decile analysis. The model developed in the study was proven to be valid in predicting the consumers who frequently eat food-away-from home(once a day or more often). This model showed that consumers eating food-away-from-home frequently tend to be younger men, living in a big city, working full time, receiving more stress and eating snacks and fried food more frequently. The model could be used to identify targets for nutrition and related education and consumer segments for the marketing of restaurant businesses.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1089-1098
• /
• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.

• Computers and Concrete
• /
• v.33 no.3
• /
• pp.285-299
• /
• 2024

Recently, many engineering computations have realized their digital transformation to Machine Learning (ML)-based systems. Predicting the behavior of a structure, which is mainly computed with structural analysis software, is an essential step before construction for efficient structural analysis. Especially in the seismic-based design procedure of the structures, predicting the lateral load capacity of reinforced concrete (RC) columns is a vital factor. In this study, a novel ML-based model is proposed to predict the maximum lateral load capacity of RC columns under varying axial loads or cyclic loadings. The proposed model is generated with a Deep Neural Network (DNN) and compared with traditional ML techniques as well as a popular commercial structural analysis software. In the design and test phases of the proposed model, 319 columns with rectangular and square cross-sections are incorporated. In this study, 33 parameters are used to predict the maximum lateral load capacity of each RC column. While some traditional ML techniques perform better prediction than the compared commercial software, the proposed DNN model provides the best prediction results within the analysis. The experimental results reveal the fact that the performance of the proposed DNN model can definitely be used for other engineering purposes as well.