• Journal of the Society of Naval Architects of Korea
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• v.43 no.3 s.147
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• pp.340-350
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• 2006

This paper is concerned with a practical guide for the ultimate longitudinal strength assessments of ships' hull structure. Rigorous non-linear structural analysis for three tanker models has been carried out to examine the ultimate strength behavior. Formula of estimating the ultimate longitudinal strength has been proposed which is modified with the results of non-linear finite element analysis of hull girders. Computational reliability and accuracy of the large-scale non-linear finite element analysis and the proposed simplified formula are verified through comparing their results with that of 1/3 scale frigate model test and DNVs program. Additionally, the ultimate longitudinal strength for ten tanker models is compared with those by the method specified in the 2nd Draft of common structural rule for tankers, which is being developed by IACS.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.65-75
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• 2008

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• Korean Management Science Review
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• v.26 no.3
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• pp.117-131
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• 2009

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• Geomechanics and Engineering
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• v.1 no.2
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• pp.155-168
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• 2009

Breakdown pressures obtained from the classic, linear elastic breakdown model are compared with the corresponding pressures obtained using a nonlinear material model. Compression test results obtained on sandstone and siltstone are used for that purpose together with previously formulated nonlinear model which introduces elasticity functions to address nonlinear stress-strain behaviour of rocks exhibiting stress-dependent mechanical properties. Linear and nonlinear collapse pressures are also compared and it is shown that material nonlinearities have significant effect on both breakdown and collapse pressures and on tangential stresses which control breakdown pressure around a borehole. This means that the estimates of ${\sigma}_H$ made using linear models give stress values which are different than the real values in the earth. Thus the importance of a more accurate analysis, such as provided by the nonlinear models, is emphasised. It is shown, however, that the linear elastic model does not necessarily over-predict borehole stresses and the opposite case can be true, depending on rock type and test interpretation.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.249-255
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• 2022

The COVID-19 virus appeared in 2019 and is extremely contagious. Because it is very infectious and has a huge impact on people's mobility. In this paper, multiple linear regression and random forest models are used to predict the number of COVID-19 cases using COVID-19 infection status data (open source data provided by the Ministry of health and welfare) and Google Mobility Data, which can check the liquidity of various categories. The data has been divided into two sets. The first dataset is COVID-19 infection status data and all six variables of Google Mobility Data. The second dataset is COVID-19 infection status data and only two variables of Google Mobility Data: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The models' performance has been compared using the mean absolute error indicator. We also a correlation analysis of the random forest model and the multiple linear regression model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.267-269
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• 2005

This paper describes a consideration on the sampling in linearly time-varying (LTV) systems in view of the convenience in digital signal processing. The relation between a continuous-time and a discrete-time system models is investigated for a simple linear time-invariant system. Based on the results of the investigation, we first consider discrete-time models for LTV systems, Then the simplicity of the models in terms of microprocessor-based digital signal processing is compared.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.120-128
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• 1993

In this paper, an implementation of high-speed parallel processing system for neural network design on the multicomputer network is presented. Linear speedup expandability is increased by reducing the synchronization penalty and the communication overhead. Also, we presented the parallel processing models and their performance evaluation models for each of the parallization methods of the neural network. The results of the experiments for the character recognition of the neural network bases on the proposed system show that the proposed approach has the higher linear speedup expandability than the other systems. The proposed parallel processing models and the performance evaluation models could be used effectively for the design and the performance estimation of the neural network on the multicomputer network.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.4
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• pp.128-134
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• 2008

Models of the deposition heights in the uncontrolled laser aided direct metal deposition process are constructed for the enhancement of the process integrity. Linear and non-linear statistical models as well as fuzzy model are utilized as the modeling methods. The predictability of the models are evaluated with the values of the sum of square error. The algorithm to use the models in the feedback controlled system is suggested to increase the deposition height accuracy within a layer.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.55-69
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• 2003

In this article, we propose the weighted LAD (least absolute deviations) estimators for multi-dimensional contingency tables and drive an estimation method to estimate the proposed estimators. To illustrate the robustness of the estimators, simulation results are presented for several models Including log-linear models and models for ordinal variables in multidimensional contingency tables. Examples were also introduced.