• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1435-1447
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• 2020

The seasonal variations in weather parameters can significantly affect the atmospheric transmission characteristics. Herein, we propose a novel augmented multiple regression algorithm for the accurate estimation of atmospheric transmittance and solar irradiance over highly localized areas. The algorithm employs 1) adaptive atmospheric model selection using measured meteorological data and 2) multiple linear regression computation augmented with the conventional application of MODerate resolution atmospheric TRANsmission (MODTRAN). In this study, the proposed algorithm was employed to estimate the solar irradiance over Taean coastal area using the 2018 clear days' meteorological data of the area, and the results were compared with the measurement data. The difference between the measured and computed solar irradiance significantly improved from 89.27 ± 48.08σ W/㎡ (with standard MODTRAN) to 21.35 ± 16.54σ W/㎡ (with augmented multiple regression algorithm). The novel method proposed herein can be a useful tool for the accurate estimation of solar irradiance and atmospheric transmission characteristics of highly localized areas with various weather conditions; it can also be used to correct remotely sensed atmospheric data of such areas.

• Water Engineering Research
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• v.4 no.2
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.957-970
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• 2015

The purpose of this study was to analyze records of Korean pro-basketball using general linear model (two-way ANOVA and hierarchical multiple regression analysis). Korea Basketball League (KBL) informed the records (2014-2015 season) of this study. The eight variables (TA, 2PA, 3PA, 2P, 3P, Ast, TFB, CH) were selected in content validity. SPSS program was used to analyze general linear model. All alpha level was set at 0.05. Major results were as follow. 3PA had significant interaction effect between victory & defeat variable and home & away variable. Victory teams showed that 3PA was higher in home games than away games, and defeat teams was the other. 2PA, AS, TFB, and CH were selected significant variables affecting victory and defeat. In result of hierarchical regression, Ast had significant moderation effect between 3PA and TS. TFB also had significant moderation effect between AS between 2P. The other construct (Ast between 2PA and TS; TFB between AS between 3P) had no significant moderation effect. In the effect of 2PA, 3PA and Ast to TS, CH also had no significant moderation effect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8791-8799
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• 2015

In this paper, One-way car-sharing System represents the verification system consider events in revenue effects. Revenue which the time and distance represented the graph, compare one-way vehicle relocate car-sharing system which proven in existing international papers with one-way vehicle relocate car-sharing system consider the event currently in the Korea. Especially, The maximum profit according to the distance and time were assessed through multiple linear regression analysis, and there are probable maximum loss allow for the maximum loss. The company suggested using the event as a discount coupon to customers through various marketing strategies, and then focused on increasing customer demand. So, Correlation analysis to determine the maximum revenue of the actual travel distance and time were carried out through Non-linear Regression.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.145-154
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• 2012

This paper analyzed the characteristics of traffic accidents in all tunnels on nationwide freeways and selected some various independent variables related to accident occurrence in tunnels. The study aims to develop reliable accident forecasting models using the various dependent variables such as the number of accident (no.), no./km, and no./MVK. Finally, reliable multiple linear regression models were proposed in this paper. This study tested the validity verification of developed models through statistics such as $R^2$, F values, multicollinearity, residual analysis. The paper selected the accident forecasting models considering the characteristics of tunnel accidents and two models were finally proposed according to two groups of tunnel length. In the selected models, natural logarithm of ln(no./MVK) is used for the dependent variable and AADT, vertical slope, and tunnel hight are used for the independent variables. The reliability of two models was proved by the comparison analysis between field data and estimating data using RMSE and MAE. These models may be not only effective in evaluating tunnel safety under design and planning phases of tunnel but also useful to reduce traffic accidents in tunnels and to manage the traffic flow of tunnel.

• Journal of Chest Surgery
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• v.54 no.6
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• pp.466-472
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• 2021

Background: Major intraoperative hemorrhage reportedly predicts unfavorable survival outcomes following surgical resection for esophageal carcinoma (EC). However, the factors predicting the amount of blood lost during thoracoscopic esophagectomy have yet to be sufficiently studied. We sought to identify risk factors for excessive blood loss during video-assisted thoracoscopic surgery (VATS) for EC. Methods: Using simple and multiple linear regression models, we performed retrospective analyses of the associations between clinicopathological/surgical factors and estimated hemorrhagic volume in 168 consecutive patients who underwent VATS-type esophagectomy for EC. Results: The median blood loss amount was 225 mL (interquartile range, 126-380 mL). Abdominal laparotomy (p<0.001), thoracic duct resection (p=0.014), and division of the azygos arch (p<0.001) were significantly related to high volumes of blood loss. Body mass index and operative duration, as continuous variables, were also correlated positively with blood loss volume in simple linear regression. The multiple linear regression analysis identified prolonged operative duration (p<0.001), open laparotomy approach (p=0.003), azygos arch division (p=0.005), and high body mass index (p=0.014) as independent predictors of higher hemorrhage amounts during VATS esophagectomy. Conclusion: As well as body mass index, operation-related factors such as operative duration, open laparotomy, and division of the azygos arch were independently predictive of estimated blood loss during VATS esophagectomy for EC. Laparoscopic abdominal procedures and azygos arch preservation might be minimally invasive options that would potentially reduce intraoperative hemorrhage, although oncological radicality remains an important consideration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.287-287
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• 2003

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.77-84
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• 1998

ARES plot (Cook and Weisberg, 1987) idea is applied to a multiple regression model in which the relation between a response variable and some independent variable is nonlinear. This method is expected to show the impact on the function to which and independent variable should be transformed, as a variable is smoothly added to the model.