• Educational Technology International
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• 제15권2호
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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.

• 한국도로학회논문집
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• 제20권2호
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• pp.75-85
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• 2018

PURPOSES : This study aimed to analyze traffic accidents at circular intersections, and discuss accident reduction strategies based on land use and vehicle type. METHODS : Traffic accident data from 2010 to 2014 were collected from the "traffic accident analysis system" (TAAS) data set of the Road Traffic Authority. To develop the accident rate model, a multiple linear regression model was used. Explanatory variables such as geometry and traffic volume were used to develop the models. RESULTS : The main results of the study are as follows. First, it was found that the null hypotheses that land use and vehicle type do not affect the accident rate should be rejected. Second, 16 accident rate models, which are statistically significant (with high $R^2$ values), were developed. Finally, the area of the central island, number of speed humps, entry lane width, circulatory roadway width, bus stops, and pedestrian crossings were analyzed to determine their effect on accidents according to the type of land use and vehicle. CONCLUSIONS : Through the developed accident rate models, it was revealed that the accident factors at circular intersections changed depending on land use and vehicle type. Thus, selecting the appropriate location of bus stops for trucks, widening entry lanes for cars, and installing splitter islands and optimal lighting for motorcycles were determined to be important for reducing the accident rate. Additionally, the evaluation showed that commercial and mixed land use had a weaker effect on accidents than residential land use.

• 생태와환경
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• 제54권2호
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• pp.102-107
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• 2021

The distribution pattern of species richness was determined by temperature. To examine the relationship between hemipteran richness and temperature, hemipteran species were collected using pitfall traps at six different oak forest sites with different annual mean temperatures in South Korea. Multiple linear regression analyses were conducted with mean annual temperature (MAT) and plant richness to evaluate differences in hemipteran richness. The influences of MAT and plant richness of study sites on hemipteran richness were examined by comparing three models (plant richness+MAT+MAT², plant richness+MAT, and MAT) or two models (plant richness+MAT and MAT). Hemipteran richness showed an inverse diversity pattern as a function of temperature, with higher species richness at lower temperature sites. Meanwhile, Aphididae showed a bell-shaped diversity pattern with the highest value at low medium temperatures. The regression analysis showed that hemipteran richness was affected by temperature and plant richness in their habitats.

• 전기학회논문지
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• 제65권12호
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• pp.2075-2083
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• 2016

The real time IAQ (Indoor Air Quality) management is very important for large buildings and underground facilities such as subways because poor IAQ is immediately harmful to human health. Such IAQ management requires monitoring, prediction and control in an integrated and real time manner. In this paper, we present three PM10 hourly prediction models for such realtime IAQ management as both Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. Both MLR and ANN models show good performances between 0.76 and 0.88 with respect to R (correlation coefficient) between the measured and predicted values, but the MLR models outperform the corresponding ANN models with respect to RMSE (root mean square error).

• 대한음성학회지:말소리
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• 제66호
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• 산업경영시스템학회지
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• 제43권4호
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.

• 산업경영시스템학회지
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• 제46권1호
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• KIEE International Transactions on Electrophysics and Applications
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• 제3C권1호
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• pp.1-4
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• 2003

This paper presents an attempt to develop probabilistic multistress life models to evaluate the lifetime characteristics of epoxy-encapsulated magnet wire with heavy build polyurethane enamel. A set of accelerated life tests were conducted over a wide range of pulsating voltages, temperatures, and frequencies. Samples of fine gauge twisted pairs of the encapsulated magnet wire were tested us-ing a pulse endurance dielectric test system. An electrical-thermal lifetime function was combined with the Weibull distribution of lifetimes. The parameters of the combined Weibull-electrical-thermal model were estimated using maximum likelihood estimation. Likewise, a generalized electrical-thermal-frequency life model was also developed. The parameters of this new model were estimated using multiple linear regression technique. It was found in this paper that lifetime estimates of the two proposed probabilistic multistress life models are good enough. This suggests the suitability of using the general electrical-thermal-frequency model to estimate the lifetime of the encapsulated magnet wire over a wide range of voltages, temperatures and pulsating frequencies.

• 대한치과교정학회지
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• 제51권1호
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• pp.15-22
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• 2021

Objective: The aim of this study was to evaluate the correlation between the vertical position of maxillary first molar and vertical skeletal measurements in lateral cephalograms by using new linear measurements on the vertical axis of coordinates with calibration. Methods: The vertical position of maxillary first molar (U6-SN), and the conventionally used variables (ConV) and the newly derived linear variables (NwLin) for vertical skeletal patterns were measured in the lateral cephalograms of 103 Korean adults with normal occlusions. Pearson correlation analyses and multiple linear regression analyses were performed with and without calibration using the anterior and posterior cranial base (ACB and PCB, respectively) lengths to identify variables related to U6-SN. Results: The PCB-calibrated statistics showed the best power of explanation. ConV indicating skeletal hyperdivergency was significantly correlated with U6-SN. Six NwLin regarding the position of palatal plane were positively correlated with U6-SN. Each multiple linear regression analysis generated a two-variable model: sella and nasion to palatal plane. Among the three models, the PCB-calibrated model yielded highest adjusted R2 value, 0.880. Conclusions: U6-SN could be determined by the vertical position of the maxilla, which could then be used to plan the amount of molar intrusion and estimate its clinical stability. Cephalometric calibration on the vertical axis of coordinates by using PCB for vertical linear measurements could strengthen the analysis itself.

• 한국수자원학회논문집
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• 제55권10호
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• pp.723-736
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• 2022

본 연구에서는 금강권역을 대상으로 최대 12개월까지 선행예측이 가능한 월 강수량 예측모형을 구축하였으며, 예측모형 구축에는 다중회귀분석과 인공신경망의 두 가지 통계적 기법을 적용하였다. 예측인자 후보로 NOAA에서 제공하는 글로벌 기후패턴 39종과 금강권역에 대한 기상인자 8종 등 총 47종의 기후지수를 활용하였다. 예측대상월을 기준으로 과거 40년간의 월 강수량과 기후지수와의 지연상관성 분석을 통해 상관도가 높은 기후지수를 예측인자로 활용하여 다중회귀모형 및 인공신경망 모형을 구축하였다. 1991~2021년에 대해 매월 예측결과의 평균값과 관측값과의 적합도를 분석한 결과, 다중회귀모형은 PBIAS -3.3~-0.1%, NSE 0.45~0.50, r 0.69~0.70으로 분석되었으며, 인공신경망모형은 PBIAS -5.0~+0.5%, NSE 0.35~0.47, r 0.64~0.70로, 다중회귀모형에 의해 도출된 예측치의 평균값이 인공신경망모형보다 관측치에 좀 더 근접한 것으로 나타났다. 각 월의 예측범위 안에 관측치가 포함될 확률을 분석한 결과에서는 다중회귀모형이 57.5~83.6%(평균 72.9%), 인공신경망모형의 경우에는 71.5~88.7%(평균 81.1%)로 인공신경망모형 결과가 우수한 것으로 나타났다. 3분위 예측확률을 비교한 결과는 다중회귀모형의 경우에는 25.9~41.9%(평균 34.6%), 인공신경망모형은 30.3~39.1%(평균 34.7%)로 비슷하며, 두 모형 모두 평균 33.3% 이상으로 월 강수량에 대한 장기예측성을 확인 할 수 있었다. 이상과 같이 두 모형의 예측성 차이는 비교적 크지 않은 것으로 나타났으나, 예측범위에 대한 적중률이나 3분위 예측확률로부터 판단할 때 예측성에 대한 월별 편차는 인공신경망모형의 결과가 상대적으로 작게 나타났다.