• Computers and Concrete
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• 제29권3호
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• pp.145-159
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• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

• 대한산업공학회지
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• 제39권5호
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.349-351
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• 2004

The classical dynamic backpropagation learning algorithm has the problems of learning speed and the determine of learning parameter. The Extend Kalman Filter(EKF) is used effectively for a state estimation method for a non linear dynamic system. This paper presents a learning algorithm using Dual Extended Kalman Filter(DEKF) for Fully Recurrent Neural Network(FRNN). This DEKF learning algorithm gives the minimum variance estimate of the weights and the hidden outputs. The proposed DEKF learning algorithm is applied to the system identification of a nonlinear SISO system and compared with dynamic backpropagation learning algorithm.

• 간호행정학회지
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• 제15권1호
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• pp.72-80
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• 2009

Purpose: To investigate the influence of a team learning climate on group performance of hospital nurses. Method: The subjects were 386 nurses who have been working in six hospitals. The data were collected by a structured questionnaire from January 20 to April 30 of 2006. The data were analyzed by SAS version 8.2, including descriptive statistics, Pearson correlation coefficient, and stepwise multiple regression. Results: The mean score of group performance was 3.38 and team learning climate was 4.89. The group performance was positively correlated with team learning climate(r=.40, p<.0001). The team learning climate explained 15% of the variance in group performance. Conclusion: The findings showed that team learning climate was an important factor in enhancing group performance in nursing organization. Therefore, the nurse manager will establish the strategies to improve the team learning climate of the nurses in order to promote organizational performance.

• 한국간호교육학회지
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• 제17권3호
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• pp.355-364
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• 2011

Purpose: This study was designed to explore the influencing factors on self-directed learning ability of nursing students and to investigate the relationship between learning style, learning attitude, and self-directed learning ability. Methods: The study sample was composed of 263 nursing students. Data were collected from March 8th to April 7th, 2011 using a questionnaire which included Kolb's learning style inventory, learning attitude inventory, and self-directed learning ability inventory. Results: Learning styles of the subjects were assimilator 33.8%, converger 31.9%, accommodator 24.7%, and diverger 9.5%. There was no significant difference in learning styles among grades. However, the total mean score of learning attitude (F=8.30, p<.001) and self-directed learning ability (F=2.85, p=.038) significantly differed among grades. Learning attitude positively correlated to self-directed learning ability (r=.62, p<.001). Learning attitude was the most significant predictor and accounted for 36.5% of the variance in self-directed leaning ability in nursing students. Conclusion: It is important for students to use all four learning styles rather than to rely solely on one style. There should be more emphasis placed on the development of positive learning attitude and self-directed learning ability of nursing students.

• International Journal of Internet, Broadcasting and Communication
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• 제11권1호
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• pp.47-53
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• 2019

The idea of ensemble learning is to train multiple models, each with the objective to predict or classify a set of results. Most of the errors from a model's learning are from three main factors: variance, noise, and bias. By using ensemble methods, we're able to increase the stability of the final model and reduce the errors mentioned previously. By combining many models, we're able to reduce the variance, even when they are individually not great. In this paper we propose an ensemble model and applied it to classification problem. In iris, Pima indian diabeit and semiconductor fault detection problem, proposed model classifies well compared to traditional single classifier that is logistic regression, SVM and random forest.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.477-483
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• 2004

One of the major paradigms for supervised learning in neural network community is back-propagation learning. The standard implementations of back-propagation learning are optimal under the assumptions of identical and independent Gaussian noise. In this paper, for regression function estimation, we introduce $\varepsilon-insensitive$ back-propagation learning algorithm, which corresponds to minimizing the least absolute error. We compare this algorithm with support vector machine(SVM), which is another $\varepsilon-insensitive$ supervised learning algorithm and has been very successful in pattern recognition and function estimation problems. For comparison, we consider a more realistic model would allow the noise variance itself to depend on the input variables.

• 디지털산업정보학회논문지
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• 제16권2호
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• pp.69-81
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• 2020

The objective of this study is to seek ways to maximize learning effects from e-learning by drawing improvement directions through investigating and analyzing an awareness of e-learning among e-learning attendees. The study was conducted among the attendees who are taking the e-learning program operated by K University and collected data from the students taking second semester in 2018 with the use of structured questionnaires. For data processing, SPSS Statistics 22.0 and AMOS were used, along with such analytical methods as frequency anslysis, descriptive statistical analysis, ANOVA (Analysis of Variance), t-analysis and cross tabulation. For significant data, it conducted an analysis by carrying out the Scheffe's test. According to the findings from this study, they showed a significant difference only in gender and curriculum desired to be opened in the question about e-learning participation motives per background factor. As for the learners' motives to study, it was confirmed that they tend to become more biased on time utilization and convenience of learning methods. The analysis of which factor of the three - learning factors, system factors and instructor's factors - has greatest effects on learning satisfaction indicated that learning factors influenced learning satisfaction the most in accordance with values for non-standard coefficient beta, followed by instructor factors which had a direct effect.

• 영재교육연구
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• 제6권1호
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• pp.93-109
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• 1996

The purpose of this study was to investigate the relationship between learning strategies and cognitive learning abilities with achievement scores of elementary school children. To achieve this purpose, 109 sixth grade children were sampled in Seoul-City, and the 'Questionnaire on the Learning Strategies and Learning Abilities Test' were administered to them. The collected data were analyzed by Pearson's Product Moment Correlation and Multiple Regression Analysis. The major findings of this study were as follows: Firstly, there appeared to be statistically significant correlations between learning strategies and achievement scores. The process of thinking variable of learning strategies were most significantly correlated with achievement scores(r=.251- .458, p<.01). The calculated R2 indicated that the combined effects of process of thinhng and affective domain on the achievement scores were about 21.5%. Secondly, there appeared to be statistically significant correlations between cognitive learning abilities and achievement scores. The verbal reasoning and verbal comprehension variable of cognitive learning abilities were most significantly correlated with achievement scores(r=.215-,493, p<.01). The calculated R2 indicated that the verbal reasoning and verbal comprehension variable of cognitive learning abilities explained about 27.6% of the variance of achievement scores. Thirdly, there appeared to be no statistically significant correlations between learning strategies and cognitive learning abilities. The results of this study shows that the development of learning strategies and cognitive learning abilities could improve the achievement scores in school learning.

• 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.