• Research in Mathematical Education
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• v.19 no.4
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• pp.211-227
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• 2015

Information Technology influenced on classroom to change the teaching and learning method. Recently, flipped learning method became a hot issue in education by using Information Technology. Learning management system that is introduced in our university in the spring semester 2015, made it possible to apply flipped learning method. So, we used the flipped learning method in a calculus course. In this paper, we found that flipped learning in Calculus we was a little bit affirmative in the aspect of motivation and active learning from students' response on flipped learning method. We analyzed the reason that students were not so positive in continuing flipped learning even though they liked flipped learning a little bit better than traditional learning. We suggest what we pay attention to for applying the flipped learning method effectively.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.19-45
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• 2010

This research has analyzed the differentiated influence of organizational variables(an organization's learning culture and organizational support networks) and personal variables(the individual's motivation to learn and self-efficacy) on the process of gaining and using of knowledge. These two variables have been regarded as the major variables for the successful learning of 6Sigma, according to Social Cognitive Theory. In addition, this research has proven the role structure of the abovementioned two variables through a suitable methodology(Hierarchical Linear Model). In regard to this methodology, the different hierarchical level of the personal variable and organizational variable was especially focused on, and the effect of interaction between the high level and the low level was considered in detail. Considering the current situation, in that the importance of organizational factor and personal factor has been emphasized but the accurate role of each variable has not been verified, the research model is thought to help to establish an effective strategy to implement 6 Sigma.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.81-90
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• 2023

Climate change has resulted in increased frequency and intensity of heat waves, which poses a significant threat to the health and safety of construction workers, particularly those engaged in labor-intensive and heat-stress vulnerable working environments. To address this challenge, this study aimed to propose an interpretable machine learning approach for forecasting personal heat strain by considering the cumulative effect of heat exposure as a situational variable, which has not been taken into account in the existing approach. As a result, the proposed model, which incorporated the cumulative working time along with environmental and personal variables, was found to have superior forecast performance and explanatory power. Specifically, the proposed Multi-Layer Perceptron (MLP) model achieved a Mean Absolute Error (MAE) of 0.034 (℃) and an R-squared of 99.3% (0.933). Feature importance analysis revealed that the cumulative working time, as a situational variable, had the most significant impact on personal heat strain. These findings highlight the importance of systematic management of personal heat strain at construction sites by comprehensively considering the cumulative working time as a situational variable as well as environmental and personal variables. This study provided a valuable contribution to the construction industry by offering a reliable and accurate heat strain forecasting model, enhancing the health and safety of construction workers.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.782-784
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• 1999

This paper deals with speed control of DC-servo motor using a Back-Propagation(BP) Learning Algorism and a PID controller Conventionally in the industrial control, PID controller has been used. But the PID controller produced suitable parameter of each system and also variable of PID controller should be changed enviroment, disturbance, load. So this paper revealed for experimental, a neural network and a PID controller combined system using developed speed characters of a Variable Load DC-servo motor. The parameters of the plant are determined by neural network perform on on-line system after training the neural network on off-line system.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.711-718
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• 2011

The k-Nearest Neighbor classifier that does not require a training phase is appropriate for a variable number of classes problem like face recognition, Recently distance metric learning methods that is trained with a given data set have reported the significant improvement of the kNN classifier. However, the performance of a distance metric learning method is variable for each application, In this paper, we focus on the face recognition and compare the performance of the state-of-the-art distance metric learning methods, Our experimental results on the public face databases demonstrate that the Mahalanobis distance metric based on PCA is still competitive with respect to both performance and time complexity in face recognition.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.3 no.2
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• pp.273-283
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• 1996

Effectiveness of the video-record learning method in teaching bowel elimination nursing skill was investigated using an experimental research methodology. Data was collected from 63 female students attending Fundamental Nursing class from a nursing college in Seoul. The subjects were randomly assigned to two groups, one is the experimental group of 29 and the other the control group of 34. The independent variable was video-record learning method and the dependent variable were the degree of knowledge achivement, nursing skill achivement, competence on practicing elimination skill, and satisfaction about the learning method. The hypotheses of the study were as following. 1) There will be significant difference between the experimental group and the control group in dependent variables. 2) There will be significant positive correlations between nursing skill achievement and other three dependent variables-interest in nursing, adaptation in nursing, and preference of nursing job. Data was analyzed using descriptive statistics, chi-square test, t-test, and Pearson's correlation coefficient with SPSS $PC^+$ program. Findings of the study are : 1) There was no significant difference between the experimental group and the control group in knowledge achievement using P<.05. 2) There was significant difference between the experimental group and the control group in nursing skill achievement using P<.05. 3) There was no significant difference between the experimental group and the control group in competence on practicing elimination skill using P<.05. 4) There was no significant difference between the experimental group and the control group in satisfaction about learning method using P<.05. 5) There was positive correlation between nursing skill achievement and the other variables but no significant difference was shown. 6) This study suggests that video-record learning method is an effective learning method for achiving basic nursing skills but is not effective in other areas such as knowledge achivement, competence in performing nursing practice, and satis-faction about the learning method. Further study with more developed research design and statistical analysis should be done to investigate the effectivenes of video-record learning method in learning basic nursing skill more accurately.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Knowledge Management Research
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• v.9 no.4
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• pp.35-49
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• 2008

The purpose of the study is to develop and validate an instructional design model from the perspective of the knowledge creation. To serve the purpose, the researcher conducted 1) literature review to find causal relationship model among knowledge creation factors and to propose a hypothetical instructional design model, 2) data analysis with 50 senior level e-Learning instructional designers, and 3) testing the fitness of the proposed model and relevant causal-relational hypotheses. Results indicate; 1) the proposed model fit to the empirical evidence, 2) 6 hypotheses among 11 were validated. A typical instructional designer's personal competency was evidenced as the most powerful independent variable that predicted knowledge acquisition, knowledge sharing, and the application of the instructional models. However, the expected effect of instructional design models toward other dependent variable was not be found. In addition, further suggestions for the future research are addressed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.59-67
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• 2013

This paper presents a neural net type process model for enhancing learning compensation function in hot strip finishing rolling mill. Adequate input and output variables of process model are chosen, the proposed model was designed as single layer neural net. Equivalent carbon content, strip thickness and rolling speed are suggested as input variables, and looper's manipulation variable is proposed as output variable. According to simulation result using process data to show the validity of the proposed process model, neural net type process model's outputs give almost similar data to process output under same input conditions.