• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1705-1714
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• 2018

This paper develops a novel controller called iterative learning sliding mode (ILSM) to control linear and nonlinear fractional-order systems. This control applies a combination structures of continuous and discontinuous controller, conducts the system output to the desired output and achieve better control performance. This controller is designed in the way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order systems. The proposed controller unlike the conventional iterative learning control for fractional systems does not need to apply direct control input to output of the system. It is shown that the controller perform well in partial and complete observable conditions. Simulation results demonstrate very good performance of the iterative learning sliding mode controller for achieving the desired control objective by increasing the number of iterations in the control loop.

• Management & Information Systems Review
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• v.27
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• pp.71-99
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• 2008

This study investigates the relationships between three BSC communication attributes(support of organizational culture, message valid, and knowledge sharing) and organizational learning and how that translates into relationship organizational performance in public organization. In this paper, first, past studies on BSC communication and organizational learning that identify the attributes of effective communication and organizational learning in organizational performance are reviewed. Second, a research model, key variables, and three hypotheses tested by PLS(partial least squares) are presented. The data was collected from BSC champions and managers of 53 public organizations in Korea. The results indicate, first, BSC communication (except for support of organizational culture) have not significant related to organizational performance. Therefore, H1 was not supported. Second, the structural path coefficient between support of organizational culture and message valid and organizational learning are statistically significant and in the hypothesized direction. But the knowledge sharing has not significant relationship with organizational learning. Therefore, H2 was partially supported. Third, organizational learning was significantly positively related to organizational performance. H3 was supported. Finally, organizational learning play a significantly positive role in mediating the relationship between BSC communication and organizational performance. The theoretical contributions, limitations, as well as future research directions are discussed at the end of the paper.

• Journal of the Korean Home Economics Association
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• v.48 no.1
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• pp.127-136
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• 2010

The purpose of this study was to investigate the relationships between the young children's creative trait and personality affecting their learning readiness. The participants were 131 young children in kindergartens or nursery schools and their mothers from Seoul, Gyeonggi and Chungnam province. The instruments were the Korea Learning Readiness Test, Young Children's Creative Traits Test, and Young Children's Personality Test. The data were analyzed by one-way ANOVA, t-test, Pearson's partial correlation, stepwise multiple regression, and Scheff$\'{e}$ test by SPSS PC(version17.0) program. The results were as follows. Firstly, there were no significant differences in young children's learning readiness, creative trait and personality by children's age, location, school type, father's age, mother's educational level and mother's job type, while there were significant differences in the creative trait and the subfactor scores of personality by children's sex, father's educational level and parents' job type. Secondly, there was a positive partial correlation(r = .20~.24) between young children's learning readiness and creative trait, while there were no correlations between their learning readiness and personality and between their personality and creative trait. Thirdly, cognitive factor of creative trait and father's educational level did not effectively explain the children's learning readiness.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.125-133
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• 2022

This study attempted to find out whether learning time has a mediating effect according to the gender, region, and grade of middle school students in the relationship between academic engagement and self-esteem. To this end, a survey of 1,045 middle school students in Gyeongsangnam-do was conducted on academic engagement, learning time, and self-esteem. Difference verification was conducted to determine the difference in academic engagement, learning time, and self-esteem according to the general characteristics of the study subjects, correlation analysis was conducted to determine the correlation between major variables, and regression analysis was conducted to verify the mediating effect of learning time. As a result of the analysis, first, there was no difference in the academic engagement of middle school students by group. In the learning time, middle school students in the city area were significantly higher than those in the township area, male students had higher self-esteem than female students, and students in the city area had significantly higher self-esteem as the grade went up. Second, as a result of correlation analysis, learning time, academic engagement, and self-esteem showed a positive correlation. Third, in the entire group not divided by group, both the direct path through which academic engagement reaches self-esteem and the partial mediating model from learning time to self-esteem showed significant effects. In the analysis by gender, only female students excluding male students showed a partial mediating effect, and the analysis results by region showed a partial mediating effect only on students in the city. The analysis results by grade showed a partial mediating effect only for second-year middle school students. In order to improve the self-esteem of middle school students, education and counseling should be conducted in consideration of not only individual differences by gender and grade, but also the region in which they live.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.591-599
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• 2020

Deep learning is widely utilized to classify or recognize objects of real-world. An abundance of data is trained on high-performance computers and a trained model is generated, and then the model is loaded in an inferencer. The inferencer is used in various environments, so that it may cause unrecognized objects or low-accuracy objects. To solve this problem, real-world objects are collected and they are trained periodically. However, not only is it difficult to immediately improve the recognition rate, but is not easy to learn an inferencer on embedded devices. We propose a real-time handwriting recognizer based on partial learning on embedded devices. The recognizer provides a training environment which partially learn on embedded devices at every user request, and its trained model is updated in real time. As this can improve intelligence of the recognizer automatically, recognition rate of unrecognized handwriting increases. We experimentally prove that learning and reasoning are possible for 22 numbers and letters on RK3399 devices.

• Journal of Korean Elementary Science Education
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• v.35 no.3
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• pp.326-335
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• 2016

The purpose of this study is to devise the concept of properties of the light on learning progressions for 3-6 grade students in the elementary school. For the purpose, this study creates a construct map of the properties of the light. Ordered multiple-choice items were developed in view of the construct map and presented to 200, 3~6 grade students in the elementary school. A partial credit model of Rasch model was used to analyze the results of those items, and the learning progressions was devised from the analysis results. The study can be summarized as follows: the construct modeling approach was used to devise the properties of light on learning progressions. As a result, the concept was selected for the core standards of construct map. Based on the construct map, the multiple-choice items were developed for students' conceptual understanding. The items were analyzed using the partial credit model, and the analysis findings showed that they were appropriate to assess students about their level of understanding of the properties of light. Finally, learning progressions were devised with the use of item analysis results.

• English Language & Literature Teaching
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• v.13 no.1
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• pp.27-46
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• 2007

Teachers can maximize students' L2 learning by knowing preferred learning styles. This paper presents the results of a survey that asked 309 English learners to identify their perceptual learning style preferences. It further compared students' favored learning styles in terms of their gender and major field of study and explored a possible link between learning styles and English achievement. Collected data using Reid's (1995) questionnaire were analyzed by descriptive statistics, MANOVA, ANOVA, correlations, multiple regressions including squared partial correlations, and Cronbach's alpha. The results indicated that Korean students favored English learning in group regardless of gender, while their preferred mode of learning was significantly different in regard to their major field of study. Certain learning styles might be profitable for English achievement. Multiple regression analyses revealed that individual mode of learning was the best predictor of students' English achievement. It furthermore showed significant relationships between visual and individual styles of learning and English performance. The findings of the study reflected students' English learning context in which English native-speaking teachers frequently used communicative pair and small group activities for speaking practices that were consonant with students' learning styles.

• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.69-78
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• 2006

Educational evaluation is not the work to rank learners hierarchically, but the thing to increase the educational effectiveness by solving a learner's problem and improving the education process from the proper evaluation. The conventional evaluation systems have measured a learner's recognition level by dichotomy. Although they support the evaluation depending on a learner's academic ability and supply the feedback for wrong selection, it is insufficient to take out study-motive and give the establishment for a guidance point of learning. In this paper, we propose the web-based individual adaptive testing system in considering partial score for a learner. Our system are effective to estimate the ability of learner by considering partial score in detail and offer an feedback study from the self-diagnosis function for a learning results.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.