• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• The Journal of Information Technology
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• v.12 no.4
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• pp.33-37
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• 2009

This paper analyzed effects Web-Based cooperation Learning have on improvement in Social Adaptability and problematic behavior, using Web-Based cooperation Learning system that is designed for Mental retardation children. Is Made Teaching Design according to students level, based on elementary school Bareunsaenghwal subject. Teaching and Learning program that is going with flash and PPT Embodied is. Designed to bulletin the evaluation data for cooperation studying after studying a part of the lesson. Verification of learning effect went with experimental group and comparison group consisted of groups of 8. Students studied the Internet web data and Teaching material paper and they took pencil test. As a result, point of post-inspection was higher than that of pre-inspection. Web-Based cooperation Learning is confirmed to be effective on Social Adaptability and problematic behavior improvement.

• Korean Journal of Child Studies
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• v.23 no.1
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• pp.55-70
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• 2002

The influence of peer collaboration on children's analogical abilities was studied with 120 9-year-old participants. After the pre-test, which determined the analogical level of the children, each child was assigned to 1 of 4 different learning conditions: cued/non-cued peer collaborative learning, or cued/non-cued individual learning conditions. The post-test showed changes in their analogical abilities. That is, results showed that cued peer collaborative learned improved the analogical abilities of the children, but the pattern of improvement was different by prior level of analogical abilities. We explained improvement in analogical ability by the context effect of peer collaborative learning and by the interactive effect of context with basic cognitive abilities of the children. We suggested implications of the present results for educational practice.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.73-79
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• 2017

The purpose of this study was to identify differences in stress, interpersonal relations, learning perceptions and self-efficacy of nursing students participating in team based learning simulation practices. The study group consisted of 24 students in the third grade who attended nursing college. Data were collected using questionnaires. The collected data were analyzed by SPSS 22 version's descriptive statistics, t-test, and paired t-test. The results showed that stress and learning perceptions were significantly different according to gender, leave of absence in pre and post - test. After the practice of team - based simulation, the stress of nursing college students decreased and interpersonal relations and self - efficacy were improved. The results of this study showed that nursing students' gender and absence of school should be considered in the team - based simulation. Further research on team based simulation practice involving interpersonal relationships is needed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1100-1104
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• 2017

A detector-based approach shows the limited performances for the defect inspections such as shallow fine cracks and indistinguishable defects from background. Deep learning technique is widely used for object recognition and it's applications to detect defects have been gradually attempted. Deep learning requires huge scale of learning data, but acquisition of data can be limited in some industrial application. The possibility of applying CNN which is one of the deep learning approaches for surface defect inspection is investigated for industrial parts whose detection difficulty is challenging and learning data is not sufficient. VOV is adopted for pre-processing and to obtain a resonable number of ROIs for a data augmentation. Then CNN method is applied for the classification. Three CNN networks, AlexNet, VGGNet, and mofified VGGNet are compared for experiments of defects detection.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Educational Technology International
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• v.18 no.2
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• pp.131-157
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• 2017

The purposes of the study were to investigate the participation of artists in paper circuit computing learning and to conduct an in-depth study on the formation and development of practical knowledge. To do this, we selected as research participants six artists who participated in the learning program of an art museum, and used various methods such as pre-open questionnaires, participation observation, and individual interviews to collect data. The collected data were analyzed based on community of practice theory. Results showed that the artists participated in the learning based on a desire to use new technology or find a new work production method for interacting with their audiences. In addition, the artists actively formed practical knowledge in the curriculum and tried to apply paper circuit computing to their works. To continuously develop the research, participants formed a study group or set up a practical goal through planned exhibitions. The results of this study can provide implications for practical approaches to, and utilization of, paper circuit computing.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.321-328
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• 2024

This paper designs a disease prediction algorithm to diagnose migraine among the types of diseases in advance by learning algorithms using machine learning-based time series analysis. This study utilizes patient data statistics, such as electroencephalogram activity, to design a prediction algorithm to determine the onset signals of migraine symptoms, so that patients can efficiently predict and manage their disease. The results of the study evaluate how accurate the proposed prediction algorithm is in predicting migraine and how quickly it can predict the onset of migraine for disease prevention purposes. In this paper, a machine learning algorithm is used to analyze time series of data indicators used for migraine identification. We designed an algorithm that can efficiently predict and manage patients' diseases by quickly determining the onset signaling symptoms of disease development using existing patient data as input. The experimental results show that the proposed prediction algorithm can accurately predict the occurrence of migraine using machine learning algorithms.

• Journal of the Korea society of information convergence
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• v.6 no.2
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• pp.15-22
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• 2013

Applying iterative learning utilizing SNS & Community to the class for pre-kindergarten teachers, the effectiveness of teaching satisfaction, self-efficacy, and curriculum understanding was verified. A iterative learning model utilizing SNS & Community in teachers leading traditional off-line teaching at college education field was applied separately into thinking to one-self by advance organizer, thinking together by presentation in the beginning of the class, and sharing the thoughts by community activities after the class. Iterative learning begins by being sent SNS to students from teachers before the class, but learners for themselves subsequently start to proceed self-directed learning activities. As a result, class satisfaction and understanding of pedagogy have been increased, and it had a positive influence on self-efficacy. Thus, it is to suggest utilizable SNS of professors and a teaching method utilizing Community to college students who need basic learning skills.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.22 no.4
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• pp.416-423
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• 2015

Purpose: The purpose of this study was to evaluate the effect of a self-evaluation method using video recording on competency in nursing skills, self-directed learning ability, and academic self-efficacy in nursing students. Methods: The study design was a non-equivalent pre-post quasi-experimental design. The experimental and control groups were randomly assigned with 35 participants in each group. Interventions for the experimental group were video recording and students' self-evaluation of what they did. Nursing skills included in the study were tube feeding, intradermal injection, subcutaneous injection, and intramuscular injection. Competency in nursing skills was measured one time at the end of the study using a checklist. Self-directed learning ability and academic self-efficacy were measured 3 times (pre-, mid-, and post-intervention) over the 8 weeks. Independent t-test, chi-square test, and repeated measures ANOVA were used for data analyses. Results: There was no statistically significant difference for competency in nursing skills and self-directed learning ability over the 8 weeks of the practice session. There was a significant difference in academic self-efficacy by groups over time. Conclusion: Results indicate that self-evaluation method using video recording is an effective learning way to improve academic achievement in nursing students.