• The Journal of Korean Association of Computer Education
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• v.18 no.1
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• pp.21-33
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• 2015

The purpose of this study has been found to be effective using web-based instruction than traditional teaching-learning method on academic achievement applying the meta-analysis method. The results of this study were as follows: First, The 85% subject of analysis of web-based instruction selected in this study turned out to be clear effective than traditional teaching-learning method in academic achievement of students. Second, Web-based instruction is more effective for academic achievement of elementary school students and university students than for middle school students and high school students relatively. Third, Web-based instruction is a most effective method in social subject and physical education but less effective in language subject. The overall results of this study concluded more powerful and big decisions which have integrated each different effects on academic achievement of studies web-based instruction method applying meta-analysis. Through this study, make better results were obtained and suggested the base line data and direction for follow up studies.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.

• Korean Medical Education Review
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• v.21 no.1
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• pp.51-58
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• 2019

Although 'assessment for learning' rather than 'assessment of learning' has been emphasized recently, student learning before examinations is still unclear. The purpose of this study was to investigate pre-assessment learning activities (PALA) and to find mechanism factors (MF) that influence those activities. Moreover, we compared the PALA and MF of written exams with those of the clinical performance examination/objective structured clinical examination (CPX/OSCE) in third-year (N=121) and fourth-year (N=108) medical students. Through literature review and discussion, questionnaires with a 5-point Likert scale were developed to measure PALA and MF. PALA had the constructs of cognitive and meta-cognitive activities, and MF had sub-components of personal, interpersonal, and environmental factors. Cronbach's ${\alpha}$ coefficient was used to calculate survey reliability, while the Pearson correlation coefficient and multiple regression analysis were used to investigate the influence of MF on PALA. A paired t-test was applied to compare the PALA and MF of written exams with those of CPX/OSCE in third and fourth year students. The Pearson correlation coefficients between PALA and MF were 0.479 for written exams and 0.508 for CPX/OSCE. MF explained 24.1% of the PALA in written exams and 25.9% of PALA in CPX/OSCE. Both PALA and MF showed significant differences between written exams and CPX/OSCE in third-year students, whereas those in fourth-year students showed no differences. Educators need to consider MFs that influence the PALA to encourage 'assessment for learning'.

• Education of Primary School Mathematics
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• v.20 no.1
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• pp.85-99
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• 2017

In order to improve mathematical problem-solving ability, there has been a need for research on practical application of meta-affect which is found to play an important role in problem-solving procedure. In this study, we analyzed the characteristics of the sociodynamical aspects of the meta-affective factor of the successful problem-solving procedure of small groups in the context of collaboration, which is known that it overcomes difficulties in research methods for meta-affect and activates positive meta-affect, and works effectively in actual problem-solving activities. For this purpose, meta-functional type of meta-affect and transact elements of collaboration were identified as the criterion for analysis. This study grasps the characteristics about sociodynamical function of meta-affect that results in successful problem solving by observing and analyzing the case of the transact structure associated with the meta-functional type of meta-affect appearing in actual episode unit of the collaborative mathematical problem-solving activity of elementary school students. The results of this study suggest that it provides practical implications for the implementation of teaching and learning methods of successful mathematical problem solving in the aspect of affective-sociodynamics.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.661-667
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• 2010

A lot of existence e-learning are connected SCORM and LMS. And u-learning was researching as one of the new trend. But there are few research paper to connect the existing SCORM and LMS. In this paper, we proposed u-learning framework with connect the SCORM and LMS. And we used the mobile equipment transform module and learning object reconstruction module to apply each different characteristics of mobile equipment. Especially, information of the mobile equipment was stored and managed using the meta-data of the equipment.

• Journal of the Korean School Mathematics Society
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• v.3 no.1
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• pp.131-148
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• 2000

Above all, the ability to solve problems must be emphasized as a basic skill of mathematics, but it is neglected when we teach. In this study, learning task means ［same meaning］ ［same form］ ［same technique］, so I tried to extend mathematical scholastic ability of the students as an extensional problem solving that is a basic element of mathematics. The purpose of this study is the investigation of level type learning, using the basic learning elements to extend thinking ability. From the constructed hypothesis as follows and then implement it. I selected basic learning elements from an analyzed textbook and then task learning material was created for each level type learning. The problem solving ability will be extended through the level type learning of the small group, using the level type learning task material. The conclusions this study are as follows. The level type learning in small group learning, using and making level type learning material, having basic learning elements in analysed text are. Basic learning content is understood clearly and deeply, so, fundamentally, it is effective in achieving the problem solving in mathematics. It is an effective method to achieve the meta-cognitive faculty because achieved the expected method of solving problems and resulted in the true learning of content.

• Advances in Computational Design
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• v.1 no.4
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.241-250
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• 2020

In this study, the methodology of the springback prediction of automotive parts applied 3rd generation AHSS was investigated using the response surface model analysis based on a regression model, and the meta model analysis based on a Kriging model. To design the learning data set for constructing the springback prediction models, and the experimental design was conducted at three levels for each processing variable using the definitive screening designs method. The hat-shaped member, which is the basic shape of the member parts, was selected and the springback values were measured for each processing type and processing variable using the finite element analysis. When the nonlinearity of the variables is small during the hat-shaped member forming, the response surface model and the meta model can provide the same processing parameter. However, the accuracy of the springback prediction of the meta model is better than the response surface model. Even in the case of the simple shape parts forming, the springback prediction accuracy of the meta model is better than that of the response surface model, when more variables are considered and the nonlinearity effect of the variables is large. The efficient global optimization algorithm-based Kriging is appropriate in resolving the high computational complexity optimization problems such as developing automotive parts.

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.781-786
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• 2009

The cloud computing environment is a new trend of web based application parts. It can be IT business model that is able to easily support learning service and allocate resources through the internet to users. U-learning also is a maximal model with efficiency of the internet based learning. Thus, in this research, we proposed a design of u-learning's teaching and learning model that is applying the internet based learning. Proposal method is to fit u-learning and has 7 steps: Preparing, planning, gathering, learning process, analysis and evaluation, and feedback. We make a cloud u-learning server and cloud LMS to process and manage the service. And We also make a mobile devices meta data to aware the model.