• Proceedings of the Korea Contents Association Conference
• /
• 2006.11a
• /
• pp.218-222
• /
• 2006

In making 3D animation with digital technology, it is necessary to increase productivity and reusability by managing production pipeline systematically through standardization of animation content. For this purpose, we try to develop the animation content management system that can manage all kind of information on the production pipeline, based on SCORM of e-teaming by considering production, publication and re-editing. A scene as the unit of visual semantics is standardize into an object that contains meta-data of place, cast, weather, season, time and viewpoint about the scene. The meta-data of content includes a lot of information of copyright, publication, description, etc, so that it plays an important role on the management and the publication. If an effective management system of meta-data such as ontology will be implemented, it is possible to search multimedia contents powerfully. Hence, it will bring on production and publication of UCC. Using the meta-data of content object, user and producer can easily search and reuse the contents. Hence, they can choose the contents object according to their preference and reproduce their own creative animation by reorganizing and packaging the selected objects.

• Journal of Korean Elementary Science Education
• /
• v.32 no.1
• /
• pp.104-112
• /
• 2013

We examined the strategies to stimulate the reflective thinking using science notebook for the improvement of problem solving ability which is one of the core skills for the future. The strategies we derived have four steps which are input, output, solving mission and reflection as my own mirror. We applied the strategies to the 6th grade class for autumn semester in order to examine the students learning process and the result. We could observe that students looked into their own learning and had a time to look back their activities in the class. We could also confirmed that science notebook would be effective to improve the problem solving as stimulating the reflective thinking. In addition, we could specify the strategy of using science notebook in the class. At a 'input' stage, students should be able to choose their own learning style as their preference and teacher need to give them proper feedback. Interaction with peers should be emphasized during the activities as 'question attack' and 'question defense' in 'output' stage and 'solving mission' stage. You should suggest the students various method to record their thought from looking back their classroom activities instead of mere writing. We also examine the students achievement from the students' notebook and Meta Cognitive Awareness test. As a result, students who had studied using science notebook showed statistically meaningful higher achievement than controlled students.

• School Mathematics
• /
• v.5 no.4
• /
• pp.477-506
• /
• 2003

In this paper, we analyze nine-grade students' conceptions of parameters, their relation to unknowns and variables and the process of understanding of letters in problem solving of equations and functions. The roles of letters become different according to the letters-used contexts and the meaning of letters Is changed in the process of being used. But, students do not understand the meaning of letters correctly, especially that of parameter. As a result, students operate letters in algebraic expressions according to the syntax without understanding the distinction between the roles. Therefore, the parameter of learning should focus on the dynamic change of roles and the flexible thinking of using letters. We develop a self-regulation model based on the monitoring working question in teaching-learning situations. We expect that this model helps students understand concepts of letters that enable to construct meaning in a concrete context.

• Nuclear Engineering and Technology
• /
• v.55 no.9
• /
• pp.3423-3440
• /
• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.17 no.4
• /
• pp.221-228
• /
• 2024

Predicting insurance claims is a key task for insurance companies to manage risks and maintain financial stability. Accurate insurance claim predictions enable insurers to set appropriate premiums, reduce unexpected losses, and improve the quality of customer service. This study aims to enhance the performance of insurance claim prediction models by applying ensemble learning techniques. The predictive performance of models such as Random Forest, Gradient Boosting Machine (GBM), XGBoost, Stacking, and the proposed Dynamic Weighted Ensemble (DWE) model were compared and analyzed. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). Experimental results showed that the DWE model outperformed others in terms of evaluation metrics, achieving optimal predictive performance by combining the prediction results of Random Forest, XGBoost, LR, and LightGBM. This study demonstrates that ensemble learning techniques are effective in improving the accuracy of insurance claim predictions and suggests the potential utilization of AI-based predictive models in the insurance industry.

• Advances in nano research
• /
• v.13 no.5
• /
• pp.499-512
• /
• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• Journal of Internet Computing and Services
• /
• v.25 no.2
• /
• pp.21-28
• /
• 2024

Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.

• Journal of Science Education
• /
• v.39 no.2
• /
• pp.165-179
• /
• 2015

In this study, the effects of writing a weekly report on the students' self-directed learning, the attitudes toward science, and the academic achievements were examined. Two hundred and three students, second graders of a high school participated. Experimental group performed writing a weekly report, while the comparative group performed regular science lessons. The results of this study are as follows: First, MSLQ test showed that there was statistically significant difference in the self-directed learning skills(p<.05). For sub-factors of motivation region, such as internal goals, extrinsic goals, learning beliefs, task value, and self-efficacy and for sub-factors of learning strategy region, such as meta-cognition, peer learning, time management, critical thinking, and demonstrations showed statistically significant results. Second, TOSRA test showed that there was no statistically significant difference in the attitudes toward science (p>.05). However, for sub-factors, such as scientific inquiry and joy to science class showed statistically significant results. Third, there was no statistically significant difference in the academic achievement in Chemistry I class (p>.05). However, top and low achievement level showed statistically significant results.

• Journal of Gifted/Talented Education
• /
• v.23 no.5
• /
• pp.697-713
• /
• 2013

The research aimed to investigate characteristics of middle school students enrolled in a science gifted education center affiliated with university in terms of multiple intelligence, self-regulated learning and personality traits. The 89 subjects in the study responded to questionnaires of multiple intelligence, self-regulated learning ability and a personality trait in October, 2011. It was found that both science and math gifted students presented intrapersonal intelligence as strength and logical-mathematical intelligence as weakness. While physics and earth science gifted ones showed spatial intelligence as strength, chemistry and biology gifted ones did intrapersonal intelligence. For self-regulated learning ability, both science and mathematics gifted students tend to show higher levels than general students, in particular, cognitive and motivation strategies comparatively higher than meta-cognition and environment condition strategies. Characteristics of personal traits widely distributed across science and mathematics gifted students, showing that each gifted student presented distinct characteristics individually. Those gifted students showing certain intelligence such as spatial, intrapersonal, or natural intelligences as strength also showed different characteristics of self-regulated learning ability and personal traits among students showing same intelligence as strength. It was concluded that science and mathematics gifted students showed various characteristics of multiple intelligences, self-regulated learning ability, and personal traits across science and mathematics areas.

• Journal of Family Resource Management and Policy Review
• /
• v.8 no.1
• /
• pp.47-60
• /
• 2004

Recognizing a tremendous increase in the Internet users and popularity of E-learning through the Internet, this study attempted to analyze interactive financial education web sites for children. Using meta search engines and major search engines, interactive financial education web sites identified based on the three criteria and analyzed in terms of the appropriateness for specific age groups, the coverage of contents related to the basic knowledge for financial literacy, and the interactive activities. The results showed that financial education web sites for children were needed to be improved in terms of both quantity and quality. The study also provides a guideline how to search for an appropriate financial education web sites for children when parents want teach about money to their children.