• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.337-357
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• 2022

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.167-177
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• 2024

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

• Journal of Information Science Theory and Practice
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• v.4 no.3
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• pp.6-27
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• 2016

There has been a growing interest and enthusiasm for the application of virtual worlds in learning and training. This research proposes a design framework of a virtual world-based learning environment that integrates two unique features of the virtual world technology, immersion and interactivity, with an instructional strategy that promotes self-regulatory learning. We demonstrate the usefulness and assess the effectiveness of our design in the context of information security learning. In particular, the information security learning module implemented in Second Life was incorporated into an Introduction to Information Security course. Data from pre- and post- learning surveys were used to evaluate the effectiveness of the learning module. Overall, the results strongly suggest that the virtual world-based learning environment enhances information security learning, thus supporting the effectiveness of the proposed design framework. Additional results suggest that learner traits have an important influence on learning outcomes through perceived enjoyment. The study offers useful design and implementation guidelines for organizations and universities to develop a virtual world-based learning environment. It also represents an initial step towards the design and explanation theories of virtual world-based learning environments.

• Journal of Science Education
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• v.46 no.3
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• pp.278-292
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• 2022

Researchers in science education have recently focused on service learning as an important educational approach to address environmental challenges including climate change. However, there are a limited number of studies focused on service learning in science education in Korea. In this study, based on existing related literature, the theoretical background of service learning is introduced and a conceptual framework that can be applied in the science education environment in Korea as a new approach to address environmental challenges is proposed. Given this, 112 relevant academic documents collected by searching a global database were analyzed. As a result, the theoretical background of service learning rooted in Dewey's experiential learning is introduced. Specifically, service learning is defined as a form of experiential learning that supports students in helping communities with structured services related to learning content, while at the same time fostering their in-depth understanding of content and engaging in civic and social responsibility as members of their community. In addition, a conceptual framework for service learning to address environmental challenges that can be applied to the science education environment in Korea is proposed. The proposed framework classifies the members of service learning into schools, students, and communities, and presents knowledge, experience, and learning through critical reflection as its core elements. It is important to actively implement service learning in science education to address various environmental challenges including climate change, biodiversity loss, air pollution, and deforestation, as just a few pressing challenges. Therefore, based on the proposed conceptual framework for service learning, studies that apply and verify diverse forms of educational approaches to addressing environmental challenges in local communities at school sites are needed.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.59-69
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• 2020

The purpose of this study is to design the framework of evaluation on learner's cognitive skill for artificial intelligence(AI) education through computational thinking. To design the rubric and framework for evaluating the change of leaner's intrinsic thinking, the evaluation process was consisted of a sequential stage with a) agency that cognitive learning assistance for data collection, b) abstraction that recognizes the pattern of data and performs the categorization process by decomposing the characteristics of collected data, and c) modeling that constructing algorithms based on refined data through abstraction. The evaluating framework was designed for not only the cognitive domain of learners' perceptions, learning, behaviors, and outcomes but also the areas of knowledge, competencies, and attitudes about the problem-solving process and results of learners to evaluate the changes of inherent cognitive learning about AI education. The results of the research are meaningful in that the evaluating framework for AI education was developed for the development of individualized evaluation tools according to the context of teaching and learning, and it could be used as a standard in various areas of AI education in the future.

• English Language & Literature Teaching
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• v.11 no.4
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• pp.335-363
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• 2005

One of the most important features in the 7th National Curriculum of English is the introduction of the middle school multi-purpose English classes. Despite the importance of the classes, there doesn't seem to be enough studies of developing learning materials for them. The purpose of the current study is, therefore, to develop English learning materials for the multi-purpose English classes based on the Task-Based Learning framework. To do so, various tasks were collected and adapted for the classes, and different teaching techniques suitable for the tasks were designed. It is hoped that this research will help teachers prepare for teaching materials for the classes, and students recognize their interests in English and to improve their English abilities.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.375-380
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• 2019

In this paper, we study learning process mining and analytic technology based on open education platform. A study on mining through personal learning history log data based on an open education platform such as MOOC which is growing in interest recently. This technology is to design and implement a learning process mining framework for discovering and analyzing meaningful learning processes and knowledge from learning history log data. Learning process mining framework technology is a technique for expressing, extracting, analyzing and visualizing the learning process to provide learners with improved learning processes and educational services.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.357-372
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• 2012

Existing e-learning systems not only require complex admission processes but also do not give stepwise education methods according to individual learners' characteristic. These circumstances cause learners to lose educational interest so that their educational efficiency decreases. In particular, the present e-learning systems do not provide educational approaches suitable for infant and elementary children. Under this system, the e-learning education for children does not proceed completely without guardians. To solve this problem, we design and implement an English e-learning system for elementary children based on friendly and comfortable user interfaces. For children, the proposed system reflects their age and individual interesting per each e-learning stage. This system supports both the Web application platform and smart phone application platform for various client requirements. The proposed system manages 3 classes as English learning content. Learners can experience their own English e-learning course in each class, which is compiled by current educational ability. In addition to the general functions in e-learning system, the proposed system develops content buffering algorithm to reduce data traffic in server.