• Journal of Fisheries and Marine Sciences Education
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• v.23 no.1
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• pp.105-115
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• 2011

Education is focused on how to nurture creative problem-solving skills talent in rapidly changing information society. The algorithm education of computer science is effective in improvement of students' logical thinking and problem solving capability. However, the algorithm education is very difficult to teach in elementary students level. Because it is difficult to understand abstract characteristic of algorithm. Therefore we developed educational contents based on the principle of the algorithm for improve students' logical thinking and problem-solving capability in this study. And educational contents contain interesting elements of the game. So, students will be interested in algorithm learning and participate actively through developed educational contents. Furthermore, students' creative problem-solving capability may improve through algorithm learning.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• Journal of Engineering Education Research
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• v.21 no.5
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• pp.32-39
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• 2018

The purpose of this study was to develop and validate a behaviorally anchored rating scale for peer evaluation in group projects based on social interdependence theory. A mixed method involving a qualitative and quantitative approach was used in this study. In the qualitative study, both the individual and group interviews were conducted to college students regarding their cooperative learning experiences. Data from this qualitative research was analyzed based on 5 elements of cooperative learning and 'critical incidents' were extracted from students' own voices that would serve as specific rating criteria in the scale. Once the 'critical incidents' have been incorporated into the scale, validation from 3 independent experts was conducted. In the quantitative study, correlations with relevant variables were analyzed to examine the criteria-referenced validity. Findings suggest that the behaviorally anchored rating scale for peer evaluation in group projects can be used in various team-based learning contexts.

• Journal of information and communication convergence engineering
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• v.17 no.3
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• pp.213-220
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• 2019

This paper shows that novel engineering (NE) is a creative learning process (CLP) based on Seymour Papert's constructionism. First, the paper introduces NE, CLP, and constructionism. Next, a sample NE lesson is explored. NE is an innovative way of integrating literacy into an engineering discipline that was developed by the Center for Engineering Education and Outreach (CEEO) at Tufts University. NE consists of seven steps: picking a book, identifying problems, designing solutions, building, feedback, upgrading solutions, and reconstructing stories. Lifelong Kindergarten by Mitchel Resnick of the MIT Media Lab describes CLP, and the four elements necessary for a lesson to be creative. NE can be viewed as one of the most creative, comprehensive learning models ever developed. NE integrates several paradigms in Korea, following all the constructs of both CLP and constructionism. The aim of this paper is to show that NE is based on both CLP and constructionism.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.30-31
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• 2021

This study aims to build a deep learning model that can predict the value of concrete mixing properties according to a given concrete strength value. A model was created for a total of 1,291 concrete data, including 8 characteristics related to concrete mixing elements and environment, and the compressive strength of concrete. As the deep learning model, DNN-3L-256N, which showed the best performance on the prior study, was used. The average value for each characteristic of the data set was used as the initial input value. In results, in the case of 'curing temperature', which had a narrow range of values in the existing data set, showed the lowest error rate with less than 1% error based on MAE. The highest error rate with an error of 12 to 14% for fly and bfs.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.89-94
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• 2024

The main purpose of the study is to determine the main elements of the use of digital technologies for learning a foreign language in educational institutions. The era of digital technologies is a transition from the traditional format of working with information to a digital format. This is the era of the total domination of digital technologies. Digital technologies have gained an unprecedented rapid and general distribution. In recent years, all spheres of human life have already undergone the intervention of digital technologies. Therefore, it is precisely the educational industry that faces a difficult task - to move to a new level of education, where digital technologies will be actively used, allowing you to conveniently and quickly work in the information field for more effective learning and development. The study has limitations and they relate to the fact that the practical activities of the process of using digital technologies in the system of preparing the study of a foreign language were not taken into account.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.3
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• pp.115-138
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• 2017

Electronic record types are becoming diverse, and "composite electronic records," which are made up of various types of electronic records associated with functionality or user interaction that does not exist in current electronic document formats, are increasing. To ensure a continuous access to composite electronic records, metadata construction is a prerequisite for electronic records archiving. In this paper, we propose a metadata that can support archiving of composite electronic records associated with interactive functionality. The common elements were derived from an analysis of both domestic and international file format registry projects, and metadata elements related to functional requirements were identified from the analysis of the records on nursing education e-learning contents. We proposed the metadata elements for archiving composite electronic records, which consist of 25 high-level elements and 138 subelements.

• Education of Primary School Mathematics
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• v.27 no.1
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• pp.57-74
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• 2024

This paper analyzed the programming education specialized lessons presented in two types of elementary school mathematics textbooks according to the revised Japanese curriculum in 2017. First, this paper presented in detail how each activity is connected to Korean mathematics areas, what elements of mathematics can be learned through programming education, how each activity is structured, and how the actual programming according to the textbook activities is structured. In Japanese textbooks, geometry and measurement areas were presented the most among Korean mathematics content areas, and mathematical elements such as sequences, rules, and algorithms were most implemented for learning. Digital learning tools that make up actual programming present more elements than those presented in the textbooks and are presented in great detail so that students can do actual programming. Lastly, in blocks, motion, control, and calculation blocks were used a lot. Based on these research results, this study provides implications when conducting programming-related education in Korea.

• Composites Research
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• v.35 no.5
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• pp.317-321
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• 2022

In this study, the PIC design method with machine learning that automatically assigning different stacking sequences according to loading types was applied bumper beam. The input value and labels of the training data for applying machine learning were defined as coordinates and loading types of reference elements that are part of the total elements, respectively. In order to compare the 2D and 3D implementation method, which are methods of representing coordinate value, training data were generated, and machine learning models were trained with each method. The 2D implementation method is divided FE model into each face and generating learning data and training machine learning models accordingly. The 3D implementation method is training one machine learning model by generating training data from the entire finite element model. The hyperparameter were tuned to optimal values through the Bayesian algorithm, and the k-NN classification method showed the highest prediction rate and AUC-ROC among the tuned models. The 3D implementation method revealed higher performance than the 2D implementation method. The loading type data predicted through the machine learning model were mapped to the finite element model and comparatively verified through FE analysis. It was found that 3D implementation PIC bumper beam was superior to 2D implementation and uni-stacking sequence composite bumper.