• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.80-83
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• 2004

The core purpose of the system proposed in this paper is to help learners pursue proactive and self-oriented education by allowing learners to proactively configure their own content, that is, learners no longer have to be restricted by prescribed sequence of lectures. Although a variety of standardization and Learning Management System (LMS) were produced to develop and effectively manage web contents in response to active diffusion of internet application, practical changes to assist online learners are not yet to be found. In this paper, I would like to introduce a LMS that can support self-leading education by providing various types of learners at Virtual University with delicately organized educational contents for maximum efficiency. The system allows a learner to select a lecture or a chapter which has been presorted to meet his educational needs and intellectual ability. In general, most LMSs cannot meet every individual's educational needs because they structure their programs by letting learners simply choose from a list of available lectures at prescribed level or difficulty. However the Self-Leading LMS eliminates such boundaries by allowing learners to choose contents and difficulty within the limit set by their own educational competence.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.167-178
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• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.243-246
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• 2011

학습자의 감성 상태가 충분히 반영되는 오프라인 수업과 달리 지금까지 대부분의 e-러닝은 학습자의 감성 정보를 수업에 효과적으로 반영하지 못했다. 이러한 한계점은 e-러닝의 학습 효과성을 저해하는 문제 중 하나로 지적되었다. 이 문제를 해결하기 위해 학습자의 뇌파를 통해 감성을 인식하고 감성 상태에 따라 적절한 학습 콘텐츠 타입을 추천하여 학습 효과를 증대 시킬 수 있는 방법론이 주목을 받고 있다. 본 논문에서는 기 수집된 학습자들의 감성(뇌파) 데이터를 분석하여 콘텐츠 타입 선호도를 파악한 후 프로파일 데이터를 활용하여 상관계수 기반 NN-Recommendation 학습 콘텐츠 타입 추천 시스템을 제안 하고자 한다. 이 시스템은 일반적인 추천시스템에서 발생하는 Cold-start 문제를 해결할 수 있으며 특히 본 연구에서는 보다나은 추천 정확도를 위해 프로파일 각 속성에 자동적으로 가중치를 부여하는 기법을 제시하여 향상된 성능을 보이게 됨을 실험을 통해 확인 하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.370-378
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• 2012

This paper proposes a method of constructing of hybrid web-based smart learning system to operable in a variety of mobile devices. To do this, the proposed system is developed a learning system with standardized and enhanced functions. In the proposed method, API specifications based on the standard functionality of smart learning system are created. And then, by building the API provider on a legacy system an organic linkage between the legacy system and the smart learning system is guaranteed. A standard API method is applied to data integration between the PC-based learning system and the smart learning system. The smart learning system interacts with legacy learning systems though Json/XML data forms via the https protocol. As a result, the legacy system using the proposed method dose not require major modifications and changes for a smart learning service.

• Computers and Concrete
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• v.20 no.6
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.

• ETRI Journal
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• v.44 no.5
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• pp.837-848
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• 2022

Analog circuit design is comparatively more complex than its digital counterpart due to its nonlinearity and low level of abstraction. This study proposes a novel low-level hybrid of the sine-cosine algorithm (SCA) and modified grey-wolf optimization (mGWO) algorithm for machine learning-based design automation of CMOS analog circuits using an all-CMOS voltage reference circuit in 40-nm standard process. The optimization algorithm's efficiency is further tested using classical functions, showing that it outperforms other competing algorithms. The objective of the optimization is to minimize the variation and power usage, while satisfying all the design limitations. Through the interchange of scripts for information exchange between two environments, the SCA-mGWO algorithm is implemented and simultaneously simulated. The results show the robustness of analog circuit design generated using the SCA-mGWO algorithm, over various corners, resulting in a percentage variation of 0.85%. Monte Carlo analysis is also performed on the presented analog circuit for output voltage and percentage variation resulting in significantly low mean and standard deviation.

• Journal of Engineering Education Research
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• v.14 no.4
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• pp.3-10
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• 2011

This study examines the possibility of a learning environment design model for enhancing creativity in engineering education. Recent studies about the online support system and the blended learning pertinent to creativity have provided us with some new perspectives on how to teach the creativity in engineering college. A learning environment design model was developed to enhance the creativity of the engineering students. A model was developed by following three steps: finding out the general principles for enhancing creativity in engineering education by reviewing the relevant literature; extracting the theoretical components for the model by categorizing the general principles; and finally, developing specific guidelines based on those theoretical components. Seven general design principles and three theoretical components could be identified, and a set of specific design guidelines was suggested. The result of this study is significant in terms of guiding the future development projects for creativity education.

• Journal of Engineering Education Research
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• v.9 no.3
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• pp.5-21
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• 2006

The engineer's creativity is becoming more important as high value-added products are required. In this paper, a new curriculum model for creative engineering education has been developed. This study proposes the method of applying Chem-E-Car to the chemical engineering education for students to develop creativity. The Chem-E-Car is used as a given problem to students for developing safety study, teamwork, communication skill and creativity. The PBL(problem based Learning) is used in the class. The problem in this case is to make the Chem-E-Car, a shoebox sized car powered only by chemical reactions. Four types of Chem-E-Car such as turbine, rocket, voltaic cell and fuel cell are developed through out this program. The fuel cell powered Chem-E-Car is emphasized to students as new problems with constraints. This paper shows how the students solved the problems with creativity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.81-87
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• 2018

Problem-based learning (PBL) appears to be a superior and effective strategy to train competent and skilled practitioners and to promote long-term retention of knowledge and skills acquired during the learning experience. This study concerns the implementation of PBL in the online environment and face-to-face PBL. An online environment allows participants to communicate with one another, view presentations or videos, interact with other participants, and engage with resources in work groups. Nowadays, education is accessible everywhere with the use of digital devices. Educational institutions subscribe to GSuite for Education, and Google introduced its Google Classroom as an e-learning platform. This study aims to analyze Google Classroom and to design PBL for Mongolian students taking Korean courses. The main objective of this paper is to identify the usability and evaluation of Google Classroom. The result of this study will be a proposed e-learning platform for Dornod University, Mongolia, which is initially needed in the Natural Science and Business Department.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.