• Journal of Engineering Education Research
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• v.13 no.6
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• pp.14-23
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• 2010

Whether the engineering department of universities employs ideal usage of e-learning or not needs to be investigated as many engineering departments diversify the use of the e-learning elements for educational purpose. Applying the teaching and learning methods and characteristics would lead to better strategies which are applied to development of contents and deployment of the e-learning courses. This study examines the characteristics and approaches of the usage of e-learning elements used by some instructors who use multimedia contents in offline teaching and learning environment. The results of this study shows that the e-learning elements assist the face-to-face course and the interactions are manifested in the classroom rather than in online setting. Lecture, hands-on-practice, simulation, and PBL(Problem-based learning) are turned out to be the major teaching and learning methods. This study signifies the need for use of various teaching and learning methods by the instructors and provision of PBL environment.

• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.40-48
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• 2022

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab's AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS's complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab's production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab's AMHS.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.385-398
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• 2020

This study aims to examine genetics problem-solving processes of high school students with different learning approaches. Two second graders in high school participated in a task that required solving the complicated pedigree problem. The participants had similar academic achievements in life science but one had a deep learning approach while the other had a surface learning approach. In order to analyze in depth the students' problem-solving processes, each student's problem-solving process was video-recorded, and each student conducted a think-aloud interview after solving the problem. Although students showed similar errors at the first trial in solving the problem, they showed different problem-solving process at the last trial. Student A who had a deep learning approach voluntarily solved the problem three times and demonstrated correct conceptual framing to the three constraints using rule-based reasoning in the last trial. Student A monitored the consistency between the data and her own pedigree, and reflected the problem-solving process in the check phase of the last trial in solving the problem. Student A's problem-solving process in the third trial resembled a successful problem-solving algorithm. However, student B who had a surface learning approach, involuntarily repeated solving the problem twice, and focused and used only part of the data due to her goal-oriented attitude to solve the problem in seeking for answers. Student B showed incorrect conceptual framing by memory-bank or arbitrary reasoning, and maintained her incorrect conceptual framing to the constraints in two problem-solving processes. These findings can help in understanding the problem-solving processes of students who have different learning approaches, allowing teachers to better support students with difficulties in accessing genetics problems.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• English Language & Literature Teaching
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• v.13 no.2
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• pp.25-45
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• 2007

In this article, I discuss an International Collaborative Writing Course on the Internet (ICWCI) that focused on the learning effectiveness Korean EFL students (KEFLSs) perceived to be necessary to exchange with international EFL students (IEFLSs). The course development was based on an internet-based instructional module, applying widely accepted EFL theories for modern foreign language instruction: collaborative learning, process writing, project-based learning, and integrated approaches. Data from online discussion forum, mid-of-semester and end-of-semester surveys, and final oral interviews are conducted and discussed. KEFLSs and IEFLSs were questioned about (a) changes in attitude towards computers assisted language learning (CALL); (b) effect of computer background on motivation; (c) perception of their acquired writing skills; and (d) attitude towards collaborative learning. The result of this study demonstrated that the majority of ICWCI participants said they enjoyed the course, gained fruitful confidence in English communication and computer skills, and felt that they made significant progress in writing skills. In spite of positive benefits created by the ICWCI, it was found that there were some issues that are crucial to run appropriate networked collaborative courses. This study demonstrates that participants' computer skills, basic language proficiency, and local time differences are important factors to be considered when incorporating the ICWCI as these may affect the quality of online instructional courses and students' motivation toward network based collaboration interaction.

• Wind and Structures
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• v.34 no.6
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• pp.511-523
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• 2022

Detecting the icing on wind turbine blades built-in cold regions with conventional methods is always a very laborious, expensive and very difficult task. Regarding this issue, the use of smart systems has recently come to the agenda. It is quite possible to eliminate this issue by using the deep learning method, which is one of these methods. In this study, an application has been implemented that can detect icing on wind turbine blades images with visualization techniques based on deep learning using images. Pre-trained models of Resnet-50, VGG-16, VGG-19 and Inception-V3, which are well-known deep learning approaches, are used to classify objects automatically. Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques were considered depending on the deep learning methods used to predict the location of icing regions on the wind turbine blades accurately. It was clearly shown that the best visualization technique for localization is Score-CAM. Finally, visualization performance analyses in various cases which are close-up and remote photos of a wind turbine, density of icing and light were carried out using Score-CAM for Resnet-50. As a result, it is understood that these methods can detect icing occurring on the wind turbine with acceptable high accuracy.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.519-525
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• 2006

Advances in information and telecommunication technology increasingly reveal the potential of computer supported education. However, most computer supported learning systems until recently did not pay much attention to different characteristics of individual learners. Intelligent learning environments adaptive to learner's preferences and tasks are desired. Each learner has different preferences and needs, so it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed using learner models, and then user interfaces are customized in an adaptive manner to accommodate the preferences. In this research, the learning user interfaces were designed based on a learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Then, a learning style modeling is done from learner behavior patterns using Decision Tree and Neural Network approaches. In this way, an intelligent learning system adaptive to learning styles can be built. Further research efforts are being made to accommodate various other kinds of learner characteristics such as emotion and motivation as well as learning mastery in providing adaptive learning support.

• Journal of Korean Elementary Science Education
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• v.30 no.4
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• pp.442-458
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• 2011

Digital textbook project is one of government-driven project to improve education due to integrating technology. Digital textbook need to be universally designed to fit for each student. Recently, universal design for learning( UDL) gains great attention as one of promising approaches for the development of the digital textbook through giving various options and flexibility to all students. UDL has three main principles, first provide multiple means of representation, second provide multiple means of action and expression, third provide multiple means of engagement. The purpose of the study is to analyze fourth grade science textbook according to three UDL guidelines and suggest implications to improve an existing science textbook. The results indicated that fourth grade science digital textbook has been partly applied UDL guidelines like implementing multimedia and multi- mode contents, learning and communication tools, and motivation strategies. But options which students can choose according to their needs and styles are insufficient and tools for expression and communication need to enhance for helping each student to overcome his/her obstacles for learning and need to be more and elaborate to support learner-centered science digital textbook.