• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.70-80
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• 2012

The marine life culture contents added with fun and learning factors are created in a 3D space, and the development of augmented reality contents concerning marine life resources in islands and the utilization method of experience-based learning are proposed. As a WYSIWYG-based authoring tool, an augmented reality authoring tool was made to easily use a authoring tool through a node structure and drag & drop. Marine life contents add the animation effect through a marker and event factors such as the change of modeling data, and also, they support real experience-based learning with the narration of marine life. Based on around 50 species of marine animals augmented reality contents, a marine animal AR book can be utilized as a textbook for elementary school classes, and as a 3D image education utilizing augmented reality, it enhances a learning effect by allowing realistic observation, various ways of thinking, and the maximum flow.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.275-278
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• 2013

Almost teachers have some basic questions about the effective teaching and learning. The purpose of this study is to develop an alternative tool to observe and analyze an instruction based on the learning theories providing the theoretical rationalities for learners to study not only in daily their life but also in their class and to apply it as an example. This study showed some suggestions analyzing an instruction theoretically through out some filters based on learning theories. Recently various learning theories have been studied and accumulated. This study developed an alternative tool to observe and analyze an instruction based on the Behaviorism, the Cognitivism and the Constructivism in the middle of these learning theories and it was applied to observe and analyze a actual class of the informatics subject. The expectation is for teachers to reflect or improve their classes using the alternative tool.

• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.207-212
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• 2004

In this paper, based on SCORM Sequencing Model, we propose the learning content structure which has structure informations of learning object and decision rules how to transfer learning object to learner. It is intended to provide the technical means for learning content objects to be easily shared and reused across multiple learning delivery environment. We develop the generation tool of learning object sequencing, for processing the learning with variable teaching methodologies. The teaming objects also are automatically packaged the PIE(Package Interchange File) to transmit with SCORM RTE(Run-Time Environment) and attached SCO(Sharable Content Object) function for tracking learner information.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.243-248
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• 2018

The most important factor for collaborative learning to be effective is the selection of tools that constitute groups. Grouping is to facilitate collaborative learning, learners form groups based on various characteristics. If a group of students fails to form properly due to the selection of the wrong tools, problems can arise where complaints from students can lead to lectures and the effects of learning. In this paper, we have implemented a group of configuration tools that considered improving learning effects and diagnosing bulling tendency. We have proposed a group composition tool that can take into consideration the learning effect and also diagnose the tendency of the bullring by constructing the group according to the teacher's preference by inputting the class preference and the student's grade through the sociometry survey. We expect that the teacher will be able to grasp the students' friendship in advance and cope with the bulling that can happen in the class, as well as the cooperative learning that can lead the class to improve the learning effect.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1570-1575
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• 1991

The feasibility of using an adaptive resonance network (ART2) with unsupervised learning capability for too] wear detection in turning operations is investigated. Specifically, acoustic emission (AE) and cutting force signals were measured during machining, the multichannel AR coefficients of the two signals were calculated and then presented to the network to make a decision on tool wear. If the presented features are significantly different from previously learned patterns associated with a fresh tool, the network will recognize the difference and form a new category m worn tool. The experimental results show that tool wear can be effectively detected with or without minimum prior training using the self-organization property of the ART2 network.

• Proceedings of the KIEE Conference
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• 2001.11a
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• pp.198-200
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• 2001

In this paper, we describe the developed learning tool of expert system for preventive diagnosis of substation power equipments. The expert system was programmed by using the diagnosis methods as like gas analysis in oil and partial discharge, hottest temperature, the current of OLTC driving meter, the current of fan and pump in MTr and driving coil current in GCB and leakage current in LA. The learning tool is composed of the expert system and the explanation of diagnosed examples and the applied rules and it well worked according to the rule.

• Journal of the Korean earth science society
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• v.24 no.2
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• pp.108-116
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• 2003

Problem Based Learning (PBL) is one of methods which has been developed to promote student-centered learning and to pursue self-directed learning for life-long learning. The purpose of this study is exploring the possibility of Problem Based Learning (PBL) in college Earth science course. The participants of this study were fourteen students attending an Earth science class at Sookmyung Women's University in Seoul. PBL was implemented in the form of group project with utilizing Web-based course tool. We provided questionnaires and conducted interviews to figure out students' perception about PBL. The findings were as follows: Through a given experiences, (1) students participated more actively than LBL (Lecture Based Learning), (2) more students were engaged with self-directed learning, and (3) students made higher cognitive efforts. LBL seemed to be more efficient way to acquire factual knowledge. In the meanwhile, PBL did not seem to affect the improvement of communication skills. Students could not make use of Web-based course tool effectively in communicating with other team members. In this study, we found that college student participants preferred problems related to everyday life, environmental issues and interesting but unusual incidents. On the other hand, they felt difficult in open-ended problems, especially when they were asked to provide their own evaluation. On the basis of PBL experiment in this paper, we present one method of successful implementation of PBL and suggest topics which should be studied in the future.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.187-192
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• 2017

In this paper, we have studied scale tools for measure programming task value and learning persistence at elementary school students. In order to develop complete test tools, we have improved the completeness by revising tests through stepwise verification. The first scales were constructed based on the previous studies. As a result of the content validity test, 5 out of 14 items of the task value test tool and 1 out of 10 items of the learning persistence test were not suitable. The second test tools were constructed by revising and supplementing the first scale, and consisted of 13 items of task value and 8 items of learning persistence. As a result of the contents validity test, all the items included in the test tool proved to be valid. The reliability of the secondary testing tools were also found to be reliable at ${\alpha}=.970$ and ${\alpha}=.975$, respectively.