• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.208-216
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• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• Educational Technology International
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• v.20 no.2
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• pp.223-255
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• 2019

The purpose of this study is to draw implications for designing online tools to support monitoring in collaborative learning. For this purpose, eighteen research papers that explored learner dashboards and group awareness tools were analyzed. The driving questions for this analysis related to the information and outcomes that must be monitored, whose performance they represent, and who monitors the extent of learning. The analytical frameworks used for this study included the following: three modes of co-regulation in terms of who regulates whose learning (self-regulation in collaborative learning, other regulation, and socially shared regulation) and four categories of dashboard information to determine which information is monitored (information about preparation, participation, interaction, and achievements). As a result, five design implications for learner dashboards that support monitoring were posited: a) Monitoring tools for collaborative learning should support multiple targets: the individual learner, peers, and the entire group; b) When supporting personal monitoring, information about the individual and peers should be displayed simultaneously to allow direct comparison; c) Information on collaborative learning achievements should be provided in terms of the content of knowledge acquired rather than test scores; d) In addition to information related to interaction between learners, the interaction between learners and learning materials can also be provided; and e) Presentation of the same information to individuals or groups should be variable.

• Design & Manufacturing
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• v.17 no.1
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• pp.27-32
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• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.129-139
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• 2004

This society is rapidly changing into an information-oriented society. As such, revolutionary and efficient teaching methods are needed in school education rather than traditional methods. To be an efficient teaching lesson, teaching plans based on learners' prior knowledge are needed. The knowledge-space theory provides the methods of efficient analysis about learners' status of knowledge. This study designs and implements the support-learning tool based on the knowledge-space theory to increase the efficiency in classroom lessons through the development of various methods of analysis of learners' knowledge status.

• Asian Journal of Business Environment
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• v.9 no.3
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• pp.27-38
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• 2019

Purpose - The aim of this study is to explore the effectiveness in educational differences between students of the government's financial-funded groups and the non-financial-funded groups at a university in Korea. Research design, data, and methodology - The study was conducted using a survey tool of National Assessment for Student Engagement in Learning. In total, 334 participants were surveyed, of which 290 students were participants in economic support program and 44 were nonattendance program students. The general characteristics of all of the participants were investigated by frequency analysis. The analysis of participants' collective characteristics used independent t and f-test, and one-way ANOVA with IBM SPSS Statistics package program 22.0. Results - The number of participating students is higher than that of non-participating students in relation to in-activities of university immersion, but the number of participating students is lower than that of non-participating students in relation to in-quality of student support. However, there was no statistical significance. The confidence coefficient of the university-immersion and student support questionnaire is 0.860 and 0.913, respectively. Conclusions - There is no significant difference in the activities of university immersion and student support between students who participate in the economic support program and those who do not.

• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.51-62
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• 2012

In this paper, we propose a self instrument learning tool using a PC-based software and an external electronic keyboard instrument with USB interface to help primary school students to learn playing piano more easily and effectively. The PC-based learning software and the external electronic keyboard instrument interact through the USB interface. This tool has a help window to provide information how to play and support interesting game mode for exercise. The external electronic keyboard instrument receives a selective information through the USB interface and display it on LEDs and 7-segment for novices to easily know the relation between the notes and the positions in the keyboard. The external keyboard instrument can detect false inputs, display them on LEDs and on the information window. We implemented a self instrument learning system and our feasibility tests showed its validity of the self learning tool to improve the learning efficiency.

• Educational Technology International
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• v.7 no.1
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• pp.39-57
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• 2006

Supporting individual or group learners through designing effective learning environment has been major concern for instructional technologists. In CSCL environment, the effectiveness of learning depends not only on the design of the learning incidences but also on that of psychological environment because in CSCL the learners encounter virtually a new environment deviate from the ordinary physical world. CSCL is one of the most demanding environment for learners and thus it requires a highly refined learner support mechanisms. The purpose of the research was to devise conceptual tools for supporting learners in CSCL environment. Especially, the researchers tried to develop special kinds of scaffolding that directly support the collaborative practice in the social and psychological dimension of the learner. Body of literature on scaffolding has been reviewed and effective CSCL environments were observed and analyzed. As a result of the study, the research proposes a new type of scaffolding, named as "collaboration scaffolding" as a conceptual tool for supporting learners in CSCL environment. Also the research suggests three subtypes of scaffolds as the most typical collaboration scaffolding; emotional scaffolds, facilitative scaffolds, and exploratory scaffolds.

• Journal of Creative Information Culture
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• v.6 no.3
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• pp.149-158
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• 2020

Corona19 has experienced radical changes in teaching methods in primary and secondary schools and higher education institutions, and the Ministry of Education has continued various attempts and support to ensure the quality of teaching and to promote learning participation in distance education. In this study, the support policy of the Ministry of Education for the post-Corona era was reviewed, and the professors' experiences in remote education were investigated and analyzed through intensive interviews. As a way to utilize programs to support participation in learning in real-time distance education, first, consideration of the proper period of concentration of learning by learners, second, coping with unexpected problems during learning activities, and using small meeting rooms and chatting as a collaboration tool were presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3307-3321
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• 2018

With the ever-increasing queries related to temporal (or time-related) information, such as the product launching time, in search engine, most web pages will be augmented with such information in the future. Meanwhile, the gradual emergence of the use of electronic textbooks (or e-Textbooks), which enrich the traditional paper-based textbooks with multimedia contents such as interactive quizzes and multimedia-based simulations, has led us to infer that e-Textbooks will be blended with temporal information to support learning. The use of temporal information helps teachers and students to understand the level of prior knowledge required to study a topic, as well as the sequence of learning activities and related sub-topics, that best attains the educational goals. This paper presents a simple yet efficient tool called TimeMap, which is based on mind mapping, to create an e-Textbook called TimeBook that takes account of time-related curriculum and the ability of students to learn via collaboration.