• East Asian mathematical journal
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• v.36 no.2
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• pp.273-289
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• 2020

This study introduces the process and results of developing an adaptive math learning program for self-directed learning. It presented the process and results of developing an adaptive math learning program that takes into account the level of learners using artificial intelligence. We wanted to get some suggestions on developing programs for artificial intelligence-based mathematics. The program was developed as Math4U, an application based on smart devices in the "character and expression" area for 7th grade. The Application Math4U may be used differently depending on its purpose. It is also expected to be a useful tool for providing self-directed learning to students as the basis for educational research using smart devices in a changing educational environment.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.709-711
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• 2002

웹 기반의 교육의 활성화로 이를 학습에 응용하기 위한 노력으로 GVA(Global Virtual Academy) 등과 같은 학습 보조 도구가 많이 발표하고 있는 설정이다. 대부분의 학습 보조 도구들은 각각의 특성들만 제시할 뿐 통합된 표준호가 되어 있지 않다. 최근 가상교육에서 학습기술이 상호운용성에 기반한 표준화의 일반적인 필요성을 인식하게 됨에 다라 가상교육의 국제표준을 소개하고 체계적으로 AICC(Aviation Industry CBT Committee), IMS Global Learning Consortium, ADL(Advanced Distributed Learning)을 중심으로 진행되어 오고 있다. 웹 기반의 교육을 통한 질의 응답의 학습방법을 고려한 도구가 없으므로 질의 응답 학습 도구(QALT)지원을 위한 표준화된 LTSA(Learning Technology Standard Architecture) 기반 시스템을 학습 객체에 대한 질의 응답과 개방형 단순 질의 응답 측면으로 구현한다. 그러므로 개방형 단순 질의 응답 측면을 구현하기 위해 학습 기술의 표준화로 제시되어 있는 LOM(Learning Object Metadata)을 통해 설계 자체를 체계화하고 전체적으로 명세 작업을 가능하게 하여 일관성을 유지하는 정련화된 문서로 질의 응답할 수 있도록 한다. 또한, Web 상에서의 Network delivery와 DTD(Document Type Definition)와 Stylesheet를 사용자가 쉽게 수정 가능하며 다양한 Linking Type을 제공하므로 단순 질의 응답 문서의 형식을 XML로 한다

• Journal of Korean Society of Industrial and Systems Engineering
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• v.5 no.6
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• pp.69-78
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• 1982

Traditional CVP (Cost-Volume-Profit) analysis employs linear cost and revenue functions within some specified time period and range of operations. Therefore CVP analysis is assumption of constant labor productivity. The use of linear cost functions implicity assumes, among other things, that firm's labor force is either a homogenous group or a collection homogenous subgroups in a constant mix, and that total production changes in a linear fashion through appropriate increase or decrease of seemingly interchangeable labor unit. But productivity rates in many firms are known to change with additional manufacturing experience in employee skill. Learning curve is intended to subsume the effects of all these resources of productivity. This learning phenomenon is quantifiable in the form of a learning curve, or manufacturing progress function. The purpose d this study is to show how alternative assumptions regarding a firm's labor force may be utilize by integrating conventional CVP analysis with learning curve theory, Explicit consideration of the effect of learning should substantially enrich CVP analysis and improve its use as a tool for planning and control of industry.

• Journal for History of Mathematics
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• v.34 no.1
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• pp.21-37
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• 2021

The purpose of the present study is to compare the differences in levels per group of high school students regarding the self-regulated learning factors for mathematics. For this purpose, a self-regulated learning measurement tool was developed and surveys were conducted. And the statistical analysis was completed using the frequency analysis, Kolmogorov-Smirnov normality test, Mann-Whitney U test and the Kruskal-Wallis H test. As a result, it is found that self-efficacy is of statistically significant differences in self-regulated learning levels regardless of the group classifications but test anxiety does not show statistically significant differences in self-regulated learning levels regardless of the group classifications.

• 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%.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Journal of Science Education
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• v.47 no.2
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• pp.154-166
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• 2023

It has been known that the tool characteristics embedded in physical tools and virtual tools act with different underlying mechanisms in a user's knowledge acquisition and conceptual understanding. This overview study examines the learning process through the use of physical and virtual tools from the perspective of conceptual frameworks, affordability that tools present, and the depth of cognitive engagement that occurs in the process of learning concepts through various learning activities. Based on the conceptual frameworks, the results of previous comparative studies were reinterpreted. It was found that what mattered for learning is the amount of new information that a tool provides and the different level of cognitive engagement that students use through various learning activities. Finally, the implications to be considered when teachers use physical and virtual tools to help students better understand various concepts are discussed.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.101-112
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• 1997

The purpose of this study was to examine usefulness of KEDI-Individual Basic Learning Skills Test as a diagnostic tool of learning disorders(LD). Learning disorder group consisted of two subgroups, verbal learning disorder group(VLD, n＝34) and nonverbal learning disorder group(NVLD, n＝14). Comparison group consisted of Dysthymia Disorder subgroup(n＝11) and Normal subgroup(n＝20). Performance of intelligence test and achievement test was examined in all 4 subgroups. In KEDI-WISC, VLD subgroup revealed primary problems in vocabulary, information and verbal-auditory attention test. NVLD group revealed primary problems in almost all performance tests such as visual acuity, psycho-motor coordination speed and visual-spatial organizations ability subtest. In KEDI-Individual Basic Learning Test, VLD group revealed primary problems in phonological coding process, word recognition and mathematics. For successful classification of LD children, the importance of achievement test and intelligence test was discussed by discriminant analysis and factor analysis. The results indicate that KEDI-Individual Basic Learning Skills is of considerable usefulness in diagnosing LD, but must be used in subtests, and additional tests must be conducted for thorough exploration of LD.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.2
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• pp.271-284
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• 2021

This study presents the educational utility of blended learning by analyzing the effectiveness of learning after class by blending team project learning and smart device app-based learning methods. Qualitative analysis and survey analysis were conducted and the results were as follows. First, team project activities based on task resolution were conducted freely through detailed activities such as sharing roles, planning meetings, and coordinating opinions. Team activities were carried out with respect and consideration, team member bonding, and a sense of responsibility. Second, the smart device app is recognized as a medium for work and communication, and fast feedback has been made, making it highly impactful on classroom activities. Third, in terms of learning satisfaction, most learners showed an interest in the course and were satisfied with the project results. The smart device app was used as a learning and communication medium for personal and team activities and was analyzed as a blended method applicable to classes that conduct practical activities as an efficient tool to further activate project activities.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1939-1951
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• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.