• Advances in nano research
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• v.13 no.5
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• pp.499-512
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• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.21-28
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• 2024

Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.

• Journal of Family Resource Management and Policy Review
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• v.8 no.1
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• pp.47-60
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• 2004

Recognizing a tremendous increase in the Internet users and popularity of E-learning through the Internet, this study attempted to analyze interactive financial education web sites for children. Using meta search engines and major search engines, interactive financial education web sites identified based on the three criteria and analyzed in terms of the appropriateness for specific age groups, the coverage of contents related to the basic knowledge for financial literacy, and the interactive activities. The results showed that financial education web sites for children were needed to be improved in terms of both quantity and quality. The study also provides a guideline how to search for an appropriate financial education web sites for children when parents want teach about money to their children.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

• Journal of the Korean School Mathematics Society
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• v.1 no.1
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• pp.111-119
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• 1998

So far the studies on mathematical problem-solving education have failed to realize the anticipated result from students. The purpose of this study is to examine the reasons from the metacognitional viewpoint, and to think of making meta-items which enables learners to study through making effective use of the meaning of problem-solving and through establishing a general, well-organized theory on metacognition related to mathematic teaching guiedance. Metacognition means the understanding of knowledge of one's own and significance in the situation that can be reflection so as to express one's own knowledge and use it effectively when was questioned. Mathematics teacher can help students to learn how to control their behaviors by showing the strategy clearly, the decision and the behavior which are used in his own planning, supervising and estimating the solution process himself. If mathematics teachers want their students to be learners not simply knowing mathematical facts and processes, but being an active and positive, they should develop effective teaching methods. In fact, mathematics learning activities are accomplished under the complex condition arising from the factors of various cognition activities. therefore, mathematical education should consider various factors of feelings as well as a factor as fragmentary mathematical knowledge.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.673-676
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• 2002

XML은 모든 분야의 데이터를 저장하고 다른 형태의 데이터로 변화될 수 있는 강한 힘을 지니고 있다. 웹에서의 가상 교육에 대한 데이터도 XML로 저장한다면 한번 저장된 데이터는 어떤 사이트에서든 조금의 수정없이 바로 사용할 수 있다. 물론 이 데이터 구조가 미리 정의되어 모든 사이트에서 이 구조대로 XML 데이터를 만들어야 가능하다. 현재 사이버 교육 사이트들의 강좌 데이터는 데이터베이스에, 데이터베이스에서 데이터를 가져오는 것은 ASP, 가져온 데이터를 사용자에게 서비스하는 최종 산출물은 HTML로 구성되어 있어 이 데이터는 더 이상 가공을 할 수 없게 된다. 즉 각각의 사이버 교육 사이트들의 데이터는 서로 공유될 수 없다. 본 논문은 현재 사이버스쿨의 한계를 벗어날 수 있도록 새로운 표준으로 제안되어진 XML을 이용하여 사이버 강좌 관리시스템을 위한 통일된 XML 데이터 구조를 정의하고 웹에서 어떻게 사용해야 하는지 모델을 제시하였다.

• Korean Journal of Child Studies
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• v.30 no.4
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• pp.87-97
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• 2009

This study used Hierarchical Linear Modeling analysis to investigate the effects of individual self-regulated cognitive strategies and public education on middle school students' academic achievement. Participants were 6389 (boys 3287, girls 3102) middle school students from the 2005 data of the Korea Education Longitudinal Study. Results were as follows : (1) there were significant differences among different schools in middle school students' academic achievement, i.e. 20% of variance in English achievement and 15% of variance in mathematics achievement were explained by school differences. (2) Students' elaboration and meta-cognitive strategy influenced academic achievement positively. (3) Predictor variables by ability grouping, supplementary class, and/or self-learning class had no significant effects on students' academic achievement.

• Annual Conference of KIPS
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• 2016.10a
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• pp.746-749
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• 2016

주제 기반 크롤링(Topical Crawling)으로 수집된 문서들은 서로 비슷한 단어들을 가지고 있기 때문에 정작 주어진 주제에 적합하지 않은 문서 들을 포함할 수 있다. 이를 해결하기 위해 특정 주제에 해당하는 문서만을 필터링하는 작업이 필요하다. 본 논문은 화재 뉴스 기사에 대한 필터링을 위해 단어 기반 속성과 어울려 화재 뉴스 기사의 특성을 고려한 메타 데이터 속성을 추출하여 이에 특화된 기계학습 메커니즘을 제안하였다. 제안 기법의 F1-측정치는 92.1 %로서, 현재 최고의 성능을 보이는 SVM, 나이브베이즈 알고리즘보다. 2~3% 개선된 것이다.

• Annual Conference of KIPS
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• 2002.11a
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• pp.247-250
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• 2002

In this paper, We are design & implement to the LMS(Learning Management System) including SCORM standard for effective reusing and management of each digital contents. This system is composed XML based meta-data manager, SQL server and ASP for LMS application, which are the user directed teaming system within effective reusing and management of each digital contents.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.