• Computers and Concrete
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• 제33권6호
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• 한국학교수학회논문집
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• 제15권2호
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• pp.259-279
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• 2012

본 연구의 목적은 개념연결표의 활용이 예비교사들의 수학 학습에 미치는 영향에 대해서 조사하는 것이다. 본 연구는 25명의 예비교사들을 대상으로 미적분학 강좌 시간을 활용하여 한 학기 동안 수행되었다. 연구에 참여한 예비교사들을 대상으로 실시한 설문조사 및 면담 결과에 의하면 개념연결표의 활용은 여러 가지 측면에서 예비교사들에게 긍정적인 영향을 미친 것으로 나타났다. 개념연결표의 활용은 예비교사들의 수학적 개념에 대한 이해와 수학적 의사소통능력을 발달시키는데 도움이 되었으며 수학의 유용성이나 가치 인식 및 자기주도적이고 적극적인 수업 참여를 유도하는데 효과적인 것으로 나타났다.

• 한국가정과교육학회지
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• 제28권3호
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• pp.65-77
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• 2016

본 연구의 목적은 중학교 기술 가정교과 의복재료 단원에서 실물 교육자료집을 좀 더 적극적으로 활용하여 의복재료에 대한 학습자의 흥미와 이해를 높이고자 물리적 섬유감별방법이 들어간 탐구활동지를 개발하는데 있다. 이를 위해 중학교 2학년 수준에 적합한 물리적 섬유감별 방법을 개발하였으며, 이를 실제 수업에 적용하고 그 효과를 분석하였다. 연구 결과 양모와 아크릴, 견과 폴리에스터를 비교하는 물리적 섬유감별방법을 개발하였으며, 이를 활용하여 탐구활동지를 개발하였다. 탐구활동지를 수업에 활용한 실험 집단과 사용하지 않은 통제 집단의 학습흥미도, 학습수용태도, 학업성취도를 비교 분석한 결과, 탐구활동지를 사용한실험집단이 통제 집단보다 모두 높은 점수를 보였으며, 유의미한 차이가 있음을 알 수 있었다. 따라서 본 연구를 통해 제작된 섬유감별방법과 탐구활동지는 의복재료에 대한 학습자의 이해를 촉진시켜줄 뿐 아니라, 의복재료에 대한 정보를 학습자에게 제공함으로써 학습자가 실생활에 적용할 수 있을 뿐 아니라 학습자의 자기 주도적 학습을 촉진할 수 있는 것으로 생각된다.

• 한국인터넷방송통신학회논문지
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• 제21권1호
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• pp.69-77
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• 2021

본 논문은 사용자가 입력한 PDF 악보를 사용자가 원하는 조(chord)의 MIDI 파일로 제공하는 앱의 개발을 다룬다. 이 앱은 사용자가 PDF 악보 파일과 바꾸고자 하는 조를 입력하면 조 변환을 위해 PDF 파일을 PNG 파일로 변환한다. 이를 영상 처리 알고리즘을 통해 악보의 음계를 인식하여 구분하고, 딥러닝을 통해 악보 음표의 박자를 인식하여 구분한다. 이를 통해 사용자가 원하는 조와 기존 악보의 MIDI 파일을 제공한다. 개발한 영상 처리 알고리즘과 딥러닝은 2, 4, 8, 16분 음표, 2, 4, 8, 16분 쉼표, 잇단 음표, 화음 음표가 인식 가능하다. 실험결과 악보의 음표 인식률 100%, 딥러닝 모델을 통한 박자 인식률은 90% 이상인 것을 확인하였다.

• 유통과학연구
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• 제21권2호
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• pp.45-52
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• 2023

Purpose: The Malaysia Education Blueprint is primarily concerned with the transformation of students' minds through the curriculum offered at the school level (2013-2025). Diversity in the application of teaching and learning methods is one means of achieving the transformation of students' minds through the Secondary School Standard Curriculum. Consequently, the production of ExSheetLink's Module for Accounting Education is the primary outcome of this study, which had three objectives: the need for ExSheetLink's Module in the process of producing financial statements for Accounting Students in secondary school to the Accounting Teacher; and the design of ExSheetLink's Module that meets the entire process in the production of financial statements for Accounting Students in secondary school based on the Documents Curriculum and the Accounting Students' needs. Research design, data and methodology: This study outlines the research framework for module development in accordance with the Design and Development Research Method, which combines multiple research techniques (Mixed Method). Results: The development of ExSheetLink's Module is completed and can be used for the level of effectiveness purposes. Conclusion: The transformation of Accounting Students' minds is a success thanks to the ExSheetLink Module. Researchers also suggested that all Malaysian Secondary School accounting students test the ExSheetLink Module.

• 컴퓨터교육학회논문지
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• 제3권2호
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• pp.59-66
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• 2000

본 논문은 새로운 교육사조로서 현재 교육 현장에 많이 시도되고 있는 구성주의의 학습 모델 중의 하나인 인지적 도제 모델을 적용하여 교사들의 정보화 소양을 높이는 데 꼭 필요한 스프레드시트 프로그램 중에서 가장 많이 사용되는 엑셀2000의 WBI를 설계하고 구현하였다. 인지적 도제 모델의 과정에 따라 학습과정을 설계하고 구성하였으며 학습은 상황에 기초하여 일어난다는 구성주의의 학습 원리에 따라 실무적인 내용을 택하고 가장 효과적인 사회적 상호작용을 위하여 교수-학습이 개별적으로 이루어지도록 하였고, 게시판이나 전자우편, 대화실 등을 통하여 교수자와 학습자 및 학습자 상호간의 상호작용이 원활히 되도록 설계하고 구현하였다.

• Advances in nano research
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• 제12권1호
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• pp.81-99
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• 2022

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE. The outcome reveals that MEE layer, temperature change, GNP weight function, and GNP distribution patterns GNP weight function have significant influence on the critical temperature and voltage of cylindrical shell made from GNP nanocomposites core with MEE face sheet on outer of the shell.

• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.464-470
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• 2018

With the introduction of the e-learning since 2000, the place of the education has not been limited to off-line, but the range of it has become broader in online. The e-learning market has evolved steadily over time. With the advent of the term "Edu-tech", which means a combination of education and technology, various IT technologies have incorporated education. Particularly, the Korean education market collects patterns by computerizing the learning history in classes taught according to curriculums. Because of that environment, various personalized learning services have been developed which maximize the effect of the learning. These services have qualitative differences depending on how many data is accumulated and algorithms are developed for the precise analysis. The purpose of this study is to recognize and data-ize OMR marking by the most suitable method to convert analog data into digital data without harming the Korean education system.

• 한국컴퓨터정보학회논문지
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• 제21권4호
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• pp.39-46
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• 2016

While the difficulty of the music can be classified by a variety of standard, conventional methods are classified by the subjective judgment based on the experience of many musicians or conductors. Music score is difficult to evaluate as there is no quantitative criterion to determine the degree of difficulty. In this paper, we propose a new classification method for determining the degree of difficulty of the music. In order to determine the degree of difficulty, we convert the score, which is expressed as a traditional music score, into electronic music sheet. Moreover, we calculate information about the elements needed to play sheet music by distance of notes, tempo, and quantifying the ease of interpretation. Calculating a degree of difficulty of the entire music via the numerical data, we suggest the difficulty evaluation of the score, and show the difficulty of music through experiments.

• 한국분무공학회지
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• 제28권1호
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.