• Computers and Concrete
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• v.33 no.6
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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.

• Journal of the Korean School Mathematics Society
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• v.15 no.2
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• pp.259-279
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• 2012

The purposes of this study were to investigate the effects of the use of link sheet in pre-service mathematics teachers' mathematics learning. The study was conducted in Calculus course during 1 semester with 25 pre-service mathematics teachers. According to the results of questionnaires and focused group interviews, the use of the link sheet helped students to develop deeper understandings of mathematical concepts and mathematical communication ability. In addition, the use of the link sheet encouraged students to realize the value of the mathematics and it also played a central role in creating active and self-directed learning atmosphere.

• Journal of Korean Home Economics Education Association
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• v.28 no.3
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• pp.65-77
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• 2016

The purpose of this study is to develop an inquiry sheet for the contents of the clothing curriculum of home economics in middle schools using a physical fiber Identification method to increase students' interests in and understanding of clothing materials. Therefore, a physical fiber Identification method suitable to middle school students was developed in actual classrooms and the effects were analyzed. As a result, the physical fiber identification method was developed to distinguish between wool and acrylic knits; moreover, the identification method between silk and polyester fiber was studied. And then the inquiry sheet using fiber identification method was also developed. When interests in learning, attitudes of acceptance toward learning, and learning achievements of the experimental group (used the inquiry sheet) and the control group (did not use the inquiry sheet) were compared, the experimental group scored higher in every category, all of which were meaningful differences. Thus, this study demonstrated that the developed fiber differentiation method and inquiry sheet improved self-directed learning as well as learners' understanding of clothing materials by enabling the application the knowledge to the learners' realities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.69-77
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• 2021

This paper deals with the development of an application that converts the PDF music score entered by the user into a MIDI file of the chord the user wants. This application converts the PDF file into a PNG file for chord conversion when the user enters the PDF music score file and the chord which the user wants to change. After recognizing the melody of sheet music through image processing algorithm and recognizing the tempo of sheet music notes through deep learning, then the MIDI file of chord for existing sheet music is produced. The OpenCV algorithm and deep learning can recognize minim note, quarter note, eighth note, semi-quaver note, half rest, eighth rest, quarter rest, semi-quaver rest, successive notes and chord notes. The experiment shows that the note recognition rate of the music score was 100% and the tempo recognition rate was 90% or more.

• Journal of Distribution Science
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• v.21 no.2
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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.

• The Journal of Korean Association of Computer Education
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• v.3 no.2
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• pp.59-66
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• 2000

This study is to design and implement Excel 2000 WBI applying the Cognitive Apprentice Model. Excel 2000 is the most used Spread Sheet program and indispensable for increasing teachers' information literacy. The Cognitive Apprentice Model is one of Constructivism learning models. Constructivism is a new educational paradigm and mainly applied in education fields. This WBI is designed and implemented according to the Cognitive Apprentice Model and composed of practical contents according to Constructivism learning principle which insists learning is occurred in real situation. For more effective interaction the teacher(computer) guides the learners individually and uses a bulletin board, E-mail and chatting room.

• Advances in nano research
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• v.12 no.1
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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.

• Journal of Korea Multimedia Society
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• v.21 no.4
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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.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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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.

• Journal of ILASS-Korea
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• v.28 no.1
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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.