• Journal of Korean Elementary Science Education
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• v.34 no.1
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• pp.1-14
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• 2015

The variety of learners include students with disabilities and general students, and an ongoing focus of inclusive education research is non-discrimination. As part of integrated education, UDL (Universal Design for Learning) for students with disabilities supports a practical approach, participation, and advancement to improve learning opportunities for all students. The purpose of this study was to examine the effects of using UDL in science classes. The dependent variables of this study were academic achievement in science, scientific attitude, and scientific motivation. In this study, the experimental groups were 9 people in the 5th grade and 11 people in 6th grade. The experimental groups were taught science class using UDL. In order to analyze the learning outcomes, the structure equation model was performed. The results of this study were as follows: First, the science achievement of learning outcomes of the science class applying UDL directly affected both scientific attitude and scientific motivation. Second, the scientific attitude of learning outcomes of the science class applying UDL directly did not affect scientific motivation. According to these results, learning outcomes for science achievement of the science class applying UDL showed that UDL affected both general students and students with disabilities. To summarize the analysis of learning outcomes, science achievement directly affected both scientific attitude and scientific motivation while scientific attitude did not affect scientific motivation. This study offered a specific implementation method for integrated education. Using the structure equation model for analyzing the effect has more significance.

• Composites Research
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• v.36 no.2
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• pp.92-100
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• 2023

In this paper, a machine learning and deep learning model for the design of circuit analog (CA) radar absorbing structure with a cross-dipole pattern on a glass fiber fabric reinforced plastic is presented. The proposed model can directly calculate reflection loss in the Ku-band (12-18 GHz) without three-dimensional electromagnetic numerical analysis based on the geometry of the Cross-Dipole pattern. For this purpose, the optimal learning model was derived by applying various machine learning and deep learning techniques, and the results calculated by the learning model were compared with the electromagnetic wave absorption characteristics obtained by 3D electromagnetic wave numerical analysis to evaluate the comparative advantages of each model. Most of the implemented models showed similar calculated results to the numerical results, but it was found that the Fully-Connected model could provide the most similar calculated results.

• Journal of Korean Elementary Science Education
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• v.21 no.2
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• pp.241-252
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• 2002

Field learning have not well performed in elementary school for various reasons, in spite of the benefits of field study. Absence of suitable geological field courses for elementary science education is one of several reasons The purpose of this study is to develop learning materials for the field geology in Hampyeong region and apply them to the geological related units for elementary science education. The 5 observation sites for the field geology learning in study area include various rocks and geological structure such as granite, gneiss, conglomerate, sandstone, mudstone, plant fossil, fold, fault and weathering phenomenon changing rocks to soil. This study area is suitable place for the field geology learning of elementary science education in Kwangju and Chonnam province because of convenience access, fresh outcrops and distribution of various geological learning materials as rocks and structure.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.216-225
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• 1998

Although artificial neural network based on backpropagation algorithm is an excellent system simulator, it has still unsolved problems of its structure-decision and learning method. That is, we cannot find a general approach to decide the structure of the neural network and cannot train it satisfactorily because of the local optimum point which it frequently falls into. In addition, although there are many successful applications using backpropagation learning algorithm, there are few efforts to improve the learning algorithm itself. In this study, we suggest a general way to construct the hidden layer of the neural network using binary genetic algorithm and also propose the various learning methods by which the global minimum value of the teaming error can be obtained. A XOR problem and line heating problems are investigated as examples.

• Journal of the Korean Chemical Society
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• v.57 no.3
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• pp.389-397
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• 2013

In this study, the effects of STAD cooperative learning strategy on students' achievement, learning motivation, perceptions of learning environment, and perceived classroom goal structure were investigated in terms of students' collectivism level. Two classes (64 students) from an elementary school were respectively assigned to a control group and a treatment group. A individualism-collectivism test, a learning motivation test, a perceptions of learning environment test, and a perceived classroom goal structure test were administered as pretests. The intervention of cooperative learning lasted for 24 class periods. After instruction, an achievement test, the learning motivation test, the perceptions of learning environment test, and the perceived classroom goal structure test were administered. The results indicated that the students of the treatment group significantly outperformed those of the control group in the achievement test. There was a significant treatment-aptitude interaction effect in the scores of the attention subcategory of the learning motivation. In the perceptions of learning environment, the score of the treatment group was significantly higher than the control group in the cohesiveness subcategory, whereas the score of the treatment group was significantly lower than their counterpart in the competitiveness subcategory. It was also found that the score of the treatment group was significantly higher than the control group in the performance subcategory of the perceived classroom goal structure.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.230-234
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• 2020

A system that uses deep-learning techniques to predict properties from molecular structures has been developed to apply to chemical, biological and material studies. Based on the database where molecular structure and property information are accumulated, a deep-learning model looking for the relationship between the structure and the property can eventually provide a property prediction for the new molecular structure. In addition, experiments on the actual properties of the selected molecular structure will be carried out in parallel to carry out continuous verification and model updates. This allows for the screening of high-quality molecular structures from large quantities of molecular structures within a short period of time, and increases the efficiency and success rate of research. In this paper, we would like to introduce the overall composition of the materiality prediction system using deep-learning and the cases applied in the actual excavation of new structures in LG Chem.

• Research in Mathematical Education
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• v.12 no.1
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• pp.1-26
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• 2008

The concept field is defined as the schema of all equivalent definitions of a mathematics concept. Concept system is defined as the schema of a group concept network where there are mathematics relations. Proposition field is defined as the schema of all equivalent proposition sets. Proposition system is defined as a schema of proposition sets where one mathematics proposition at least is "derived" from the other proposition. CPFS structure that consists of concept field, concept system proposition field, proposition system describes more precisely mathematics cognitive structure, and reveals the unique psychological phenomena and laws in mathematics learning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.122-125
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• 2003

By advent of NNC(Neural Network Chip), it is possible that process in parallel and discern the importance of signal with learning oneself by experience in external signal. So, the design of general purpose operation unit using VHDL(VHSIC Hardware Description Language) on the existing FPGA(Field Programmable Gate Array) can replaced EN(Expert Network) and learning algorithm. Also, neural network operation unit is possible various operation using learning of NN(Neural Network). This paper present general purpose operation unit using hierarchical structure of EN EN of presented structure learn from logical gate which constitute a operation unit, it relocated several layer The overall structure is hierarchical using a module, it has generality more than FPGA operation unit.

• ETRI Journal
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• v.33 no.1
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• pp.39-49
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• 2011

A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary-based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.