• The Mathematical Education
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• v.58 no.3
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• pp.383-401
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• 2019

This study started with the idea that it is necessary to focus on common concepts and ideas among the subjects when conducting integrated education in high school. This is a preliminary study for developing materials that can be taught in mathematics in the context of already learning scientific concepts in high school. For this purpose, Mendel 's law of genetics was studied among the contents of biological subjects which are known to have relatively little connection with mathematics. The more common links between the two subjects are, the better, in order to integrate math and other subjects and develop materials for teaching. Therefore, in this study, we investigated not only the probability domain but also the concept of statistical domain. We have been wondering if there is a more abundant idea to connect between 'Mendel's law' and 'probability and statistics'. Through these anxieties, we could find that concepts such as 'likely equality' and 'permutation and combination' including 'a large number of laws' can be a link between two subjects. Based on this, we were able to develop class materials that correspond to classes. This study is expected to help with research related to development of integrated education support materials, focusing on mathematics.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.183-189
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• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.

• Journal of Mechanical Science and Technology
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• v.14 no.6
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• pp.622-632
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• 2000

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on. The cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling processes for various cutting conditions, their mathematical model is important and the model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging forces of cutting tests. In this paper the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in the learning stage as the omitted number of experimental data increase the average errors increase as well.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.6
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• pp.912-921
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• 2006

This study examines greenhouse gas reduction potentials in cement manufacturing industry of Korea. An energy system model in the MARKAL (MARKet ALlocation) modeling framework was used in order to identify appropriate energy technologies and to quantify their possible implications In terms of greenhouse gas reduction. The model is characterized as mathematical tool for the long term energy system analysis provides an useful informations on technical assessment. Four scenarios are developed that covers the ti me span from 2000 to 2020. Being technology as a fundamental driving factor of the evolution of energy systems, it is essential to study the basic mechanisms of technological change and its role in developing more efficient, productive and clean energy systems. For this reasons, the learning curves on technologies for greenhouse gas reduction is specially considered. The analysis in this study shows that it is not easy to mitigate greenhouse gas with low cost in cement manufacturing industry under the current cap and trade method of Kyoto protocol.

• IE interfaces
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• v.16 no.spc
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• pp.28-32
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• 2003

In this paper, the fuzzy group method data handling-type(GMDH) neural networks and their application to the forecasting of mobile communication system are described. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neuro-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the neuro-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the neuro-fuzzy GMDH. The computer program is developed and successful applications are shown in the field of estimating problem of mobile communication with the number of factors considered.

• Education of Primary School Mathematics
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• v.24 no.1
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• pp.1-20
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• 2021

Factors influencing mathematics academic achievement are constantly changing and have direct and indirect effects on mathematics achievement, so longitudinal studies that can predict and analyze their growth are needed. This study uses longitudinal data on students from 2011 (5th grade of elementary school) to 2015 (2nd grade of middle school) of the Seoul Education Longitudinal Study, and divides them into groups with similar longitudinal changes in mathematics academic achievement. The direct and indirect effects of learning attitudes and achievement goals were examined. As a result of the study, it was found that learning effort and learning attitude had a direct effect on mathematics achievement in 1 group (2277 students, 67.7%), and learning attitude had a direct effect on mathematics achievement in 3 groups (958 students, 28.5%). And it was found that learning effort h ad an indirect effect. In addition, it was found that both learning attitudes, learning efforts, and achievement goals had no effect on the academic achievement of mathematics in the second group (127 students, 3.8%).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.997-1004
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• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• Journal of Science Education
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• v.43 no.1
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• pp.17-42
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• 2019

This study, as an attempt to integrate mathematics and information for convergence education, was conducted to develop teaching-learning materials on mathematics education combined with coding education, which has recently been emphasized. We chose the subject of digital signature for coding education, and used SageMath as a coding program. In this study, we overview mathematics used in the elliptic curve digital signature algorithm, one of the many methods for digital signature, and developed the teaching-learning materials on the algorithm for mathematics education integrated with information education based on coding. The elliptic curve digital signature algorithm utilized in transactions of Bitcoin, which many people recently are interested in, is a good example, showing students that mathematics is applied to problem-solving in the real world and provides an optimal environment for implementation by coding. Accordingly, we expect that a class on algorithm will provide a specific teaching-learning program to achieve the goal of integrated mathematics education. By comprehensively considering the opinions of mathematicians, mathematics teachers and mathematics education experts, we expect that the teaching-learning program will be realized as a meaningful class in science high schools, high school's math clubs, and 'number theory' class in colleges.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2610-2612
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• 1999

In controlling vector of induction motor, PID controller is required much time as the expert should control manually a gain of controller according to plant or a change of circumstances. Accordingly, this paper has gotten a gain of PID controller used neural network by self-funning method in order to settle above problem. The neural network can describe an input/output features in spite of non-linear system which is hard to get mathematical model by controlling the strength of connection by learning. It has a strong character against a distortion and noise of input information, and is suitable modeling of diver-variable system which is composed of several input/output. This paper has represented the self-tunning method for gain of PID controller used neural network when using PID controller to control speed of induction motor, and has checked strong characters against distortion and noise of input information through simulation.

• Journal of Korea Foundry Society
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• v.34 no.3
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• pp.107-111
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• 2014

Permeability is the ability of a material to transmit fluid/gases. It is an important material property and it depends on mould parameters such as grain fineness number, clay, moisture, mulling time, and hardness. Modeling the relationships among these variable and interactions by mathematical models is complex. Hence a biologically inspired artificial neural-network technique with a back-propagation-learning algorithm was developed to estimate the permeability of green sand. The developed model was used to perform a sensitivity analysis to estimate permeability. The individual as well as the combined influence of mould parameters on permeability were simulated. The model was able to describe the complex relationships in the system. The optimum process window for maximum permeability was obtained as 8.75-10.5% clay and 3.9-9.5% moisture. The developed model is very useful in understanding various interactions between inputs and their effects on permeability.