• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.3
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• pp.60-67
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• 2008

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideration of the performance deterioration consist of the compressor, the gas generation turbine and the power turbine. Compared to the on-design point, the teaming data has been increased 200 times in case off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimal division has been proposed for learning time decrease as well as the high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been confirmed under 5 %.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.3
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• pp.238-247
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• 2007

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to learning algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generator turbine and power turbine are considered for engine performance deterioration and estimation for performance deterioration of a single component at design point was conducted. As a result of that, defect diagnostics has been conducted. The input criteria for the genetic algorithm to guarantee the high stability and reliability was discussed as increasing learning data sets. As a result, the accuracy of defect estimation and diagnostics were verified with its RMS error within 3%.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.452-455
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• 1999

We introduce an evolutionary approach to on-line learning for mobile robot control using reconfigurable hardware. We use genetic programming as an evolutionary engine. Control programs are encoded in tree structure. Genetic operators, such as node mutation, adapt the program trees based on a set of training cases. This paper discusses the advantages and constraints of the evolvable hardware approach to robot learning and describes a FPGA implementation of the presented genetic programming method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.267-270
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• 1997

In this paper, we proposed an optimazation method using Genetic Algorithm for nonlinear system modeling. Fuzzy Neural Network(FNNs) was used as basic model of nonlinear system. FNNs was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, We used FNNs which was proposed by Yamakawa. The FNNs was composed Simple Inference and Error Back Propagation Algorithm. To obtain optimal model, parameter of membership function, learning rate and momentum coefficient of FNNs are tuned using genetic algorithm. And we used simplex algorithm additionaly to overcome limit of genetic algorithm. For the purpose of evaluation of proposed method, we applied proposed method to traffic choice process and waste water treatment process, and then obtained more precise model than other previous optimization methods and objective model.

• School Mathematics
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• v.11 no.3
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• pp.431-462
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• 2009

Introduction of logarithm in mathematics textbook in the 7th national curriculum of mathematics is the inverse of exponent. This introduction is happened that students don't know the necessity for learning logarithm and the meaning of logarithm. Students also have solved many problems of logarithm by rote. Therefore, we try to present teaching unit for the logarithm according to the historico-genetic principle. We developed the learning-teaching module of unit for the logarithm according to historico-genetic principle, especially reinvention for real contexts based RME. Loaming-teaching module is carried out as the performance assessment. As a results, We find out that this module helps students understand concepts of logarithm meaningfully Also, mathematical errors of logarithm is revised after the application of learning-teaching module.

• Computers and Concrete
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• v.30 no.3
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• pp.225-236
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• 2022

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.712-717
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• 2008

In swarm robot systems, each robot must act by itself according to the its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.350-353
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• 2007

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideriation of the performance deterioration is consist of the compressor, the gas generation turbine and the power turbine, repectively. Compared to the on-design point on the sea-level condition, the learning data has been increased 200 times in case of the off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimum division has been proposed to decrease learning time as well as to obtain high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been estimated under 5 %.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.17-29
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• 2017

IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.

• Journal of The Korean Association For Science Education
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• v.6 no.2
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• pp.35-42
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• 1986

Recent studies on the learning of the science concepts indicate that most students have misconceptions of the science concepts. The misconceptions have their roots in the various aspects of teaching and learning situations. The textbooks used in schools have been substantiated as one of the sources of the misconceptions. Genetics has been recognized as one of the most difficult areas for high school students to learn. Therefore, this study had its objective to identify the misconceptions of genetics held by high school students and analyze the high school biology textbook as the source of the misconceptions. In order to indentify the misconceptions of the genetic concepts, the volunteer students were interviewed and genetic content and its sequence in the high school biology textbooks were analyzed. The misconceptions identified in this study are as follow: gamete formation, mitosis, trait expression, and allele and gene behavior in meiosis. This study found that the high school biology textbooks might be the source of those misconceptions. Based on the misconceptions identified, this study proposed direction for efficient instruction of genetics in high schools.