• School Mathematics
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• v.17 no.3
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• pp.447-467
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• 2015

The purpose of this study is to examine how the learning experience understanding degree and radian as the measurement of arc affects the conceptual understanding of radian and measuring angle. For this purpose, we investigated pre-service teachers' understanding about measurement of angle using a length of arc, and then conducted a teaching experiment with two middle school students. The results of analyzing pre-service teachers' and students' response are as follows. Students' experience interpreting the concept of degree into measurement of arc had a positive effect on understanding of radian and students' learning process in which they got measurement of angle as measurement of arc enabled conceptual understanding of 'linear measuring'. Also a circle context and a strategy dividing by arc operated as effective strategies for solving various problems about an angle. Finally, we confirmed that providing direct manipulative activities as a chance to explore relationships between an angle and arc measure can help students' conceptual understanding of measuring angle.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.450-453
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• 1997

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. The result of simulations shows that the crane is just controlled for a very large swing angle of 1 radian within nearly one cycle.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Composites Research
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• v.36 no.1
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• pp.48-52
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• 2023

Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.2
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• pp.42-48
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• 2010

For a basic engineering education Problem-Based Learning (PBL) method was adopted in order to pursuit the learner acquisition of critical knowledge, problem solving proficiency, and self-directed learning strategies by measurements of various physical and biological units, by calculation of errors in experimental data, by leraning energy conservation law and equation of motion, and, by analysis ability on data patterns through Elliptical Trainer(ET) exercise. The results show the ET may be a good experimental tool for understanding the PBL method. A sample syllabus was provided for one semester use, and by use of data obtained by self-directed and creative learning, the results of three groups for the PBL problems proposed by using ET were (1) the slope of angle was 23.5o in the diagram of energy exhaustion against velocity (GROUP A), (2) the angle range between the maximal and minimal energy exhaustion against weight loss was 15.0o ~ 26.5o (GROUP B), and finally (3) the angle was varied by 51.0o in the diagram of weight loss against distance (GROUP C).

• Research in Mathematical Education
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• v.13 no.2
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• pp.91-101
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• 2009

The mathematics curriculum reform has been carried out for almost five years (2004-2008) in the mainland China. But the teaching and learning in mathematics classrooms still are traditional in nature. Analyzing from the cultural angle, some reasons can be found: the orientation of teachers' role, teaching, and learning, the relationships between a teacher and the students, understanding the mathematics, and examination.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.339-344
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• 1993

A study for a lathe to machine workpieces with noncircular corss-sections is presented. The noncircular cutting is accomplished by controlling the radial tool position synchronized with the revolution angle of spindle. A learning control algorithm is suggested for the toll positioning, of which the control performances are analyzed and simulated on a numerical computer that the effectiveness of the control is convinced. The learning control is tested on a NC-lathe which shows successful results.

• Journal of ILASS-Korea
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• v.27 no.2
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• pp.57-65
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• 2022

The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R² and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.129-136
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• 2006

A new guidance synthesis for anti-ship missiles to control impact angle and impact time is proposed in this paper. The flight vehicle is assumed as a 1st order lag system to consider more practical system. The proposed guidance synthesis enhances the survivability of anti-ship missiles because multiple anti-ship missiles with the proposed synthesis can hit the target simultaneously. The control input to satisfy constraints of zero miss distance and impact angle, and the feedforward bias control input to control impact time constitute the guidance law. The former is from trajectory shaping guidance, the latter is from neural network. And particle swarm optimization method is introduced to furnish reference input and output for learning in neural network. The performance of the proposed synthesis in the accuracy of impact time and angle is validated by numerical examples.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.1
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.