• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1186-1193
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• 1991

This paper proposed a trajectory controlmethod for a robot manipulator by using neural networks. The total torque for a manipulator is a sum of the linear feedback controller torque and the neural network feedfoward controller torque. The proposed neural network is a multilayer neural network with time delay elements, and learns the inverse dynamics of manipulator by means of PD(propotional denvative)controller error torque. The error backpropagation (BP) learning neural network controller does not directly require manipulator dynamics information. Instead, it learns the information by training and stores the information and connection weights. The control effects of the proposed system are verified by computer simulation.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.705-709
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• 1995

The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it was used as a commanded feedforward input generator. A PD feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the excavator as well as the PD feedback error. By using the BP network as a feedforward controller, no a priori knowledge on system dynamics is need. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbancen and performance improvement with the on-line learning in the position control of excavator attachment.

• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.383-388
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• 2004

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty problem. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model, Nueral network and PID control is used as a feedback controller. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance

• English Language & Literature Teaching
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• v.16 no.4
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• pp.1-18
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• 2010

Recasts, which are defined as implicit types of corrective feedback, have been the focus of numerous SLA researchers for more than a decade. A range of classroom-based observational and experimental research studies have explored how and when language teachers provide recasts to learners' ill-formed utterances and aimed to understand the role of recasts in language acquisition or learning. On the basis of previous studies on recasts, our study investigated when an ESL teacher provided recasts and how recasts were provided in his class. The research questions were as follows: (1) When does an ESL teacher provide recasts? (2) How does the teacher provide recasts? The data came from observations of one ESL classroom as well as consecutive-semi structured interviews with the teacher. The data analysis included transcriptions of teacher-student interactions in the target setting and categories of recasts according to the linguistic phenomena, which prompted recasting. Based on the findings, practical suggestions for ESL teachers were provided. [156 words].

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.113-117
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• 2002

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PR controller based on an Immune feedback mechanism and a gradient descent teaming. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor Is peformed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.194-204
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• 2022

This study is a one-group pretest-posttest design to evaluate the effect of problem-based learning (PBL) for patient safety on self-leadership, patient safety competencies, and reflective thinking of nursing students. The research was conducted from March 2 to April 15, 2022, in which 57 nursing students participated. PBL for patient safety was examined in a total of 8 sessions in the order of motivation, problem identification, task performance planning, problem-solving methods, summary and solution, presentation, and evaluation. The following topics of patient safety were selected for each team: nursing records, high-alert medication, medication error and intravenous fluid regulation, blood transfusion care, fall, bedsore, infection control, and pain management. We provided feedback on the learning process and outcomes of nursing students. According to the results, self-leadership showed a statistically significant improvement in self-expectations (t=2.60, p=0.01), goal setting (t=2.84, p<0.01), self-reward (t=3.32, p<0.01), and self-criticism (t=2.32, p=0.02). Patient safety competencies showed a statistically significant improvement in patient safety knowledge (t=13.05, p<0.001) and patient safety skills (t=4.87, p<0.001) but not in reflective thinking. The results prove that PBL for patient safety is an effective teaching-learning strategy to improve self-leadership and patient safety competencies. Future studies must develop and validate specific and long-term teaching-learning methods to improve reflective thinking.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.377-399
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• 2010

Mixed calculations involving fractions and decimals covered in the unit 6-Na in elementary school math class cause students difficulties, leading them make lots of errors. If students fail to understand temporarily or partly what the teacher taught or lose confidence and continue to have difficulty due to a lack of understanding and skills of algorithm, though they properly understand the concept and principle of the learning content, it should be resolved through intensive teaching. For students suffering from this problem, a correct diagnosis and appropriate treatment are required. Therefore, this study developed a feedback program after diagnosing students' errors through evaluating them in order to continuously assist them to fully understand contents regarding mixed calculations involving fractions and decimals.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.