• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.520-522
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• 1996

The Direct Drive Arm(DDA) is a SCARA typed direct drive manipulator with two degrees-of-freedom(DOF) using the direct drive motor of the NSK company. A controller system for the SCARA robot of DDA is designed using a DSP (TMS32Oc3O), which has the highest performance among the third DSP chips in the TI company. The design objective of the system is to implement dynamic control algorithms and neural control algorithms for real time learning which require a lot of calculations and large memory and have been tested only by simulations so far. The controller uses a DSP, a high speed D/A, 32-bit Counter and a large DRAM to implement advanced robot control algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2266-2270
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• 2005

In this paper, an internet based control application for dynamical systems is implemented. This implementation is maily targeted for the part of advanced control education. Intelligent control algorithms are implemented in a PC so that a client can remotely access the PC to control a dynamical system through the internet. Neural network is used as an on-line intelligent controller. To have on-line learning and control capability, the reference compensation technique is implemented as intelligent control hardware of combining a DSP board and an FPGA chip. GUIs for a user are also developed for the user's convenience. Actual experiments of motion control of a DC motor have been conducted to show the performance of the intelligent control though the internet and the feasibility of advanced control education.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.635-641
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• 2016

In many industrial applications that use the electric motors robust controllers are needed. The method using a neural network in order to design a robust controller when a disturbance occurs is studied. Backpropagation algorithm, which is used in a conventional neural network controller is used in many areas, but when the number of neurons in the input layer, hidden layer and output layer of the neural network increases the processing speed of the learning process is slow. In this paper an adaptive PI(Proportional and Integral) controller based on CTRNN(Continuous Time Recurrent Neural Network) for permanent magnet synchronous motors is presented. By varying the load and the speed the validity of the proposed method is verified through simulation and experiments.

• The Journal of Korean Physical Therapy
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• v.20 no.4
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• pp.15-20
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• 2008

Purpose: We investigated the effects of physical practice, mental practice, and cross education using serial reaction time (SRT). We recruited 21 right-handed healthy males and females who gave consent and had no clinical history for their upper limbs. Methods: The subjects were divided into three groups; actual practice (n=7), mental practice (n=7), and controls (n=7), who performed actual training, mental training, or no intervention respectively for three weeks. Super lab 4.0 displayed four symbols on the monitor and subjects pushed on the matching button, with reaction time assessed pre- and post-intervention. Results: Reaction time was significantly lower after actual or mental practice (p<0.05). Actual practice also decreased left hand reaction time. Conclusion: Actual practice and mental practice can improve motor learning, but mental practice is not sufficient for cross education.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.28 no.2
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• pp.144-150
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• 1992

This paper describes an application of artificial neural network to diagnose the defects of rotating machiner. Induction motor was used to the object of defect diagnosis. For defect diagnosis, the frequency spectrum of vibration was utilized. Learning method of applied neural network was back propagation. Neural network has following advantage; Once it has been learned, inference time is very short and it can provide a reasonable conclusion regardless of insufficient input data. So, this defect diagnosis system can be used superiorly to rule based expert system as quality inspection of rotating machinery in the shop.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.139-142
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• 2002

The brain of the human is the best model for the artificial intelligence and is studied by many natural, medical scientists and engineers. In the engineering department, the brain model becomes a main subject in the area of development of a system that can represent and think like human. In this paper, we approach and define the function of the brain biologically and especially, make a model for the function of cerebral cortex, known as a part that performs behavior inference and decision for sensitive information from the thalamus. Therefore, we try to make a model for the transfer process of the brain. The brain takes the sensory information from sensory organ, proceeds behavior inference and decision and finally, commands behavior to the motor nerves. We use the modular neural network in this model. finally, we would like to design the intelligent system that can sense, recognize, think and decide like the brain by learning the information process in the brain with the modular neural network.

• The Journal of Korean Physical Therapy
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• v.22 no.6
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• pp.91-98
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• 2010

Neurostimulation approaches have been developed and explored to modulate neuroplastic changes of cortical function in human brain. As one of the most primary noninvasive tools, transcranial direct current stimulation (tDCS) was extensively studied in the field of neuroscience. The alternation of cortical neurons depending on the polarity of the tDCS has been used for improving cognitive processing including working memory, learning, and language in normal individuals, as well as in patients with neurological or psychiatric diseases. In addition, tDCS has great advantages: it is a non-invasive, painless, safe, and cost-effective approach to enhance brain function in normal subjects and patients with neurological disorders. Numerous previous studies have confirmed the efficacy of tDCS. However, tDCS has not been considered for clinical applications and research in the field of physical therapy. Therefore, this review will focus on the general principles of tDCS and its related application parameters, and provide consideration of motor behavioral research and clinical applications in physical therapy.

• Journal of Korean Physical Therapy Science
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• v.17 no.3_4
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• pp.11-21
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• 2010

Purpose: To Provide 3 main principles that needs to be changed for clinical practice and suggest the frame of clinical practice in accordance with the change of generation. Methods: We reviewed literatures related with Clinical Practice. Results: The purpose of physical therapy is to maintain the client's motor and functional ability and enhance the quality of lifestyle. To carry out the principles of clinical practice effectively all the physical therapist must be able to comprehend as follows. First, physical therapy can be different generation by generation. Second, technical terminology must be used when communicate. Third, There are certain ways of process in physical therapy. Conclusion: Physical therapist that is heath care professional occupation in health related, is in need of constant endeavor. Also physical therapist must train oneself self-directed learning.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.1
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• pp.30-36
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• 2001

This paper introduces a new indirect rotor position estimation algorithm for the SRM sensorless control, based on the magnetizing curves of aligned and unaligned rotor positions. Through the basic test method, the complete SRM magnetizing characterization is first constructed using a neural network training, and then used to estimate the rotor position. And also, the optimal phase is selected by the phase selector. In order to verify this approach, the proposed rotor position estimation algorithm using a neural network learning data is investigated. The experimental results show that the proposed control algorithm can be effectively applied to SRM sensorless control.