• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.400-410
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• 1992

This paper deals with a design of neural network control architectures for robot motion. Three types of control architectures are designed as follows : 1) a neural network control architecture which has the same characteristics as computed torque method 2) a neural network control architecture for compensating the control error on computed torque method with fixed feedback gain 3) neural network adaptive control architecture. Computer simulation of PUMA manipulator with 6 links is conducted for robot motion in order to examine the proposed neural network control architectures.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.4
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• pp.37-42
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• 1984

In this paper, effective methods of symbol timing in a 9600 bps modem are presented. The symbol timing circuit consists of a square-low device followed by a high-Q narrow band-pass filter tuned to a symbol frequency. Also, the advantages of using a digital phase-tooted loop (DPLL) to suppress side tones are described, and statistical properties of timing wave are derived. In addition, the overall performances of the symbol timing circuit are verified by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.441-446
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• 1992

We propose a control architecture for the fuzzy logic control of robot manipulators and a rule base derivation method for a fuzzy logic controller(FLC) using a neural network. The control architecture is composed of FLC and PD(positional Derivative) controller. And a neural network is designed in consideration of the FLC's structure. After the training is finished by BP(Back Propagation) and FEL(Feedback Error Learning) method, the rule base is derived from the neural network and is reduced through two stages - smoothing, logical reduction. Also, we show the performance of the control architecture through the simulation to verify the effectiveness of our proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.117-122
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• 1992

The fuzzy-neural hybrid control system utilizing the fuzzy-neural network(FNN) will be presented in this paper. The basic structure of the controller is the parallel combination of a conventional P-controller and a FNN. Such a combination can guarantee the stability of a plant at initial stage before the rules are completely created. And a method how to automatically tunning the parameters of the FNN will be proposed with error back-propagation(BP) algorithm. Finally the effectiveness of the proposed strategy will be verified by computer simulations using a two DOF robot manipulator.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.215-220
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• 2002

This paper proposes a method to apply the Backpropagation(BP) algorithm of MVL(Multi-Valued Logic) Neural Network to pattern recognition. It extracts the property of an object density about an original pattern necessary for pattern processing and makes the property of the object density mapped to MVL. In addition, because it team the pattern by using multiple valued logic, it can reduce time f3r pattern and space fer memory to a minimum. There is, however, a demerit that existed MVL cannot adapt the change of circumstance. Through changing input into MVL function, not direct input of an existed Multiple pattern, and making it each variable loam by neural network after calculating each variable into liter function. Error has been reduced and convergence speed has become fast.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.40-45
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• 2016

This paper presents the neuro-fuzzy control method for balancing a two-wheel mobile robot. A two-wheel mobile robot is built for the experimental studies. On-line learning algorithm based on the back-propagation(BP) method is derived for the Takagi-Sugeno(T-S) neuro-fuzzy controller. The modified error is proposed to learn the B-P algorithm for the balancing control of a two-wheel mobile robot. The T-S controller is implemented on a DSP chip. Experimental studies of the balancing control performance are conducted. Balancing control performances with disturbance are also conducted and results are evaluated.

• IE interfaces
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• v.9 no.3
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• pp.298-305
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• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. Most of rolling processes use mathematical models to predict rolling force which is very important to decide the resultant thickness of a coil. In general, these mathematical models are not flexible for variant coil types and cannot handle various elements which is practically important to decide accurate rolling force. A corrective neural network is proposed to improve the accuracy of rolling force prediction. Additional variables-composition of the coil, coiling temperature and working roll parameters-are fed to the network. The model uses an MLP with BP to predict a corrective coefficient. The test results using 1,586 process data collected at POSCO in early 1995 show that the proposed model reduced the prediction error by 30% on average.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.21-23
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• 1994

The degree of forecast accuracy with BP-algorithm largely depends upon the neuron number in hidden layer. In order to construct the optimal structure, first, we prescribe the error bounds of learning procedure, and then, we provid the method of incrementing the number of hidden neurons by using the derivative of errors with respect to an output neuron weights. For the case study, we apply the proposed method to forecast the component-wise residential load, and compare this results to that of time series forecasting.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.45-51
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• 2001

The daily inflow is apparently one of nonlinear and complicated phenomena. The nonlinear and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear schemes. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the back propagation algorithm which is one of neural network structures is modified by combining a regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using tole regression scheme in BP algorithm is showed in the low and high ends of inflows.

• Journal of Microbiology and Biotechnology
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• v.17 no.7
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• pp.1090-1097
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• 2007

Genomic analysis of Thermococcus sp. NA1 revealed the presence of a 3,927-base-pair (bp) family B-type DNA polymerase gene, TNA1_pol. TNA1_pol, without its intein, was overexpressed in Escherichia coli, purified using metal affinity chromatography, and characterized. TNA1_pol activity was optimal at pH 7.5 and $75^{\circ}C$. TNA1_pol was highly thermostable, with a half-life of 3.5h at $100^{\circ}C$ and 12.5h at $95^{\circ}C$. Polymerase chain reaction parameters of TNA1_pol such as error-rate, processivity, and extension rate were measured in comparison with rTaq, Pfu, and KOD DNA polymerases. TNA1_pol averaged one incorrect bp every 4.45 kilobases (kb), and had a processivity of 150 nucleotides (nt) and an extension rate of 60 bases/s. Thus, TNA1_pol has a much faster elongation rate than Pfu DNA polymerase with 7-fold higher fidelity than that of rTaq.