• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.344-349
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• 1990

Under the assumption that process input/output data are sufficiently rich to allow reasonable plant identification, a long-range predictive control method for SISO bilinear plant is derived. In order to ensure offset-free behaviour of the control method, a new bilinear CARIMA model with variable dead-time is introduced. Furthermore, to extend the maximum output prediction horizon, the future predicted outputs in the bilinear term are assumed to be equal to the known future set-points. With a classical recursive adaptation algorithm, the proposed control scheme is capable of stable control of bilinear plants with variable parameters, with variable dead-time, and with a model order which changes instantaneously. Several simulation results demonstrate the characteristics of the proposed bilinear model predictive control method.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.85-88
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• 2006

Many control techniques have been proposed in order to improve the control performance of the discrete-time domain control system. In the position control system, the output of a controller is generally used as the input of a plant but the undesired noise is include in the output of a controller. In this paper, the neuro-network 2-DOF PID Controller is designed by a neural network and the gains of this controller are adjusted automatically by the back-propagation algorithm of the neural network when the response characteristic of system is changed under a condition.

• Journal of Welding and Joining
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• v.11 no.1
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• pp.52-61
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• 1993

Laser welding of ASTM no. 1060 Al plate with a pulsed Nd: YAG laser of 200W average power was performed for end capping of KMRR nuclear fuel elements In this research, we performed basic welding experiments. Firstly, laser output parameters which affect laser welding parameters were studied by changing laser input parameters for effective welding of 1060 Al plates. We found that laser power density and pulse energy are important parameters for smooth bead shape. Secondly, welding parameters which affect weld width-to-depth ratio were studied by changing power density and pulse energy, shielding gas, and defocusing. We found that power density must be higher than 0.3 Mw/cm$^{2}$ pulse energy must be higer than 3 J. travel speed must not exceed 200mm/sec, laser focus must be existed beneath 2-3mm from plate surface and helium is proper shielding gas. Thirdly, we studied the weld defects of Al-1060 such as crack and porosity in lap-joint welding. We designed new welding geometry for crack free welding of Al-1060 plates, and obtained crack free weldment but with lack of fusion. However, with Ti, Zr grain refiner elements, we can weld Al plates without solidification hot crack. Finally, we studied the origin of porosity by changing shielding gas. And we found that porosity was resulted from entrapment of shielding gas by the collapsing keyhole.

• Korean Journal of Optics and Photonics
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• v.23 no.6
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• pp.246-250
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• 2012

We derive analytical expressions for the output spectral density and the noise power $P_{\beta}$ in noise loading analysis using Volterra kernels to characterize fiber nonlinearities. The bandwidth of the input noise source has little effect on $P_{\beta}$, but the power of the input noise source and the dispersion parameter value of the fiber have a significant effect on $P_{\beta}$. The Volterra method predicts ${\Delta}P_{\beta}[dB]$ = 30 dB/decade, which agrees very accurately over a wide range of fiber parameters compared with the numerical results by the split-step Fourier method. Therefore the Volterra method could be useful to predict the performance of a dense WDM system when we plan to upgrade fiber or increase signal power.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.1
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• pp.37-44
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• 1983

A class of adaptive control algorithms applied to discrete-time single-input single-output deterministic linear systems is analyzed by using direct model reference adaptive control. Controller parameters are identified with weighted least square Method. And computer simulations reveal that proposed weighted least square method in which the value of depends on the identification error can be used regardless of the sufficient condition of reference input signal.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.5
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• pp.321-327
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• 2018

DC microgrid system composed of multiple converters has a tendency to make the system unstable due to the interaction of converters. To solve this problem, in this paper, the interaction between cascaded converters with LC input filter is analyzed with impedance modeling using g-parameter. The input impedance and the output impedance of the system can be obtained through this technique. The stability of the system can be determined by applying Middlebrook's stability criterion to the impedance. Virtual impedance is added to the controller to enhance stability. The validity of the analysis is verified by the result of several simulations and experiments.

• Fire Science and Engineering
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• v.24 no.5
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• pp.60-67
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• 2010

Fire model shall be verified and validated to reliably predict the consequences of fires within its limitations. Generally the verification and validation procedures are conducted by comparison with experimental test data. This study aims to evaluate predictive capabilities of FDS in the pool fire with two rooms and the sensitivity between input parameters such as heat release rate and ventilation rate and the output values like temperature, concentration, and heat flux. The predictive capabilities of FDS will be evaluated by comparing FDS simulation results with PRISME experimental data which result from the international fire test project. The sensitivity analysis will be conducted to decide which one of input parameters affects outcomes by comparison of FDS results with ${\pm}$ 10% changes of input parameter. From this study, the FDS predictive capabilities are within 20% error range. Heat release rate as input parameter affects most of outcomes and flow rate only has relation with concentration of oxygen and combustion products.

• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.69-81
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• 1997

In this paper, first we propose an architecture of fuzzy neural networks with triangular fuzzy weights. The proposed fuzzy neural network can handle fuzzy input vectors. In both cases, outputs from the fuzzy network are fuzzy vectors. The input-output relation of each unit of the fuzzy neural network is defined by the extention principle of Zadeh. Also we define a cost function for the level sets(i. e., $\alpha$-cuts)of fuzzy outputs and fuzzy targets. Then we derive a learning algorithm from the cost function for adjusting three parameters of each triangular fuzzy weight. Finally, we illustrate our a, pp.oach by computer simulation examples.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.4
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• pp.84-92
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• 1998

A program was developed to analyze the electrical characteristics and harmonic distrotion in a two-dimensional silicon BJT. The finite difference equations of the small signal and its second and thired harmonics for basic semiconductor equations are formulated treating the nonlinearity and time dependence with Volterra series and Taylor series. The soluations for three sets of simultaneous equations were obtained sequantially by a decoupled iteration method and each set was solved by a modified Stone's algorithm. Distortion magins and ac parameters such as input impedance and current gains are calculated with frequency and load resistance as parameters. The distortion margin vs. load resistancecurves show cancellation minima when the pahse of output voltage shifts. It is shown that the distortionof small signal characteristics can be reduced by reducing the base width, increasing the emitter stripe length and reducing the collector epitaxial layer doping concentration in the silicon BJT structure. The simulation program called TRADAP can be used for the design and optimization of transistors and circuits as well as for the calculation of small signal and distortion solutions.

• Computers and Concrete
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• v.13 no.5
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• pp.621-632
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• 2014

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the compressive strength of High Performance Concrete (HPC) containing binary and quaternary blends. The investigations were done on 23 HPC mixes, and specimens were cast and tested after 7, 28 and 56 days curing. The obtained experimental datas of 7, 28 and 56 days are trained using ANN which consists of eight input parameters like cement, metakaolin, blast furnace slag and fly ash, fine aggregate, coarse aggregate, superplasticizer and water binder ratio. The corresponding output parameters are 7, 28 and 56 days compressive strengths. The predicted values obtained using ANN show a good correlation between the Experimental data. The performance of the 8-9-3-3 architecture was better than other architectures. It concluded that ANN tool is convenient and time saving for predicting compressive strength at different ages.