• Nuclear Engineering and Technology
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• v.35 no.5
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• pp.399-411
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• 2003

A model predictive control method is applied to design an automatic controller for thermal power control in a reactor core. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the second optimal control input is not implemented and the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize the difference between the output and the desired output and the variation of the control rod position. The nonlinear PWR plant model (a nonlinear point kinetics equation with six delayed neutron groups and the lumped thermal-hydraulic balance equations) is used to verify the proposed controller of reactor power. And a controller design model used for designing the model predictive controller is obtained by applying a parameter estimation algorithm at an initial stage. From results of numerical simulation to check the controllability of the proposed controller at the $5\%/min$ ramp increase or decrease of a desired load and its $10\%$ step increase or decrease which are design requirements, the performances of this controller are proved to be excellent.

• Journal of IKEEE
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• v.11 no.1 s.20
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• pp.1-14
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• 2007

In this paper, a design of an integral augmented nonlinear optimal variable structure system(INOVSS) is presented for the prescribed output control of uncertain MIMO systems under persistent disturbances. This algorithm basically concerns removing the problems of the reaching phase and combining with the nonlinear optimal control theory. By means of an integral nonlinear sliding surface, the reaching phase is completely removed. The ideal sliding dynamics of the integral nonlinear sliding surface is obtained in the form of the nonlinear state equation and is designed by using the nonlinear optimal control theory, which means the design of the integral nonlinear sliding surface and equivalent control input. The homogeneous $2{\upsilon}(\kappa)$ form is defined in order to easily select the $2{\upsilon}$ or even $(\kappa)-form$ higher order nonlinear terms in the suggested sliding surface. The corresponding nonlinear control input is designed in order to generate the sliding mode on the predetermined transformed new surface by means of diagonalization method. As a result, the whole sliding output from a given initial state to origin is completely guaranteed against persistent disturbances. The prediction/predetermination of output is enable. Moreover, the better performance by the nonlinear sliding surface than that of the linear sliding surface can be obtained. Through an illustrative example, the usefulness of the algorithm is shown.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.296-300
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• 1989

The paper proposes a method to eliminate harmonics of PWM inverter fed induction motor system using Walsh series. In other words, this paper presents technique of the selective harmonics elimination(SHE) by W-FT series in three phase PWM inverter output waveform. A microprocessor(8086 CPU) - controlled three phase induction motor system in order to verify this algorithm is present. It is designed for a three output voltage in the 1$\sim$60 Hz inverter with the 5th and 7th harmonics, 5th, 7th, 11th, and 13th, harmonics eliminated, and with the fundamental wave amplitude proportional to the output frequency. In the PWM inverter, dead time circuit is inserted in the switching si gnats to prevent the de link shortage. This paper is deals with quantative prediction of dead-time effect and its compensation in PWM inverters. The performance of the compensation circuits is confirmed by the experiment.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2143-2145
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• 2003

In the linear system identification using the discrete time constant coefficients, there is a subspace method based on 4SID recently much suggested instead of the parametric method like as the maximum likelihood method. The subspace method is not related with the impulse response and difference equation in its input-output equation, but with the system matrix of the direct state space model from the input-output data. The subspace method is a very useful tool to adopt in the multivariable system identification, but it has a shortage unable to adopt in the closed-loop system identification. In this paper, we are suggested the methods to get rid of the shortage of the subspace method in the closed-loop system identification. The subspace method is used in the estimate of the output prediction values from the estimating of the state space vector. And we have compared the results with the outputs of the recursive least square method in the numerical simulation.

• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.690-703
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• 2010

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between $5^{th}$ and $95^{th}$ percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.10
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• pp.87-97
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• 2020

In general, tires require various sensors and power supply devices, such as batteries, to obtain information such as pressure, temperature, acceleration, and the friction coefficient between the tire and the road in real time. However, these sensors have a size limitation because they are mounted on a tire, and their batteries have limited usability due to short replacement cycles, leading to additional replacement costs. Therefore, vibration energy harvesting technology, which converts the dynamic strain energy generated from the tire into electrical energy and then stores the energy in a power supply, is advantageous. In this study, the output voltage and power generated from piezoelectric elements are predicted through finite element analysis under static state and transient state conditions, taking into account the dynamic characteristics of tires. First, the tire and piezoelectric elements are created as a finite element model and then the natural frequency and mode shapes are identified through modal analysis. Next, in the static state, with the piezoelectric element attached to the inside of the tire, the voltage distribution at the contact surface between the tire and the road is examined. Lastly, in the transient state, with the tire rotating at the speeds of 30 km/h and 50 km/h, the output voltage and power characteristics of the piezoelectric elements attached to four locations inside the tire are evaluated.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.47-50
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• 1991

An automatized latency calculation method of evoked otoacoustic emission is proposed. Averaged e-OAE signal is measured from normal adult and it is processed by IBM-AT computer for latency calculation. After partition of stimulated and emitted signal on the time axis by converted linear prediction coding spectrum, latency is calculated by cross correlation method. The output is also compared with those of conventional autocorrelation and pure cross correlation methods. The result show that proposed method has better performance than those of conventional ones.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.102-105
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• 2002

A noise reduction technique to reduce background noise in corrupted voice is proposed. The proposed method is based on linear prediction and takes advantages of periodicity of voiced speech. A voiced sound is regarded as a periodic stationary signal in short time interval. Therefore, the current voice signal is correlated with the voice signal delayed by a pitch period. A linear predictor can estimate only the current signal correlated with the delayed signal. Therefore, the enhanced voice can be obtained as output of the linear predictor. Simulation results show that the proposed method is able to reduce the background noise.