• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2126-2128
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• 2001

This paper suggests that the immune network algorithm based on fuzzy set can effectively be used in tuning of a PID controller for multivariable process or nonlinear process. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that from a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. Along with these, this paper used the fuzzy set in order that the stimulation and suppression relationship between antibody and antigen can be more adaptable controlled against the external condition, including noise or disturbance of plant. The immune network based on fuzzy set suggested here is applied for the PID controller tuning of multivariable process with two inputs and one output and is simulated.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.220-225
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• 2010

This paper deals with the robust stability analysis of STATCOM (Static Synchronous Compensator) power system for power quality enhancement and power system stability. The STATCOM plays an important role in controlling the reactive power flow to the power network and hence the system voltage fluctuations and stability. The control areas of this plant are very large and the overall composition of the system is nonlinear. Also, STATCOM is influence of the uncertainties so that it is necessary to apply the new control technique. For solving these problems, we perform the fuzzy modeling and robust analysis for STATCOM system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.538-543
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• 2016

For a good product quality and plant safety, it is necessary to implement the on-line monitoring and diagnosis schemes of industrial processes. Combined with monitoring systems, reliable diagnosis schemes seek to find assignable causes of the process variables responsible for faults or special events in processes. This study deals with the real-time diagnosis of complicated industrial processes from the intelligent use of multivariate statistical techniques. The presented diagnosis scheme consists of a classification-based diagnosis using nonlinear representation and filtering of process data. A case study based on the simulation data was conducted, and the diagnosis results were obtained using different diagnosis schemes. In addition, the choice of future estimation methods was evaluated. The results showed that the performance of the presented scheme outperformed the other schemes.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.210-215
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• 2004

The dynamic model of ac servo motor is influenced very much due to rotor resistance change and nonlinear characteristic. By using the sliding mode control the dynamic behavior of system can be made insensitive to plant parameter change and external disturbance. This paper describes the application of the sliding mode control for position control of ac servo motor. The control scheme is derived and designed. A design method based on external load parameters has been developed for the robust control of ac induction servo drive. The proposed control scheme are given based on the variable structure controller and slip frequency vector control. Simulated results are given to verify the proposed design method by adoption of sliding mode and show robust control for a change of shaft initial J, viscous friction B and torque disturbance.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.859-865
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• 2014

Optimization of an electric machine is mainly a nonlinear multi-modal problem. For the optimization of the multi-modal problem, many function calls are required with much consumption of time. To address this problem, this paper proposes a novel hybrid algorithm in which function calls are less than conventional methods. Specifically, the proposed method uses the kriging metamodel and the fill-blank technique to find an approximated solution in a whole problem region. To increase the convergence speed in local peaks, a parallel gradient assisted simplex method is proposed and combined with the kriging meta-model. The correctness and usefulness of the proposed hybrid algorithm is verified through a mathematical test function and applied into the practical optimization as the cogging torque minimization for an interior permanent magnet synchronous machine.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.6
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• pp.109-115
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• 2008

This paper proposes an adaptive Dynamic Matrix Control (DMC) and its application to boiler-turbine system In a conventional DMC, object system is described as a Step Response Model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are prepared. On-line interpolation is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory results with a wide range operation of the boiler-turbine system.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.10-12
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• 2003

This study was to monitor changes of leaf area index (LAI) and normalized difference vegetation index (NDVI), calculated from ground-based remotely sensed high resolution reflectance spectra, during rice (Oryza sativa L. cv. TNG 67) growth so as to determine their relationships and the optimum time period to use these parameters for yield prediction. Field experiments were conducted at the experimental farm of TARI to obtain various scales of grain yield and values of LAI and NDVI in the first and the second cropping seasons of 2001-2002. It was found that LAI and NDVI can be mutually estimated through an exponential relationship, and hence plant growth information and spectral remote sensing data become complementary counterparts through this linkage. Correlation between yield and LAI was best fitted to a nonlinear function since about 7 weeks after transplanting (WAT). The accumulated and the mean values of LAI from 15 days before heading (DBH) to 15 days after heading (DAH) were the optimum time period to predict rice yield for First Crops, while values calculated from 15 DBH to 10 DAH were the optimal timing for Second Crops.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1993.10a
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• pp.35-41
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• 1993

산업사회가 발전함에 따라 거대한 플랜트(plant)를 포함하여, 원자력 발전소 와 로켓트 등과 같이 구조적 안전성과 정확한 성능의 확보가 극히 중요시되 는 설비들이 많이 등장하고 있다. 이들 설비를 구성하고 있는 파이프계 (piping system)는 강성 또는 연성재료 및 각종 valve등으로 구성되어 있기 때문에, 고온 고압 고속의 유체가 파이프 내부를 흐를 때 일으키는 진동현상 및 플랜트의 과도운전 상황에서의 일어나는 수격현상(water hammer 또는 steam hammer)과 이로 기인한 제반 진동문제는 안전성확보 측면에서 많은 관심이 고조되고 있다. 이와 관련되어 유체유발진동에 관한 많은 연구들이 수행되었으며, 파이프계에 장착된 밸브에 대한 연구는 Weaver등에 의하여 실험적, 이론적으로 수행되었다. 그들은 유동방정식에서 비정상 베르누이 방 정식을 사용하여 내부유동방정식을 간략하게 유도하였으나, jet flow에 의한 유체의 운동량변화를 고려하지 않고 해석되어 그 결과에 의문이 제기된다. 그러므로 본 연구에서는 체크밸브(check valve)가 부착된 파이프계에서 일어 나는 유체유발진동 및 안정성에 관한 이론적 연구를 수행하여, 파이프계의 설계에 필요한 파라미터의 영향을 파악하고자 한다. 원자력 발전소와 같이 구조적 안정성이 요구되는 플랜트를 국내 기술로 설계 시공하려는 국내 실 정에서 이로 인한 파이프계에 발생할 수 있는 유체유발진동과 안정성을 예 측할 수 있는 해석이론은 파이프계의 설계 및 운전조건의 선정에 있어서 그 의의가 크다고 할 수 있겠다.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.894-903
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• 2019

An approach for collapse risk assessment is proposed to evaluate the vulnerability of electric cabinet in nuclear power plants. The lognormal approaches, namely maximum likelihood estimation and linear regression, are introduced to establish the fragility curves. These two fragility analyses are applied for the numerical models of cabinets considering various boundary conditions, which are expressed by representing restrained and anchored models at the base. The models have been built and verified using the system identification (SI) technique. The fundamental frequency of the electric cabinet is sensitive because of many attached devices. To bypass this complex problem, the average spectral acceleration $S_{\bar{a}}$ in the range of period that cover the first mode period is chosen as an intensity measure on the fragility function. The nonlinear time history analyses for cabinet are conducted using a suite of 40 ground motions. The obtained curves with different approaches are compared, and the variability of risk assessment is evaluated for restrained and anchored models. The fragility curves obtained for anchored model are found to be closer each other, compared to the fragility curves for restrained model. It is also found that the support boundary conditions played a significant role in acceleration response of cabinet.