• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2192-2195
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• 2002

Even though, tanks are used at the many industry plants, it is very difficult to control the tank level without any overflow and shortage; moreover, cause of its complication of dynamics and nonlinearity, it's impossible to realize the accurate control using the mathematical model which can be applied to the various operation modes. However, the sliding mode controller(SMC) is known as having the robust variable structures for the nonlinear control systems with the parametric perturbations and with the sudden disturbances. It's difficult to find SMC's parameters, and SMC is bring chattering which injures actuator and increases error. In this paper, Genetic Aloglism based Fuzzy Sliding Mode Controller(GA-FSMC) for the precise control of the coupled tank level was proposed. Genetic Algolism and Fuzzy logic are adapted to find SMC's parameters and reduce the chattering. The simulation result is shown that the tank level could be satisfactorily controlled with less overshoot and steady-state error by the proposed GA-FSMC.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.1
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• pp.87-94
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• 2003

A geometric non-linear finite element formulation of CRTS reflector subjected to displacement loads, corresponding to the successional assembly steps of the reflector, is presented in order to determine the initial static equilibrium state based on the displacement incremental method. Parametric analyses of the influence of cables and mechanical properties of the reflector on the shape error between reference and equilibrium surfaces have been studied. These results of the present study are compared with the others using Galerkin mothod and NASS 98 program to demonstrate the feasibility.

• Earthquakes and Structures
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• v.9 no.3
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• pp.455-480
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• 2015

In the last decade, displacement-based (DB) methods have become established design procedures for reinforced concrete (RC) structures. They use strain and displacement measures as seismic performance control parameters. As for other simplified seismic design methods, it is of great interest to prove if they are usually conservative in respect to more refined, nonlinear, time history analyses, and can estimate design parameters with acceptable accuracy. In this paper, the current Direct Displacement-Based Design (DDBD) procedure is evaluated for designing simple single degree of freedom (SDOF) systems with specific reference to simply supported RC bridge piers. Using different formulations proposed in literature for the equivalent viscous damping and spectrum reduction factor, a parametric study is carried out on a comprehensive set of SDOF systems, and an average error chart of the method is derived allowing prediction of the expected error for an ample range of design cases. Following the chart, it can be observed that, for the design of actual RC bridge piers, underestimation errors of the DDBD method are very low, while the overestimation range of the simplified displacement-based procedure is strongly dependent on design ductility.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.2
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• pp.55-61
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• 2000

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kc, $\tau$I, $\tau$D by the Ziegler-Nichols formula using the ultimate gain and ultimate period from a relay tuning experiment. We get error and error change of plant output correspond to the initial value and new proportion gain(Kc) and integral time($\tau$I) from fuzzy tunner. This fuzzy tuning algorithm for PID controller considerably reduced overshoot and rise time compare to any other PID controller tuning algorithms. In real parametric uncertainty systems, the PID controller with Fuzzy auto-tuning give appreciable improvement in the performance. The significant properties of this algorithm is shown by simulation In this paper, we proposed a new PID algorithm by the fuzzy set theory to improve the performance of the PID controller.

• International Journal of Ocean System Engineering
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• v.3 no.2
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• pp.50-60
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• 2013

This paper presents a novel approach to the design of an adaptive fuzzy sliding mode controller for depth control of an autonomous underwater vehicle (AUV). So far, AUV's dynamics are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to estimate, because of the variations of these coefficients with different operating conditions. These kinds of difficulties cause modeling inaccuracies of AUV's dynamics. Hence, we propose an adaptive fuzzy sliding mode control with novel fuzzy adaptation technique for regulating vertical positioning in presence of parametric uncertainty and disturbances. In this approach, two fuzzy approximator are employed in such a way that slope of the linear sliding surface is updated by first fuzzy approximator, to shape tracking error dynamics in the sliding regime, while second fuzzy approximator change the supports of the output fuzzy membership function in the defuzzification inference module of fuzzy sliding mode control (FSMC) algorithm. Simulation results shows that, the reaching time and tracking error in the approaching phase can be significantly reduced with chattering problem can also be eliminated. The effectiveness of proposed control strategy and its advantages are indicated in comparison with conventional sliding mode control FSMC technique.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.387-393
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• 2017

This paper focuses on a horizontal waypoint tracking and a speed control of large diameter unmanned underwater vehicles (LDUUVs) with X-stern configuration plane. The concerned design problem is converted into an asymptotic stabilization of the error dynamics with respect to the desired yaw angle and surge speed. It is proved that the error dynamics under the proposed control scheme based on the linear control and the feedback linearization can be considered as a cascade system; the cascade system is asymptotically stable if its nominal systems are so. This stability connection enables to separately deal with the waypoint tracking problem and the speed control one. By using the sector nonlinearity, the nominal system with nonlinearities is modeled as a polytopic linear parameter varying (LPV) system with parametric uncertainties. Then, sufficient linear matrix inequality (LMI) conditions for its asymptotic stabilizability are derived in the sense of Lyapunov stability criterion. An example is given to show the validity of the proposed methodology.

• 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.53 no.6
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• pp.1796-1809
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• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

• Computers and Concrete
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• v.31 no.3
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• pp.173-184
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• 2023

In this study, the flexural behaviors of one- and two-way reinforced concrete (RC) slabs strengthened with carbon-fiber-reinforced polymer (CFRP) strips under impact loads were investigated. The flexural strengthening of RC slabs under simulated static monotonic loads has been comprehensively studied. However, the flexural behavior of RC slabs strengthened with CFRP strips has not been investigated extensively, particularly those conducted numerically. Nonlinear three-dimensional finite element models were developed, executed, and verified against previous experimental results, producing satisfactory models with approximately 4% error. The models were extended to a parametric study, considering three geometric parameters: the slab rectangularity ratio, CFRP strip width, and CFRP strip configuration. Finally, the main results were used to derive a new formula for predicting the total deflection of RC slabs strengthened with CFRP strips under impact loads with an error of approximately 10%. The proposed equation reflected the slab rectangularity, CFRP strip width, equivalent slab stiffness, and dropped weight. Results indicated that the use of CFRP strips enhanced the overall impact performance, the wider the CFRP width, the better the enhancement. Moreover, the application of diagonally oriented CFRP strips diminished the cracking zone compared to straight strips. Additionally, the diagonal orientation of CFRP strips was more efficient for two-way slabs while the vertical orientation was found to be better in the case of one-way slabs.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.