• Journal of the Society of Naval Architects of Korea
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• v.44 no.2 s.152
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• pp.130-138
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• 2007

A coupled variational equation for fluid-structure interaction (FSI) problems is derived from a steady state Navier-Stokes equation for incompressible Newtonian fluid and an equilibrium equation for geometrically nonlinear structures. For a fully coupled FSI formulation, between fluid and structures, a traction continuity condition is considered at interfaces where a no-slip condition is imposed. Under total Lagrange formulation in the structural domain, finite rotations are well described by using the second Piola-Kirchhoff stress and Green-Lagrange strain tensors. An adjoint shape design sensitivity analysis (DSA) method based on material derivative approach is applied to the FSI problem to develop a shape design optimization method. Demonstrating some numerical examples, the accuracy and efficiency of the developed DSA method is verified in comparison with finite difference sensitivity. Also, for the FSI problems, a shape design optimization is performed to obtain a maximal stiffness structure satisfying an allowable volume constraint.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.4
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• pp.75-80
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• 2001

In this paper, we propose a new chaotic synchronization algorithm of using HVPM(Hyperchaotic Volume Preserving Maps) model. The proposed chaotic equation, that is, HVPM model which consists of three dimensional discrete-time simultaneous difference equations and shows uniquely random chaotic attractor using nonlinear maps and modulus function. Pecora and Carrol have recently shown that it is possible to synchronize a chaotic system by sending a signal from the drive chaotic system to the response subsystem. We proposed coupled synchronization algorithm in order to accomplish discrete time hyperchaotic HVPM signals. In the numerical results, two hyperchaotic signals are coupled and driven for accomplishing to the chaotic synchronization systems. And it is demonstrated that HVPM signals have shown the chaotic behavior and chaotic coupled synchronization.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.148-154
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• 2009

In this paper, an embedded Kalman filter for a time-delayed controller is designed on an FPGA to estimate accelerations of the robot arm. When the time-delayed controller is used as a controller, the inertia estimation along with accelerations is needed to form the control law. Although the time-delayed controller is known to be robust to cancel out uncertainties in the nonlinear systems, performances are very much dependent upon estimating the acceleration term ${\ddot{q}}(t-{\lambda})$ along with inertia estimation ${\hat{D}}(t-{\lambda})$. Estimating accelerations using the finite difference method is quite simple, but the accuracy of estimation is poor specially when the robot moves slowly. To estimate accelerations more accurately, various filters such as the least square fit filter and the Kalman filter are introduced and implemented on an FPGA chip. Experimental studies of following the desired trajectory are conducted to show the performance of the controller. Performances of different filters are investigated experimentally and compared.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.3
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• pp.78-86
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• 2016

In this paper we propose a new frame transfer function model describing the variations of a heat release rate in response to an external flow oscillation in gas turbine systems. A critical difference of our model compared to the so-called $n-{\tau}$ model which has been widely used for a prediction of combustion instability (CI), is that our model is able to describe a nonlinear relation between phase and frequency. In contrast, the phase part of the $n-{\tau}$ model is a pure time delay and thus the phase should be a linear function of frequency, which is inconsistent with many experimental results of real combustion systems. For an illustration, our new model is applied to experimental data and the effect of phase nonlinearity is investigated in the context of combustion instability.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1033-1036
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• 2004

The study on speech recognition and understanding has been done for many years. In this paper, we propose a new type of recurrent neural network architecture for speech recognition, in which each output unit is connected to itself and is also fully connected to other output units and all hidden units [1]. Besides that, we also proposed the new architecture and the learning algorithm of recurrent neural network such as Backpropagation Through Time (BPTT, which well-suited. The aim of the study was to observe the difference of Arabic's alphabet like "alif" until "ya". The purpose of this research is to upgrade the people's knowledge and understanding on Arabic's alphabet or word by using Recurrent Neural Network (RNN) and Backpropagation Through Time (BPTT) learning algorithm. 4 speakers (a mixture of male and female) are trained in quiet environment. Neural network is well-known as a technique that has the ability to classified nonlinear problem. Today, lots of researches have been done in applying Neural Network towards the solution of speech recognition [2] such as Arabic. The Arabic language offers a number of challenges for speech recognition [3]. Even through positive results have been obtained from the continuous study, research on minimizing the error rate is still gaining lots attention. This research utilizes Recurrent Neural Network, one of Neural Network technique to observe the difference of alphabet "alif" until "ya".

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2092-2100
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• 2003

Sensitivity information has been used for linearization of nonlinear functions in optimization. Basically, sensitivity is a derivative of a function with respect to a design variable. Design sensitivity is repeatedly calculated in optimization. Since sensitivity calculation is extremely expensive, there are studies to directly use the sensitivity in the design process. When a small design change is required, an engineer makes design changes by considering the sensitivity information. Generally, the current process is performed one-by-one for design variables. Methods to exploit the sensitivity information are developed. When a designer wants to change multiple variables with some relationship, the directional derivative can be utilized. In this case, the first derivative can be calculated. Only small design changes can be made from the first derivatives. Orthogonal arrays can be used for moderate changes of multiple variables. Analysis of Variance is carried out to find out the regional influence of variables. A flow is developed for efficient use of the methods. A software system with the flow has been developed. The system can be easily interfaced with existing commercial systems through a file wrapping technique. The sensitivity information is calculated by finite difference method. Various examples are solved to evaluate the proposed algorithm and the software system.

• Geomechanics and Engineering
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• v.5 no.1
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• pp.37-55
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• 2013

An important issue in the design of soil-nailing systems, as long-term retaining walls, is to assess their stability during seismic events. As such, this study is aimed at simulating the dynamic behavior and failure pattern of nailed structures using two series of numerical analyses, namely dynamic time history and pseudo-static. These numerical simulations are performed using the Finite Difference Method (FDM). In order to consider the actual response of a soil-nailed structure, nonlinear soil behaviour, soil-structure interaction effects, bending resistance of structural elements and construction sequences have been considered in the analyses. The obtained results revealed the efficiency of both analysis methods in simulating the seismic failure mechanism. The predicted failure pattern consists of two sliding blocks enclosed by three slip surfaces, whereby the bottom nails act as anchors and the other nails hold a semi-rigid soil mass. Moreover, it was realized that an increase in the length of the lowest nails is the most effective method to improve seismic stability of soil-nailed structures. Therefore, it is recommended to first estimate the nails pattern for static condition with the minimum required static safety factor. Then, the required seismic stability can be obtained through an increase in the length of the lowest nails. Moreover, placement of additional long nails among lowest nails in existing nailed structures can be considered as a simple retrofitting technique in seismic prone areas.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.