• 대한조선학회논문집
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• 제44권2호
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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.

• 한국산업정보학회논문지
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• 제6권4호
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• pp.75-80
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• 2001

본 논문에서는 복잡한 하이퍼카오스 신호를 발생시키는 HVPM(Hyperchaotic Volume Preserving Maps) 모델을 이용한 카오스 동기화 알고리즘을 제안하고자 한다. 제안한 HVPM 모델은 3차원 이산시간(discrete-time) 연립 차분방정식으로 구성되어 있으며, 비선형 사상(maps)과 모듈러(modulus) 함수를 사용하여 랜덤한 카오스 어트랙터(attractor)를 발생시킨다. Pecora와 Caroll은 최근 카오스 시스템이 카오스 신호를 이용하여 동기화가 가능하다고 보고하였다. 따라서 본 논문에서는 하이퍼카오스 신호를 발생시키는 HVPM 모델간의 동기화를 위하여 결합동기(coupled synchronization) 알고리듬을 제안하였다. 모의실험에서 카오스 시스템과 하이퍼카오스 신호를 결합하여 카오스 동기화 현상을 확인할 수 있었다.

• 제어로봇시스템학회논문지
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• 제15권2호
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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.

• 한국추진공학회지
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• 제20권3호
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• pp.78-86
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• 2016

본 논문은 가스터빈시스템에서 외부 유량의 섭동과 이에 따른 열발생의 섭동 사이의 관계를 표현하는 새로운 형태의 화염전달함수(flame transfer function))를 제안한다. 연소의 불안정성을 예측하기 위하여 그 동안 널리 이용되어 왔던 $n-{\tau}$ 모델과 본 논문에서 제안하는 모델과의 가장 큰 차이점은 주파수 변화에 따른 위상의 비선형적인 관계를 표현 할 수 있다는 점에 있다. 기존의 $n-{\tau}$ 모델의 경우 위상은 항상 주파수의 선형함수로 주어지는데 이는 실제 연소시스템의 실험에서 자주 관측되는 결과와 일치하지 않았다. 실험 결과를 바탕으로 새로운 화염전달함수 모델을 구성하였고 연소불안정성의 측면에서 위상의 비선형성이 미치는 영향에 관하여 분석하였다.

• Nuclear Engineering and Technology
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• 제27권3호
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• pp.336-343
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• 1995

적응제어의 한 방식인 자기동조제어(STR) 방식이 비선형 노심 모델의 출력 조정에 적용된다. 적응제어는 비선형, 시변 및 확률(Stochastic) 시스템을 위한 준최적 제어기를 설계하기 위한 적절한 제어 방식이다. 제어계통은 미지의 시변 파라메타를 갖는 3차 선형 모델에 기초한다. 파라메타는 가변 망각계수를 도입한 늑장 최소자승법에 의하여 매시간(Time Step) 순환적으로 평가된다. 평가된 파라메타를 이용하여 한 스텝 먼저 냉자재 평균온도가 예측되고 이 예측된 값과 Setpoint 값과의 차이를 최소화함은 물론, 제어봉의 움직임을 막고자 가중(Weighted) One-step-ahead 제어기가 설계된다. 또한 적분동작이 첨가되어 정상상태 에러가 제거된다. 넓은 운전영역을 포괄하는 비선형 PWR 모델이 원자로 출력 조정을 위한 본 제어기를 시뮬레이션하는데 이용되었다. 시뮬레이션 결과로부터 본 제어기의 성능이 우수한 것으로 판명되었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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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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• 제어로봇시스템학회 2004년도 ICCAS
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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".

• 대한기계학회논문집A
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• 제27권12호
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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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• 제5권1호
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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.

• 전력전자학회논문지
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• 제26권1호
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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.