• Journal of Navigation and Port Research
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• v.34 no.2
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• pp.117-122
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• 2010

Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains and to analyse the stability compared to conventional PID controllers. This paper proposes a fuzzy PD+I controller for tracking control which uses a linear fuzzy inference(product-sum-gravity) method based on a conventional linear PID controller. In this scheme the fuzzy PD+I controller works similar to the control performance as the linear PD plus I(PD+I) controller. Thus it is possible to analyse and design an fuzzy PD+I controller for given systems based on a linear fuzzy PD controller. The scaling factors tuning scheme, another topic of fuzzy controller design procedure, is also introduced in order to fine performance of the fuzzy PD+I controller. The scaling factors are adjusted by a real-coded genetic algorithm(RCGA) in off-line. The simulation results show the effectiveness of the proposed fuzzy PD+I controller for tracking control problems by comparing with the conventional PID controllers.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.04a
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• pp.245-249
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• 2007

In this study a model-based diagnostic method using the Neural Network was proposed for PW206C turbo shaft engine and performance model was developed by SIMULINK. Fault and test database to build the NN was obtained at various off-design operating range such as flight altitude, flight Mach number and gas generator rotational speed variation. According to the fault detection analysis results, it was confirmed that the proposed fault detection method could find well the fault of compressor, compressor turbine and power turbine at on-design point as well as off-design point conditions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.27-31
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• 2011

차량의 대형화 및 고속화, 그리고 기존 교량의 노후화를 고려하였을 때, 교량의 건전성 평가는 매우 중요해지고 있다. 거동을 예측하는데 사용되는 유한요소 모델의 신뢰도는 이상적인 가정과 모델링 오차, 교량의 노후화 등에 의해 실제 거동을 반영하지 못하는 경우가 많다. 유한요소 모델의 신뢰도를 높이기 위해, 실제 교량의 거동을 계측하여, 이를 기반으로 물리적 의미를 가지는 변수들과 지점의 조건을 수정하는 모델의 개선이 주로 행해진다. 이러한 모델 개선은 최적화 기법을 통해 수행된다. 본 연구에서는 목적함수간 가중치에 의한 모델 개선 결과의 영향과 다중 목적 함수 최적화 기법을 통해, 가중치의 영향을 줄이고, 다양한 개선 모델들을 구하는데 적용하였다. 팔곡 3교의 실제 계측 데이터를 이용하여 단일 다중 목적 함수 기반의 모델 개선을 수행하였다. 단일 목적 함수의 경우, 정의되는 목적함수는 주로 고유진동수와 모드 형상에 관한 차이의 가중치 합으로 표현되어 지며, 이러한 가중치에 따라, 모델 개선의 결과에 영향을 가함을 확인하였다. 다중 목적 함수 기반의 모델 개선을 통해, 구해진 모델 개선 결과를 단일 목적 함수 기반 모델 개선의 결과들과 비교하였으며, 모델 개선에 대한 다중 목적 함수 최적화 적용을 분석하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.702-705
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• 2011

본 논문에서는 초고층 전단벽-아웃리거 시스템에 대해, 기존의 근사해석법과 유전알고리즘을 이용하여, 물량최적설계 기반의 구성요소 단면 및 아웃리거 최적위치 결정에 관해 연구를 진행하였다. 아웃리거 시스템의 최적성은 아웃리거의 위치와 아웃리거 시스템을 구성하는 전단벽-아웃리거, 외곽기둥의 단면 성능의 복잡한 관계에 의해 역학적으로 결정된다. 하지만 기존의 아웃리거 시스템의 최적화 연구는 대부분 전단벽과 아웃리거, 외곽기둥의 단면은 고정된 상태에서, 아웃리거의 위치만 설계변수로 하여 아웃리거의 최적위치를 찾는 연구에 국한되어 있다. 이에 본 연구에서는 G.A.를 이용하여, 아웃리거 설치위치뿐만 아니라 전단벽과 아웃리거, 외곽기둥의 단면까지 설계변수로 하여 물량최적설계 조건을 만족시키는 아웃리거시스템의 최적설계 연구를 진행하였다. 또한 반복 계산의 시간을 줄이기 위해 기존의 근사해석법을 사용하였다. 본 연구의 결과는 초고층 구조물의 초기 설계 시에 구성요소의 단면 및 아웃리거 설치 층의 선정에 적극 활용될 수 있을 것이다.

• Journal of the Korea Society for Simulation
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• v.23 no.2
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• pp.25-33
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• 2014

In this study, the border security problem of the ROK Army is examined by applying the agent-based modeling and simulation (ABMS) concept as well as its platform, MANA. Based on the approximately optimized behavior of the infiltrator obtained using genetic algorithm (GA), we evaluate the GOP border security system which consists of human resources, surveillance, as well as command and control (C2) systems. We use four measures of effectiveness (MOEs) to evaluate its performance, and we apply a near optimal latin hypercube (NOLH) design to deal with the large number of factors of interest in our model. By using a NOLH design, our simulation runs are implemented efficiently. We hope the results of this study provide valuable data for deciding the configuration of the border security system structure and the number of soldiers assigned in the platoon.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.

• Computational Structural Engineering : An International Journal
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• v.3 no.1
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• pp.61-68
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• 2003

A recently developed semi-active control system employing magneto-rheological (MR) fluid dampers is applied to vibration control of a wind excited tall building. The semi-active control system with MR fluid dampers appears to have the reliability of passive control devices and the adaptability of fully active control systems. The system requires only small power source, which is critical during severe events, when the main power source may fail. Numerical simulation studies are performed to demonstrate the efficiency of the MR dampers on the third ASCE benchmark problem. Multiple MR dampers are assumed to be installed in the 76-story building. Genetic algorithm is applied to determine the optimal locations and capacities of the MR dampers. Clipped optimal controller is designed to control the MR dampers based on the acceleration feedback. To verify the robustness with respect to the variation of the external wind force, several cases with different wind forces are considered in the numerical simulation. Simulation results show that the semi-actively controlled MR dampers can effectively reduce both the peak and RMS responses the tall building under various wind force conditions. The control performance of the MR dampers for wind is found to be fairly similar to the performance of an active tuned mass damper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1032-1037
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• 2003

Although the holes on the shell case are very important fer the acoustic performance, it is difficult to solve the problem because the case includes thin bodies. Hence, in the past, only the method of trial and error, which depends on the engineer's intuition and experience, was available fur the design of holes. Many researchers have tried to solve the thin-body acoustic problems, since the conventional boundary element method (BEM ) using the Helmholtz integral equation fails to yield a reliable solution fer the numerical modelling of radiation anti scattering of sound from thin bodies. In the area of the analysis of thin-body acoustic problem, three approaches are generally used; the multi-domain BEM, the indirect variational BEM, and the normal derivative integral equation And there has been just a f9w study reported on the design optimization for the acoustic radiation problems by using only the conventional BEM. For the thin-body acoustics, however, no further study in the optimization fields has been reported. In this research, the normal derivative integral equation is adopted as an analysis formulation in the thin-body acoustics, and then used fur the optimization. The analytical approaches for the design of holes are proposed by using a topology optimization technique and a genetic algorithm. The proposed approaches are implemented and validated using numerical examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.1-6
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• 2003

The Delayed Feedback Control method (DFC) proposed by Pyragas applies an input based on the difference between the current state of the system, which is generating chaos orbits, and the $\tau$-time delayed state, and stabilizes the chaos orbit into a target. In DFC, the information about a position in the state space is unnecessary if the period of the unstable periodic orbit to stabilize is known. There exists the fault that DFC cannot stabilize the unstable periodic orbit when a linearlized system around the periodic point has an odd number property. There is the chaos control method using the prediction of the $\tau$-time future state (PDFC) proposed by Ushio et al. as the method to compensate this fault. Then, we propose a method such as improving the fault of the DFC. Namely, we combine DFC and PDFC with parameter W, which indicates the balance of both methods, not to lose each advantage. Therefore, we stabilize the state into the $\tau$ periodic orbit, and ask for the ranges of Wand gain K using Jury' method, and determine the quasi-optimum pair of (W, K) using a genetic algorithm. Finally, we apply the proposed method to a discrete-time chaotic system, and show the efficiency through some examples of numerical experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.52-57
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• 2003

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection. The classifier system of Holland can evaluate the usefulness of rules represented by classifiers with repeated learning. FCS makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies the method of machine learning to the concept of fuzzy logic. It is that the antecedent and consequent of classifier is same as a fuzzy rule. In this paper, the FCS is the Michigan style. A single fuzzy if-then rule is coded as an individual. The average gray levels which each group of neighbor pixels has are represented into fuzzy set. Then a pixel is decided whether it is edge pixel or not using fuzzy if-then rules. Depending on the average of gray levels, a number of fuzzy rules can be activated, and each rules makes the output. These outputs are aggregated and defuzzified to take new gray value of the pixel. To evaluate this edge detection, we will compare the new gray level of a pixel with gray level obtained by the other edge detection method such as Sobel edge detection. This comparison provides a reinforcement signal for FCS which is reinforcement learning. Also the FCS employs the Genetic Algorithms to make new rules and modify rules when performance of the system needs to be improved.