• Journal of Power Electronics
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• 제13권4호
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• pp.679-691
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• 2013

High-power electromagnetic transmitter power supplies are an important part of deep geophysical exploration equipment. This is especially true in complex environments, where the ability to produce a highly accurate and stable output and safety through redundancy have become the key issues in the design of high-power electromagnetic transmitter power supplies. To solve these issues, a high-frequency switching power cascade based emission power supply is designed. By combining the circuit averaged model and the equivalent controlled source method, a modular mathematical model is established with the on-state loss and transformer induction loss being taken into account. A triple-loop control including an inner current loop, an outer voltage loop and a load current forward feedback, and a digitalized voltage/current sharing control method are proposed for the realization of the rapid, stable and highly accurate output of the system. By using a new algorithm referred to as GAPSO, which integrates a genetic algorithm and a particle swarm algorithm, the parameters of the controller are tuned. A multi-module cascade helps to achieve system redundancy. A simulation analysis of the open-loop system proves the accuracy of the established system and provides a better reflection of the characteristics of the power supply. A parameter tuning simulation proves the effectiveness of the GAPSO algorithm. A closed-loop simulation of the system and field geological exploration experiments demonstrate the effectiveness of the control method. This ensures both the system's excellent stability and the output's accuracy. It also ensures the accuracy of the established mathematical model as well as its ability to meet the requirements of practical field deep exploration.

• Smart Structures and Systems
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• 제26권4호
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• pp.451-468
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• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• 산업경영시스템학회지
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• 제44권4호
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• pp.43-52
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• 2021

In weapon assignment studies to defend against threats such as ballistic missiles and long range artillery, threat assessment was partially lacking in analysis of various threat attributes, and considering the threat characteristics of warheads, which are difficult to judge in the early flight stages, it is very important to apply more reliable optimal solutions than approximate solution using LP model, Meta heuristics Genetic Algorithm, Tabu search and Particle swarm optimization etc. Our studies suggest Generic Rule based threat evaluation and weapon assignment algorithm in the basis of various attributes of threats. First job of studies analyzes information on Various attributes such as the type of target, Flight trajectory and flight time, range and intercept altitude of the intercept system, etc. Second job of studies propose Rule based threat evaluation and weapon assignment algorithm were applied to obtain a more reliable solution by reflection the importance of the interception system. It analyzes ballistic missiles and long-range artillery was assigned to multiple intercept system by real time threat assessment reflecting various threat information. The results of this study are provided reliable solution for Weapon Assignment problem as well as considered to be applicable to establishing a missile and long range artillery defense system.

• Composites Research
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• 제35권5호
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• pp.340-346
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• 2022

본 연구에서는 복합재료 블레이드에 대한 최적 구조설계 프레임워크를 구성하고, 이를 헬리콥터 블레이드에 적용하여 최적 구조설계를 수행하였다. 단면 형상의 경우 C형 및 D형 스파를 선택할 수 있게 구성하였으며, 최적설계 프레임워크는 유전자 알고리즘과 입자 군집 최적화 알고리즘을 결합한 PSGA를 활용하였다. 단면의 기하학적 모델링은 B-spline을 이용하여 구현하였고, 유한요소 모델 생성 프로그램 Gmsh를 통해 단면 유한요소모델을 만든 뒤 단면 해석 프로그램인 Ksec2D를 사용하여 구조해석 결과를 도출하였다. 본 최적설계 프레임워크를 HART II 블레이드에 적용하여 최적 구조설계를 수행한 결과, C형 스파 모델은 기준 형상 대비 무게 7.39%, D형 스파 모델은 6.65% 감소하였으며, 이때 전단중심은 모두 공력중심과 인접한(5% 이내) 결과를 도출하였다. 본 연구를 통해 일반적인 헬리콥터 블레이드의 단면에 적용할 수 있는 최적 구조설계 프레임워크의 유효성을 확인하였다.

• 한국산학기술학회논문지
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• 제19권10호
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• pp.330-338
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• 2018

비례-적분-미분(PID) 제어기는 단순한 구조와 넓은 범위의 운전영역에서 견고한 성능으로 인해 산업계에서 널리 사용되고 있다. 그러나 제어대상으로서 AVR(Automatic Voltage Regulator)은 전력 시스템의 파라미터의 변동에 강인하지 않다. 따라서 PID 제어기를 사용하여 AVR 시스템의 안정성과 성능을 향상시키는 것이 필요하다. 본 논문에서는 PSO(Partial Swarm Optimization) 알고리즘을 사용하여 AVR 시스템을 위한 최적 PID 제어기 파라미터를 결정하는 새로운 설계 방법을 제시한다. 제안하는 접근법은 쉬운 구현뿐만 아니라 안정된 수렴 특성 및 양호한 계산 효율과 우수한 특성을 갖는다. 또한, 제안 된 PSO-PID 제어기의 성능을 평가하기 위해 새로운 목적함수를 정의한다. 이 목적함수는 최대백분율 오버슈트와 정정시간이 설계사양으로 주어진 경우 이를 반영하기 위한 것이다. 이를 위해 ITAE 평가함수에 제약 조건을 위반하면 벌점을 부과하도록 하여 PSO 알고리즘이 PID 제어기 파라미터를 탐색할 때 설계사양을 만족하도록 하게 한다. 최종적으로 컴퓨터 시뮬레이션을 통해 제안한 PSO-PID 제어기는 단자전압 계단응답에 대해 주어진 설계사양을 만족할 뿐만 아니라 다른 유사한 최근의 연구보다 더 우수한 제어 성능을 보임을 확인하였다.

• 전기학회논문지
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• 제62권4호
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• pp.544-553
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• 2013

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

• 한국유체기계학회 논문집
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• 제16권1호
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• pp.11-16
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• 2013

A numerical study was conducted to improve the performance of an impeller of centrifugal compressor. Nine design variables were chosen with constraints. Only meridional contours and blade profile were adjusted. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. At first, ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After $7^{th}$ generation, the prediction difference of efficiency and pressure ratio between ANN and CFD was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.

• IEIE Transactions on Smart Processing and Computing
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• 제4권5호
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• pp.311-317
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• 2015

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline. based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1956-1957
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• 2011

본 연구는 White LED를 이용하여 주변 밝기 변화에 빠르게 적응하는 퍼지 뉴로 Dimming Control System을 설계한다. 본 논문에서는 방사형기저함수 신경회로망(Radial Basis Function Neural Network: RBFNN)을 설계하여 실제 White LED Dimming Control System에 적용시켜 모델의 근사화 및 일반화 성능을 평가한다. 제안한 모델에서의 은닉층은 방사형기저함수를 사용하여 적합도를 구현하였고, 후반부의 연결가중치는 경사하강법을 사용한다. 이때 멤버쉽 함수의 중심점은 HCM 클러스터링 (Hard C-Means Clustering)을 적용하여 결정한다. 연결가중치는 4가지 형태의 다항식을 대입하여 출력을 평가하였다. 최종 출력의 최적화를 위하여 PSO(Particle Swarm Optimization)을 이용하여 은닉층 노드수 및 다항식 형태를 결정한다. 본 논문에서 제안한 LED Dimming Control System은 Atmega8535를 사용하여 PWM 제어 방식을 사용하고, 조도계(Cds)를 이용하여 LED의 밝기에 따른 주변의 밝기를 감지하여 조명에 적응시키는 방법을 적용하였다.

• Journal of Electrical Engineering and Technology
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• 제6권6호
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• pp.750-759
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• 2011

This paper focuses on the control of bidirectional power flow in the electric shipboard power systems, especially in the Medium-Voltage Direct Current (MVDC) shipboard power system. Bidirectional power control between the main MVDC bus and the local zones can improve the energy efficiency and control flexibility of electric ship systems. However, since the MVDC system contains various nonlinear loads such as pulsed power load and radar in various subsystems, the voltage of the MVDC and the local zones varies significantly. This voltage variation affects the control performance of the bidirectional DC-DC converters as exogenous disturbances. To improve the control performance regardless of uncertainties and disturbances, this paper proposes a novel controller design method of the bidirectional DC-DC converters using $L_1$ control theory and intelligent optimization algorithm. The performance of the proposed method is verified via large-scale real-time digital simulation of a notional shipboard MVDC power system.