• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.12
• /
• pp.2511-2519
• /
• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.12
• /
• pp.2322-2329
• /
• 2008

In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of particle swarm optimization(PSO) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling facrors) of each fuzzy controller using PSO. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on PSO, is presented in comparison with the conventional PD cascade controller based on serial genetic alogritms.

• Special Issue of the Society of Naval Architects of Korea
• /
• 2015.09a
• /
• pp.93-95
• /
• 2015

In case of crude oil tanker, loading condition must be satisfied MARPOL damage stability criteria (Reg.28). But some specific demands are hard to content the criteria. So, it takes a lot of time and efforts to make loading condition reflecting these demands. In this study, PSO (Particle Swarm Optimization) is used to make loading condition to be satisfied the criteria. Study result is applied 'CROSSWAY (160,000 DWT Crude Oil Tanker)' in NAPA. The result shows that satisfy the criteria and other constraints and limitation.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.6
• /
• pp.355-365
• /
• 2022

This paper addresses the problem of real-time tracking a high-speed ballistic target. Particle filters can be considered to overcome the nonlinearity in motion and measurement models in the ballistic target. However, it is difficult to apply particle filters to real-time systems because particle filters generally require much computation time. This paper proposes an accelerated particle filter using graphics processing unit (GPU) for real-time ballistic target tracking. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional particle filter on CPU (central processing unit) showed that the proposed method improved the real-time performance by reducing computation time significantly.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.2
• /
• pp.399-406
• /
• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.8
• /
• pp.624-631
• /
• 2018

To meet the requirements of various satellite synthetic aperture radar(SAR) system performance parameters, the characteristics of the antenna pattern should be analyzed. In this paper, we propose a method to improve the SAR system performance using an effective technique for optimizing antenna pattern synthesis in the presence of element failure. The desired antenna pattern can be synthesized by referring to the optimized antenna mask templates using the particle swarm optimization algorithm. In the simulation, the performance of the proposed method is verified by analyzing characteristics related to the SAR system performance parameters using antenna pattern regeneration.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.11
• /
• pp.963-971
• /
• 2012

In this paper, development of optimal design tools for wing structure is described including multi load spectra condition and fatigue analysis. Two dimensional CFD result are used for calculating aerodynamic force. Design variables are composed of a number of rib and spar, positions, and thickness of each structural member. The mission profile for fatigue analysis is composed based upon the results of CFD analysis, the flight-by-flight spectra method, the excessive curves for gust loads. Minor's rule was used to deal with multi-load condition. Stress analysis and fatigue analysis are performed to calculate objective functions. Particle Swarm Optimization(PSO) algorithm was used to apply to problems which have dozens of design variables.

• Journal of KIISE
• /
• v.43 no.2
• /
• pp.204-211
• /
• 2016

In this paper, we propose an automatic melody composition system that can generate a sophisticated melody by adding non-harmony tone in the given chord progression. An overall procedure consists of two steps, which are the rhythm generation and melody generation parts. In the rhythm generation part, we designed new fitness functions for rhythm that can be controlled by a user setting parameters. In the melody generation part, we designed new fitness functions for melody based on harmony theory. We also designed evolutionary operators that are conducted by considering a musical context to improve computational efficiency. In the experiments, we compared four metaheuristics to optimize the rhythm fitness functions: Simple Genetic Algorithm (SGA), Elitism Genetic Algorithm (EGA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Furthermore, we compared proposed genetic algorithm for melody with the four algorithms for verifying performance. In addition, composition results are introduced and analyzed with respect to musical correctness.

• Composites Research
• /
• v.35 no.1
• /
• pp.31-37
• /
• 2022

In this study, an optimal structural design framework has been developed for the structural design of composite helicopter blades. The optimal design framework is constructed using PSGA (Particle Swarm assisted Genetic Algorithm), which combines the genetic algorithm and particle swarm optimizer. The optimization process consists of a finite element (FE) modeling over the blade section, two-dimensional (2D) cross-sectional FE analysis, and 1D rotating blade analysis. In the design process, the geometric curves and surfaces are formed using the B-spline scheme while discretizing the sections via a FE mesh generation program Gmsh. The blade cross-sections are created in accordance with the design variables when performing the blade structural analysis. The proposed optimization design framework is applied to a modernization of the HART II (Higher-harmonic Aeroacoustics Rotor Test II) blades. It is demonstrated that an improved blade design is reached through the current optimization framework with the satisfaction of all design requirements set for the study.

• Korean Journal of Optics and Photonics
• /
• v.33 no.1
• /
• pp.11-21
• /
• 2022

We have investigated the optimal design of an aperiodic optical phased array (OPA) for use in light detection and ranging applications. Three optimization algorithms - particle-swarm optimization (PSO), a genetic algorithm (GA), and a pattern-search algorithm (PSA) - were employed to obtain the optimal arrangement of optical antennas comprising an OPA. The optimization was performed to obtain the minimal side-lobe level (SLL) of an aperiodic OPA at each steering angle, using the three optimization algorithms. It was found that PSO and GA exhibited similar results for the SLL of the optimized OPA, while the SLL obtained by PSA showed somewhat different features from those obtained by PSO and GA. For an OPA optimized at a steering angle <45°, the SLL value averaged over all steering angles increased as the angle of optimization decreased. However, when the angle of optimization was larger than 45°, low average SLL values of <13 dB were obtained for all three optimization algorithms. This implies that an OPA with high signal quality can be obtained when the arrangement of the optical antennas is optimized at a large steering angle.