• Journal of computational fluids engineering
• /
• v.4 no.1
• /
• pp.34-40
• /
• 1999

Drag reduction on vehicles are the main concern for the body shape designers in order to lower the fuel consumption rate and to aid the driving stability. The drag of bluff bodies like transportation vehicles is mostly pressure drag due to the flow separation, which can be minimized by controlling the location and size of the separation bubble. In the present study, the TURBO-3D code is incorporated with optimal algorithm based on analytical approximation method to obtain an optimal afterbody shape of the MIRA Model corresponding to the lowest drag coefficient. For this purpose three mutually independent afterbody angles are chosen as design variables, while the drag coefficient is chosen as an objective function. It is demonstrated in the present study that an optimal body shape having the lowest drag coefficient which is about 6% lower than that of the original shape has been successfully obtained within number of iterations of tile optimal design loop.

• Journal of Advanced Marine Engineering and Technology
• /
• v.40 no.10
• /
• pp.935-941
• /
• 2016

Predicting and evaluating ship manoeuvring characteristics are very important not only for the design stage, but also for the existing vessels. There are several ways to predict ship's manoeuvrability and most of them are highly connected with the estimation of hydrodynamic coefficients. This paper presents a new estimation method using the system identification with mathematical algorithms for estimating hydrodynamic coefficient in the ship's mathematical model. Specifically a double ended ferry which equips four azimuth propulsion systems were chosen as benchmark ship and a set of benchmark data which is generated in the fast time simulation software was provided to conduct mathematical optimization process. Also the initial values for the optimization were borrowed from the empirical regression formulas of the simulation software of Rheinmetall Defence ship simulator. Therefore the newly suggested mathematical optimization algorithm gave a successful result for estimation hydrodynamic coefficients. Proper optimization conditions of the objective function and constraints were also verified during the study.

• Journal of Digital Convergence
• /
• v.13 no.6
• /
• pp.151-156
• /
• 2015

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.39 no.5
• /
• pp.435-441
• /
• 2015

In this study, a method for optimal design of impeller and diffuser blades in the centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was evaluated. A numerical simulation was conducted using ANSYS CFX with various values of impeller and diffuser parameters, which consist of leading edge (LE) angle, trailing edge (TE) angle, and blade thickness. Each of the parameters was divided into three levels. A total of 45 design points were planned using central composite design (CCD), which is one of the design of experiment (DOE) techniques. Response surfaces that were generated on the basis of the results of DOE were used to determine the optimal shape of impeller and diffuser blade. The entire process of optimization was conducted using ANSYS Design Xplorer (DX). Through the optimization, isentropic efficiency and pressure recovery coefficient, which are the main performance parameters of the centrifugal compressor, were increased by 0.3 and 5, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.6
• /
• pp.468-476
• /
• 2016

In this study, the conic winglet which is made by rotating wing tip airfoil by each 3 axis is applied to the dual type combined fan to reduce the wing tip leakage loss. Computational Fluid Dynamics is used to calculate the loss and optimum technique is used to get minimum loss. Optimization results shows that total pressure loss coefficient was reduced by 3.4 %, and optimization model was a bended shape at the end of wing forward to pressure side.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.12
• /
• pp.1661-1666
• /
• 2014

In this paper, a compact planar ultra wideband (UWB, 3.1~10.6GHz) multiple-input multiple-output (MIMO) antenna is proposed. This antenna consists of two monopole planar UWB antennas and T-shaped stub decoupling between two antennas. The T-shaped stub improve the isolation characteristic at the wide band. The evolution strategy(ES) algorithm is employed to optimized design. As a result, optimized antenna has a return loss less than -10dB and the isolation less than -15dB from 3.1GHz to 10.6GHz. During the optimization process, the antenna gain is enhanced at lower band and the envelope correlation coefficient(ECC) is lower than 0.003.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2002.10a
• /
• pp.65-84
• /
• 2002

The Observational Method proposed by Terzaghi can be applied for the safe and economic construction projects where the exact prediction of the behavior of the structures is difficult as in the underground excavation. The method consists of measuring lateral displacement, ground settlement and axial force of supports in the earlier stage of the construction and back analysis technique to find the best fit design parameters such as earth pressure coefficient, subgrade reaction etc, which will minimize the gap between calculated displacement and measured displacement. With the results, more reliable prediction of the later stage can be obtained. In this study, back analysis programs using the Direct Method, based on the Hill Climbing Method were made and evaluated, and to overcome the limits of the method, Genetic Algorithm(GA) was applied and tested for the actual construction cases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.1
• /
• pp.228-246
• /
• 2011

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.

• Journal of Korea Society of Industrial Information Systems
• /
• v.4 no.3
• /
• pp.79-88
• /
• 1999

One of the most popular algorithm in adaptive signal processing is the least mean square(LMS) algorithm. The majority of these papers examine the LMS algorithm with a constant step size. The choice of the step size reflects a tradeoff between misadjustment and the speed of adaptation. Subsequent works have discussed the issue of optimization of the step size or methods of varying the step size to improve performance. However there is as yet no detailed analysis of a variable step size algorithm that is capable of giving both the speed of adaptation and convergence. In this paper we propose a new variable step size algorithm where the step size adjustment is controlled by square of the prediction error. The simulation results obtained using the new algorithm about noise canceller system and system identification are described. They are compared to the results obtained for other variable step size algorithm. function.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.1
• /
• pp.135-142
• /
• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.