• Journal of ILASS-Korea
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• v.28 no.4
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• pp.161-168
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• 2023

Droplet impingement on solid surfaces is pivotal for a range of spray and heat transfer processes. This study aims to optimize the cooling performance of single droplet impingement on heated textured surfaces. We focused on maximizing the cooling effectiveness or the total contact area at the droplet maximum spread. For efficient estimation of the optimal values of the unknown variables, we introduced an enhanced Genetic Algorithm (GA) and Particle swarm optimization algorithm (PSO). These novel algorithms incorporate its developed theoretical backgrounds to compare proper optimized results. The comparison, considering the peak values of objective functions, computation durations, and the count of penalty particles, confirmed that PSO method offers swifter and more efficient searches, compared to GA algorithm, contributing finding the effective way for the spray and droplet impingement process.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.1029-1037
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• 2008

When a humanoid robot pushes an object with its force, it is essential to adequately control its posture so as to maximize the surplus torque far all joints. For such purpose, this study proposes a method to find an optimal posture of a humanoid robot using a genetic algorithm in such a way that the surplus torque for all joints is maximized. In this study, pushing motion of an 18-DOF humanoid robot is considered. When the robot takes a cooperative motion to push an object, the palms and soles are assumed to be fixed at the object and ground respectively, and are subjected to sense the reaction force from the object and the ground. Then, the torques for all joints are calculated and reflected to fitness function of the genetic algorithm. To verify the effectiveness of the proposed method, a number of simulations with different fitness functions are carried out. The simulation result shows that the proposed method can be adopted to find optimized posture in cooperative motion of a humanoid robot.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.10
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• pp.1021-1030
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• 2009

We propose optimization method of two-inductor loaded small loop antennas using simple genetic algorithm. To optimize the loop antennas for the RFID and the mobile phone band, we changed positions and values of the two inductors in the loop antenna. Visual basic was used to make genetic algorithm and to calculate fitness values by controlling the commercial EM software. The bandwidth of the optimized RFID loop antenna is 10 MHz at the center frequency of 922 MHz and that of the mobile phone antenna are 84 MHz and 266 MHz at the center frequency of 948 MHz(GSM band) and 1.81 GHz(DCS band), respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.676-683
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• 2005

In this paper, we proposed a method that optimal values of fuzzy control rule base and quantization of membership function are searched by genetic algorithm. Proposed method searched the optimal values of membership function and control rules using genetic algorithm by off-line. Then fuzzy controller operates using these values by on-line. Proposed fuzzy control system is optimized the control rule base and membership function by genetic algorithm without expert's knowledge. We investigated proposed method through simulation and experiment using DC motor and one link manipulator, and confirmed the following usefulness.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.59-69
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• 1998

A regression model with basin physiographic characteristics as independent variables was calibrated for regional flood frequency analysis. In case that high correlations existing among the independent variables the ridge regression has been known to have capability of overcoming the problems of multicollinearity. To optimize the ridge regression model the cost function including regularization parameter must be minimized. In this research the genetic algorithm was applied on this optimization problem. The genetic algorithm is a stochastic search method that mimic the metaphor of natural biological heredity. Using this method the regression model could have optimized and stable weights of variables.

• Structural Engineering and Mechanics
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• v.17 no.5
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• pp.611-626
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• 2004

On the basis of a structural analysis of radial gate (i.e. Tainter gate), the current paper focuses on weight minimization according to the location of the arms on a radial gate. In spite of its economical significance, there are hardly any previous studies on the optimum design of radial gate. Accordingly, the present study identifies the optimum position of the support point for a radial gate that guarantees the minimum weight satisfying the strength constraint conditions. This study also identifies the optimum position for 2 or 3 radial arms with a convex cylindrical skin plate relative to a given radius of the skin plate curvature, pivot point, water depth, ice pressure, etc. These optimum designs are then compared with previously constructed radial gates. Local genetic and hybrid-type genetic algorithms are used as the optimum tools to reduce the computing time and enhance the accuracy. The results indicate that the weights of the optimized radial gates are appreciably lower than those of previously constructed gates.

• Structural Engineering and Mechanics
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• v.68 no.5
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• pp.549-561
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• 2018

Sobol method is applied as a powerful variance decomposition technique in the field of global sensitivity analysis (GSA). The paper is devoted to increase convergence speed of the extracted Sobol indices using a new proposed sampling technique called genetic based Latine hypercube sampling (GBLHS). This technique is indeed an improved version of restricted Latine hypercube sampling (LHS) and the optimization algorithm is inspired from genetic algorithm in a new approach. The new approach is based on the optimization of minimax value of LHS arrays using manipulation of array indices as chromosomes in genetic algorithm. The improved Sobol method is implemented to perform factor prioritization and fixing of an uncertain comprehensive high speed rotor-bearing system. The finite element method is employed for rotor-bearing modeling by considering Eshleman-Eubanks assumption and interaction of axial force on the rotor whirling behavior. The performance of the GBLHS technique are compared with the Monte Carlo Simulation (MCS), LHS and Optimized LHS (Minimax. criteria). Comparison of the GBLHS with other techniques demonstrates its capability for increasing convergence speed of the sensitivity indices and improving computational time of the GSA.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.161-169
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• 2012

Recently various attempts have been made to apply HSPF model to calculate runoff and diffuse pollution loads of stream and reservoir watersheds. Because the role of standard flow is very important in the water quality modelling of Total Water Pollution Load Management, HSPF was used as a means of estimating standard flow. In this study, BASINS 4.0 and WinHSPF was applied to the Gomakwoncheon watershed, genetic algorithm(GA) and influence coefficient algorithm were used to calibrate the runoff parameters of the WinHSPF. The objective function is the sum of the squares of the normalized residuals of the observed and calculated flow and it is optimized using GA. Estimates of the optimum runoff parameters are made at each iteration of the influence coefficient algorithm. The calibration results showed a relatively good correspondence between the observed and the calculated values. The standard flow(Q275) of the Gomakwoncheon watershed was estimated using the ten years of weather data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.85-91
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• 2015

The press-fit shaft is an important part used in automobiles, vessels, and trains. This study proposes an optimized design method to reduce damage that may occur in the press-fitted shaft by modifying the shape of the boss step of the press-fitted shaft. To reduce the time and cost of running the optimized design method, an approximate design optimization is applied and an optimized algorithm is generated using a genetic algorithm that is widely used in engineering fields and an approximate model using a response surface method. The planned experiments for the data that are needed to generate the approximate model use a central composite design (CCD) and Latin hypercube sampling (LHS), and the results of the approximate optimization using the above two design of experiments are to be compared.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.297-300
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• 2005

In this study, we proposed genetically optimized self-organizing fuzzy polynomial neural network based on information granulation and evolutionary algorithm (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structural Iy and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.