• Korean Journal of Materials Research
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• v.25 no.7
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• pp.322-329
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• 2015

The magnitude of wear should be at a minimum for numerous automobile and aeronautical components. In the current work, composites were prepared by varying the cenosphere content using the conventional stir casting method. A uniform distribution of particles was ensured with the help of scanning electron microscopy (SEM). Three major parameters were chosen from various factors that affect the wear. A wear test was conducted with a pin-on-disc apparatus; the controlling parameters were volume percentages of reinforcement of 5, 10, 15, and 20%, applied loads of 9.8, 29.42, and 49.03 N, and sliding speeds of 1.26, 2.51, and 3.77 m/s. The design of the experiments (DOE) was performed by varying the different influencing parameters using the full factorial method. An analysis of variance (ANOVA) was used to analyze the effects of the parameters on the wear rate. Using regression analysis, a response curve was obtained based on the experimental results. The parameters in the resulting curve were optimized using the Genetic Algorithm (GA). The GA results were compared with those of an alternate efficient algorithm called Neural Networks (NNs).

• Smart Structures and Systems
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• v.3 no.1
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• pp.51-68
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• 2007

High-strength concrete (HSC) is becoming increasingly attractive for various construction projects since it offers a multitude of benefits over normal-strength concrete (NSC). Unfortunately, current design provisions for shear capacity of RC slender beams are generally based on data developed for NSC members having a compressive strength of up to 50 MPa, with limited recommendations on the use of HSC. The failure of HSC beams is noticeably different than that of NSC beams since the transition zone between the cement paste and aggregates is much denser in HSC. Thus, unlike NSC beams in which micro-cracks propagate around aggregates, providing significant aggregate interlock, micro-cracks in HSC are trans-granular, resulting in relatively smoother fracture surfaces, thereby inhibiting aggregate interlock as a shear transfer mechanism and reducing the influence of compressive strength on the ultimate shear strength of HSC beams. In this study, a new approach based on genetic algorithms (GAs) was used to predict the shear capacity of both NSC and HSC slender beams without shear reinforcement. Shear capacity predictions of the GA model were compared to calculations of four other commonly used methods: the ACI method, CSA method, Eurocode-2, and Zsutty's equation. A parametric study was conducted to evaluate the ability of the GA model to capture the effect of basic shear design parameters on the behaviour of reinforced concrete (RC) beams under shear loading. The parameters investigated include compressivestrength, amount of longitudinal reinforcement, and beam's depth. It was found that the GA model provided more accurate evaluation of shear capacity compared to that of the other common methods and better captured the influence of the significant shear design parameters. Therefore, the GA model offers an attractive user-friendly alternative to conventional shear design methods.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1447-1456
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• 2017

Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.

• Journal of the korean society of oceanography
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• v.32 no.4
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• pp.163-170
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• 1997

Some CMP gathers acquired from shallow marine seismic reflection survey in offshore Korea do not show the hyperbolic trend of moveout. It originated from so-called swell effect of source and streamer, which are towed under rough sea surface during the data acquisition. The observed time deviations of NMO-corrected traces can be entirely ascribed to the swell effect. To correct these time deviations, a residual statics is introduced using Genetic Algorithms (GA) into the swell correction. A new class of global optimization methods known as GA has recently been developed in the field of Artificial Intelligence and has a resemblance with the genetic evolution of biological systems. The basic idea in using GA as an optimization method is to represent a population of possible solutions or models in a chromosome-type encoding and manipulate these encoded models through simulated reproduction, crossover and mutation. GA parameters used in this paper are as follows: population size Q=40, probability of multiple-point crossover P$_c$=0.6, linear relationship of mutation probability P$_m$ from 0.002 to 0.004, and gray code representation are adopted. The number of the model participating in tournament selection (nt) is 3, and the number of expected copies desired for the best population member in the scaling of fitness is 1.5. With above parameters, an optimization run was iterated for 101 generations. The combination of above parameters are found to be optimal for the convergence of the algorithm. The resulting reflection events in every NMO-corrected CMP gather show good alignment and enhanced quality stack section.

• Structural Engineering and Mechanics
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• v.67 no.2
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• pp.105-113
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• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.462-462
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• 2013

Gallium Oxide (Ga2O3) has been widely investigated for the optoelectronic applications due to its wide bandgap and the optical transparency. Recently, with the development of fabrication techniques in nanometer scale semiconductor materials, there have been an increasing number of extensive reports on the synthesis and characterization of Ga2O3 nano-structures such as nano-wires, nanobelts, and nano-dots. In contrast to typical vaporliquid-solid growth mode with metal catalysts to synthesis 1-dimensional nano-wires, there are several difficulties in fabricating the nanostructures by using sputtering techniques. This is attributed to the fact that relatively low growth temperatures and higher growth rate compared with chemical vapor deposition method. In this study, Ga2O3 chestnut burr were synthesized by using radio-frequency magnetron sputtering method. In contrast to typical sputtering method with sintered ceramic target, a Ga2O3 powder (99.99% purity) was used as a sputtering target. Several samples were prepared with varying the growth parameters, especially he growth time and the growth temperature to investigate the growth mechanism. Samples were characterized by using XRD, SEM, and PL measurements. In this presentation, the details of fabrication process and physical properties of Ga2O3 nano chestnut burr will be reported.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.3
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• pp.76-87
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• 1993

We present a self-consistent, 2-dimensional solution of the Poisson and Sch rodinger equation based on the finite difference method with a nonuniform mesh size for a AlGaAs/GaInAs/GaAs HEMT devide. During the interative self-consistent calculation, however, we calculate Schrodinger equation only a some region of device, not a fully region in order to save the moemory and the speed-up of computation, and then use the approximated data for the other region using by a interpolation method with a given values. Also we adopt the proper matrix transformation method that allows preservation of the symmetric, form of the discretized Schrodinger equation, even with the use of a nonumiform mesh size, therefor, can reduce the computation time. We calculate the wavefunction, eigenstates and the electron concentration uat channel layer nder the thermal equilibrium and the biased conditions, respectively. Also,these parameters are used to solve 2-dimensional tdistribution of potential in he entire region of device. It is proved that the method is very efficient in finding eigenstages extending over relatively large spatial area without loss of accuracy. So, it can be used rather easily in any sarbitrary modulation doped utucture.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1284-1288
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• 2021

In this work, we propose and design a GaN-based diode with a p-doped GaN (p-GaN) multi-well structure for high efficiency betavoltaic (BV) cells. The short-circuit current density (J_SC) and opencircuit voltage (V_OC) of the devices were investigated with variations of parameters such as the doping concentration, height, width of the p-GaN well region, well-to-well gap, and number of well regions. The J_SC of the device was significantly improved by a wider depletion area, which was obtained by applying the multi-well structure. The optimized device achieved a higher output power density by 8.6% than that of the conventional diode due to the enhancement of J_SC. The proposed device structure showed a high potential for a high efficiency BV cell candidate.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.4
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• pp.424-429
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• 2014

This paper investigates the problem of synchronization of the Pehlivan chaotic system based on sliding mode control and GA. For this, a brief overview of the Pehlivan chaotic system is given. Then, the conventional sliding mode control technique is described and a synchronization method using GA strategy is proposed. The proposed method is that the GA searched the parameters including sliding plane and control gains) selected by the designer in the sliding mode control are searched optimally through the GA. The GA in the MATLAB Toolbox was used and simulation work is shown to illustrate the effectiveness of the synchronization schemes for the chaotic system.