• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.319-325
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• 2010

For decades, researchers have rigorously studied the characteristics of flow traveling around blunt objects in order to gain greater understanding of the flow around aircraft, vehicles or vessels. Many different types of flow exist, such as boundary layer flow, flow separation, laminar and turbulent flow, vortex and vortex shedding; such types are especially observed around circular cylinders. Vortex shedding around a circular cylinder exhibits a two-dimensional flow structure possessing a Reynolds number within the range of 47 and 180. As the Reynolds number increases, the Karman vortex changes into a three-dimensional flow structure. In this paper, a numerical analysis was performed examining the flow and aero-acoustic field characteristics around a circular cylinder using an optimized high-order compact scheme, which is a high order scheme. The analysis was conducted with a Reynolds number ranging between 300 and 1,000, which belongs to B-mode flow around a circular cylinder. For a B-mode Reynolds number, a proper spanwise length is analyzed in order to obtain the characteristics of three-dimensional flow. The numerical results of the Strouhal number as well as the lift and drag coefficients according to Reynolds numbers are coincident with the other experimental results. Basic research has been conducted studying the effects an unstable three-dimensional wake flow on an aero-acoustic field.

• Preventive Nutrition and Food Science
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• v.4 no.1
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• pp.79-83
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• 1999

A potentiometric biosensor employing a CO3-2 ion-selective electrode(ISE) and malic enzyme immobilization in al flow injection analysis (FIA) system was constructed. Analytical parameters were optimized for L-malate determination . The CO3-2 -ISE-FIA system was composed of a pump, an injector, a malic enzyme (EC1.1.1.40) reactor, a CO3-2 ion-selective electrode, a pH/mV meter and a recorder. Cofactor NADP was also injected with substrate for theenzyme reaction into the system. Optimized analytical parameters for L-malate determination in the CO3-2 ISE-FIA system were as follows ; flow rate, 14.5ml/hr ; sample injection volume, 100ul; enzyme loading in the reactor, 20 units ; length of the enzyme reactor , 7 cm ; tubing length form the enzyme reactor to the detector as a geometric factor in FIA, 15 cm . The response time for measuring the entire L-malate concentration range (10-2 ~10-5 mol/L ; 4 injections )was <15minutes . In this CO3-2 -ISE-FIA system, the potential differences due to th eformation of CO3-2 by the reaction of malic enzyme on L-malate were correlated to L-malate concentration in the range of 10-2 ~10-5mol/L ; the detection limit was 10-5 mol/L. This potentionmetric CO3-2 ISE--FIA system was found to be useful for L-malate measurement.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.3
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• pp.201-209
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• 2013

Much numerical and experimental research has been done for the flow around an oscillating airfoil. The main research topics are vortex shedding, dynamic stall phenomenon, MAV's lift and thrust generation. Until now, researches mainly have been concentrated on analyzing the wake flow for the variation of frequency and amplitude at a low angle of attack. In this study, wake structures and acoustic wave propagation characteristics were studied for a plunging airfoil at high angle of attack. The governing equations are the Navier-Stokes equation with LES turbulence model. OHOC (Optimized High-Order Compact) scheme and 4th order Runge-Kutta method were used. The Mach number is 0.3, the Reynolds number is, and the angle of attack is from $20^{\circ}$ to $50^{\circ}$. The plunging frequency and the amplitude are from 0.05 to 0.15, and from 0.1 to 0.2, respectively. Due to the high resolution numerical method, wake vortex shedding and pressure wave propagation process, as well as the propagation characteristics of acoustic waves can be simulated. The results of frequency analysis show that the flow has the mixed characteristics of the forced plunging frequency and the vortex shedding frequency at high angle of attack.

• YAKHAK HOEJI
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• v.55 no.2
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• pp.91-97
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• 2011

To search a new anti-depressant agents against para-chloroamphetamine-induced excitation, three dimensional quantitative-structure relationships (3D-QSAR) models between structure of 3a,4-dihydro-3H-[1]-benzopyronao[4,3]isoxazoles (1-30) and thieir inhibitory activity against para-chloroamphetamine-induced excitation were performed and discussed quantitatively using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. From these basis on the findings, the optimized CoMSIA-2F model ($q^2$=0.793 and $r^2$=0.952) showed the best statistical results. And also, it is found that the para-chloroamphetamine inhibitory activity from the optimized CoMSIA-2F model was dependent on steric field (35.2%) and electrostatic field (64.8%) of tricyclic isoxazoles. Particularly, it is predicted that the inhibitory activity against para-chloroamphetamine-induced excitation will be able to increase by the designed compounds from the CoMSIA-2F model.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2312-2322
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• 2021

In recent studies, the creep rate of Zircaloy-4, one of the basic property parameters of the nuclear fuel code, has been commonly used with the axial creep model proposed by Rosinger et al. However, in order to calculate the circumferential deformation of the fuel cladding, there is a limitation that a difference occurs depending on the anisotropic coefficients used in deriving the circumferential creep equation by using the axial creep equation. Therefore, in this study, the existing axial creep law and the derived circumferential creep results were analyzed through a circumferential creep test by the internal pressurization method in the isothermal conditions. The circumferential creep deformation was measured through the optical image analysis method, and the results of the experiment were investigated through constructed IDECA (In-situ DEformation Calculation Algorithm based on creep) code. First, preliminary tests were performed in the isotropic β-phase. Subsequently in the anisotropic α-phase, the correlations obtained from a series of circumferential creep tests were compared with the axial creep equation, and optimized anisotropic coefficients were proposed based on the performed circumferential creep results. Finally, the IDECA prediction results using optimized anisotropic coefficients based on creep tests were validated through tube burst tests in transient conditions.

• YAKHAK HOEJI
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• v.54 no.6
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• pp.429-440
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• 2010

HS-SPME-GC/MS was studied and optimized for the determination of 17 orgarnophosphorous pesiticides (OPPs: chlorpyrifos, chlorpyrifos-methyl, demeton-s-methyl, diazinon, dimethoate, EPN, fenitrothion, fenthion, malathion, methidathion, monocrotophos, parathion, phenthoate, phosphamidon, sulfotep, terbufos, triazophos) in blood. Optimum SPME parameters were selected: choice of SPME fiber (85 ${\mu}m$ polyacrylate), pH effect (0.5 N HCl), salt effect ($Na_2SO_4$, 0.2 g; 20%), headspace incubation temperature ($80^{\circ}C$), headspace incubation time (1 min), headspace adsorption time (30 min) and GC desorption time (2 min). These parameters were optimized using HS-SPME autosampler coupled with gas chromatography-mass spectrometry (GC-MS). Method validation was carried out in terms of linearity, limit of detection (LOD), limit of quantitation (LOQ) and recovery in blood. The assay was linear over 0.5~5.0 mg/l ($r^2$=0.955~1.000). Limit of detection (LOD) and limit of quantitation (LOQ) in blood were determined 0.03~0.3 mg/l (S/N=3) and 0.1~1.1 mg/l (S/N=10), respectively. Relative recovery with 0.5, 1 and 5 mg/l (in blood) were 90.8%, 98.5% and 94.1%, respectively. This method will be applied to the determination of the orgarnophosphorous pesticides in postmortem blood. The proposed protocol can be an attractive alternative to be used in routine toxicological analysis.

• Journal of Plant Biotechnology
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• v.2 no.3
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• pp.157-163
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• 2000

Optimization of chemicals in the large scale sea water medium and inoculum for biomass and natural colourants production in the marine cyanobacteria, Phomidium tenue BDU 46241 (phycoerythrin producer) and P.valderianum BDU 30501 (phycocyanin producer) was carried out by experiments in L8 orthogonal array. Mathematical analysis revealed the significance of these factors. The factor(s) that critically control the yield varied with the organism and the end-product further, the desirable level of these factors between the normal and a higher level tested was identified and improved media were evolved. In both cyanobacteria, higher level of $K_2$$HPO_4$, $NaNO_3$ and inoculum with normal level of ferric ammonium citrate was found to be desirable for biomass production and additionally, higher level of $MgSO_4$ for pigment production. The level of other factors varied with the organism and the end-product. Confirmation experiments showed that the clues obtained based on mathematical experimentation are valid. In P.tenue, the medium optimized for biomass production increased the yield of biomass by 495% and the medium optimized for phycoerythrin production increased the yield of biomass by 408% with 30% increase in phycoerythrin content of the biomass. Similarly in P.valderianum, the medium optimized for biomass production increased the yield of biomass by 224% and the medium optimized for phycocyanin production increased the yield of biomass by 143% with 44% increase in phycocyanin content of the biomass.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• KIEAE Journal
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• v.16 no.3
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• pp.89-94
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• 2016

Purpose: This study aimed at developing an artificial neural network (ANN) model to predict the optimal start moment of the setback temperature during the normal occupied period of a building. Method: For achieving this objective, three major steps were conducted: the development of an initial ANN model, optimization of the initial model, and performance tests of the optimized model. The development and performance testing of the ANN model were conducted through numerical simulation methods using transient systems simulation (TRNSYS) and matrix laboratory (MATLAB) software. Result: The results analysis in the development and test processes revealed that the indoor temperature, outdoor temperature, and temperature difference from the setback temperature presented strong relationship with the optimal start moment of the setback temperature; thus, these variables were used as input neurons in the ANN model. The optimal values for the number of hidden layers, number of hidden neurons, learning rate, and moment were found to be 4, 9, 0.6, and 0.9, respectively, and these values were applied to the optimized ANN model. The optimized model proved its prediction accuracy with the very storing statistical correlation between the predicted values from the ANN model and the simulated values in the TRNSYS model. Thus, the optimized model showed its potential to be applied in the control algorithm.