• Current Optics and Photonics
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• v.6 no.1
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• pp.1-9
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• 2022

This paper proposes a robust, simple zonal wavefront-estimation method in a grid sampling model. More slopes are added to the integral equation of the algorithm to improve the accuracy and convergence rate of this approach, especially for higher-order optical aberrations. The Taylor theorem is applied to clarify the mathematical description of the remaining error in the proposed method. Several numerical simulations are conducted to ensure the performance and improvement in comparison to the Southwell and previous algorithm. An experiment is also conducted according to deflectometry output and the results are verified using a reference measured with a stylus system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.73-79
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• 2009

For the shake of control for movement object, control theory like neural network, nonlinear model predictive control(NMPC) is realized on digital high speed computer. Predictor of flight control system(FCS) based nonlinear model predictive control has to be satisfied with response for hard real-time to perform applications on each module in the FCS. Simultaneously, It gives a serious consideration accuracy to give full play to FCS's performance. Error of mathematical aspect affects realization of whole algorithm. But factors of bring mathematical error is not considered to calculate final accuracy on parameter of predictor. In this paper, Predictor was made using load control model on the digital computer for design FCS at hard real-time and is shown response time on realization algorithm. And is shown realization algorithm of high effective predictor over the accuracy. The predictor was realized on the load control model using Euler method, Heun method, Runge-Kutta and Taylor method.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.2
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• pp.126-131
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• 2007

In this paper, Monte Carlo Simulation (MCS) method was used as an uncertainty assessment tool for air flow, net thrust measurement. Uuncertainty sources of the net thrust measurement were analyzed, and the probability distribution characteristics of each source were discussed. Detailed MCS methodology was described including the effect of the number of simulation. Compared to the conventional sensitivity coefficient method, the MCS method has advantage in the uncertainty assessment. The MCS is comparatively simple, convenient and accurate, especially for complex or nonlinear measurement modeling equations. The uncertainty assessment result by MCS was compared with that of the conventional sensitivity coefficient method, and each method gave different result. The uncertainties in the net thrust measurement by the MCS and the conventional sensitivity coefficient method were 0.906% and 1.209%, respectively. It was concluded that the first order Taylor expansion in the conventional sensitivity coefficient method and the nonlinearity of model equation caused the difference. It was noted that the uncertainty assessment method should be selected carefully according to the mathematical characteristics of the model equation of the measurement.

• Structural Engineering and Mechanics
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• v.74 no.6
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• pp.809-819
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• 2020

For the first time, the influence of in-plane magnetic field on wave propagation of Graphene Nano-Platelets (GNPs) polymer composite nanoplates is investigated here. The impact of three- parameter Kerr foundation is also considered. There are two different reinforcement distribution patterns (i.e. uniformly and non-uniformly) while the material properties of the nanoplate are estimated through the Halpin-Tsai model and a rule of mixture. To consider the size-dependent behavior of the structure, Eringen Nonlocal Differential Model (ENDM) is utilized. The equations of wave motion derived based on a higher-order shear deformation refined theory through Hamilton's principle and an analytical technique depending on Taylor series utilized to find the wave frequency as well as phase velocity of the GNPs reinforced nanoplates. A parametric investigation is performed to determine the influence of essential phenomena, such as the nonlocality, GNPs conditions, Kerr foundation parameters, and wave number on the both longitudinal and flexural wave characteristics of GNPs reinforced nanoplates.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.97-102
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• 2016

Deep learning enables machines to have perception and can potentially outperform humans in the medical field. It can save a lot of time and reduce human error by detecting certain patterns from medical images without being trained. The main goal of this paper is to build the optimal model for breast mass classification by applying various methods that influence the performance of Convolutional Neural Network (CNN). Google's newly developed software library Tensorflow was used to build CNN and the mammogram dataset used in this study was obtained from 340 breast cancer cases. The best classification performance we achieved was an accuracy of 0.887, sensitivity of 0.903, and specificity of 0.869 for normal tissue versus malignant mass classification with augmented data, more convolutional filters, and ADAM optimizer. A limitation of this method, however, was that it only considered malignant masses which are relatively easier to classify than benign masses. Therefore, further studies are required in order to properly classify any given data for medical uses.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.4
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• pp.57-66
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• 2016

Foreign Direct Investment (FDI) plays a vital role in economic growth of the countries. The present study analyses the impact of the FDI on economic growth of South Asian Association of Regional Cooperation countries by using the pooled data for the period 1990-2014. Neo-classical production function has been used for analysis and getting stock-to-flow estimation, Taylor series approximation has applied. Fixed Effects Model has been used to investigate the impact of FDI, domestic capital, labour and government expenditures on economic growth. It is the evident from the results that both domestic investment and FDI have been a positive effect on economic growth. The study finds that the contribution of domestic private investment is more trustworthy than the contribution of FDI. Consequently, FDI loses its attraction as an engine of growth if the adverse balance of payment consequence of the resulting profit repatriating is also taken into account. The labour has positive and significant association with GDP. The effect of government expenditure is negligible on economic growth. The findings suggest that growth strategy cannot yield the long term benefits if it neglects investments on human capital.

• Macromolecular Research
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• v.17 no.12
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• pp.943-949
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• 2009

A highly dispersed W/O emulsion of silicone oil (cyclomethicone)/water system was prepared with a nonionic surfactant. The surface and interfacial tension between the oil and water were characterized in terms of the droplet size distribution and viscosity change of the emulsion. When the dispersed phase concentration was relatively high, the viscosity of the emulsion was rapidly increased and the droplet size of the emulsion was decreased. The rheological behavior of the emulsion system showed non-Newtonian and shear thinning phenomena depending upon the content of the dispersed phase. The droplet size of the emulsion was decreased with increasing surfactant content and water concentration. The relative viscosity of the emulsion was better predicted with the Choi-Schowalter model than with the Taylor model. The value of the complex modulus increased with increasing surfactant concentration. The linear viscoelastic region was expanded with a dispersed phase concentration. According to the change in the viscosity, the behavior was classified into three distinct regions: [I] linear viscoelastic, [II] partially viscoelastic, and [III] viscous. The creep/recovery behaviors in each region were characterized.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.3
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• pp.252-261
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• 2016

A spar-type floating substructure that is being widely used for offshore wind power generation is vulnerable to resonance in the heave direction because of its small water plane area. For this reason, the stable dynamic response of this floating structure should be ensured by accurately identifying the resonance characteristics. The purpose of this study is to analyze the characteristics of the combination resonance between the excitation frequency of a regular wave and natural frequencies of the floating substructure. First, the nonlinear equations of motion with two degrees of freedom are derived by assuming that the floating substructure is a rigid body, where the heaving motion and pitching motions are coupled. Moreover, to identify the characteristics of the combination resonance, the nonlinear term in the nonlinear equations is approximated up to the second order using the Taylor series expansion. Furthermore, the validity of the approximate model is confirmed through a comparison with the results of a numerical analysis which is made by applying the commercial software ANSYS AQWA to the full model. The result indicates that the combination resonance occurs at the frequencies of ${\omega}{\pm}{\omega}_5$ and $2{\omega}_{n5}$ between the excitation frequency (${\omega}$) of a regular wave and the natural frequency of the pitching motion (${\omega}_{n5}$) of the floating substructure.

• Journal of Hydrogen and New Energy
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• v.27 no.6
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• pp.727-736
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• 2016

Numerical simulations of n-heptane spray characteristics in a constant volume combustion chamber under diesel engine like conditions with increasing ambient gas density ($14.8-142kg/m^3$) and ambient temperature (800-1000 K) respectively were performed to understand the non-vaporizing and vaporizing spray behavior. The effect of fuel temperature (ranging 273-313 K) on spray characteristics was also simulated. In this simulation, spray modeling was implemented into ANSYS FORTE where the initial spray conditions at the nozzle exit and droplet breakups were determined through nozzle flow model and Kelvin-Helmholtz/Rayleigh-Taylor (KH-RT) model. Simulation results were compared with experimentally obtained spray tip penetration result to examine the accuracy. In case of non-vaporizing condition, simulation results show that with an increment of the magnitude of ambient gas density and pressure, the vapor penetration length, liquid penetration length and droplet mass decreases. On the other hand vapor penetration, liquid penetration and droplet mass increases with the increase of ambient temperature at the vaporizing condition. In case of lower injection pressure, vapor tip penetration and droplet mass are increased with a reduction in fuel temperature under the low ambient temperature and pressure.