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The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.
The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.
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Therefore, further work is required to accurately extract Tolman lengths and rigidities of alkanols because DFT with PCP-SAFT does not accurately predict surface tensions of these fluids.
Therefore, further work is required to accurately extract Tolman lengths and rigidities of alkanols because DFT with PCP-SAFT does not accurately predict surface tensions of these fluids.
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Predict Surface sentence examples within Could Predict Surface
ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs.
ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs.
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A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx.
A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx.
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Predict Surface sentence examples within predict surface roughnes
The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit.
The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit.
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Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.
Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.
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Predict Surface sentence examples within predict surface strain
A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
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A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
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Predict Surface sentence examples within predict surface water
Urban flood modelling tools are in demand to predict surface water
inundation caused by intense rainfall and to manage associated flood risks
in urban areas.
Urban flood modelling tools are in demand to predict surface water
inundation caused by intense rainfall and to manage associated flood risks
in urban areas.
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The results indicate that the FNN based soft-sensor can predict surface water quality simultaneously with suitable prediction accuracy.
The results indicate that the FNN based soft-sensor can predict surface water quality simultaneously with suitable prediction accuracy.
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Predict Surface sentence examples within predict surface tension
On the other hand, for future experimental measurements of phase equilibria, our results could serve as an initial approximation of equilibrium, and the correlations obtained for the binary parameters of the linear gradient theory and parachor method can be used to predict surface tension at other temperatures outside the range 288.
On the other hand, for future experimental measurements of phase equilibria, our results could serve as an initial approximation of equilibrium, and the correlations obtained for the binary parameters of the linear gradient theory and parachor method can be used to predict surface tension at other temperatures outside the range 288.
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Finally, the model is extended to predict surface tension for mixture solutions, considering both independent and dependent adsorptions of different solute species to the liquid-vapor interface.
Finally, the model is extended to predict surface tension for mixture solutions, considering both independent and dependent adsorptions of different solute species to the liquid-vapor interface.
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Predict Surface sentence examples within predict surface subsidence
The accumulative change value for surface subsidence is subject to logarithmic law, and our results can be used to quantitatively guide the collaborative relationship between tunnel excavation and support, dynamically adjust TBM construction parameters, and predict surface subsidence and surrounding rock deformation, thereby providing guidance and reference for similar projects.
The accumulative change value for surface subsidence is subject to logarithmic law, and our results can be used to quantitatively guide the collaborative relationship between tunnel excavation and support, dynamically adjust TBM construction parameters, and predict surface subsidence and surrounding rock deformation, thereby providing guidance and reference for similar projects.
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This paper introduced the fundamental principle of the SWBM technique and then presented a model based on the theory of equivalent mining height (EMH) to predict surface subsidence.
This paper introduced the fundamental principle of the SWBM technique and then presented a model based on the theory of equivalent mining height (EMH) to predict surface subsidence.
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Predict Surface sentence examples within predict surface integrity
Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters.
Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters.
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The obtained result revealed that the model of the quadratic polynomial is fitted to predict surface integrity.
The obtained result revealed that the model of the quadratic polynomial is fitted to predict surface integrity.
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Predict Surface sentence examples within predict surface temperature
Predict Surface sentence examples within predict surface quality
We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.
We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.
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Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality.
Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality.
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Predict Surface sentence examples within predict surface soil
In addition to multispectral images, we use terrain attributes derived from a digital
elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture.
In addition to multispectral images, we use terrain attributes derived from a digital
elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture.
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The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.
The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.
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10.3390/ma14051089
We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.
We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.
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10.1051/e3sconf/202129303017
A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation.
A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation.
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10.1016/J.MFGLET.2021.04.002
Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters.
Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters.
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10.1109/TIE.2020.2987286
We predict surface overlaps using an estimated OctoMap.
We predict surface overlaps using an estimated OctoMap.
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10.5194/HESS-25-2843-2021
Urban flood modelling tools are in demand to predict surface water
inundation caused by intense rainfall and to manage associated flood risks
in urban areas.
Urban flood modelling tools are in demand to predict surface water
inundation caused by intense rainfall and to manage associated flood risks
in urban areas.
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10.3390/rs13163181
5, which is acceptable to predict surface albedo.
5, which is acceptable to predict surface albedo.
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10.5194/HESS-25-2739-2021
In addition to multispectral images, we use terrain attributes derived from a digital
elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture.
In addition to multispectral images, we use terrain attributes derived from a digital
elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture.
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10.1007/s12665-020-09345-0
The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.
The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.
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10.1098/rsos.210414
These optical methods could help predict surface drying state, thereby improving the accuracy of dust emissions risk assessment protocols that support mining industries intervention and mitigation strategies.
These optical methods could help predict surface drying state, thereby improving the accuracy of dust emissions risk assessment protocols that support mining industries intervention and mitigation strategies.
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10.1016/J.CEMCONCOMP.2020.103870
Regression modeling was able to predict surface resistivity using porosity data and thus may be a valuable concrete mixture screening tool.
Regression modeling was able to predict surface resistivity using porosity data and thus may be a valuable concrete mixture screening tool.
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10.1177/16878140211040647
Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality.
Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality.
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10.1785/0120200235
Therefore, simplified models for effective stress ground analysis should be used with caution by practicing engineers to predict surface spectra but can be used confidently to assess the potential for liquefaction triggering.
Therefore, simplified models for effective stress ground analysis should be used with caution by practicing engineers to predict surface spectra but can be used confidently to assess the potential for liquefaction triggering.
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10.1007/s00170-020-06399-z
Stage 1 includes thermomechanical SLM simulation to predict surface geometry and is applied to model four 50-μm layers of a 316L part having a 4 mm × 4 mm footprint.
Stage 1 includes thermomechanical SLM simulation to predict surface geometry and is applied to model four 50-μm layers of a 316L part having a 4 mm × 4 mm footprint.
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10.1063/5.0041273
Our study shows that the surface diffusion coefficient can be used to quantitatively predict surface crystallization rates in a chemically diverse range of materials.
Our study shows that the surface diffusion coefficient can be used to quantitatively predict surface crystallization rates in a chemically diverse range of materials.
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10.1109/MERCon52712.2021.9525734
As an alternative to the traditional methods, this study developed a fast and computationally economical machine learning-based model to predict surface-averaged external pressure coefficients of a building with an unconventional configuration using three tree-based regressors: Adaboost, Extra Tree, and Random Forest.
As an alternative to the traditional methods, this study developed a fast and computationally economical machine learning-based model to predict surface-averaged external pressure coefficients of a building with an unconventional configuration using three tree-based regressors: Adaboost, Extra Tree, and Random Forest.
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10.1016/J.APPLTHERMALENG.2021.117436
In the present study, a mathematical model based on volume-averaged porous media equations is employed to predict surface operation time.
In the present study, a mathematical model based on volume-averaged porous media equations is employed to predict surface operation time.
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10.17559/tv-20190522104029
The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit.
The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit.
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10.1007/S10765-021-02815-Y
On the other hand, for future experimental measurements of phase equilibria, our results could serve as an initial approximation of equilibrium, and the correlations obtained for the binary parameters of the linear gradient theory and parachor method can be used to predict surface tension at other temperatures outside the range 288.
On the other hand, for future experimental measurements of phase equilibria, our results could serve as an initial approximation of equilibrium, and the correlations obtained for the binary parameters of the linear gradient theory and parachor method can be used to predict surface tension at other temperatures outside the range 288.
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10.1073/pnas.2102705118
Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity.
Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity.
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10.5194/GMD-14-2603-2021
We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.
We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.
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10.1016/J.IJMACHTOOLS.2021.103704
To predict surface evolution, one should ideally consider the tool as a set of grains and take into account the cutting action of each grain.
To predict surface evolution, one should ideally consider the tool as a set of grains and take into account the cutting action of each grain.
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10.1038/s41524-021-00618-1
Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance.
Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance.
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10.3390/ma14051311
Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.
Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.
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10.3389/fmars.2021.672477
In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman.
In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman.
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10.4028/www.scientific.net/AST.106.109
These models latter can be used to predict surface roughness according to technological variables.
These models latter can be used to predict surface roughness according to technological variables.
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10.1016/j.diamond.2020.108191
Based on theories of Gaussian pulse beam and picosecond laser ablation material, machined surface morphology model was developed to predict surface roughness Sa and removal depth of CVDD.
Based on theories of Gaussian pulse beam and picosecond laser ablation material, machined surface morphology model was developed to predict surface roughness Sa and removal depth of CVDD.
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10.2139/ssrn.3860382
Based on those observations a new parameter involving these three geometrical factors was developed to predict surface crack initiation sites.
Based on those observations a new parameter involving these three geometrical factors was developed to predict surface crack initiation sites.
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10.1016/j.agwat.2020.106597
Hydrological simulation models predict surface and artificial subsurface flow at different scales.
Hydrological simulation models predict surface and artificial subsurface flow at different scales.
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10.1021/acs.jpca.0c10232
Finally, the model is extended to predict surface tension for mixture solutions, considering both independent and dependent adsorptions of different solute species to the liquid-vapor interface.
Finally, the model is extended to predict surface tension for mixture solutions, considering both independent and dependent adsorptions of different solute species to the liquid-vapor interface.
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10.1016/j.ecolind.2020.106889
The model output shows the importance of slope and ground heating index (GHI) – an estimation of the amount of energy transferred to the ground, to predict surface displacements independently from the type of considered processes.
The model output shows the importance of slope and ground heating index (GHI) – an estimation of the amount of energy transferred to the ground, to predict surface displacements independently from the type of considered processes.
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10.1016/J.MATPR.2021.05.118
In this present study, an experimental investigation has been done to predict surface roughness by taking into consideration of the following parameters: cutting speed, rate of feed, cutting depth and nose radius in the hard turning of Duplex 2205 (ASTM A276) round bar material utilizing carbide tip tool material.
In this present study, an experimental investigation has been done to predict surface roughness by taking into consideration of the following parameters: cutting speed, rate of feed, cutting depth and nose radius in the hard turning of Duplex 2205 (ASTM A276) round bar material utilizing carbide tip tool material.
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10.21203/RS.3.RS-425549/V1
In addition, a linear model generated to predict surface roughness performed best at moderate to medium level of processing (fluences in the region of 0.
In addition, a linear model generated to predict surface roughness performed best at moderate to medium level of processing (fluences in the region of 0.
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10.1007/s10706-021-01965-y
The accumulative change value for surface subsidence is subject to logarithmic law, and our results can be used to quantitatively guide the collaborative relationship between tunnel excavation and support, dynamically adjust TBM construction parameters, and predict surface subsidence and surrounding rock deformation, thereby providing guidance and reference for similar projects.
The accumulative change value for surface subsidence is subject to logarithmic law, and our results can be used to quantitatively guide the collaborative relationship between tunnel excavation and support, dynamically adjust TBM construction parameters, and predict surface subsidence and surrounding rock deformation, thereby providing guidance and reference for similar projects.
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10.1785/0120200346
We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles.
We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles.
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10.1007/s40544-021-0527-z
Finite element method (FEM) was used to study elastic deformation effects, explain the observed friction difference, and predict surface material influence.
Finite element method (FEM) was used to study elastic deformation effects, explain the observed friction difference, and predict surface material influence.
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10.3390/ma14195701
In this paper, a radial basis neural network is proposed to predict surface roughness.
In this paper, a radial basis neural network is proposed to predict surface roughness.
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10.1177/16878140211017632
ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs.
ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs.
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10.3390/ma14092297
Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures.
Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures.
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10.1007/s11665-021-05991-y
This article investigates the influence of LP on surface roughness and wettability of AISI 316L SS produced by DED and proposes equations that predict surface roughness and remelted layer thickness (RLT) as a function of laser power (P).
This article investigates the influence of LP on surface roughness and wettability of AISI 316L SS produced by DED and proposes equations that predict surface roughness and remelted layer thickness (RLT) as a function of laser power (P).
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10.2339/POLITEKNIK.892146
The developed mathematical models for ceramic and coated carbide inserts are capable to predict surface roughness with 97.
The developed mathematical models for ceramic and coated carbide inserts are capable to predict surface roughness with 97.
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10.1109/TASE.2020.3001047
This method to predict surface temperature is then combined into an optimization model for real-time layer time control.
This method to predict surface temperature is then combined into an optimization model for real-time layer time control.
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10.1016/J.TRIBOINT.2019.04.040
This model can be used to predict surface behaviour when cavitation induced by e.
This model can be used to predict surface behaviour when cavitation induced by e.
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10.1109/ACCESS.2019.2944769
An effective and efficient methodology is proposed to predict surface roughness by online monitoring of surface quality using accelerometer signals.
An effective and efficient methodology is proposed to predict surface roughness by online monitoring of surface quality using accelerometer signals.
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10.1093/mnras/stz1234
We use these simulations to predict surface brightnesses in Hα, which we show to have a characteristic ring-shaped morphology for haloes in a narrow mass range between ≃ 109.
We use these simulations to predict surface brightnesses in Hα, which we show to have a characteristic ring-shaped morphology for haloes in a narrow mass range between ≃ 109.
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10.26649/musci.2019.008
The best way to predict surface movement processes is exploring sensitive areas of surface movement.
The best way to predict surface movement processes is exploring sensitive areas of surface movement.
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10.1201/9781351063265-92
To predict the correct time for rehabilitation or routine maintenance, it is necessary to have functions or performance models to predict surface deterioration.
To predict the correct time for rehabilitation or routine maintenance, it is necessary to have functions or performance models to predict surface deterioration.
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10.1016/J.CIRP.2019.04.082
However, it is difficult to guarantee shape accuracy and predict surface roughness evolution.
However, it is difficult to guarantee shape accuracy and predict surface roughness evolution.
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10.1115/1.4043872
A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
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10.1364/JOSAB.36.002346
We also predict surface second harmonic conversion efficiencies of order 0.
We also predict surface second harmonic conversion efficiencies of order 0.
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10.1525/elementa.387
An evaluation of the model setup is performed, which exhibited the model’s ability to predict surface meteorological and chemical variables well compared with observations, and consistent with other studies.
An evaluation of the model setup is performed, which exhibited the model’s ability to predict surface meteorological and chemical variables well compared with observations, and consistent with other studies.
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10.1080/10426914.2018.1532587
On this basis, a new approach was developed to measure the amount of grinding wheel wear (GWW) and predict surface roughness (SR) in accordance with the compressed air measuring head and hybrid algorithms fuzzy neural networks (ANFIS)–Gaussian regression function (GPR) and Taguchi empirical analysis.
On this basis, a new approach was developed to measure the amount of grinding wheel wear (GWW) and predict surface roughness (SR) in accordance with the compressed air measuring head and hybrid algorithms fuzzy neural networks (ANFIS)–Gaussian regression function (GPR) and Taguchi empirical analysis.
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10.1108/ILT-07-2018-0259
The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis.
The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis.
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10.1007/s11998-019-00258-7
A novel pneumatic monodisperse spray was fabricated to validate the results of a probabilistic model and a stochastic model that were developed to predict surface area coverage of the monodisperse spray.
A novel pneumatic monodisperse spray was fabricated to validate the results of a probabilistic model and a stochastic model that were developed to predict surface area coverage of the monodisperse spray.
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10.1007/978-3-030-21359-6_52
The obtained results showed a promising capability of the tool to swiftly predict surface settlements in mechanized tunneling projects.
The obtained results showed a promising capability of the tool to swiftly predict surface settlements in mechanized tunneling projects.
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10.15244/PJOES/94814
This paper introduced the fundamental principle of the SWBM technique and then presented a model based on the theory of equivalent mining height (EMH) to predict surface subsidence.
This paper introduced the fundamental principle of the SWBM technique and then presented a model based on the theory of equivalent mining height (EMH) to predict surface subsidence.
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10.1115/1.4043872
A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.
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10.23919/ChiCC.2019.8866494
The results indicate that the FNN based soft-sensor can predict surface water quality simultaneously with suitable prediction accuracy.
The results indicate that the FNN based soft-sensor can predict surface water quality simultaneously with suitable prediction accuracy.
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10.1007/s40430-019-2014-2
The obtained result revealed that the model of the quadratic polynomial is fitted to predict surface integrity.
The obtained result revealed that the model of the quadratic polynomial is fitted to predict surface integrity.
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10.1007/S00170-018-2910-0
This paper proposes a novel approach with time-invariant transition matrix to predict surface location error (SLE) for low radial immersion milling.
This paper proposes a novel approach with time-invariant transition matrix to predict surface location error (SLE) for low radial immersion milling.
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10.1063/1.5135288
Therefore, further work is required to accurately extract Tolman lengths and rigidities of alkanols because DFT with PCP-SAFT does not accurately predict surface tensions of these fluids.
Therefore, further work is required to accurately extract Tolman lengths and rigidities of alkanols because DFT with PCP-SAFT does not accurately predict surface tensions of these fluids.
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10.2298/TSCI170303198G
Cubic spline methods of the 1-D heat conduction equation are used to predict surface heat flux and compared with input heat loads.
Cubic spline methods of the 1-D heat conduction equation are used to predict surface heat flux and compared with input heat loads.
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10.1039/c8an01768a
This work explores the potential of multivariate PLSR modelling in conjunction with Attenuated Total Reflection - Fourier Transform Infrared (ATR-FTIR) chemical imaging to investigate whether experimentally characterised surface chemistry can be used to predict surface protein adhesion.
This work explores the potential of multivariate PLSR modelling in conjunction with Attenuated Total Reflection - Fourier Transform Infrared (ATR-FTIR) chemical imaging to investigate whether experimentally characterised surface chemistry can be used to predict surface protein adhesion.
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10.1109/ICCVW.2019.00501
We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image.
We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image.
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10.1016/J.IJGGC.2019.07.005
In this work, we also developed a simple linear regression model to predict surface uplift using top vertical reservoir displacement data.
In this work, we also developed a simple linear regression model to predict surface uplift using top vertical reservoir displacement data.
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10.1016/J.RINP.2019.01.054
A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx.
A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx.
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10.3847/1538-4357/ab55e8
These yields are combined with estimated impact rates for the Early Earth to predict surface boundary conditions for an atmospheric model.
These yields are combined with estimated impact rates for the Early Earth to predict surface boundary conditions for an atmospheric model.
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10.1007/S12008-018-0490-8
The aim of the model is to minimise the contribution of uncertainty errors due to the stochastic distribution of the phases present within the material microstructure, to better predict surface roughness under different cutting conditions.
The aim of the model is to minimise the contribution of uncertainty errors due to the stochastic distribution of the phases present within the material microstructure, to better predict surface roughness under different cutting conditions.
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10.3390/LUBRICANTS7010009
Research was carried out to develop a mathematical model based on the cutting tool surface profile geometry to predict surface roughness in face milling.
Research was carried out to develop a mathematical model based on the cutting tool surface profile geometry to predict surface roughness in face milling.
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10.1016/j.envpol.2019.113486
The Pesticide Water Calculator (PWC), a model used for the regulation of pesticides in the US, was used to predict surface water and sediment pore water pesticide concentrations in vernal pool habitats.
The Pesticide Water Calculator (PWC), a model used for the regulation of pesticides in the US, was used to predict surface water and sediment pore water pesticide concentrations in vernal pool habitats.
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10.1007/S00170-018-2852-6
On the basis of the analysis, a model that can predict surface roughness is established that provides a means of optimizing micro-textured cutter design and machining inclination angles when milling titanium alloy.
On the basis of the analysis, a model that can predict surface roughness is established that provides a means of optimizing micro-textured cutter design and machining inclination angles when milling titanium alloy.
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10.3847/1538-4357/ab184a
By using 3D models, we can self-consistently predict surface temperatures and the location of water vapor, clouds, and surface ice in the climate system.
By using 3D models, we can self-consistently predict surface temperatures and the location of water vapor, clouds, and surface ice in the climate system.
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10.1590/2318-0331.241920170199
The Curve Number (CN) method is extensively used for predict surface runoff from storm events.
The Curve Number (CN) method is extensively used for predict surface runoff from storm events.
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10.1177/0040517518811940
It is shown that the Delany–Bazley and Miki models can accurately predict surface impedance of multi-component polyester nonwovens, but the Komatsu model is less accurate, especially at the low-frequency range.
It is shown that the Delany–Bazley and Miki models can accurately predict surface impedance of multi-component polyester nonwovens, but the Komatsu model is less accurate, especially at the low-frequency range.
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More Predict Surface sentence examples
10.1039/c9cp03984h
We also developed a statistical model to predict surface-dependent segregation energies.
We also developed a statistical model to predict surface-dependent segregation energies.
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10.3390/HYDROLOGY6010008
Applying the Van Genuchten relationship with measured groundwater data allows us to predict surface moisture maps.
Applying the Van Genuchten relationship with measured groundwater data allows us to predict surface moisture maps.
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10.1080/21681163.2018.1442751
The two-step framework uses a multibody musculoskeletal model to predict tibiofemoral kinematics which are then imposed on a deformable model to predict surface strains.
The two-step framework uses a multibody musculoskeletal model to predict tibiofemoral kinematics which are then imposed on a deformable model to predict surface strains.
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10.1109/JSTARS.2019.2925595
While approximate analytical electromagnetic (EM) models paired with surface gravity wave spectra have been used to predict surface roughness emissivity enhancement, these methods do not reveal details of the response to foam, breaking wave, and WC geometry.
While approximate analytical electromagnetic (EM) models paired with surface gravity wave spectra have been used to predict surface roughness emissivity enhancement, these methods do not reveal details of the response to foam, breaking wave, and WC geometry.
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More Predict Surface sentence examples
10.1021/acs.langmuir.9b00457
Until recently, superhydrophobicity was considered as a hint to predict surface icephobicity, an association of concepts that is by no means universal and that has been proven to depend on different experimental factors and material properties, including the actual morphology and chemical state of surfaces.
Until recently, superhydrophobicity was considered as a hint to predict surface icephobicity, an association of concepts that is by no means universal and that has been proven to depend on different experimental factors and material properties, including the actual morphology and chemical state of surfaces.
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