Linear Interpolation Method Research Articles

K-Edge Measurement Technology Simulation Model Design

Abstract Based on the principle of K-edge technology and particle transport theory, the article establishes the calculation model of K-edge measurement technology, which includes key components such as the continuous X-ray light source, high-purity germanium detector, cylindrical sample, collimator, and so on. In terms of light source setting, the light source function was obtained by linear interpolation method according to the X-ray light source model formula; in terms of K-edge absorption distance, the average K-edge absorption distance calculation method was adopted to improve the concentration calculation formula. The calibration factor was obtained under the experimental conditions with 8mm diameter sample bottles. Under identical experimental conditions, the validation was tested out using samples with concentrations of 150g/L and 300g/L. The reconstructed Uranium concentrations were 148.5g/L and 294g/L, with deviations of 1% and 2% between the initial values, respectively. To further validate the correctness of the modified concentration calculation formula, the diameters of the sample bottles were altered to 6mm and 10mm, and the corresponding calibration factors were obtained respectively. Verification experiments were subsequently carried out using solutions with concentrations of 150g/L and 300g/L. The simulation outcomes validate the accuracy of the model and the modified concentration calculation formula. The calculation model constructed in the article combines with actual application scenarios to provide preliminary technical assurance for the expansion and optimization of K-edge measurement technology.

Wind Power Prediction Based on The SENet-CNN-LSTM Model

aims: To improve the accuracy of wind power prediction background: In recent years, wind power has developed rapidly nationwide and worldwide.How to improve the prediction precision and accuracy of wind power has become a research hotspot in this field objective: This paper aims to construct a wind power forecasting system based on SENet-CNN-LSTM to improve the accuracy and stability of wind power forecasting method: Firstly, the isolated forest algorithm is used to detect abnormal values in wind power historical data, and the linear interpolation method is used to fill in the missing data. Secondly, CNN is used to extract the spatio-temporal characteristics of wind power data, SENet is used to assign different weights to the extracted feature information, and LSTM’s unique gating mechanism is used to memorize and forget the information. Finally, taking the measured historical data of a wind power farm as the sample, six algorithms including CNN, LSTM, CNN-LSTM, SENet-CNN, SENet-LSTM and SENet-CNN-LSTM are used to predict the example result: The prediction results show that the RMSE, MAE and MAPE of SENet-CNN-LSTM are significantly reduced compared with the other five prediction models conclusion: The wind power prediction model based on SENet-CNN-LSTM has high prediction accuracy and good performance other: There is no.

Study on Shear Postbuckling and Failure of Perforated Plates: Modeling and Experiments

Based on the Kirchhoff–Karman nonlinear theory, the postbuckling bearing capacity (PBC) of perforated plates under shearing load was studied and compared using a self-developed quasi-conforming element program with singular point identification and switching approach. The influences of geometric parameters on the PBC were analyzed. During calculation, the edge beams were introduced to simulate effects of boundary on PBC. The perforated plates were manufactured and tested to verify the accuracy of the numerical results. The results show that the influence of in-plane constraints along the normal of edges on the PBC is more significant. The PBC increases with the increase of plate thickness but is not sensitive to the change of the hole’s location. The reinforcement can reduce the stress concentration and increase the PBC by 7–133%. The PBC is greatly affected by the aperture D, and it is found that the optimal aperture D* exists when the side length a is constant. The PBC under the optimal aperture is higher than that under other apertures. To facilitate the optimization design, a more refined geometric parameter library is provided by the least-squares method combined with the linear interpolation method. The interpolation results are verified by the existing numerical results.

Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows

Abstract Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as laser interferometer space antenna, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector’s response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling.

Leveraging multi-level correlations for imputing monitoring data in water supply systems using graph signal sampling theory

Data missing and anomalies in monitoring equipment have become critical barriers to developing intelligent Water Supply Systems (WSS). The valid data preceding and after the missing segments can be utilized to impute missing values. However, traditional imputation methods, such as linear interpolation and prediction-based methods, have limited capacity to use data relationships or can only utilize information before the missing values. Therefore, existing methods still need to work on efficiently and conveniently achieving high-accuracy imputation. According to the continuity and periodicity of WSS data, missing values often exhibit multi-level correlations with valid data. This paper innovatively employs graph structures to analyze the multi-level correlations at different timestamps and applies graph signal sampling algorithms to extract low-frequency features for imputation. A novel Graph-based Data Imputation (GDI) method has been developed, which leverages multi-level correlations to propagate information and completes imputation tasks without requiring complex feature engineering and pre-training processes. Results indicate that GDI outperforms Holt-Winters, Support Vector Regression, and Gated Recurrent Unit in the task of imputing continuous missing data. It can still achieve R2>0.8 even when the proportion of missing values reaches 80 %. These results demonstrate that GDI ensures a more streamlined and efficient imputation with high robustness and accuracy.

Eliminating metal artifacts in dental computed tomography using an elaborate sinogram normalization interpolation method with CNR-based metal segmentation

Metal artifact reduction (MAR) in dental cone-beam computed tomography (CBCT) is critical for improving its clinical usefulness and remains a challenging problem. This study presents an elaborate sinogram normalization interpolation (SNI) method with contrast-to-noise ratio (CNR)-based metal segmentation to eliminate metal artifacts in dental CBCT. The proposed MAR method involves three main steps: (1) CNR-based segmentation of a metal trace in the sinogram domain; (2) generation of a residual artifact-reduced prior sinogram; and (3) sinogram completion using the prior sinogram, followed by filtered-backprojection (FBP)-based CBCT reconstruction and metal insertion processes. To verify the efficacy of the proposed method, simulations and experiments were performed using numerical and physical dental phantoms with several metal inserts. The quality of the resulting CBCT images was quantitatively evaluated using the structural similarity (SSIM) metric and compared to those obtained with other interpolation-based MAR methods. A tabletop setup for the micro-CT system was used in the experiment, which comprised an X-ray tube operated under tube conditions of 70 kV p and 5 mA and a flat-panel detector with a pixel size of 198 μm. Our results indicate that the CNR-based metal segmentation method precisely identified traces of metallic objects on the sinogram, and thereby, the proposed SNI-based MAR method considerably reduced metal artifacts in dental CBCT images without introducing any contrast anomalies. The SSIM values of the CBCT images obtained with the proposed MAR method were 0.99 and 0.95 for the simulation and experiment, respectively, which are approximately 11% and 9% greater than those with a simple threshold-based linear interpolation method, respectively, improving the image quality. The proposed MAR method can be applied to reduce metal artifacts in real-world dental CBCT systems.

Optimal DG hosting capacity evaluation method based on energy storage configuration and network reconfiguration strategy in power supply unit cluster

Abstract For a single node in the power supply unit cluster, the effect of the node’s energy storage configuration capacity on improving the capacity of distributed power supply (DG) is analyzed, considering the energy storage access. Network reconfiguration is performed during operation to enhance DG capacity further. Firstly, a DG capacity evaluation model is constructed. The model maximizes DG capacity under various energy storage capacities for a single node, transforming it into a MILP model. Secondly, corresponding points of total cost and DG capacity under different storage capacities are obtained, and a piecewise linear interpolation method is used to plot the relationship curve. Thirdly, the optimal energy storage configuration and corresponding optimal DG capacity are determined by calculating the slope of the curve. Finally, the proposed model and method are verified on the IEEE-33 node system.

CHANGEPOINTS DETECTION OF PANDEMIC WAVE IN REAL-TIME: APPLICATIONS TO THE TRANSMISSION OF COVID-19

The COVID-19 pandemic has spread throughout the world. Most countries experienced the pandemic in multiple waves. The Richards model predicts when a pandemic will peak and end in a particular area. However, this model can only be used in the single-wave case. The research aims to identify a changepoint detection method capable of delineating pandemic wave boundaries, thus enabling the resolution of multiple wave cases using the Richards model. This article uses two methods to detect changepoints: the Pruned Exact Linear Time (PELT) and the interpolation method. PELT method determines the changepoint based on changes in the statistical properties of the sequence of observations which can be in the form of differences in the mean or variance of each set of observations. In contrast, the linear interpolation method determines the changepoint based on the slope of a data pattern. The two methods complement each other, where the interpolation method is used to determine whether the pandemic is still in a single wave or has multiple waves, followed by determining wave boundaries using the PELT method. Richards model parameter estimation is carried out after the wave boundaries are obtained, and initial data is taken from the last wave using the PELT method. The prediction results show the peak of the pandemic in a particular region and when it will end, which can be used to inform medium-term strategies for the government to overcome the ongoing pandemic. This information helps prevent a resurgence of infections, which would negatively affect the COVID-19 mortality rate and the area's economic situation.

An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations

AbstractObservations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen & Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (>3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays.

Gap filling of missing satellite data from MODIS and CMEMS for chlorophyll-a in the waters of Aceh, Indonesia

The motivation behind our study is to identify a robust method to enhance the accuracy of missing data, particularly chlorophyll-a data, which often goes undetected due to various factors. This study analyzes chlorophyll-a concentrations and sea level changes due to tides using three methods: Linear Interpolation, Fillgaps, and Modified Fillgaps. Two experiments were conducted: Experiment I involved random data removal (60% and 70%), and Experiment II combined sequential and random data removal (25% sequentially on the right, 35% and 45% randomly on the left). In Experiment I, the Modified Fillgaps method showed high correlation coefficients (up to 0.96) between original and reconstructed data, demonstrating its effectiveness in accurately filling significant data gaps. This method also exhibited low Root Mean Square Error and Mean Absolute Error values, confirming its predictive precision. In Experiment II, despite structured and realistic data loss patterns, the method maintained high correlation and low prediction errors, with low Normalized Root Mean Squared Error and Mean Absolute Percentage Error values, further validating its reliability. Additionally, the method excelled in two-dimensional chlorophyll-a maps, outperforming Linear Interpolation and Fillgaps methods in scenarios with 50% and 60% data loss, achieving higher correlation and lower prediction errors. These findings are crucial for environmental and climatological studies relying on satellite-derived data, confirming the Modified Fillgaps method as the most reliable and effective for handling significant data loss in chlorophyll-a map analyses. Future research should explore its application to other environmental data types and more complex data loss patterns.

Prediction of second-hand sailboat prices based on multivariate linear interpolation methods

According to the requirements of the topic, our team has established a mathematical model based on a large amount of relevant data and conducted a quantitative analysis of factors such as the price of sailboats and their length, types, materials, etc. In the process of data analysis and model building, we mainly use interpolation method and error variance analysis, and display these results through charts and figures. In addition, we have used the obtained model to analyze and estimate the price of sailboats in Hong Kong.

Prediction of second-hand sailboat prices based on multivariate linear interpolation methods

According to the requirements of the topic, our team has established a mathematical model based on a large amount of relevant data and conducted a quantitative analysis of factors such as the price of sailboats and their length, types, materials, etc. In the process of data analysis and model building, we mainly use interpolation method and error variance analysis, and display these results through charts and figures. In addition, we have used the obtained model to analyze and estimate the price of sailboats in Hong Kong.

Robust H-Infinity Speed Controller based Optimization Technique for Doubly Salient Singly Excited Motor Drive

This research presents a novel robust controller for Doubly Salient Singly Excited motor (DSSEM) speed controlling. This algorithm aims to minimize the influence of disturbances that affect motor function and offer the best duty cycles possible to accomplish tracking performance. The H-infinity () tracking control approach can optimally solve the tracking game algebraic Riccati equation online by using reinforcement learning. This method enhances the Doubly Salient Singly Excited Motor (DSSEM) system model with the reference current model to produce a quadratic value function. The quadratic value function facilitates the implementation of a linear quadratic tracker, similar to control. By partitioning and organizing the nonlinear domain of the DSSEM into a table of nodes, the system can manage complex nonlinearities efficiently. Each node in the table represents a specific region of the control space, allowing for precise and robust tracking of desired reference signals. This structure ensures that the DSSEM achieves optimal performance and stability, adapting dynamically to operational changes. Furthermore, the tracking control is outfitted with a linear interpolation method to facilitate a seamless transition between matrices in the table. Finally, this paper presents simulation results to validate the proposed control algorithm.

Predicting the entire pyrolysis process of representative charring material infiltrated with kerosene using an improved artificial neural network

It is crucial to predict the entire pyrolysis process of charring materials infiltrated with flammable liquids to provide guidance for reducing the fire hazards and implementing numerical simulations of fire accidents caused by the leakage of flammable liquids. In the present study, pyrolysis data obtained from the thermogravimetric experiments were preprocessed using linear interpolation and data normalization methods. The processed data set was then used to train the artificial neural network (ANN) framework to predict the pyrolysis behaviors of typical charring material (Mongolian Scots pine) infiltrated with typical flammable liquid (kerosene) under three different conditions: (1) pyrolysis at a constant mass fraction of kerosene with multiple heating rates; (2) pyrolysis at multiple mass fractions of kerosene with a constant heating rate; and (3) pyrolysis at multiple mass fractions of kerosene and multiple heating rates. The ANN, with the double hidden layer structure (6−3), was capable to well predict the entire pyrolysis behaviors of Mongolian Scots pine under all the three scenarios. Besides, five data transformation function (multiplication, exponential function with base e, logarithmic function with base e, square root, and exponentiation function) were applied to the present three input variables (temperature, mass fraction of kerosene, and heating rate) to generate new input variables in order to extract more characteristics among the pyrolysis data. Therein, the ANN utilizing the data transformation functions of multiplication and exponential function with base e exhibited the best predictive ability. Among the three input variables in the ANN (temperature, mass fraction of kerosene, and heating rate), temperature was identified as the most sensitive one to the prediction performance, followed by mass fraction of kerosene, with heating rate being the least sensitive.

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.

Nonlinear Dynamic Analysis of High-Strength Concrete Bridges under Post-Fire Earthquakes Considering Hydrodynamic Effects

This study employed the linear interpolation method to ascertain the curve relationship between the elastic modulus and stress of high-strength concrete C60 with temperature, and the nonlinear dynamic analysis of high-strength concrete bridge structures subjected to post-fire earthquake action at varying water levels was subsequently evaluated. It was established that both the hydrodynamic effects and the temperature effects have a considerable impact on the structural dynamic response of bridges. The presence of water has been observed to increase the dynamic response of pier structures. At water levels of 0 m and 10 m, the temperature effect results in a reduction in the fundamental frequencies of acceleration and displacement responses by 73.68% and a decrease in the fundamental frequency of stress responses by 83.33%. At a water level of 20 m, the fundamental frequencies of the acceleration, displacement, and stress responses decrease by 53.49%. In consideration of the acceleration and displacement at the pier top and stress at the pier base at a water depth of 10 m, the superposition of temperature effects and hydrodynamic effects results in an increase of 59.06% in acceleration, 25.93% in displacement, and 49.53% in stress than combination effects, respectively. At a water depth of 20 m, the superposition of temperature and hydrodynamic effects results in an increase of 92.82%, 100%, and 127.85% in acceleration, displacement, and stress, respectively. The combined effects of hydrodynamic and temperature effects cannot be described merely as a linear superposition of the two single actions. The research findings provide a significant theoretical basis for understanding the impact of multiple disasters, such as fires and earthquakes, on bridge structures.

Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.

To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.

A Study on the Color Prediction of Ancient Chinese Architecture Paintings Based on a Digital Color Camera and the Color Design System

Color paintings such as painted facades and interiors are important decoration elements of ancient Chinese architectures. The color of the paintings usually fades over time due to exposure to strong light, high humidity, high temperatures, and other environmental factors. In order to restore or reproduce the color appearance of ancient architecture paintings correctly, it was necessary to study the color degradation process of such paintings. To meet the needs of on-site colorimetric measurement of the paintings on ancient Chinese architectures, we propose using a digital color camera and the CDS (Color Design System) to measure and evaluate the colors of such paintings. The CDS is a color order system recommended by the Chinese national technical committee for color standardization (SAC/TC 120) in 2017 (GB/Z 35473-2017). The current version of the CDS atlas contains about 2740 samples which were uniformly distributed on the whole color space, and can be used to set up the colorimetric characterization model for the digital camera. Particularly, the digital CDS lookup table contains over 400 thousand samples, and it can be used to express the color of paintings on ancient Chinese architectures. In the experiment, a digital color camera was used to capture the colors of the paintings on the ancient Chinese architectures of different years based on the CDS and polynomial transform method. Moreover, a linear interpolation method was proposed for calculating and predicting the color degradation of such paintings. The experimental results show that with the increase in years, the color hue of the paintings changes slowly, while the lightness and the chroma of them fade obviously. In the future, more ancient architectures of different years and from different places should be selected as experimental samples to improve the method and the results of the paper.

Analysis of passenger transportation and the public transportation impact on the reduction in a smart city

The state of atmospheric pollution is determined by the growth of the population, the amount of transport and the generated volumes of emissions. The object is the process of analyzing passenger transportation in the city. The subject is passenger transport analysis methods. Purpose: analysis of passenger transportation and approaches to optimization of public transport based on the concept of a smart city. Tasks: analysis of passenger transportation, classification of existing conceptual approaches to optimization of public transport with low carbon emissions, systematization of existing methods, means and types of neural networks in smart cities, analysis of successful implementation projects. Methods of statistical analysis, linear and non-linear interpolation, logical generalization, comparison, grouping, analysis and synthesis. Results: the analysis of passenger transportation in the city revealed that statistical data sets indicate a decrease in the main indicators of passenger traffic and an increase in the volume of emissions of carbon-containing compounds. The classification of existing approaches to the optimization of public transport is carried out according to the priority of public transport, hybridization and electrification of vehicles and the implementation of IT monitoring. During the systematization of methods and means in smart cities, the following are highlighted: smart transport systems; electric vehicles; transport sharing networks; smart applications and information systems; innovative payment systems; unmanned vehicles; information boards and announcement systems; networks of bicycle paths and equipped sidewalks; environmental monitoring systems. Among neural networks, recurrent, convolutional, and deep neural networks have been proposed as those that contribute to route optimization and traffic prediction. Conclusions: the statistical analysis of passenger transportation established that reducing carbon dioxide emissions is an unresolved task for both public transport and the transportation system. It is proposed to include methods and means that optimize public transport, reducing the carbon footprint of the initiatives of implementing the concept of a smart city, which are successful all over the world. It is proposed to use recurrent, convolutional and deep neural networks to optimize passenger transportation in smart cities.

Prediction of the minimum fluidization velocity of different biomass types by artificial neural networks and empirical correlations

PurposeThis paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.Design/methodology/approachAn extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.FindingsThe performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.Originality/valueTechniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed.