Fitting Error Research Articles

The Method of Multi-Angle Remote Sensing Observation Based on Unmanned Aerial Vehicles and the Validation of BRDF

The measurement of bidirectional reflectivity for ground-based objects is a highly intricate task, with significant limitations in the capabilities of both ground-based and satellite-based observations from multiple viewpoints. In recent years, unmanned aerial vehicles (UAVs) have emerged as a novel remote sensing method, offering convenience and cost-effectiveness while enabling multi-view observations. This study devised a polygonal flight path along the hemisphere to achieve bidirectional reflectance distribution function (BRDF) measurements for large zenith angles and all azimuth angles. By employing photogrammetry’s principle of aerial triangulation, accurate observation angles were restored, and the geometric structure of “sun-object-view” was constructed. Furthermore, three BRDF models (M_Walthall, RPV, RTLSR) were compared and evaluated at the UAV scale in terms of fitting quality, shape structure, and reflectance errors to assess their inversion performance. The results demonstrated that the RPV model exhibited superior inversion performance followed, by M_Walthall; however, RTLST performed comparatively poorly. Notably, the M_Walthall model excelled in capturing smooth terrain object characteristics while RPV proved applicable to various types of rough terrain objects with multi-scale applicability for both UAVs and satellites. These methods and findings are crucial for an extensive exploration into the bidirectional reflectivity properties of ground-based objects, and provide an essential technical procedure for studying various ground-based objects’ in-plane reflection properties.

An accurate full-dimensional interaction potential energy surface of CO2+N2 incorporating ∆-machine learning approach via permutation invariant polynomial-neural network

The interaction between CO2 and N2, both as essential components of the Earth’s atmosphere, plays a crucial role in investigating the greenhouse effect. In this work, we sampled 40,930 data points within the full-dimensional configuration space of CO2 and N2 and performed calculations at the level of explicitly correlated coupled cluster single, double, and perturbative triple level with the augmented correlation corrected valence triple-ζ basis set (CCSD(T)-F12a/AVTZ). To ensure computational accuracy while reducing computational costs, we employed the recently proposed Δ-machine learning (Δ-ML) method based on Permutation Invariant Polynomial-Neural Network (PIP-NN) for basis set superposition error (BSSE) correction. By leveraging the limited extrapolation capability of NN, efficient sampling was performed within the existing dataset, enabling the construction of the potential energy surface (PES) incorporating BSSE correction with only a small number of data points for BSSE calculations. A total of approximately 1100 data points were selected from the initial dataset to construct a BSSE correction PES. Utilizing this correction PES, BSSE predictions were carried out for all remaining data points, resulting in the successful development of a high-precision full-dimensional PES with BSSE correction for the CO2 + N2 system. The PIP-NN based Δ-ML method significantly reduced the required BSSE calculations by approximately 97.2%, resulting in a final PES with a fitting error of merely 0.026 kcal/mol.

A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data.

We didactically derive a correlated traits correlated (methods - 1) [CTC(M - 1)] multitrait-multimethod (MTMM) model for dyadic round-robin data augmented by self-reports. The model is an extension of the CTC(M - 1) model for cross-classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round-robin designs. It can be specified as a traditional structural equation model. We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter estimation inaccuracy. Further, neglecting the reciprocity covariance-structure of the augmented round-robin data does not severely bias the remaining parameter estimates. All analyses (including data, R scripts, and results) and the simulation study are provided in the Supporting Information. Implications and limitations are discussed.

Stochastic Precipitation Generation for the Xilingol League Using Hidden Markov Models with Variational Bayes Parameter Estimation

Precipitation modeling holds significant importance in various fields such as agriculture, animal husbandry, weather derivatives, hydrology, and risk and disaster preparedness. Stochastic precipitation generators (SPGs) represent a class of statistical models designed to generate synthetic data capable of simulating dry and wet precipitation stretches for a long duration. The construction of Hidden Markov Models (HMMs), which treat latent meteorological circumstances as hidden states, is an efficient technique for simulating precipitation. Considering that there are many choices of emission distributions used to generate positive precipitation, the characteristics of different distributions for simulating positive precipitation have not been fully explored. The paper includes a simulation study that demonstrates how the Pareto distribution, when used as the distribution for generating positive precipitation, addresses the limitations of the exponential and gamma distributions in predicting heavy precipitation events. Additionally, the Pareto distribution offers flexibility through adjustable parameters, making it a promising option for precipitation modeling. We can estimate parameters in HMMs using forward–backward algorithms, Variational Bayes Expectation-Maximization (VBEM), and Stochastic Variational Bayes (SVB). In the Xilingol League, located in the central part of the Inner Mongolia Autonomous Region, China, our study involved data analysis to identify crucial locations demonstrating a robust correlation and notable partial correlation between the Normalized Difference Vegetation Index (NDVI) and annual precipitation. We performed fitting of monthly dry days ratios and monthly precipitation using seasonal precipitation and year-round precipitation data at these crucial locations. Subsequently, we conducted precipitation predictions for the daily, monthly, and annual time frames using the new test dataset observations. The study concludes that the SPG fits the monthly dry-day ratio better for annual daily precipitation data than for seasonal daily precipitation data. The fitting error for the monthly dry day ratio corresponding to annual daily precipitation data is 0.053 (exponential distribution) and 0.066 (Pareto distribution), while for seasonal daily precipitation data, the fitting error is 0.14 (exponential distribution) and 0.15 (Pareto distribution). The exponential distribution exhibits the poorest performance as a model for predicting future precipitation, with average errors of 2.49 (daily precipitation), 40.62 (monthly precipitation), and 130.40 (annual precipitation). On the other hand, the Pareto distribution demonstrates the best overall predictive performance, with average errors of 0.69 (daily precipitation), 34.69 (monthly precipitation), and 66.42 (annual precipitation). The results of this paper can provide decision support for future grazing strategies in the Xilingol League.

Anti-Spoofing Method for Improving GNSS Security by Jointly Monitoring Pseudo-Range Difference and Pseudo-Range Sum Sequence Linearity.

Spoofing interference is one of the most emerging threats to the Global Navigation Satellite System (GNSS); therefore, the research on anti-spoofing technology is of great significance to improving the security of GNSS. For single spoofing source interference, all the spoofing signals are broadcast from the same antenna. When the receiver is in motion, the pseudo-range of spoofing signals changes nonlinearly, while the difference between any two pseudo-ranges changes linearly. Authentic signals do not have this characteristic. On this basis, an anti-spoofing method is proposed by jointly monitoring the linearity of the pseudo-range difference (PRD) sequence and pseudo-range sum (PRS) sequence, which transforms the spoofing detection problem into the sequence linearity detection problem. In this paper, the model of PRD and PRS is derived, the hypothesis based on the linearity of PRD sequence and PRS sequence is given, and the detection performance of the method is evaluated. This method uses the sum of squares of errors (SSE) of linear fitting of the PRD sequence and PRS sequence to construct detection statistics, and has low computational complexity. Simulation results show that this method can effectively detect spoofing interference and distinguish spoofing signals from authentic signals.

Evaluation of runoff estimation from GRACE coupled with different meteorological gridded products over the Upper Blue Nile Basin

Study RegionThe Upper Blue Nile (UBN) basin, Ethiopia. Study FocusIn efforts to accurately close the water balance equation for the UBN basin using remote sensing products, river runoff is calculated as a residual from the water balance equation by incorporating Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and remote sensing products for precipitation (P) and evapotranspiration (ET). The calculated river runoff is then compared to the gauge records located at the basin’s outlet. The best performing combination among the various combinations is chosen by aggregating rankings attributed to both error and linear fit metrics. The errors associated with each satellite product were assessed by forcing the In-Situ runoff to estimate the P, ET, and TWS. This methodology helps in addressing the uncertainty linked with each hydrological component. New Hydrological Insights for the RegionThe best P, ET, and TWS combination performance products to estimate runoff are SM2RAIN-CCI, GLEAM, and GRACE Spherical Harmonic products, respectively. The statistical results for the six metrics are R2 = 0.7, slope = 1.6, y-intercept = - 0.5 cm, RMSE = 3 cm, MAE = 2.8 cm, and PBIAS = 36%. The uncertainty from each hydrological component was quantified and showed that improving the accuracy of P and ET estimation is a crucial step to successfully close the water balance.

Optimization and microbial community analysis for anaerobic digestion of water hyacinth (Eichhornia crassipes) with waste sludge at different solid contents and temperatures

In this study, optimum operating conditions for anaerobic digestion of water hyacinth (Eichhornia crassipes) with waste sludge was investigated at varying total solid (TS) content of the digestion solution (3.3%, 4.3%, 5.3%, 6.3%, 7.3% and 8.3%) and digestion temperature (35, 40, 45, 50 and 55 °C) to produce methane with high yield. The highest biogas yield reached experimentally was 163.2 mL/g TVS (with high CH4 content of 81.1 vol%) at 6.3% of TS content and digestion temperature of 35 °C. Kinetic parameters were estimated by using modified Gompertz, Cone and first-order kinetic models in which modified Gompertz model gave the lowest fitting errors (−1.0 to 3.7%). The effect of temperature on methane production rate was simulated by Ratkowsky model and optimum digestion temperature was found between 41.5 and 44 °C for maximum methane production at 6.3% TS. The experimental methane yields were 61.9–132.3 mL CH4/g TVS. The metagenomic sequencing was used to characterize microbial community structure of the anaerobic digestate at varying conditions and acetoclastic/hydrogenothrophic methanogenesis was equally dominated methane-producing pathways. The most abundant bacterial populations were found to be Proteobacteria, Firmicutes, Chloroflexi and Actinobacteria. Based on alpha diversity indices, it can be concluded that effect of digestion temperature on microbial richness and diversity was higher than the effect of TS content.

Feature selection using differential evolution for microarray data classification

The dimensions of microarray datasets are very large, containing noise and redundancy. The problem with microarray datasets is the presence of more features compared to the number of samples, which adversely affects algorithm performance. In other words, the number of columns exceeds the number of rows. Therefore, to extract precise information from microarray datasets, a robust technique is required. Microarray datasets play a critical role in detecting various diseases, including cancer and tumors. This is where feature selection techniques come into play. In recent times, feature selection (FS) has gained significant importance as a data preparation method, particularly for high-dimensional data. It is preferable to address classification problems with fewer features while maintaining high accuracy, as not all features are necessary to achieve this goal. The primary objective of feature selection is to identify the optimal subset of features. In this context, we will employ the Differential Evolution (DE) algorithm. DE is a population-based stochastic search approach that has found widespread use in various scientific and technical domains to solve optimization problems in continuous spaces. In our approach, we will combine DE with three different classification algorithms: Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). Our analysis will include a comparison of the accuracy achieved by each algorithmic model on each dataset, as well as the fitness error for each model. The results indicate that when feature selection was used the results were better compared to the results where the feature selection was not used.

Determining the effect of cardiac blood volume on accuracy of uptake rate constants by simulation

Objective. There is great interest in better understanding coronary microvascular disease using mouse models. Typical quantification requires dynamic imaging to estimate the rate constant K 1 of the tracer moving from the blood into the myocardium. In addition to K 1, it is also desirable to determine blood volume fraction V, which if known allows for more accurate fitting of K 1. Our previously published kinetic modeling software did not consider the effect of V. To ensure a better fit of experimental data to the model for myocardial SPECT imaging, in this work we updated our kinetic modeling software to include a blood volume fraction V, which adds a fraction of the arterial activity concentration into the tissue concentration. Approach. The tissue and blood time-activity curves (TACs) used for fit input were generated using ideal equations with known values in MATLAB. This allowed post-fit results to be compared to known values to determine fit errors. Parameters that were varied in generating the TACs included blood volume fraction (0, 0.05, 0.1, 0.2 and 0.3), K 1 (0.5, 1.5, 2.5 ml min−1 g−1), frame length (1, 2, 5, 10, 15, 20 s), FWHM of the input Gaussian (10, 20, 40 s), and time of the injection peak relative to frame duration. Blood volume-fraction results have low error when blood volume is lowest, but results worsen as frame length and K 1 increase. Main results. We demonstrated that blood volume can be accurately determined, and also show how fit accuracy varies across TACs with different input properties. Significance. This information allows for robust use of the fitting algorithm and aids in understanding fit performance when used in animal studies.

Existence analysis of hydraulic conductivity representative elementary volume in fractured rocks based on three-dimensional discrete fracture network method

The complexity of fractures in rocks results in heterogeneity and anisotropy of their hydraulic properties. The existence of hydraulic conductivity representative elementary volume (KREV) is a fundamental question in comprehending the hydraulic behavior of fractured rock masses. This article introduces an analytical procedure for determining the existence of KREV in fractured rock masses, which considers the distribution of underground apertures using a discrete fracture network (DFN) model. The procedure follows four basic steps: inverse modeling to generate a 3D DFN; calibration of the effective aperture by combining the results of packer tests and flow simulation using finite difference methods; calculation of the equivalent conductivities in 10 domains and 31 different flow directions; analysis of the fit error between the conductivity ellipsoid to determine the existence of KREV. The proposed approach is applied to the fractured rocks surrounding the 3013 exploration adit near the underground powerhouse of the Three Gorges Project as a case study. The results show that most fractures in the study area have apertures ranging from 0.06 to 0.25 mm and follow a lognormal distribution. The KREV size is 14–16 times the fracture spacing. Investigation of this model has significant implications for understanding flow through fracture networks in related fields.

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

Three dimensional (3D) robot measurement systems, with binocular planar structured light cameras (3D cameras) installed at the terminal flange of robots, are widely used to measure complete workpieces by stitching point clouds obtained from various sampled poses. To ensure accurate stitching, hand–eye calibration is necessary. However, 3D cameras often demonstrate low precision in measuring jumping edges and vertices, resulting in challenges when selecting appropriate calibrators and calibration methods for hand–eye calibration. We propose a simple and high‐precision hand–eye calibration method for 3D robot measurement systems. Initially, a single sphere is utilized as the calibrator, and its poses are observed using specific robotic motions. Subsequently, the hand–eye calibration problem is formulated as a well‐known equation, , relying solely on the high‐precision relative pose of the robot. Finally, a novel simultaneous solution for and the corresponding closed‐form initial solution are introduced. Simulations and experiments confirm that the proposed method exhibits high noise resistance and achieves high‐precision calibration, even with a limited calibration data quantity. Compared with traditional methods, the fitting error of the proposed method can be reduced from over 0.9 mm to less than 0.6 mm.

Enhancing bio-hydrogen and bio-methane production of concentrated latex wastewater (CLW) by Co-digesting with palm oil mill effluent (POME): Batch and continuous performance test and ADM-1 modeling

This study aimed at investigating the biohydrogen and biomethane potential of co-digestion from palm oil mill effluent (POME) and concentrated latex wastewater (CLW) in a two-stage anaerobic digestion (AD) process under thermophilic (55 ± 3 °C) and at an ambient temperature (30 ± 3 °C) conditions, respectively. The batch experiments of POME:CLW mixing ratios of 100:0, 70:30, 50:50, 30:70, and 0:100 was investigated with the initial loadings at 10 g-VS/L. The highest hydrogen yield of 115.57 mLH2/g-VS was obtained from the POME: CLW mixing ratio of 100:0 with 29.0 of C/N ratio. While, the highest subsequent methane production yield of 558.01 mLCH4/g-VS was achieved from hydrogen effluent from POME:CLW mixing ratio of 70:30 0 with 21.8 of C/N ratio. This mixing ratio revealed the highest synergisms of about 9.21% and received maximum total energy of 19.70 kJ/g-VS. Additionally, continuous hydrogen and methane production were subsequently performed in a series of continuous stirred tank reactor (CSTR) and up-flow anaerobic sludge blanket reactor (UASB) to treat the co-substate. The results indicated that the highest hydrogen yield of POME:CLW mixing ratio at 70:30 of 95.45 mL-H2/g-VS was generated at 7-day HRT, while methane production was obtained from HRT 15 days with a yield of 204.52 mL-CH4/g-VS. Thus, the study indicated that biogas production yield of CLW could be enhanced by co-digesting with POME. In addition, the two-stage AD model under anaerobic digestion model no. 1 (ADM-1) framework was established, 9.10% and 2.43% of error fitting of hydrogen and methane gas between model simulation data and experimental data were found. Hence, this research work presents a novel approach for optimization and feasibility for co-digestion of POME with CLW to generate mixed gaseous biofuel potentially.

Multi-compartment head modeling in EEG: Unstructured boundary-fitted tetra meshing with subcortical structures.

This paper introduces an automated approach for generating a finite element (FE) discretization of a multi-compartment human head model for electroencephalographic (EEG) source localization. We aim to provide an adaptable FE mesh generation tool for EEG studies. Our technique relies on recursive solid angle labeling of a surface segmentation coupled with smoothing, refinement, inflation, and optimization procedures to enhance the mesh quality. In this study, we performed numerical meshing experiments with the three-layer Ary sphere and a magnetic resonance imaging (MRI)-based multi-compartment head segmentation which incorporates a comprehensive set of subcortical brain structures. These experiments are motivated, on one hand, by the sensitivity of non-invasive subcortical source localization to modeling errors and, on the other hand, by the present lack of open EEG software pipelines to discretize all these structures. Our approach was found to successfully produce an unstructured and boundary-fitted tetrahedral mesh with a sub-one-millimeter fitting error, providing the desired accuracy for the three-dimensional anatomical details, EEG lead field matrix, and source localization. The mesh generator applied in this study has been implemented in the open MATLAB-based Zeffiro Interface toolbox for forward and inverse processing in EEG and it allows for graphics processing unit acceleration.

Effects of pre-deformation and annealing on the mechanical properties and microstructure of miniaturized FeCrAl-Y-Si alloys

The evolution of mechanical properties and microstructure in FeCrAl alloys with added Y and Si was investigated using miniaturized specimens. The FeCrAl alloys underwent warm rolling, resulting in thicknesses of 1 mm and 2 mm, followed by annealing at temperatures ranging from 600 °C to 1050 °C. Mechanical properties were determined through three distinct methods: conventional uniaxial tensile testing (UTT), micron indentation hardness testing (MIHT), and small punch testing (SPT). Microstructure evolutions were characterized using various techniques, primarily focusing on electron backscatter diffraction (EBSD). Two types of precipitates containing Y, Si, and Al were observed during annealing. Fracture morphologies revealed that as the annealing temperature (AT) exceeded 900 °C, size effect and brittle failures became prominent in SPT specimens. Brittle failures in SPT can be attributed to the segregation of Y, Si, and Al along the grain boundaries, the size effect, and the dispersed distribution of grain sizes. The optimum inter-pass annealing temperature was identified as 700 °C based on grain orientation spread (GOS) maps. SPT and MIHT results were correlated with UTT results when conducted at room temperature (RT, 25 ℃), and a Hall-Petch equation was derived. It was observed that the correlation equations for SPT were significantly influenced by brittle failures, resulting in relatively larger fitting errors. However, these SPT correlation equations proved effective in predicting total elongation compared to MIHT correlation equations and the Hall-Petch equation. The Hall-Petch equation exhibited the lowest average fitting error though limited by deformed grains. MIHT exhibited the strongest correlation with UTT, particularly in terms of yield strength and ultimate tensile strength. The three fitting methods have distinct advantages and shortcomings in characterizing the mechanical properties of Y and Si-added FeCrAl alloys.

Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.

Nonlinear dynamic model for COVID-19 epidemics using the Gaussian distributed wiring small-world network technique

The coronavirus (COVID-19) pandemic began in late 2019, unleashing a severe human health crisis that lasted three years and caused immeasurable economic losses worldwide. Although the World Health Organization declared the end of its emergency phase in May 2023, formulating measures by predicting transmission trends of the epidemic is one of the crucial ways to monitor and contain this disease. On the other hand, epidemiological studies have shown that the spatial structure of the population is a vital factor in the spread of epidemics. When infectious diseases invade a region, epidemics are often transmitted through connectivity between hosts. Therefore, in this study, we propose a small-world network model to simulate the transmission of the COVID-19 epidemic. However, the small-world network is organized by Gaussian distributed wiring that satisfies the frequency-distance rule in human mobility theory. Based on its nonlinear dynamic properties, an epidemic prediction model is developed. We performed numerical simulations and evaluated the accuracy of the prediction model. The results show that a high level of accuracy was achieved. The prediction model is examined using the MoH Malaysia COVID-19 dataset. When compared, our model showed lower fitting errors and was closer to the actual curves than the SIR model.

Investigating the technical feasibility of magnetoencephalography during transcranial direct current stimulation.

Magnetoencephalography (MEG) can measure weak magnetic fields produced by electrical brain activity. Transcranial direct current stimulation (tDCS) can affect such brain activities. The concurrent application of both, however, is challenging because tDCS presents artifacts on the MEG signal. If brain activity during tDCS can be elucidated by MEG, mechanisms of plasticity-inducing and other effects of tDCS would be more comprehensively understood. We tested the technical feasibility of MEG during tDCS using a phantom that produces an artificial current dipole simulating focal brain activity. An earlier study investigated estimation of a single oscillating phantom dipole during tDCS, and we systematically tested multiple dipole locations with a different MEG device. A phantom provided by the manufacturer was used to produce current dipoles from 32 locations. For the 32 dipoles, MEG was recorded with and without tDCS. Temporally extended signal space separation (tSSS) was applied for artifact rejection. Current dipole sources were estimated as equivalent current dipoles (ECDs). The ECD modeling quality was assessed using localization error, amplitude error, and goodness of fit (GOF). The ECD modeling performance with and without tDCS, and with and without tSSS was assessed. Mean localization errors of the 32 dipoles were 1.70 ± 0.72 mm (tDCS off, tSSS off, mean ± standard deviation), 6.13 ± 3.32 mm (tDCS on, tSSS off), 1.78 ± 0.83 mm (tDCS off, tSSS on), and 5.73 ± 1.60 mm (tDCS on, tSSS on). Mean GOF findings were, respectively, 92.3, 87.4, 97.5, and 96.7%. Modeling was affected by tDCS and restored by tSSS, but improvement of the localization error was marginal, even with tSSS. Also, the quality was dependent on the dipole location. Concurrent tDCS-MEG recording is feasible, especially when tSSS is applied for artifact rejection and when the assumed location of the source of activity is favorable for modeling. More technical studies must be conducted to confirm its feasibility with different source modeling methods and stimulation protocols. Recovery of single-trial activity under tDCS warrants further research.

Forecasting the renewable energy consumption of Australia by a novel grey model with conformable fractional opposite-direction accumulation.

The accurate prediction of renewable energy consumption (REC) is of great significance to ensure energy security, reduce dependence on fossil energy, and promote sustainable economic and social development. In this paper, a novel grey model with conformable fractional opposite-direction accumulation (CFOA), abbreviated as the CFOGM (1,1) model, is proposed to forecast REC in Australia. The new model is discussed in detail with a new CFOA operation and the GM (1,1) model and can take full advantage of the information carried by the original data. The CFOGM (1,1) model has lower modeling error and better fitting and forecasting accuracy than other grey, Holt, and ARM models and can better capture the change trend of REC and achieve accurate prediction. The forecasting results present that the REC in Australia is 497-581 petajoules in 2021, 596-728 petajoules in 2022, and 715-912 petajoules in 2023, indicating that the REC in Australia is still accelerating.

ASIDS: A Robust Data Synthesis Method for Generating Optimal Synthetic Samples

Most existing data synthesis methods are designed to tackle problems with dataset imbalance, data anonymization, and an insufficient sample size. There is a lack of effective synthesis methods in cases where the actual datasets have a limited number of data points but a large number of features and unknown noise. Thus, in this paper we propose a data synthesis method named Adaptive Subspace Interpolation for Data Synthesis (ASIDS). The idea is to divide the original data feature space into several subspaces with an equal number of data points, and then perform interpolation on the data points in the adjacent subspaces. This method can adaptively adjust the sample size of the synthetic dataset that contains unknown noise, and the generated sample data typically contain minimal errors. Moreover, it adjusts the feature composition of the data points, which can significantly reduce the proportion of the data points with large fitting errors. Furthermore, the hyperparameters of this method have an intuitive interpretation and usually require little calibration. Analysis results obtained using simulated original data and benchmark original datasets demonstrate that ASIDS is a robust and stable method for data synthesis.

Comparative study of two equivalent circuit models for electrochemical impedance spectroscopy analysis of epoxy zinc-rich coatings

ABSTRACT In this paper, the impedance spectra of an epoxy zinc-rich coating (ZRC) and its free film immersed in 3.5% NaCl solution are investigated, the fitting error and model parameter trends of series and nested equivalent circuit fits are compared, and the differences in the high-frequency components of the ZRC and the epoxy coatings are analyzed. The results show that the high-frequency impedance of ZRC is the contact impedance between the zinc powders (Zm) rather than the film impedance (Zf). The corrosion reaction of the ZRCs is controlled by the diffusion of oxygen. For ZRCs, the series equivalent circuits are more reasonable compared to the nested equivalent circuit. Based on the series equivalent circuit, the variation law of the equivalent circuit elements (resistors, capacitors and dispersion coefficient) and the correlation of these elements with low-frequency impedance modulus (|Z|0.01Hz) and open circuit potential (OCP) are investigated in this paper.