Curve Fitting Method Research Articles

Associations between the waist-to-height ratio index and migraine: A cross-section study of the NHANES 1999-2004.

The importance of obesity as a factor that increases the probability of migraine episodes is increasingly acknowledged. Thus, this study aimed to explore the potential correlation between central obesity and migraine, emphasizing the waist-to-height ratio (WHtR) as a key measure in assessing this relationship. This cross-sectional analysis included 13,344 individuals who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999-2004. To investigate the association associations between WHtR and migraine, we utilized refined multivariate logistic regression models, smoothing curve fitting methods, subpopulation analysis, and interactive testing. Of the 13,344 participants, 2,764 (20.72%) had migraines. A significant positive correlation was observed between the WHtR and migraine incidence in both the partially adjusted model (3.08 [95% CI: 1.92-4.94]) and the crude model (1.95 [95% CI: 1.23-3.08]). The participants in the highest quartile of the WHtR had a 13% greater incidence of migraine than those in the lowest quartile [1.13(0.99,1.28)]. The interaction analysis revealed a statistically significant difference (p<0.01) in this relationship among the subgroups. Notably, the correlation between WHtR and migraine risk was not significant and negative in patients ≥60 years, indicating that obesity has a mitigating role in preventing migraine in this elderly population. The incidence of migraine increased concomitantly with increased WHtR. However, central obesity has a protective effect against migraine occurrence in individuals ≥60 years. Thus, our findings underscore the importance of WHtR in migraine prevention and management strategies and highlight its potential as a critical biomarker for mitigating migraine incidence.

Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.

Modelling of a Cylindrical Battery Mechanical Behavior under Compression Load

The extensive utilization of lithium-ion (Li-ion) batteries within the automotive industry necessitates rigorous measures to ensure their mechanical robustness, crucial for averting thermal runaway incidents and ensuring vehicle safety. This paper introduces an innovative methodology aimed at homogenizing the mechanical response of Li-ion batteries under compression load, using Finite Element Method (FEM) techniques to improve computational efficiency. A novel approach is proposed, involving the selective application of compression loads solely to the Jelly Roll and its casing, achieved by cutting the battery heads. Through this method, distinct mechanical behaviors are identified within the battery force displacement curve: an elastic region, a zone characterized by plastic deformation, and a segment exhibiting densification. By delineating these regions, our study facilitates a comprehensive understanding of the battery’s mechanical response under compression. Two battery models were employed in this study: one representing the battery as a solid volume, and another featuring the jelly roll as a solid volume enclosed by a shell representing the casing. The material utilized was LS Dyna MAT24, chosen for its piecewise characteristics’ definition, and its validation was primarily conducted through the curve fitting method applied to the force–displacement curve, taking in account the three regions of the compression force behavior. This approach not only optimizes computational resources but also offers insights crucial for enhancing the mechanical stability of Li-ion batteries in automotive applications.

Increased virtual resolution for sub-pixel displacement algorithm optimization in digital image correlation for AISI 1020 steel

The development of the digital image correlation (DIC) technique has required the use of cameras with increasingly higher resolution, as well as requiring complex sub-pixel tracking algorithms with exponentially increasing computational cost. This article proposes a DIC algorithm based on the correlation coefficient curve-fitting method, minimizing SSD or ZNSSD correlation criteria, with the help of increasing the virtual resolution of the images through bicubic interpolation, for obtain an accurate, robust and reliable algorithm according to the highest computational efficiency. The developed algorithm was virtually validated using synthetic images and experimentally using AISI 1020 steel samples test in tension, performed with strain gauges. The results showed a reduction in the error observed in synthetic images with increased magnification and good agreement with the deformation data obtained using the conventional extensometry technique.

Impact of Data Corruption and Operating Temperature on Performance of Model-Based SoC Estimation

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

Research on dynamic performance and mechanical model of a novel magnetorheological shear thickening damper with adaptive variable damping gap

Abstract Magnetorheological shear thickening fluid (MRSTF) is an intelligent composite material, which shows considerable potential in semi-active vibration control. However, the current research is mostly limited to the study of the rheological properties of materials, and there is still a lack of damper structures that can effectively utilize the dual-control characteristics of MRSTF. In this paper, a novel damper structure with adaptive adjustable damping gap length is proposed. Its unique design integrates an adaptive internal damping gap that changes with the movement of the piston and an external gap controlled by the magnetic field, providing a relatively wide range of current-controlled damping force and reliable adaptive adjustment capabilities. Based on the M-S shear stress constitutive model and structural characteristics, the mechanical model is derived from the force balance of the micro-element, and verified by performance test and curve fitting. The effectiveness of the new damper structure proposed in this paper is verified by dynamic performance test. The results show that the damper has a wide range of current controlled damping force at low speed and reliable adaptive adjustment capability at high speed. Finally, the accuracy and universality of the modified mechanical model of damper are verified by curve fitting method. All these studies can offer an effective guidance for further application of MRSTF in the field of semi-active vibration control.

Synthetic minority oversampling and iterative fluorescence-suppression integrated algorithm for Raman spectrum pesticide detection system

Raman spectrum preprocessing method for automatic denoising and suppression of the fluorescent background. In this method, noise is reduced using wavelet transform, and a modified polynomial curve fitting method is implemented such that an algorithm can independently identify the optimal curve parameters for fluorescent background suppression. To address the problem of imbalanced datasets, the present study employed a synthetic minority oversampling technique to increase the volume of data in minority classes. This technique enables the prediction of pesticides that are otherwise difficult to detect, and the prediction accuracy is comparable to that of detection with large data volumes. The proposed convolutional neural network model was verified to accurately identify the type of single pesticides and composition of mixed pesticides. The prediction accuracy for mixed pesticides reached 99.1%.

Determination of Protein-Ligand Binding Affinities by Thermal Shift Assay.

Quantification of protein-ligand interactions is crucial for understanding the protein's biological function and for drug discovery. In this study, we employed three distinct approaches for determination of protein-ligand binding affinities by a thermal shift assay using a single ligand concentration. We present the results of the comparison of the performance of the conventional curve fitting (CF) method and two newly introduced methods - assuming zero heat capacity change across small temperature ranges (ZHC) and utilizing the unfolding equilibrium constant (UEC); the latter has the advantage of reducing calculations by obtaining the unfolding equilibrium constant directly from the experimental data. Our results highlight superior performance of the ZHC and UEC methods over the conventional CF method in estimating the binding affinity, irrespective of the ligand concentration. In addition, we evaluated how the new methods can be applied to high-throughput screening for potential binders, when the enthalpy (ΔH L) and molar heat capacity change (ΔC PL) of ligand binding are unknown. Our results suggest that, in this scenario, using the -300 cal K-1 mol-1 assumption for ΔC pL and either -5 kcal mol-1 or the average enthalpy efficiency-based estimation for ΔH L(T) can still provide reasonable estimates of the binding affinity. Incorporating the new methods into the workflow for screening of small drug-like molecules, typically conducted using single-concentration libraries, could greatly simplify and streamline the drug discovery process.

A method for compressing AIS trajectory based on the adaptive core threshold difference Douglas–Peucker algorithm

Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect.

The response of mesophyll conductance to short-term CO2 variation is related to stomatal conductance.

The response of mesophyll conductance (gm) to CO2 plays a key role in photosynthesis and ecosystem carbon cycles under climate change. Despite numerous studies, there is still debate about how gm responds to short-term CO2 variations. Here we used multiple methods and looked at the relationship between stomatal conductance to CO2 (gsc) and gm to address this aspect. We measured chlorophyll fluorescence parameters and online carbon isotope discrimination (Δ) at different CO2 mole fractions in sunflower (Helianthus annuus L.), cowpea (Vigna unguiculata L.), and wheat (Triticum aestivum L.) leaves. The variable J and Δ based methods showed that gm decreased with an increase in CO2 mole fraction, and so did stomatal conductance. There were linear relationships between gm and gsc across CO2 mole fractions. gm obtained from A-Ci curve fitting method was higher than that from the variable J method and was not representative of gm under the growth CO2 concentration. gm could be estimated by empirical models analogous to the Ball-Berry model and the USO model for stomatal conductance. Our results suggest that gm and gsc respond in a coordinated manner to short-term variations in CO2, providing new insight into the role of gm in photosynthesis modelling.

An Image Processing-Based Correlation Method for Improving the Characteristics of Brillouin Frequency Shift Extraction in Distributed Fiber Optic Sensors

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (Brillouin optical time domain analysis/reflectometry) principles. First, the spectra corresponding to different spatial coordinates of the fiber sensor are resampled. Subsequently, the resampled spectra are aligned by the position of the maximum by shifting in frequency relative to each other. The spectra aligned by the position of the maximum are then averaged, which effectively increases the signal-to-noise ratio (SNR). Finally, the Lorentzian curve fitting (LCF) method is applied to the spectrum with improved characteristics, including a reduced scanning step and an increased SNR. Simulations and experiments have demonstrated that the method is particularly efficacious when the signal-to-noise ratio does not exceed 8 dB and the frequency scanning step is coarser than 4 MHz. This is particularly relevant when designing high-speed sensors, as well as when using non-standard laser sources, such as a self-scanning frequency laser, for distributed fiber-optic sensing.

Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants' fundus images.

To develop a computer-aided diagnostic system for retinopathy of prematurity (ROP) disease using retinal vessel morphological features. A total of 200 fundus images from 136 preterm infants with stage 1 to 3 ROP were analysed. Two methods were developed to measure vessel tortuosity: the peak-and-valley method and the polynomial curve fitting method. Correlations between temporal artery tortuosity (TAT) and temporal vein tortuosity (TVT) with ROP severity were investigated, and vessel tortuosity relationships with vessel angles (TAA and TVA) and vessel widths (TAW and TVW). A separate dataset from Japan containing 126 images from 97 preterm patients was used for verification. Both methods identified similar tortuosity in images without ROP and mild ROP cases. However, the polynomial curve fit method demonstrated enhanced tortuosity detection in stages 2 and 3 ROP compared to the peak and valley method. A strong positive correlation was revealed between ROP severity and increased arterial and venous tortuosity (P < 0.0001). A significant negative correlation between TAA and TAT (r = -0.485, P < 0.0001) and TVA and TVT (r = -0.281, P < 0.0001), and a significant positive correlation between TAW and TAT (r = 0.204, P value = 0.0040) were identified. Similar results were found in the test dataset from Japan. ROP severity was associated with increased retinal tortuosity and retinal vessel width while displaying a decrease in retinal vascular angle. This quantitative analysis of retinal vessels provides crucial insights for advancing ROP diagnosis and understanding its progression.

Rheological Behavior of Drilling Muds and Developing a Rheological Model

In our research described here, a rheological model was proposed to be used to determine the rheological behavior of drilling mud, which is one of the most important factors in determining a suitable mud used in drilling. The parameters of the rheological model were determined by a curve fitting method using more than 95 experimental data pairs available in the literature for seven different viscoelastic drilling muds. The relationship between shear stress and shear rate obtained from the proposed model was compared with the relationships obtained from the Herschel–Bulkley, Power-law, and Bingham plastic models. The data obtained from the proposed model was also compared with the experimental data existing in the related literature. The average percentage differences between the experimental data and the data obtained from the proposed model for the drilling fluids were 5.16, 0.623, and 8.8, respectively, which were considerably lower than the average differences between the experimental data and the data acquired from the three mentioned models. The Power-law model outperformed our proposed model for only three drilling muds. As a result, we suggest that the rheological model proposed here can be easily used to determine the rheological properties of some kinds of drilling muds. In addition to deriving the rheological model for drilling muds, a comprehensive literature review on the subject was conducted.

DS based 2-DOF PID controller for various integrating processes with time delay

This study proposes a direct synthesis-based two-degree-of-freedom (2-DOF) controller for various types of integrating processes with time delays. This 2-DOF controller includes a proportional-integral-derivative (PID) controller to enhance load disturbance rejection performance and a set-point filter to improve servo response performance. The main PID controller parameters are expressed as process model parameters and a single adjustment variable, while the set-point filter is composed of PID controller parameters with weighted factors. The adjustment variable is tuned to achieve an optimal balance between response performance and robustness, based on the maximum magnitude of the sensitivity function (Ms). Controller parameters for various Ms values and guidelines for setting these parameters are provided in a consistent formulaic form using a curve-fitting method. These parameter-setting formulas facilitate the accurate implementation of PID controllers with specified Ms values and allow the controller design to be extended to processes with larger dimensionless time delays for a given Ms value. Although a 2-DOF controller was proposed, the adjustment variable for setting the parameters of the main PID controller and the set-point filter was solely the desired time constant. The proposed method was applied to various integrating processes with time delays, and its performance was compared with existing methods reported in the literature, based on performance indices such as settling time, overshoot, integral of absolute error, total variation in input usage, and global performance index. Simulations were conducted using six examples of various integrating processes with time delays to verify the effectiveness and applicability of the proposed controller.

Beta radiation-induced thermoluminescence in pure and rare earth doped ZrO2 prepared by pechini-type sol-gel

Thermoluminescence properties of ZrO2, ZrO2:Gd3+, ZrO2:Gd3+–Dy3+, ZrO2:Gd3+–Yb3+, ZrO2:Gd3+–Er3+ and ZrO2:Gd3+–Sm3+ phosphors synthesized by Pechini method were investigated. XRD measurement suggested that all the samples are monoclinic and tetragonal multiphase. Crystallite size measured using SEM is around 200 nm–250 nm. The Tmax–TSTOP, variable dose and glow curve fitting methods were applied to the phosphors and reusability, dose responses and fading characteristics were examined. The phosphors exhibit a natural TL emission which disappeared after heat treatment. The glow curves of the pure ZrO2 have thirteen individual glow peaks, which include the peaks reported for the monoclinic and tetragonal phases. Although some peaks emerge at low irradiation doses, others can only be observed at high doses. Moreover, although the dopes blanched some of the peaks of pure ZrO2, they made others more intense. These peaks can be interpreted as if they emerged caused by the rare earth doping into the matrix material. On the other hand, Gd3+, Dy3+, Yb3+, Er3+ and Sm3+ dopes did not alter the peak temperatures of the matrix material. As a result, the individual peak intensities are very sensitive to the dopes. Fundamental trapping parameters of the trap levels were calculated, and the results are in good agreement with the given values of activation energies in the literature. Fading experiments show the materials have both normal and abnormal fading characteristics in long and short-term storage.

Power Factor Analysis of Grid-Connected Solar Inverter under Different Irradiance Levels throughout the Day

The power factor (PF) plays a crucial role in determining the quality of energy produced by grid-connected photovoltaic (PV) systems. When irradiation levels are high, typically during peak sunlight hours, the PV panels generate more electricity. In this scenario, the PF tends to be higher because the real power output closely matches the apparent power drawn from the grid. Whereas, when irradiation levels are low, such as during cloudy weather or nighttime, the PV panels produce less electricity. In these conditions, the power factor may decrease because the real power output diminishes compared to the apparent power drawn from the grid. This could be due to reduced efficiency or increased reactive power flow. PF decreases linearly at solar irradiance values lower than 220 (W/m2). At the same time, it approaches unity at higher solar irradiance values than 220 (W/m2). In this study, the variation of the power coefficient of the grid-connected PV solar system depending on solar irradiation was modeled and analyzed using MATLAB/Simulink 41016490. The analytical expression of the power factor depending on the change in solar irradiation was found using the curve fitting method.

Enhancing practical modeling: A neural network approach for locally-resolved prediction of elastohydrodynamic line contacts

When modeling bearings in the context of entire transmissions or drivetrains, there are practical limits to the calculation resources available to calculate single bearings or even contacts. In settings such as these, curve-fitting methods have historically been deployed to estimate the elastohydrodynamic lubrication conditions. Machine learning methods have the potential to enable more sophisticated physical modeling in the context of larger computation environments, as the evaluation time of a trained model is typically negligible. We present a neural network that accurately evaluates the locally variable elastohydrodynamic film pressure and film thickness distributions and explore its application to (e.g.) cylindrical roller bearings. Employing a neural network for the EHL film thickness calculations rather than the curve-fitted, simplified methods that are today’s standard can enable a more physically precise modeling strategy at almost no additional computational cost.

Ultrafast measurement of photoacoustic parameters with mid-infrared frequency comb transients.

The photoacoustic (PA) effect has been widely used in various applications, including highly sensitive spectroscopy and label-free, non-invasive imaging. In this work, we demonstrate a fast and precise measurement of PA parameters for light-absorbing liquids using mid-infrared asynchronous sampling pump-probe measurements. To simulate the observed PA oscillation signals and extract various PA parameters as a function of pump power, we derived analytical solutions of the PA wave equation driven by a train of ultrashort Gaussian pump pulses. By fitting the analytical solution to the measured PA signals using a nonlinear curve fitting method, we could measure the PA parameters, including damping rate, viscosity, and natural frequency. Furthermore, the dynamic response of thermophysical properties of the chloroform solution is also investigated by measuring the variation of the Grüneisen parameter with pump power. We anticipate that this work will open new possibilities for precise in situ measurements of the thermal properties of light-absorbing liquids.

Direct Field Curve Fitting Method to Determine Hardening Soil Parameter for Geotechnical Projects

Abstract In a wide archipelago country like Indonesia, transporting soil samples for laboratory tests are quite a bit of challenge in terms of cost and time as those need to be transported for weeks to nearest big cities. In-situ tests and correlation methods can be used as an alternative solution for preliminary or detailed design beside having on site laboratory. Correlating in-situ test results to hardening soil parameter (e.g. E 50) is common but it is not clear how those should be adjusted for Plaxis input. Two deep excavation projects and a lateral pile load test are used to see the performance of direct field curve fitting method utilizing SPT correlation to HS parameter. From the analysis, using E 50=4000N for clay soils and E 50=2800N for sand soils as upper bound parameters produce the best results with lesser discrepancy with the field measurements compared to calculations with lower bound parameters. For the deep excavation cases (Case 1 and Case 2), calculated lateral displacement results using upper bound parameters overestimates inclinometers measurement for less than 1.5 cm for both cases. For the case of lateral pile load test (Case 3), calculation using upper bound parameters succeed to capture a high detail of unloading-reloading curves similar to the actual measurement. This method can be used as a technique to design geotechnical projects and it proposes a framework for future research regarding a new possibility to establish various E 50 correlations.

Measurement error in hydrostatic leveling system due to temperature effect and their reduction method.

Temperature is a crucial factor influencing the accuracy of the hydrostatic leveling system (HLS), necessitating a temperature compensation test for HLS. This paper investigates HLS temperature compensation through theoretical correction and experimental verification. The influence of temperature on the accuracy of hydrostatic level products is determined through temperature tests on 34 hydrostatic level products. The optimal temperature compensation formula is derived using the non-linear curve fitting method. The HLS is enhanced with a temperature compensation algorithm and temperature sensor, resulting in a new, high-precision, and high-stability hydrostatic leveling product with temperature compensation. Experimental findings reveal that the stability of the improved hydrostatic leveling product exceeds 30% compared to products on the market and surpasses 70% compared to the original products.