Fitting Method Research Articles

The influence of b-values, noise levels, range of parameters, and ROI characteristics on the bi-exponential IVIM model and its fitting methods

The influence of b-values, noise levels, range of parameters, and ROI characteristics on the bi-exponential IVIM model and its fitting methods

Artificial Intelligence-based Estimation of Fetal Head Circumference with Biparietal and Occipitofrontal Diameters Using 2D Ultrasound Images

Abstract One of the most essential biometrics for measuring fetal growth during prenatal ultrasound exams is the head circumference (HC). However, manual measurement of this biometric by doctors often needs substantial experience. We developed a state-of-the-art image processing algorithm that utilizes biometry and segmented image and employed a fast ellipse fitting method to measure the head circumference (HC), head orientation (angle) along with biparietal (BPD), and occipitofrontal (OFD) diameter automatically. Additionally, To the best of our knowledge, this is the first work that adopts a segmented image from any model or technique and provides significant fetal parameters during ultrasonography in a single shot. The suggested technique is lightweight, real-time, and easily integrate with any segmentation model or technique. We have used three well-known segmentation models for demonstration and compared their performance using dice scores as an evaluation parameter. Among the three, HRNET shows the best results with an average dice score of 0.96; due to the high dice score, we have chosen HRNET over the other two models and proceeded further and implemented our novel algorithm on that to predict the required fetal key measurements. The study's findings showed that our proposed technique estimates fetal head circumference along with biparietal and occipitofrontal diameter, which has significant use in fetal analysis and monitoring.

Null Phase Assumption-Based Technique for Constructing the Target Model of Seismic Irregularity on High-Speed Railways

High-speed railways are “lifeline projects” that shoulder the heavy responsibility of transporting relief supplies and medical forces for the first time after earthquakes. To ensure the train’s safety after earthquakes, it is of great urgency to ascertain a post-earthquake speed threshold. To that end, a target model for seismic irregularity emerges as a key parameter. In this paper, a null phase assumption-based technique for the mutual conversion between evolutionary power spectral density and non-stationary signal was proposed. Taking a high-speed railway track-bridge system as the research object, the target model of seismic irregularities was constructed based on the proposed technique. The rationality of the target model of seismic irregularities was verified, and the construction parameter settings were discussed. Moreover, a simplified frequency-domain fitting method for the target model of seismic irregularities was proposed based on the spectral decomposition theory. According to the research findings, the null phase assumption-based technique is capable of performing interconversion between seismic irregularity and its evolutionary power spectral density with satisfactory accuracy. It is recommended to set the minimum number of seismic irregularities, spatial sampling interval, hop size, and length of window function as 50, 0.25[Formula: see text]m, 1, and 40 to 100, respectively.

Fast Three-Dimensional Profilometry with Large Depth of Field.

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time-domain Gaussian fitting method is proposed in this paper. The concept of a time-domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time-domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time-domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.

Extraction of Moso Bamboo Parameters Based on the Combination of ALS and TLS Point Cloud Data.

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35-0.48 m, while the R2 of the DBH fit was increased to a range of 0.97-0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001-0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.

Internal Temperature Estimation of Lithium Batteries Based on a Three-Directional Anisotropic Thermal Circuit Model

In order to improve the accuracy of internal temperature estimation in batteries, a 10-parameter time-varying multi-surface heat transfer model including internal heat production, heat transfer and external heat transfer is established based on the structure of a lithium iron phosphate pouch battery and its three directional anisotropic heat conduction characteristics. The entropy heat coefficient, internal equivalent heat capacity and internal equivalent thermal resistance related to the SOC and temperature state of the battery were identified using experimental tests and the least square fitting method, and were then used for online calculation of internal heat production and heat transfer in the battery. According to the time-varying and nonlinear characteristics of the heat transfer between the surface and the environment of the battery, an internal temperature estimation algorithm based on the square root cubature Kalman filter was designed and developed. By iteratively calculating the estimated surface temperature and the measured value, dynamic tracking and online correction of the internal temperature of the battery can be achieved. The verification results using FUDS and US06 dynamic working condition data show that the proposed method can quickly eliminate the influence of initial temperature deviations and accumulated process errors and has the characteristics of a high estimation accuracy and good robustness. Compared with the estimation results of the adaptive Kalman filter, the proposed method improves the estimation accuracy of FUDS and US06 working conditions by 67% and 54%, respectively, with a similar computational efficiency.

Electroluminescence and infrared imaging of fielded photovoltaic modules: A complementary analysis of series resistance-related defects

Fielded photovoltaic (PV) modules often exhibit series resistance-related defects, mainly due to solder bond degradation and metallization corrosion. To quantitatively analyze the series resistance increase in fielded modules, we conducted a detailed investigation on two sets of modules, employing two complementary techniques, electroluminescence (EL) and infrared (IR) imaging. The dependence of EL image characteristics on module temperature, as revealed in IR images, was a key focus of this study. The first and second sets of modules were exposed in Florida (hot and humid climate) and Arizona (hot and dry climate) over 10 years and 18 years, respectively. The resistive defect patterns obtained using EL and IR images showed a closer correlation for the Florida modules compared to the Arizona modules as the Florida modules primarily experience solder bond degradation. EL and IR images were acquired at five current injection levels (i.e., 0.1, 0.3, 0.5, 0.7, 1.0 x short circuit current) and two exposure times (i.e., 60 s and 300 s) and used to develop and report a new curve fitting method for estimating the external series resistance. The results indicate that inaccurate temperature determinations from IR images can lead to underestimations (up to 23 %) in EL-based external series resistance estimates. For the most accurate series resistance estimation, especially in modules with severe thermal defects and series resistance deterioration, the study recommends obtaining EL and IR images within 60 s of the current injection time. This study also reports a Monte Carlo simulation assessing the impact of EL and IR characteristics on the accuracy of external series resistance estimations.

Experimental investigation of performance, emissions, combustion characteristics, cyclic variations, and exergy efficiency of CI engine fueled with Karanja-camphor oil blends and diesel-camphor oil blends.

This article compares the influence of blending the low-viscous oxygenated camphor oil with hydrocarbon diesel fuel and high-viscous oxygenated Karanja oil. The experiment is conducted in a four-stroke one-cylinder naturally aspirated Kirloskar compression ignition (CI) engine coupled with an eddy current dynamometer. The three types of fuel blends are prepared by blending the camphor oil with Karanja oil on the volume ratio of 30:70 (C30K70), 50:50 (C50K50), and 70:30 (C70K30), and the other three types of fuels are prepared by blending the camphor oil with diesel on the volume ratio of 30:70 (C30D70), 50:50 (C50D50), and 70:30 (C70D30). The experimental efficiency results show higher thermal efficiency of 31.86% and 30.84% for C70D30 and C70K30 at rated operating conditions. The brake-specific energy consumptions of C70D30 and C70K30 were found to be 11.29 and 11.67MJ/kWh, respectively, at rated operating conditions. The lowest CO, CO2, HC, and smoke emissions are found for C70D30 at rated operating conditions, which are 96.58%, 6.15%, 34.20%, and 7.59% lower than diesel. However, the NO emissions were found to be 27.62% higher for C70D30 than diesel at full load. The rate of pressure rise, net heat release rate, and cyclic variations were found to increase with increase in proportion of the camphor oil. The P-v diagram also confirms the lower heat addition period for the C70D30 and C70K30 with an increase in brake thermal efficiency. The actual compression ratio and the actual cutoff ratio are found to have a reasonable correlation with the thermal efficiency of the engine. The exergy efficiency of C30K70 is found higher which is 2.11% higher than diesel fuel at rated power. Second-order polynomial equations were obtained for the engine characteristics using the curve fitting method, and the characteristic equations confirmed the confidence level of 95%.

Performance tweaking of Co3O4 UV radiation detector via zirconium dopant microstructural and photo-electronic engineering

Incorporation of dopant impurities in semiconducting metal oxide nanostructures has been presumed to increase the pace at which photo-induced holes and electrons separate due to possible bandgap engineering and oxygen vacancy modulations. In this study, we have adopted an electrodeposition technique using a three-electrode configuration for the preparation of Co3O4 thin film samples and investigated the effect of Zr as a dopant ion on the UV radiation sensing performance of nanostructured Co3O4 host material via energy band engineering and microstructural modulation. Consistency in optical energy band gap measurements of the deposited Zr-doped Co3O4 (Zr@Co3O4) was unveiled with the aid of two models: Tauc’s and absorption spectrum fitting (ASF) methods. Ag/Zr@Co3O4/ITO/glass UV radiation detector was fabricated and a sample with 3 % Zr-dopant content demonstrated high UV-365 nm detector performance: detectivity, 1.2 ×1011 Jones, and responsivity, 1818 mA W−1, with high cycling stability, attributable to the host's dopant microstructural tailoring, and the enhancement in its electronic charge carrier number and their scattering potentials within the host's band structure, thanks to the synergistic impacts of dopant ion incorporation and UV irradiation.

A new method for predicting consolidation settlements of soft ground based on monitoring results

Excessive settlement or differential settlement of subgrade will lead to the deterioration of line operational conditions, the reduction of passenger comfort, and even endanger the safety of traffic. Therefore, it is of great significance to study the settlement prediction of subgrade. In order to predict the settlement of foundation under the next level of loading earlier during the embankment construction process, a new method of predicting settlement of soft soil subgrade is proposed. Firstly, based on monitoring results of soft soil foundation, the consolidation parameters of soil layer are back-calculated according to the three-point method. Then, combined with the theory of the consolidation degree of graded loading, the formula that can predict settlement under different loading conditions are derived. Eventually, the practical application of the method is verified by the prediction and comparative analysis of measured settlements based on engineering examples. The result of research shows that the method can predict the foundation settlement after loading during construction of engineering fill. This method has obvious advantages over the traditional curve fitting method and can guide the actual engineering construction.

Electromechanical properties of BS-PT ceramics under uniaxial pressure and hydrostatic pressure

The increasing demands of device applications in harsh environments have led to higher expectations for temperature and pressure resistance in high-temperature piezoelectric materials. In order to understand the performance characteristics of BiScO3-PbTiO3 (BS-PT) high-temperature piezoelectric materials in practical device applications, this study focuses on analyzing the dielectric, piezoelectric, and ferroelectric properties of BS-64PT ceramics under uniaxial stress up to 150 MPa, as well as its electromechanical performance under a hydrostatic pressure of up to 400 MPa. As the uniaxial pressure increases, both the bias-field and large-signal piezoelectric coefficients exhibit a pattern of initially increasing and subsequently decreasing. Furthermore, the bias-field piezoelectric coefficient exceeds 450 pC/N and the large-signal piezoelectric coefficient surpasses 630 pC/N under uniaxial pressures below 100 MPa. This highlights their exceptional resistance to depolarization caused by uniaxial stress. To obtain more precise piezoelectric properties for BS-64PT ceramics in the 31 and 33 modes under hydrostatic pressure, the admittance fitting method was utilized. This method takes into account the significant losses at higher pressures. Within the pressure range of 0–400 MPa, the values of d33 and d31 for BS-64PT ceramics exhibited a minor change of 7.6% and 8%, respectively. These findings indicate that BS-64PT ceramics exhibit more stable piezoelectric properties under both uniaxial and hydrostatic pressures when compared to the majority of Pb-based perovskite-structured materials. The exceptional stability of piezoelectric properties in BS-PT ceramics can be primarily attributed to their elevated anisotropy energy or coercivity field compared to other perovskite-structured Pb(Mg1/3Nb2/3)O3 (PMN) / Pb(Zr,Ti)O3 (PZT)-based ceramics reported.

The wave-like disc oscillations of mono-age stellar populations in the Solar neighbourhood from Gaia DR3

ABSTRACT The North–South asymmetry in the number density and bulk velocity of stars in the Solar neighbourhood provides valuable insights into the formation and evolution of the Milky Way disc. Our objective is to investigate the wave-like disc oscillations of mono-age stellar populations in the Solar neighbourhood using data from Gaia Data Release 3. We have selected a comprehensive sample of main-sequence turn-off stars. The ages of these stars can be accurately determined using isochrone fitting methods. Our findings indicate that the North–South density and mean vertical velocity asymmetries remain consistent across all age groups. The uniformity of perturbations across all subsamples suggests that all populations are responding to the same external influence, which likely affects them irrespective of their age. Moreover, the fact that these perturbations appear consistently implies they could be either ongoing or recent. Regarding vertical velocity dispersions, we observe that older stars exhibit larger dispersions.

Robust inverse parameter fitting of thermal properties from the laser-based Ångstrom method in the presence of measurement noise using physics-informed neural networks (PINNs)

The two-dimensional laser-based Ångstrom method measures the in-plane thermal properties for anisotropic film-like materials. It involves periodic laser heating at the center of a suspended film sample and records its transient thermal response by infrared imaging. These spatiotemporal temperature data must be analyzed to extract the unknown thermal conductivity values in the orthotropic directions, an inverse parameter fitting problem. Previous demonstration of the metrology technique used a least-squares fitting method that relies on numerical differentiation to evaluate the second-order partial derivatives in the differential equation describing transient conduction in the physical system. This fitting approach is susceptible to measurement noise, introducing high uncertainty in the extracted properties when working with noisy data. For example, when noise of a signal-to-noise ratio of 10 is added to simulated amplitude and phase data, the error in the extracted thermal conductivity can exceed 80%. In this work, we introduce a new alternative inverse parameter fitting approach using physics-informed neural networks (PINNs) to increase the robustness of the measurement technique for noisy temperature data. We demonstrate the effectiveness of this approach even for scenarios with extreme levels of noise in the data. Specifically, the PINN-approach accurately extracts the properties to within 5% of the true values even for high noise levels (a signal-to-noise ratio of 1). This offers a promising avenue for improving the robustness and accuracy of advanced thermal metrology tools that rely on inverse parameter fitting of temperature data to extract thermal properties.

Exploring groundwater depletion and land subsidence dynamics in Taiwan’s Choushui river alluvial fan: insights from integrated GNSS and hydrogeological data analysis

The Choushui River Alluvial Fan (CRAF) is a major agricultural area in Taiwan with heavy groundwater usage. The extraction of groundwater here has caused land subsidence, which is now a significant global environmental issue. This study analyzes land subsidence in the CRAF by integrating hydrogeological data from 233 groundwater monitoring stations across four aquifers (CRAF Groundwater_NET) and data from 50 continuous GNSS stations (CRAF GNSS_NET). We developed an automated processing flow for GNSS static surveying within CRAF GNSS_NET, and further employed a time-series fitting method to examine the long-term trends and annual changes for both GNSS and groundwater level data. Our analysis of the time-series data from the past decade identifies areas of significant groundwater level depletion and subsidence hotspots. We explore the relationship between groundwater level variations and surface displacements within CRAF, utilizing GNSS data to analyze horizontal and vertical displacement trends, as well as annual changes. We integrate these findings with hydrogeological data to understand regional subsidence patterns. Our results indicate that CRAF is characterized by distinct hydrogeological features. The study finds that the amplitudes of annual changes in both groundwater level and vertical displacement generally increase from northeast to southwest in the analyzed region. One particular area shows significant groundwater level decline, with the most severe rate recorded at 0.54 m/year. Similarly, GNSS analysis indicates pronounced subsidence trends in the same area, with rates ranging from 4.2 to 5.2 cm/year. These findings highlight the critical need for the development of effective groundwater management strategies to ensure sustainable use of groundwater resources and to implement mitigation measures against land subsidence in similar multiple-aquifer settings.

On the Onset of Plasticity: Determination of Strength and Ductility

The analysis of the work hardening variation with stress reveals insight to operative stress-strain mechanisms in material systems. The onset of plasticity can be assessed and related to ensuing plastic deformation up to the structural instability using one constitutive relationship that incorporates both behaviors of rapid work hardening (Stage 3) and the asymptotic leveling of stress (Stage 4). Results are presented for the mechanical behavior analysis of Ti-6Al-4V wherein the work hardening variation of Stages 3 and 4 are found to: be dependent through a constitutive relationship; be useful in a Hall-Petch formulation of yield strength; and provide the basis for a two point-slope fit method to model the experimental work hardening and stress-strain behavior.

Effect of mixed carbon phase state on detonation parameters and reaction zone of explosives under extreme condition

A precise description of the thermodynamic states of gaseous and solid detonation products is essential when using thermodynamic calculations to determine the detonation performance and destructive power of explosives. For high oxygen-lean explosives (the oxygen contained in explosives is insufficient to completely oxidize combustible elements and excess solid carbon will be generated in the detonation products), the phase state of solid carbon product affects the Chapman–Jouguet (CJ) detonation performance parameters, reaction zone, and energy release process. However, the recovery of detonation products demonstrates that the actual detonation carbon product is primarily a mixed state of diamond/graphite stack, as opposed to the existing thermodynamic codes, which essentially treat detonation carbon as single-phase carbon. To understand the thermodynamic effect of the mixed carbon phase state on the non-ideal detonation behavior, in this work, the matching relationship among the VINET equation of state parameters, thermodynamic potential parameters of the solid products of the equivalent system and the phase mixed system was constructed by using the nonlinear fitting method. The relationship between the carbon phase composition at the CJ point and the explosive composition structure was researched. Investigations were conducted into how the mixed carbon phase affected the volume and content of gas products as well as the composition at CJ points. Diamond formation in products is good for enhancing explosive's working capacity. Based on mixed-state potential parameters, the correlation mechanism between the mixed carbon phase and the chemical reaction zone was investigated, and it was found that intramolecular carbon/intermolecular carbon and more detonation graphite/diamond products all would lead to the extension of the reaction zone.

State of charge estimation with hysteresis-prone open circuit voltage in lithium-ion batteries using the trajectory correction hysteresis (TCH) model

State-of-the-art lithium-ion cell chemistries with pronounced open-circuit voltage hysteresis (OCV), characterised by asymmetry and directional dependence, present a challenge for estimating the state of charge (SOC). Without understanding the hysteresis behaviour, OCV measurement points that lie within the hysteresis window cannot be used for SOC correction. After obtaining the data efficiency of the trajectory correction hysteresis (TCH) model with the introduction of the transfer fit (TF) method, this work applies the TF TCH for OCV-based SOC correction. The TF method plays a key role as it enables the cell-specific adaptation of an existing TCH model - ageing update is achieved with solely 12 (SOC/OCV) measurement points. With the precise hysteresis model, the developed framework successfully corrects the faulty SOC history, which could originate from a vehicle data logger. Given that two OCV measurement points are available that arbitrarily lie within the SOC history, the SOC correction is achieved by minimising the voltage deviation between the measurement points and the TCH model’s simulation. Identifying the two SOC parameters shift and scale enables subsequent SOC estimation until an additional OCV measurement is available for a further update. The functionality of the presented SOC correction framework is demonstrated using two validation profiles.

Evaluation of The Implementation of The Laboratory Information System Using the Hot Fit Method in Maxima Clinical Laboratory

Evaluation of information systems in the health domain goes beyond exclusively technological considerations but also includes evaluation of human and organizational dimensions. The information system evaluation model frequently health used is called HOT-Fit (Human Organization Technology-Fit). This model considers the complex interactions between human, organizational, and technological elements in the context of health information systems. This research aims to analyze the implementation of the Laboratory Information System using the HOT Fit method at the Maxima Clinical Laboratory. The research method used is quantitative with a cross-sectional research design using a survey approach. Research subjects include laboratory managers, laboratory staff, information system users, and patients. Primary data was collected through questionnaires and interviews, while secondary data was obtained from related documents. The data analysis technique used is descriptive analysis and Partial Least Square ( PLS ). The research results show that there is a significant influence between system quality (p-value = 0.041), information quality (p-value = 0.003), service quality (p-value = 0.003), and user satisfaction (p-value = 0.041). Research findings can be used as evaluation and improvement material for laboratory managers to increase the effectiveness and efficiency of health services by optimizing the use of information systems.

Wavelet–ANN hybrid model evaluation in seepage prediction in nonhomogeneous earthen dams

ABSTRACT In this study, novel methods such as wavelet–artificial neural network hybrid models and artificial neural network models were used to predict seepage from the Zonouz earthen dam. The dataset consisted of 972 piezometric data points. Statistical fitting methods such as root mean squared error, determination coefficient, scatter plots, and data distribution diagrams were used to evaluate the results. The findings indicated that the wavelet–artificial neural network hybrid model was more accurate than the artificial neural network model. Specifically, during training, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.820, 0.680, 743.39, and 792.52, while the artificial neural network model had 0.700, 0.600, 426.39, and 131.45. Similarly, during validation, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.700, 0.600, 426.39, and 131.45, while the artificial neural network model had 0.823, 0.680, 743.39, and 792.52. Therefore, the wavelet–artificial neural network hybrid model can be proposed as a precise method for predicting seepage in earthen dams and is more accurate than the artificial neural network model. This study highlights the importance of preventing dam failures and using advanced modeling techniques for better predictions and preventive measures.

An iterative weighted least-square fitting method for crustal anisotropy using receiver functions

SUMMARY The harmonic variation of the P-to-S converted phases (i.e. Pms) observed from receiver functions (RFs) includes information on crustal azimuthal anisotropy. However, this harmonic analysis is easily influenced by low-quality RF traces, and the measurements may be misleading. Here, we propose an improved method, named the iterative weighted least-square method (IWLS), to extract the splitting parameters of the crust and simultaneously retrieve the two- and four-lobed components of backazimuthal variation. The quality and weights of different RF traces are estimated properly in the IWLS method. The weight function is related to the sharpness of the Pms phase and the smearing of other signals. We conduct many synthetic tests, and the IWLS method provides stable measurements for poor backazimuthal coverage, strong noise, weak P-wave azimuthal anisotropy and multiple anisotropic layers. We apply the IWLS method to observational data from two temporary stations on the southeastern Tibetan Plateau and North China Craton, respectively. The measurements are comparable to previous results and provide insight into crustal deformation.