Traditional Fitting Research Articles

Modeling wide-area tropospheric delay corrections for fast PPP ambiguity resolution

The performance of precise point positioning (PPP) has been significantly improved thanks to the continuous improvements in satellite orbit, clock, and ambiguity resolution (AR) technologies, but the convergence speed remains a limiting factor in real-time PPP applications. To improve the PPP precision and convergence time, tropospheric delays from a regional network can be modeled to provide precise correction for users. We focus on the precise modeling of zenith wet delay (ZWD) over a wide area with large altitude variations for improving PPP-AR. By exploiting the water vapor exponential vertical decrease, we develop a modified optimal fitting coefficients (MOFC) model based on the traditional optimal fitting coefficients (OFC) model. The proposed MOFC model provides a precision better than 1.5 cm under sparse inter-station distances over the Europe region, with a significant improvement of 70% for high-altitude stations compared to the OFC model. The MOFC model with different densities of reference stations is further evaluated in GPS and Galileo kinematic PPP-AR solutions. Compared to the PPP-AR solutions without tropospheric delay augmentation, the positioning precision of those with 100-km inter-station spacing MOFC and OFC is improved by 25.7% and 17.8%, respectively, and the corresponding time to first fix (TTFF) is improved by 36.9% and 33.0% in the high-altitude areas. On the other hand, the OFC model only slightly improves the TTFF and positioning accuracy when using the 200 km inter-station spacing modeling and even degrades the positioning for high-altitude stations, whereas using the MOFC model, the PPP-AR solutions always improve. Moreover, the positioning precision improvement of MOFC compared with OFC is about 22.1%, 21.7%, and 25.7% for the Galileo-only, GPS-only, and GPS + Galileo PPP-AR solutions, respectively.

Using Sport Education to Implement Game-Based HIIT in Physical Education

Most of the materials published in relation to the Sport Education model have developed games and sports seasons. However, the potential of Sport Education has been tested with other content, such as fitness activities. The educational potential of Sport Education and motivational impact on students might make it an alternative to the traditional fitness methodologies in physical education (PE). Therefore, combining Sport Education with functional training exercises and foundational movements (FTE-FM) and High Intensity Interval Training (HIIT), which have proven to be especially effective in achieving health and fitness goals, could make fitness as PE content more motivating and educational. This article describes how to integrate FTE-FM and HIIT into a season of fitness, using Sport Education in PE with a game-based approach (GB-HIIT).

Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge for parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator–ferromagnetic insulator heterostructure Bi2Se3/EuS exhibiting proximity magnetism in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator–antiferromagnet heterostructure (Bi,Sb)2Te3/Cr2O3 and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.

Evaluation of the Effectiveness of Artificial Neural Network Based on Correcting Scoliosis and Improving Spinal Health in University Students.

In recent years, spinal disorders, spinal deformities, and scoliosis have become more frequent, especially chronic systemic inflammatory diseases, such as early-onset scoliosis, idiopathic scoliosis, and ankylosis spondylitis, which have unknown causes, insidious onset, and progressive development, resulting in irreversible spinal joint deformities and stiffness at advanced stages and high disability rates. As one of the traditional Chinese health and fitness qigong exercises, Taijiquan has the effect of strengthening the waist and kidneys, sparing the tendons and softening the body, unblocking the meridians, unblocking the qi and blood, and strengthening the body and increasing intelligence. To this end, this paper proposes a data enhancement method that uses the spine line as the control curve to deform the human contours, drawing on the moving least squares deformation method to fit a variety of scoliosis cases and increase the diversity of the dataset, thereby improving the generalisation capability of the network. The colour map and depth map information are then integrated using a four-channel method and a dual feature extraction network structure to improve the accuracy of segmentation.

Characterisation and validation of the JWL equation of state parameters for PE4

This investigation focused on determining the Jones-Wilkins-Lee (JWL) equation of state (EoS) parameters for the Australian made explosive PE4-MC. Four cylinder expansion (CYLEX) experiments were conducted using the T-20 methodology with measurements of the time of arrival of the copper cylinder on the perspex plate made using optical fibres connected to an optical pulse timer. Three different model parameter fits were then generated from the CYLEX test data. These were based on; (1) Traditional least-squares fit of CYLEX data to JWL EoS and (2) Inverse modeling method using LS-DYNA and LS-OPT (two different fits). The three parameter sets were then compared in terms of the accuracy of their predictions against two sets of blast loading experiments. The first set of blast loading experiments evaluated the different parameter fits at a scaled distance (Z) of 1.77–2.55 m/kg1/3, representing a mid-field blast loading scenario while the second set of blast loading experiments represented a near-field blast loading scenario (Z = 0.08–0.1 m/kg1/3). The JWL EoS parameters which were derived using the standard fitting method from the CYLEX test data (CYLEX) were shown to provide accurate predictions (within 5% of experiments) in the near-field but under-predicted the peak reflected pressure in the mid-field by more than 20%. The best predictions across both the near and mid-field blast loading scenarios was provided by the two parameters sets (LS-OPT Final and LS-OPT Select), which were derived using an inverse modeling approach, with the LS-OPT Select set of parameters being the preferred option. Additionally, comparisons between the experimental data and predictions using the JWL EoS parameters for the US made C4 explosive indicated that the blast loading in the mid-field was similar between C4 and PE4-MC, which was consistent with the previous literature. However, in the near-field C4 was predicted to provide higher blast loadings. This highlights the importance of validating an explosives EoS across a range of different scaled distances.

Effects of integrative neuromuscular training intervention on physical performance in elite female table tennis players: A randomized controlled trial.

ObjectivesTo investigate the effects of integrative neuromuscular training (INT) on physical performance in elite female table tennis players.MethodsTwenty-four Chinese elite female table tennis players were randomized into either INT (n = 12) group or control group (CON; n = 12). INT group performed four INT sessions every week for 8 weeks, while CON group performed traditional physical fitness training. One repetition maximal (1RM), vertical jump, Y balance test and 30-meter sprinting performance were tested before and after intervention.ResultsNo between groups differences were detected for any tests before intervention. Significant group by time (before or after intervention) interaction effects were observed in 1RM, vertical jump, bilateral lower limb reaching distance at posteromedial and posterolateral directions, and right leg at the anterior direction for the Y balance test (all p < 0.05), but not for the left leg at the anterior direction or the 30-meter sprinting performance (both p > 0.05). Post-hoc analysis for measurements with significant interactions revealed that all significant changes were at the ING group (all p < 0.05), while no changes for the CON group were observed (all p > 0.05).ConclusionEight weeks INT significantly improved strength, power and balance in Chinese elite female table tennis players. Adopting INT in table tennis players may improve their physical performance and lead to better sports performance.

Variation law and prediction model to determine the moisture content in tea during hot air drying

AbstractTo study the changes in the moisture content of tea leaves during the hot air drying process of tea production, this study designed an experiment to monitor the dynamic change of moisture content of rolled green tea during the hot air drying process under different feeding amounts (800–1,200 g), drying temperature (90–120°C), and drum speed (20–30 rpm/min). We established a dynamic prediction model to determine the moisture content using the BP, Elman, and Particle swarm optimization Elman neural network (PSO Elman) algorithms. The prediction calculation was conducted by considering the drying temperature, drum speed, drying initial water, and prediction time as input and the water content prediction result during the tea drying process as the output. Additionally, we analyzed the significant factors to explore the dynamic changes in the water content of tea leaves under different drying conditions. Experimental results showed that the temperature, rotational speed, and feeding rate significantly affected the drying effect. Furthermore, we established a traditional multiple linear regression fitting model was established to compare and analyze with the above three neural network models. The verification and error analysis results of the four water prediction models showed that the PSO–Elman model could better predict the changes in the water content during the drying process.Practical ApplicationsThe change of moisture content in the process of tea hot air drying research can provide a theoretical basis for the hot air drying technology and process of tea, and have important significance for guiding tea processing and production, improving processing efficiency and tea quality.

Practical Basis of the Geometric Mean Fitness and its Application to Risk-Spreading Behavior.

Temporal variations in population size under unpredictable environments are of primary concern in evolutionary ecology, where time scale enters as an important factor while setting up an optimization problem. Thus, short-term optimization with traditional (arithmetic) mean fitness may give a different result from long-term optimization. In the long-term optimization, the concept of geometric mean fitness has been received well by researchers and applied to various problems in ecology and evolution. However, the limit of applicability of geometric mean has not been addressed so far. Here we investigate this problem by analyzing numerically the probability distribution of a random variable obeying stochastic multiplicative growth. According to the law of large number, the expected value (i.e., arithmetic mean) manifests itself as a proper measure of optimization as the number of random processes increases to infinity. We show that the finiteness of this number plays a crucial role in arguing for the relevance of geometric mean. The geometric mean provides a satisfactory picture of the random variation in a long term above a crossover time scale that is determined by this number and the standard deviation of the randomly varying growth rates. We thus derive the applicability condition under which the geometric mean fitness is valid. We explore this condition in some examples of risk-spreading behavior.

Error Calibration for Full Tensor Magnetic Gradiometer Probe Based on Coordinate Transformation Method

In order to calibrate the zero offset error and triaxial non-orthogonal error of full tensor magnetic gradiometer (FTMG) probe composed of superconducting quantum interference device (SQUID) magnetometers, we propose a new calibration method in this article. With the help of the triaxial Helmholtz coil, we can generate a magnetic field with known parameters. By establishing the objective functions containing the error parameters for the outputs of the SQUIDs and solving them with the least-squares fitting algorithm, the included angle between the SQUID sensor and the Helmholtz coil can be obtained, then all the required error parameters can be calculated by coordinate transformation method. Compared with the traditional method, this method does not need to rotate the SQUID magnetometers-based probe to collect the data set for calibration, thus avoiding the influence of motion induced noise on the calibration results. In order to verify the effectiveness of the proposed method, we adopted the widely used ellipsoid fitting method as a comparison method and conducted field comparison experiments. The results show that the proposed calibration method is better than the traditional ellipsoid fitting calibration method under the same experimental environment. The root mean square errors of five independent magnetic gradient tensor components are reduced to 0.39, 0.35, 0.41, 0.31 and 0.57 nT/m from 1630.55, 756.59, 3097.56, 1051.09 and 1245.64 nT/m by the proposed method.

Phase demodulation of fiber vibration sensing by modified ellipse fitting algorithm based on local outlier factor optimization

In the existing ellipse fitting algorithms, the Lissajous figure is used to solve the demodulation error caused by the non-ideal 3×3 couplers. However, the influence of circuit noise and phase noise on Lissajous figure are not fully considered in the studies. In this work, an ellipse fitting demodulation method based on local outlier factor (LOF) algorithm is proposed, which can effectively eliminate the outlier points in Lissajous figure caused by noises. The proposed method proves to achieve the accurate demodulation of the signals by numerical simulations. In addition, the Monte Carlo analysis is used to obtain the comprehensive error rate of about 0.13%, which verifies the stability of the proposed method. Compared with the traditional least square fitting method, this method improves the stability and accuracy of demodulated signals.

Safety research on stabilization of autonomous vehicles based on improved-LQR control

Mediating the divergent interest of vehicle stability and strengthened path tracking performance when aiming at the design of a path tracking controller for autonomous vehicles is a challenging issue. Accordingly, this paper proposes an improved-LQR (linear quadratic regulator) control applied using an improved path planning algorithm. A feedforward and feedback LQR control is constructed by applying the path optimization solution method, which is a different traditional polynomial trajectory fitting method, and then solving the path planning information and the control input parameter in real time to make the tracking error as convergent as possible. To verify the superiority of the improved-LQR, this study compares the proposed controller and model predictive control by the traditional path solving method on a closed-loop test road using Carsim/Simulink. The comparative results show the efficiency, accurate tracking, vehicle stability, and reliability of the proposed controller.

Internet of Things for in Home Health Based Monitoring System: Modern Advances, Challenges and Future Directions

IOT has carried out important function in converting the traditional fitness care corporation. With developing call for in population, traditional healthcare structures have reached their outmost functionality in presenting sufficient and as plenty as mark offerings. The worldwide is handling devastating developing antique population disaster and the right want for assisted-dwelling environments is turning into inevitable for senior citizens. There furthermore a determination by means of the use of way of countrywide healthcare organizations to increase crucial manual for individualized, right blanketed care to prevent and manipulate excessive coronial situations. Many tech orientated packages related to Health Monitoring have been delivered these days as taking advantage of net boom everywhere on globe, manner to improvements in cellular and in IOT generation. Such as optimized indoor networks insurance, community shape, and fairly-low device fee performances, advanced tool reliability, low device energy consumption, and hundreds higher unusual common usual performance in network safety and privacy. Studies have highlighted fantastic advantages of integrating IOT with health care location and as era is improving the rate also cannot be that terrific of a problem. However, many challenges in this new paradigm shift notwithstanding the fact that exist, that need to be addressed. So the out most purpose of this research paper is 3 essential departments: First, evaluation of key elements that drove the adoption and boom of the Internet of factors based totally domestic some distance off monitoring; Second, present fashionable improvement of IOT in home a long manner off monitoring shape and key building gadgets; Third, communicate future very last effects and distinct guidelines of such type a long way off monitoring packages going ahead. Such Research is a wonderful manner in advance now not outstanding in IOT Terminology but in standard fitness care location.

An Improved Genetic Algorithm for UWB Localization

The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.

Estimation of Arc Welding Pressure Pipeline Weld Peaking Parameters Based on Data Prediction

Peaking parameter is the key content in the regular inspection of the pressure pipeline. Solving the problem of the peaking measurement method defined by a standard cannot be applied to a situation in which there exists a weld surface with reinforcement and misalignment. In this paper, a peaking estimation method based on data prediction was proposed to estimate the contour information of the base metal at the weld joint using the contour point set data of the base metal part of the weld. Herein, the longitudinal weld peaking estimation method based on a piecewise logistic regression (PLR) and the girth weld peaking estimation method based on a piecewise Bayesian linear regression (PBLR) were studied, and the midpoint of the two symmetrical points of the base metal on either side of the weld was used as a reference for calculating the peaking. Finally, we collected the surface profile data of longitudinal weld pressure pipes with diameters of 155 mm, 255 mm, 550 mm, and 600 mm and the surface profile data of girth weld pressure pipes with diameters of 120 mm, 130 mm, 140 mm, and 170 mm. This weld seam data used the data estimation method proposed in this article and traditional long short-term memory and fitting methods. The results showed that the proposed data prediction method could accurately predict the position of the base metal, and the theoretical mean absolute error of the peaking estimation based on the PBLR and PLR could attain 0.06 mm and 0.07 mm, respectively, which meets the parameter measurement requirements of related verification fields.

Wet tropospheric delay estimation of satellite altimeters based on machine learning

Wet path delay (WPD) is an important source of satellite altimeters’ transmission error, which must be fully corrected to ensure data quality. After decades of study, WPD can now be accurately negated by measuring the brightness temperature with a microwave radiometer (MWR). However, due to hardware and financial limitations, many satellites are not equipped with MWRs, and WPD can only be corrected by mathematical models. Currently available empirical models, which are mostly established by traditional fitting methods, are unable to reach the accuracy required by altimeters, so it is necessary to further improve the correction accuracy of the models. Based on three machine learning algorithms, we established three high-precision wet tropospheric correction (WTC) models suitable for satellite altimeters. We analyzed the global navigation satellite system dataset and three MWR datasets, i.e., Jason-3, Sentinel-3A, and Sentinel-3B, for data quality and consistency and combined these datasets for modeling. In addition, several input features were extracted from existing knowledge on tropospheric delay. Through a comparative analysis of different feature combinations, temperature (T), water vapor pressure (e), and total content of water vapor were selected as the input features. Then we established three WTC models based on multilayer perceptron, random forest, and the adaptive network-based fuzzy inference system (ANFIS), respectively. With a dataset from Jason-2 that was not used during modeling as the verification set, we verified the generalizability of these WTC models from the global, latitudinal, and seasonal perspectives. Moreover, three traditional WTC models were compared with the ones developed here. The results show that the WTC models established based on machine learning algorithms outperform traditional WTC models. The WTC model based on ANFIS boasts the best calculation accuracy in all aspects, over 50% higher than that of traditional WTC models.

Prediction of Uniaxial Compression Strength of Limestone Based on the Point Load Strength and SVM Model

Uniaxial compression strength (UCS) is a fundamental parameter to carry out geotechnical engineering design and construction. It is simple and efficient to predict UCS using point load strength (PLS) at engineering sites. However, the high dispersion of rock strength limits the accuracy of traditional fitting prediction methods. In order to improve the UCS prediction accuracy, 30 sets of regular cylindrical specimen tests between PLS and UCS are conducted on limestone mines. The correlation relationship between PLS and UCS is found by using four basic fitting functions. Then, a prediction model is established by using SVM algorithm. Multiple training test data are used to achieve high-precision prediction of UCS and the results show it is less different from the actual values. Especially, the R2 coefficient reached 0.98. The SVM model prediction performance is significantly better than the traditional fitting function. The constructed SVM model in this study can accurately predict the UCS using the PLS obtained in the field, which has a great significance to the rock stability judgment in the actual construction environment.

PGNets: planet mass prediction using convolutional neural networks for radio continuum observations of protoplanetary discs

ABSTRACT We developed convolutional neural networks (CNNs) to rapidly and directly infer the planet mass from radio dust continuum images. Substructures induced by young planets in protoplanetary discs can be used to infer the potential young planets’ properties. Hydrodynamical simulations have been used to study the relationships between the planet’s properties and these disc features. However, these attempts either fine-tuned numerical simulations to fit one protoplanetary disc at a time, which was time consuming, or azimuthally averaged simulation results to derive some linear relationships between the gap width/depth and the planet mass, which lost information on asymmetric features in discs. To cope with these disadvantages, we developed Planet Gap neural Networks (PGNets) to infer the planet mass from two-dimensional images. We first fit the gridded data in Zhang et al. as a classification problem. Then, we quadrupled the data set by running additional simulations with near-randomly sampled parameters, and derived the planet mass and disc viscosity together as a regression problem. The classification approach can reach an accuracy of 92 per cent, whereas the regression approach can reach 1σ as 0.16 dex for planet mass and 0.23 dex for disc viscosity. We can reproduce the degeneracy scaling α ∝ $M_\mathrm{ p}^3$ found in the linear fitting method, which means that the CNN method can even be used to find degeneracy relationship. The gradient-weighted class activation mapping effectively confirms that PGNets use proper disc features to constrain the planet mass. We provide programs for PGNets and the traditional fitting method from Zhang et al., and discuss each method’s advantages and disadvantages.

Detectability of exomoons by examining the signals from a model of transiting exoplanets with moons

Exomoons are natural satellites of exoplanets. Nowadays, none has been confirmed. However, a number of detection techniques have been proposed, including Transit Timing Variations (TTV) and Transit Duration Variations (TDV) techniques. From a recent study, fitting observed transit with the traditional photocentric fitting model shows unique features around the primary and secondary exomoon transits in TDV and transit depth signals, which might reduce the detectability. The aim of this work is to retrieve the variation of TTV, TDV and transit depth signals of exomoon systems with the photocentric fitting model. One year star-planet-moon transit light curves are simulated with LUNA algorithm and fit with TransitFit. The results show that neglecting the TDV and transit depth data with phase around exomoon’s primary and secondary transits improve the exomoon detectability by a factor of ten and the systems with large moon orbital semi-major axis with nearly edge-on orbit around low mass stars can be detected.

On the Impact of Inclination-dependent Attenuation on Derived Star Formation Histories: Results from Disk Galaxies in the Great Observatories Origins Deep Survey Fields

Abstract We develop and implement an inclination-dependent attenuation prescription for spectral energy distribution (SED) fitting and study its impact on derived star formation histories. We apply our prescription within the SED fitting code Lightning to a clean sample of 82, z = 0.21–1.35 disk-dominated galaxies in the Great Observatories Origins Deep Survey North and South fields. To compare our inclination-dependent attenuation prescription with more traditional fitting prescriptions, we also fit the SEDs with the inclination-independent Calzetti et al. (2000) attenuation curve. From this comparison, we find that fits to a subset of 58, z &lt; 0.7 galaxies in our sample, utilizing the Calzetti et al. (2000) prescription, recover similar trends with inclination as the inclination-dependent fits for the far-UV-band attenuation and recent star formation rates. However, we find a difference between prescriptions in the optical attenuation (A V ) that is strongly correlated with inclination (p‐value &lt; 10−11). For more face-on galaxies, with i ≲ 50°, (edge-on, i ≈ 90°), the average derived A V is 0.31 ± 0.11 magnitudes lower (0.56 ± 0.16 magnitudes higher) for the inclination-dependent model compared to traditional methods. Further, the ratio of stellar masses between prescriptions also has a significant (p‐value &lt; 10−2) trend with inclination. For i = 0°–65°, stellar masses are systematically consistent between fits, with log 10 ( M ⋆ inc / M ⋆ Calzetti ) = − 0.05 ± 0.03 dex and scatter of 0.11 dex. However, for i ≈ 80°–90°, the derived stellar masses are lower for the Calzetti et al. (2000) fits by an average factor of 0.17 ± 0.03 dex and scatter of 0.13 dex. Therefore, these results suggest that SED fitting assuming the Calzetti et al. (2000) attenuation law potentially underestimates stellar masses in highly inclined disk-dominated galaxies.

Frugal Method of Notch Modification of Scleral Contact Lenses in the Setting of Complex Ocular Surface Anatomy.

Scleral contact lenses (ScCLs) have gained popularity as a treatment of refractive errors in patients with complex anterior segment pathology. Patients with mechanical abnormalities of the ocular surface may be unsuccessful with traditional ScCL fitting. Scleral contact lens modifications, such as notching and microvaulting, typically incur additional financial costs and require the services of professional laboratories. We describe a frugal method of ScCL notch modification that can be performed by a practitioner using readily available tools in a single office visit. Two patients with abnormal ocular surface anatomy were fit with the practitioner-modified ScCL and achieved successful visual rehabilitation. We offer this method as a potentially economical and effective technique to achieve successful ScCL fitting in this challenging patient population with pathologies that may preclude standard ScCL usage.