Parameter Estimation Research Articles

Application of Frequency Response Analysis for Parameters Estimation of Switched Reluctance Motors

Application of Frequency Response Analysis for Parameters Estimation of Switched Reluctance Motors

A reaction–diffusion model of major emerging infectious diseases in a spatially heterogeneous environment and case study

During the outbreak of major emerging infectious diseases, virus droplets can survive in the environment as aerosols for hours to days, and their impact on human infectious diseases is often overlooked. In addition, the speed of transmission of infectious diseases is often closely related to transportation. Therefore, studying the impact of environmental viruses and transportation on disease development is significant for effective infectious disease prevention and control. We proposed a degenerate reaction–diffusion infectious disease model ([Formula: see text]) considering environmental virus interference and established a well-posedness and threshold system for this model. We have obtained the system solution approaches the disease-free equilibrium ([Formula: see text]) when the basic reproduction number [Formula: see text]. The system has at least one positive steady state solution (PSS) when [Formula: see text]. This paper used a non-standard finite difference method discretization model while data fitting and parameter estimation were performed based on data provided by the Health Commission. Further sensitivity analysis was conducted on [Formula: see text]. At the same time, we also discussed the impact of various parameters in the early stages of the outbreak of major emerging infectious diseases on the development of the disease. Research found that even if the contact rate between people is controlled at a shallow level, the disease may persist. In the early stages of major emerging outbreak of infectious diseases, immediately reducing the use of transportation can effectively reduce the speed of disease spread.

The Influence of Different Solar System Planetary Ephemerides on Pulsar Timing

Pulsar timing offers a comprehensive avenue for exploring diverse topics in physics and astrophysics. High-precision solar system planetary ephemeris is crucial for pulsar timing as it provides the positions and velocities of solar system planets including the Earth. However, it is inevitable that inherent inconsistencies exist in these ephemerides. Differences between various ephemerides can significantly impact pulsar timing and parameter estimations. Currently, pulsar timing highly depends on the JPL DE ephemeris, for instance, the Pulsar Timing Array data analysis predominantly utilizes DE436. In this study, we examine inconsistencies across various ephemeris series, including JPL DE, EPM, and INPOP. Notably, discrepancies emerge particularly between the current ephemeris DE436 and the earliest released ephemeris DE200, as well as the most recent ephemerides, e.g., DE440, INPOP21A, and EPM2021. Further detailed analysis of the effects of ephemeris on geometric correction procedures for the conversion of measured topocentric times of arrival is presented in this study. Our researches reveal that variations in the Roemer delays across different ephemerides lead to distinct differences. The timing residuals and the fact that these discrepancies can be readily incorporated into the subsequent pulsar parameters, leading to inconsistent fitting estimates, suggest that the influence of errors in the ephemeris on the timing process might currently be underappreciated.

Genetic parameters for oocytes and embryo production and their association with linear type traits in dairy Gyr Cattle

In vitro embryo production is one of the main reproductive techniques used in dairy Gyr cattle. In addition, linear type measures are well characterized and have been used in dairy Gyr breed selection for the last 4 decades. The estimation of genetic parameters for the number of aspirated oocytes and in vitro-produced embryos associated with the linear type measures would support genetic progress for animal breeding programs toward embryo production. This study aimed to estimate genetic parameters for aspirated oocytes, embryo in vitro production, and linear type traits, exploring the association between them. The repeatability model was applied to 14,251 ovum pick-up events from 1,916 Gyr donors. A subset of 604 donors from the same group had their body measurements taken. Single- and 2-trait analyses were carried out using the BLUPF90 family programs. Heritability estimates of 0.38, 0.34, and 0.20 were obtained for total oocytes, viable oocytes, and embryos, respectively,-and the heritability of the linear type traits ranged from 0.22 to 0.40. High genetic correlations between total oocytes and viable oocytes (0.99), and between oocytes (total and viable) and embryos (0.83) were obtained. Low to high genetic (-0.07 to 0.92) and phenotypic (0.32 to 0.86) correlations were obtained between the linear type traits. Moreover, low phenotypic correlations (0.01 to 0.13) were observed for oocytes (total and viable) and embryos with the linear type traits, whereas low to moderate genetic correlations (0.07 to 0.42) were observed between the same traits, especially for ilium width (0.42), rump area (0.38), and hip height (0.33). Thus, selection for in vitro production is achievable in Gyr dairy cattle, and superior genetic progress is associated with the selection of oocytes (total and viable). Furthermore, the moderate genetic association between oocytes and embryos with linear type traits, especially ilium width suggests that progress on in vitro embryo production may be achieved by accessing these measurements.

Examining innovative unsupervised learning techniques for automated characterization of complex groundwater systems

This research proposes an innovative approach utilizing geophysical well logging data analyzed with multiple machine learning (ML) methods including, self-organizing maps (SOMs), k-means cluster analysis (CA), and most frequent value-assisted cluster analysis (MFV-CA), to automatically identify lithological variation within a complex groundwater system. The MFV method is introduced to enhance cluster center identification. In MFV, an automatedly weighted Euclidean distance is applied in which closer data points are assigned higher weights that emphasize their proximity within clusters. This method proved to be efficient, however, to ensure robustness, an innovative histogram-based selection method is employed for initial cluster center positioning to minimize the risk of choosing an inappropriate starting value. The proposed methodology is tested on the Quaternary aquifer system in the Debrecen area, Eastern Hungary which is known for its high lithological heterogeneity. The results were evaluated in both 1D and 2D to reveal the vertical and horizontal distribution of the lithofacies. Accordingly, the histogram-based MFV-CA demonstrated exceptional noise rejection capabilities and efficient recognition of lithological clusters. Moreover, the continuous estimation of hydraulic parameters including hydraulic conductivity and critical flow velocity along the hydrostratigraphical units showed a close agreement with the identified lithological units.

Cause-specific hazard Cox models with partly interval censoring - Penalized likelihood estimation using Gaussian quadrature.

The cause-specific hazard Cox model is widely used in analyzing competing risks survival data, and the partial likelihood method is a standard approach when survival times contain only right censoring. In practice, however, interval-censored survival times often arise, and this means the partial likelihood method is not directly applicable. Two common remedies in practice are (i) to replace each censoring interval with a single value, such as the middle point; or (ii) to redefine the event of interest, such as the time to diagnosis instead of the time to recurrence of a disease. However, the mid-point approach can cause biased parameter estimates. In this article, we develop a penalized likelihood approach to fit semi-parametric cause-specific hazard Cox models, and this method is general enough to allow left, right, and interval censoring times. Penalty functions are used to regularize the baseline hazard estimates and also to make these estimates less affected by the number and location of knots used for the estimates. We will provide asymptotic properties for the estimated parameters. A simulation study is designed to compare our method with the mid-point partial likelihood approach. We apply our method to the Aspirin in Reducing Events in the Elderly (ASPREE) study, illustrating an application of our proposed method.

Optimization design method for multi-stress accelerated degradation test based on Tweedie exponential dispersion process

In the operational environment of electronic products, simultaneous multiple stresses contribute to product degradation, with their interactions often being intricate. To effectively analyze and optimize multi-stress accelerated degradation tests, we introduce a Multi-Stress Generalized Coupling accelerated degradation model based on the Tweedie exponential dispersion process, coupled with a multi-decision optimization approach for test design. The model integrates stress interactions elucidated through fuzzy correlation analysis, reflecting the accelerated degradation behavior consistent with the Tweedie exponential dispersion process. Addressing the complexities of multi-parameter estimation, we propose a parameter estimation method underpinned by the Sine Cosine Algorithm for multi-objective optimization within the proposed framework. Considering budgetary constraints, D-optimization and V-optimization strategies are deployed to finetune test scheme allocations, optimizing resource utilization. Validation through a three-stress accelerated degradation test on LED chips confirms the model's efficacy in reliability assessment. Our findings indicate the superior descriptive power of the Tweedie exponential dispersion model for complex degradation phenomena and the proposed model's closer approximation to stress conditions in real-world settings, with a methodological error maintained below 2 %. Furthermore, the test optimization method enhances the D-optimization and V-optimization objective values by 21.20 % and reduces them by 4.08 %, respectively, marking a substantial improvement over traditional schemes and endorsing the holistic advancement of multi-stress accelerated degradation testing methodologies.

Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach.

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people's intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual's social reactivity - their emotional response to social situations - can be represented as the association between repeated measurements of the individual's social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level1 and using the latent representations of the slopes to predict outcomes on level2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes - a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs - such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

Classical and Bayesian estimations of performance measures in G e o / G e o / 1 queue

. In this article, we consider an early arrival G e o / G e o / 1 queue from the statistical perspective. The estimations of parameters and system performances under Classical and Bayesian frameworks are considered. Based on queue length data, we attempt to study the uniform minimum-variance unbiased estimators (UMVUEs) and the closed Bayesian estimators for various queueing characteristics. We compare their properties using Monte Carlo (MC) simulations. As a comparison and improvement, based on arrival and departure data over a period, we use a Bivariate Beta distribution as joint prior, and solve the performance estimators using the properties of Gaussian hypergeometric function. We derive Bayesian estimators and conduct numerical simulations using Markov Chain Monte Carlo (MCMC) method.

ASASSN-21js: A multi-year transit of a ringed disc

The early-type star ASASSN-21js started to fade in 2021, as was detected by the All Sky Automated Survey for Supernovae, undergoing a multi-year eclipse that is still underway. We interpret this event as being due to a structured disc of material transiting in front of the star. The disc is in orbit around a substellar object with the mass and luminosity of a brown dwarf or smaller. We want to determine the expected duration and ending date of the eclipse. We modelled a tilted and inclined azimuthally symmetric ring system around an unseen companion and calculated the resulting time-varying light curve as the object transited in front of the star. We made an initial estimate of the ring parameters and used these as inputs to an MCMC algorithm to determine the geometric properties of the rings with associated uncertainties. The model most consistent with the light curve to date is a two-ring system at high inclination with respect to the line of sight that has a semi-major axis of 71.6 stellar radii. With an estimate of the stellar radius, the transverse velocity is around 0.7 which if bound to the star is an orbit with a semi-major axis of around 13000 au, placing it in the Oort cloud of the parent star. The transit is ongoing and will finish around MJD 61526 (May 1 2027). We encourage the community to continue observing this object in order to understand its properties.

Fractional Negative Order Moment Parameter Estimator of Compound-Gaussian Clutter with Lognormal Texture

Fractional Negative Order Moment Parameter Estimator of Compound-Gaussian Clutter with Lognormal Texture

Mapping the Milky Way in 5D with 170 Million Stars

We present Augustus, a catalog of distance, extinction, and stellar parameter estimates for 170 million stars from 14 mag < r < 20 mag and with ∣b∣ > 10° drawing on a combination of optical to near-infrared photometry from Pan-STARRS, 2MASS, UKIDSS, and unWISE along with parallax measurements from Gaia DR2 and 3D dust extinction maps. After applying quality cuts, we find 125 million objects have “high-quality” posteriors with statistical distance uncertainties of ≲10% for objects with well-constrained stellar types. This is a substantial improvement over the distance estimates derived from Gaia parallaxes alone and in line with the recent results from Anders et al. We find the fits are able to reproduce the dereddened Gaia color–magnitude diagram accurately, which serves as a useful consistency check of our results. We show that we are able to detect large, kinematically coherent substructures in our data clearly relative to the input priors, including the Monoceros Ring and the Sagittarius Stream, attesting to the quality of the catalog. Our results are publicly available at doi:10.7910/DVN/WYMSXV. An accompanying interactive visualization can be found at http://allsky.s3-website.us-east-2.amazonaws.com.

Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery.

Spectral feature informed variational model and its applications to machinery fault diagnosis

Variational mode extraction (VME), a novel signal decomposition method based on a frequency-domain filter in essence, has recently become a potential tool in fault diagnosis. However, the original VME algorithm is not provided with full self-adaptation, and its performance in the extraction of fault features is subject to predefining the initial parameters, including initial center frequency (ICF) and balance parameter. To address these issues, a spectral feature informed variational model (SFIVM) algorithm is constructed to overcome the defects of parameters setting and efficiently realize the fault diagnosis without prior knowledge. Specifically, a spectral feature detector inspired by the convergence property of ICF is first developed to reveal the spectral features, including the detected center frequencies and boundary frequencies. Then, a balance parameter estimation formula is designed to adaptively determine the target balance parameter by taking advantage of the above spectral features. Finally, a highly efficient decomposition model is proposed to extract the fault-related mode from the vibration signal, where iterative optimization is unnecessary. The effectiveness of the proposed SFIVM method is verified by one simulated and two experimental cases. Moreover, its superiority and high efficiency are demonstrated by comparing it with some advanced and classical fault diagnosis methods.

Bayesian expectation maximization-maximization for robust estimation in proton exchange membrane fuel cells: A comparative study

In this paper, we present a novel approach based on Bayesian Expectation Maximization-Maximization (BEMM) to address this challenge. Unlike traditional optimization methods, which may struggle with high-dimensional and nonlinear optimization problems, BEMM offers a robust framework that combines the benefits of Bayesian inference with the flexibility of expectation maximization and maximization techniques. By iteratively updating parameter estimates based on observed data and maximizing the likelihood of the model, BEMM effectively navigates the solution space to converge on accurate estimates of the unspecified variables in Proton Exchange Membrane Fuel Cells (PEMFCs)models. Through extensive experimentation and comparison with other metaheuristic techniques, including Arithmetic Optimization Algorithm (AOA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), and Biogeography-Based Optimization (BBO), we demonstrate the superior performance of our BEMM approach. Our results show that BEMM outperforms these alternative methods in terms of precision, convergence speed, and stability across various scenarios involving different numbers of unspecified variables. The implications of our findings are significant for both researchers and practitioners in the field of PEMFC modeling and optimization. By providing a more efficient and reliable method for estimating model parameters, our approach can facilitate more accurate predictions of PEMFC performance, leading to better-informed decision-making in the design, operation, and optimization of PEMFC systems. Furthermore, the robustness and versatility of BEMM make it well-suited for a wide range of optimization problems beyond PEMFC modeling, highlighting its potential impact across various domains of engineering and scientific research. In the sensitivity analysis, as the population size increases from 10 to 40, there is a significant improvement in solution quality by approximately 100 %. However, beyond a population size of 40, the marginal gains diminish, with only marginal improvements of less than 1 % observed despite further increases in population size up to 200.

Parameter estimation from the Lyα forest in the Fourier space using an information-maximizing neural network

Our aim is to present a robust parameter estimation with simulated forest spectra from Sherwood-Relics simulations suite by using an information-maximizing neural network (IMNN) to extract maximal information from 1D-transmitted flux in the Fourier space. We performed 1D estimations using IMNN for intergalactic medium (IGM) thermal parameters $T_0$ and gamma at $z=2-4$, and cosmological parameters $ and s $ at $z=3-4$. We compared our results with estimates from the power spectrum using the posterior distribution from a Markov Chain Monte Carlo (MCMC). We then checked the robustness of IMNN estimates against deviation in spectral noise levels, continuum uncertainties, and instrumental smoothing effects. Using mock forest sightlines from the publicly available CAMELS project, we also checked the robustness of the trained IMNN on a different simulation. As a proof of concept, we demonstrated a 2D-parameter estimation for $T_0$ and photoionization rates, $ HI We obtain improved estimates of $T_0$ and gamma using IMNN over the standard MCMC approach. These estimates are also more robust against signal-to-noise deviations at $z=2$ and 3. At $z=4$, the sensitivity to noise deviations is on par with MCMC estimates. The IMNN also provides $T_0$ and gamma estimates that are robust against continuum uncertainties by extracting small-scale continuum-independent information from the Fourier domain. In the cases of $ and s $, the IMNN performs on par with MCMC but still offers a significant speed boost in estimating parameters from a new dataset. The improved estimates with IMNN are seen for high instrumental resolution (FWHM=6 At medium or low resolutions, the IMNN performs similarly to MCMC, suggesting an improved extraction of small-scale information with IMNN. We also find that IMNN estimates are robust against the choice of simulation. By performing a 2D-parameter estimation for $T_0$ and $ HI $, we also demonstrate how to take forward this approach observationally in the future.

Groundwater level fluctuation caused by tide and groundwater pumping in coastal multi-layer aquifer system

This paper considered the groundwater head fluctuation induced by tide and pumping in the coastal multi-layered aquifer system. The multi-layered aquifer system comprises an unconfined aquifer, an upper confined aquifer, and a lower confined aquifer. An aquiclude exists between each two aquifers. All the layers terminate at the coastline. The new analytical solutions describing groundwater head variation in the coastal multi-confined aquifer system are derived. Superposition principle and image methods are used for the derivation of the analytical solutions. Analytical solutions of different situations of without considering pumping, of without considering tidal effect, and of N-layered confined aquifers are also derived. The impacts of the parameters of the initial phase shift of tide, pumping rate, position of the pumping well, storage coefficient, and transmissivity on the groundwater head fluctuation are discussed. The analytical solutions are applied with application examples in fitting field observations and parameter estimations. The estimated values of the hydraulic conductivities in the upper and lower confined aquifers are within the range of the values obtained from the field experiments. The fitted results of the analytical solutions capture the main characteristics of groundwater head fluctuation affected by the tide and groundwater pumping. The study of groundwater head fluctuation in the coastal zone is helpful to understand the mechanism of seawater intrusion under the influence of tide and groundwater pumping.

Topology Testing and Demographic Modeling Illuminate a Novel Speciation Pathway in the Greater Caribbean Sea Following the Formation of the Isthmus of Panama.

Recent genomic analyses have highlighted the prevalence of speciation with gene flow in many taxa and have underscored the importance of accounting for these reticulate evolutionary processes when constructing species trees and generating parameter estimates. This is especially important for deepening our understanding of speciation in the sea where fast moving ocean currents, expanses of deep water, and periodic episodes of sea level rise and fall act as soft and temporary allopatric barriers that facilitate both divergence and secondary contact. Under these conditions, gene flow is not expected to cease completely while contemporary distributions are expected to differ from historical ones. Here we conduct range-wide sampling for Pederson's cleaner shrimp (Ancylomenes pedersoni), a species complex from the Greater Caribbean that contains three clearly delimited mitochondrial lineages with both allopatric and sympatric distributions. Using mtDNA barcodes and a genomic ddRADseq approach, we combine classic phylogenetic analyses with extensive topology testing and demographic modeling (10 site frequency replicates x 45 evolutionary models x 50 model simulations/replicate = 22,500 simulations) to test species boundaries and reconstruct the evolutionary history of what was expected to be a simple case study. Instead, our results indicate a history of allopatric divergence, secondary contact, introgression, and endemic hybrid speciation that we hypothesize was driven by the final closure of the Isthmus of Panama and the strengthening of the Gulf Stream Current ~3.5 million years ago. The history of this species complex recovered by model-based methods that allow reticulation differs from that recovered by standard phylogenetic analyses and is unexpected given contemporary distributions. The geologically and biologically meaningful insights gained by our model selection analyses illuminate what is likely a novel pathway of species formation not previously documented that resulted from one of the most biogeographically significant events in Earth's history.

Irradiance separation model parameter estimation from historical cloud cover statistical properties

Irradiance separation models allow the decomposition of Global Horizontal Irradiance into Diffuse Horizontal and Direct Normal Irradiances. These models need fitting to the irradiance characteristics of the location of interest using locally measured ground data. For locations that only measure Global Horizontal Irradiance, current state of the art establishes the use of parameters obtained for another location that measures the three components, with similar climate characteristics. Nevertheless, this results in a lack of localized character for estimates and requires fitting model parameters for all possible climates, which can be infeasible given data availability. This work presents a novel approach based on the hypothesis that the separation model's parameters are a function of the statistical properties of satellite-derived cloud cover estimates. The proposed methodology was evaluated in 23 sites covering all main Köppen-Geiger climatic types and different cloud coverage properties using the Boland-Ridley-Lauret diffuse fraction model. The model performs similarly as locally adjusting the model, with Root Mean Square Errors of 0.087–0.15 diffuse fraction units versus 0.077–0.127 for locally optimized parameters, and offers adequate performance across climates and cloud characteristics. These results encourage future research by generalizing parameter estimation for other diffuse fraction models. The main applications for this research are the estimation of irradiance components where no local data is available for model fitting and the enhancement or complementarity of satellite estimates of surface irradiance. Furthermore, it allows the estimation of missing irradiance components due to equipment failure in locations with insufficient data for a representative, locally adapted model.

CML based parameter estimation of FH signals in alpha stable noise environment

CML based parameter estimation of FH signals in alpha stable noise environment