Model Parameters Research Articles

Structural analysis of the dynamic response of a shape memory alloy based damper

Shape memory alloys (SMAs) are promising candidates for use in sensors, actuators, or passive dampers. This paper investigates the dynamic response of a superelastic NiTi holed disk to assess its damping performance relative to frequency and temperature for SMA-based damper applications. This study involved several key steps. Initially, the superelastic behavior of the SMA was experimentally characterized through tensile tests. This testing campaign provided the required data to identify material parameters of a thermomechanical constitutive model, already implemented in the finite element code Abaqus. Using the identified parameters, a finite element based structural analysis was conducted to predict the disk’s operational range, ensuring it remained within the superelastic domain without incurring potential damage. Following this static analysis, a dynamic mechanical analysis (DMA) was performed on the disk. By employing a complex stiffness approach, we further examined the disk’s damping effects. This dynamic method enabled a detailed description of the apparent stiffness and damping characteristics based on solicitation frequency, test temperature, vibration amplitude, and a predefined static displacement. The results indicated a clearly predominant structural effect over the phase transformation effect, despite the disk’s substantial damping potential.

Surrogate-based worst-case analysis of a knee joint model using Genetic Algorithm

Verification, validation, and uncertainty quantification is generally recognized as a standard for assessing the credibility of mechanical models. This is especially evident in biomechanics, with intricate models, such as knee joint models, and highly subjective acquisition of parameters. Propagation of uncertainty is numerically expensive but required to evaluate the model reliability. An alternative to this is to analyze the worst-case models obtained within the specific bounds set on the parameters. The main idea of the paper is to search for two models with the greatest different response in terms of displacement-load curve. Real-Coded Genetic Algorithm is employed to effectively explore the high-dimensional space of uncertain parameters of a 2D dynamic knee model, while Radial Basis Function surrogates reduce the computation by orders of magnitude to near real-time, with negligible impact on the quality. It is expected that the studied knee joint model is very sensitive to uncertainty in the geometrical parameters. The obtained worst-case knee models showcase unrealistic behavior with one of them unable to fully extend, and the other largely overextending. Their relative difference in extension is up to 35% under ±1 mm bound set on the geometry. This unrealistic behavior of knee joint model is confirmed by the large standard deviation obtained from a classical sampling-based sensitivity analysis. The results confirm the viability of the method in assessing the reliability of biomechanical models. The proposed approach is general and could be applied to other mechanical systems as well.

Research on Mathematical Model and Process Parameter Optimization of Rotary Hearth Furnace Process Toward Energy and Cost Saving

Research on Mathematical Model and Process Parameter Optimization of Rotary Hearth Furnace Process Toward Energy and Cost Saving

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

AbstractThe service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

Inhomogeneous magnetization transfer (ihMT) imaging reveals variable recovery profiles of active MS lesions according to size and localization

Abstract This work aims at exploiting the unique myelin specificity of the inhomogeneous magnetization transfer (ihMT) technique to characterize the recovery dynamics of active multiple sclerosis (MS) lesions. IhMT and three other myelin-sensitive techniques, conventional MT, T1-weighted, and diffusion tensor imaging, were applied in a 12-month longitudinal study performed on relapsing-remitting MS patients. An exponential recovery model was used to fit the variations over time of the metrics derived from each MR technique within new active lesions. A principal component analysis was performed on the model parameters obtained for all MR myelin-sensitive techniques across all active lesions of all patients to identify specific recovery profiles. The results show that the recovery profiles of myelin-sensitive MR metrics in active MS lesions vary according to the localization and size of lesions. The distance of lesions from the ventricles is positively associated with the recovery rates of ihMTR and T1w-MPRAGE: the further the lesion is from the ventricles, the higher the recovery rate of these metrics. Lesion size is positively associated with initial loss and negatively associated with final recovery of ihMTR and other MR metrics: small lesions have lower initial loss and greater final recovery of MR metrics than large lesions. Thanks to the specificity of the ihMT technique for myelin, these features can be interpreted in terms of remyelination. This study thus provides longitudinal in vivo support for the pathological observations of higher remyelination in small lesions compared with large ones and faster remyelination in lesions away from the ventricles. These results support the use of ihMT and other measures for quantifying remyelination rates in clinical studies of remyelination therapies.

An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model

As renewable energy sources such as wind and photovoltaics continue to enter the grid, their intermittency and instability leads to an increasing demand for peaking and frequency regulation. An efficient dynamic monitoring method is necessary to improve the safety level of intelligent operation and maintenance of power stations. To overcome the insufficient detection accuracy and poor adaptability of traditional methods, a novel fault early warning method with careful consideration of dynamic characteristics and model optimization is proposed. A combined loss function is proposed based on the dynamic time warping and the mean square error from the perspective of both shape similarity and time similarity. A prediction model of steam turbine intermediate-stage extraction temperature based on the gate recurrent unit is then proposed, and the change in prediction residuals is utilized as a fault warning criterion. In order to further improve the diagnostic accuracy, a human evolutionary optimization algorithm with lens opposition-based learning is proposed for model parameter adaptive optimization. Experiments on real-world normal and faulty operational data demonstrate that the proposed method can improve the detection accuracy by an average of 1.31% and 1.03% compared to the long short-term memory network, convolutional neural network, back propagation network, extreme learning machines, gradient boosting decision tree, and LightGBM models.

Efficient characterization of a double quantum dot using the Hubbard model

Semiconductor quantum dots are favorable candidates for quantum information processing due to their long coherence time and potential scalability. However, the calibration and characterization of interconnected quantum dot arrays have proven to be challenging tasks. One method to characterize the configuration of such an array involves using the Hubbard model. In this paper, we present an efficient characterization algorithm that efficiently extracts the Hubbard model parameters, including tunnel coupling and capacitive coupling energy, from experimental stability diagrams. Leveraging the dual-annealing optimizer, we determine the set of Hubbard parameters that best characterize the experimental data. We compare our method with an alternate, well-established measure of tunnel coupling and find good agreement within the investigated regime. Our extracted tunnel couplings range from 69 to 517 μeV, and we discuss the limiting factors of our method.

Crow Search Algorithm for Modelling an Anaerobic Digestion Process: Algorithm Parameter Influence

Corn steep liquor is a waste product from the process of treating corn grain for starch extraction. It is used as a substrate in anaerobic digestion with simultaneous hydrogen and methane production in a cascade of two anaerobic bioreactors. For process research and optimisation, adequate mathematical models are required. So, the authors aim to present a high-quality model of the corn steep liquor process for the sequential production of H2 and CH4. This paper proposes a technique for identifying the best mathematical model of the process using the metaheuristics crow search algorithm (CSA). The CSA was applied for the first time to mathematical modelling of the considered two-stage anaerobic digestion process, using real experimental data. Based on the analysis of the numerical data from the model parameter identification procedures, the influence of the main CSA parameters—the flight length, fl, and the awareness probability, AP—was investigated. Applying classical statistical tests and an innovative approach, InterCriteria Analysis, recommendations about the optimal CSA parameter tuning were proposed. The best CSA algorithm performance was achieved for the AP = 0.05, fl = 3.0, followed by AP = 0.10, fl = 2.5, and AP = 0.15, fl = 3.0. The optimal tuning of the CSA parameters resulted in a 29% improvement in solution accuracy. As a result, a mathematical model of the considered two-stage anaerobic digestion process with a high degree of accuracy was developed.

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.

Modeling and Comparison Study of Industrial AC-Arcs

AbstractElectric arcs are a necessary heat source in many industrial processes that take place in Submerged Arc Furnaces (SAFs). Arcs exhibit non-linear electrical characteristics and behave in a complex manner. Therefore, an improved understanding of their behavior enables better control of furnace operation. Modeling of industrial arcs is a multiphysics process that involves simultaneously solving several coupled physical phenomena, such as electromagnetics, fluid dynamics, and heat transfer, including a radiative heat transfer from the plasma arc. Coupling fluid dynamics and electromagnetics is known as Magnetohydrodynamics (MHD). For practical applications, however, there are also simpler approaches to arc modeling, either based on simplified physical principles or empirical behavior. In this paper, a combined Cassie–Mayr model (CMM) and a channel arc model (CAM) are implemented and coupled with a submerged arc furnace electrical circuit model. The complete circuit model parameters such as resistances and inductances are estimated using modeling of a full size furnace, and then, actual measurements from a SAF are used to validate the models by comparing current and voltage waveform. Both models are then used to estimate harmonic distortion in a SAF for different arc current ratios, which should help operators to estimate the arc current in real time thus be able to lower and raise the electrode to keep operating conditions constant.

Causal computations of supplementary motor area on spatial impulsivity

Spatial proximity to important stimuli often induces impulsive behaviour. How we overcome impulsive tendencies is what determines behaviour to be adaptive. Here, we used virtual reality to investigate whether the spatial proximity of stimuli is causally related to the supplementary motor area (SMA) functions. In two experiments, we set out to investigate these processes using a virtual environment that recreates close and distant spaces to test the causal contributions of the SMA in spatial impulsivity. In an online first experiment (N = 93) we validated and measured the influence of distant stimuli using a go/no-go task with close (21 cm) or distant stimuli (360 cm). In experiment 2 (N = 28), we applied transcranial static magnetic stimulation (tSMS) over the SMA (double-blind, crossover, sham-controlled design) to test its computations in controlling impulsive tendencies towards close vs distant stimuli. Reaction times and error rates (omission and commission) were analysed. In addition, the EZ Model parameters (a, v, Ter and MDT) were computed. Close stimuli elicited faster responses compared to distant stimuli but also exhibited higher error rates, specifically in commission errors (experiment 1). Real stimulation over SMA slowed response latencies (experiment 2), an effect mediated by an increase in decision thresholds (a). Current findings suggest that impulsivity might be modulated by spatial proximity, resulting in accelerated actions that may lead to an increase of inaccurate responses to nearby objects. Our study also provides a first starting point on the role of the SMA in regulating spatial impulsivity.

Pulsar Timing Arrays Require Hierarchical Models

Pulsar timing array (PTA) projects have found evidence of a stochastic background of gravitational waves (GWB) using data from an ensemble of pulsars. In the literature, minimal assumptions are made about the signal and noise processes that affect data from these pulsars, such as pulsar spin noise. These assumptions are encoded as uninformative priors in Bayesian searches, though frequentist approaches make similar assumptions. Uninformative priors are not suitable for (noise) properties of pulsars in an ensemble, and they bias estimates of model parameters such as gravitational-wave signal parameters. Both frequentist and Bayesian searches are affected. In this article, more appropriate priors are proposed in the language of hierarchical Bayesian modeling, where the properties of the ensemble of pulsars are jointly described with the properties of the individual components of the ensemble. Results by PTA projects should be reevaluated using hierarchical models.

Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology.

Model-independent parameterization of H(z) and its implications for cosmic evolution

We investigate the model-independent parameterization of H(z) in spatially homogeneous and isotropic FLRW space-time using Cosmic Chronometers (31 data points) and Pantheon+SH0ES (1701 points) datasets. The best-fit values for the model parameters (H0,A1,A2,A3) are obtained through MCMC analysis. Our results show that the EoS parameter ω transitions from a matter-dominated era through quintessence to ω=−1, indicating convergence to ΛCDM at late times. The current ω values suggest an accelerating universe. Moreover, the model's squared sound speed vs2 evolves from negative to positive, indicating increasing stability. The deceleration parameter q shows a smooth transition from deceleration to acceleration, with transition redshift zt between 0.5 and 0.8. Further, the energy conditions are satisfied, and SEC violation at z=0 supports current acceleration. The ω−ω′ plane lies in the freezing region, and the r−s plane approaches the ΛCDM limit at late times.

Differential image motion in astrometric observations with very large seeing-limited telescopes

We investigate how to quantitatively model the observed differential image motion (DIM) in relative astrometric observations. As a test bed we used differential astrometric observations from the FORS2 camera of the Very Large Telescope (VLT) obtained during 2010--2019 under several programs of observations of southern brown dwarfs . The measured image motion was compared to models that decompose atmospheric turbulence in frequency space and translate the vertical turbulence profile into DIM amplitude. This approach accounts for the spatial filtering by the telescope's entrance pupil and the observation parameters (field size, zenith angle, reference star brightness and distribution, and exposure time), and it aggregates that information into a newly defined metric integral term. We demonstrate excellent agreement (within 1<!PCT!>) between the model parameters derived from the DIM variance and determined by the observations. For a 30 s exposure of a typical 1 field close to the Galactic plane, image motion limits astrometric precision to sim 60 mu as when sixth-order transformation polynomial is applicable. We confirm that the measured image motion variance is well described by Kolmogorov-type turbulence with exponent 11/3 dependence on the field size at effective altitudes of 16--18 km, where the best part of the DIM is generated. Extrapolation to observations with extremely large telescopes enables the estimation of the astrometric precision limit for seeing-limited observations of sim 5 mu as, which has a variety of exciting scientific applications.

Analysis of Shared Parking Game Model under Dynamic Parking Pricing

In recent years, the problem of "parking difficulty" has led to a large number of illegal parking incidents. The shared parking mode has been considered as an effective way to alleviate the conflict between parking supply and demand and urban traffic pressure. To address the issue of illegal parking and promote the development of shared parking, this paper constructs an evolutionary game model with shared platforms and motor vehicle drivers as the main entities. The study investigates the evolutionary stability strategy of the model, conducts sensitivity analysis on model parameters, and further analyzes the impact of highly sensitive parameters on the evolutionary paths of both players in the game. Finally, numerical simulations are performed on dynamic parking pricing standard. The research findings demonstrate that the sensitivity of discounts received by drivers from shared platforms and the additional revenue gained by the shared platform is higher than that of other parameters. Moderately increasing the penalty for illegal parking and the additional revenue of the shared platform can encourage drivers to choose legal parking and promote the development of shared parking. Under given parameterized and periodic parking pricing standard, finally, according to the particle swarm optimization algorithm, a set of relatively optimal parameter values is derived to enable the model to evolve rapidly into a stable state where drivers choose to park legally and the shared platform selects surrounding parking lots. It can effectively reduce the frequency of illegal parking.

Mathematical modeling of drying kinetics of two varieties of cacao cultivated in the brazilian amazon region

Drying kinetics, along with mathematical modeling, provides means for simulations and predictions to achieve process optimizations. This study investigated the drying kinetics of Criollo and Forastero cocoa beans, varieties cultivated in the Amazon region, at three different temperatures (50 °C, 60 °C, and 70 °C). The results showed that the two varieties exhibited distinct behaviors during the drying process. The temperature of 60 °C was the most effective, as it provided seed water content closer to 8% for the Criollo (8.15% d.b.) and Forastero (8.89% d.b.) varieties. Mathematical models were fitted to the experimental data to analyze the drying kinetics. The Midili model (R² > 0.998, DQM < 0.02) provided the best fits for both varieties at all tested temperatures. The model parameters indicated that the drying rate increased with higher temperature and longer exposure time to drying air. Internal resistance to water transfer varied in some models, indicating a possible influence of thermal conditions. This study contributes to a better understanding of cocoa bean drying, providing important information for optimizing industrial drying processes. The results emphasize the importance of considering the specific characteristics of different cocoa varieties when developing efficient drying strategies.

Cosmological constraints on the background dynamics of a two-field nonsingular bounce model

Abstract In this paper we consider a nonsingular bounce scenario with non-minimal kinetic coupling between two scalar fields.We derive the constraints on the model parameters from the finiteness of the physical quantities at the classical level and from the relation between the late-time accelerated expansion and particle production up to the bounce phase. We then determine the allowed parameter space for the model.

Augmenting Knowledge for Individual NVR Prediction in Different Spatial and Temporal Cross-Building Environments

Natural ventilation is a critical method for reducing energy consumption for heating, cooling, and ventilating buildings. Recent research has focused on utilizing environmental IoT data from both inside and outside buildings for NVR prediction based on a deep learning model. To design an accurate NVR prediction model while considering individual building environments, various knowledge-sharing methods can be applied, such as transfer learning and ensemble models for cross-building prediction. However, the characteristics of learning data and model parameters should be considered when applying transfer learning and ensemble models to predict NVR with different spatial and temporal domains. In this paper, we propose a way to design an NVR prediction model for a cross-building environment by normalizing the training data, selecting transfer learning layers that are well-suited to the data environment, and augmenting NVR knowledge via ensemble methods. Based on the experimental results, we confirm that the proposed knowledge-sharing deep learning approach, while considering the normalizing of training data, the selecting transfer learning layers, and augmenting the NVR knowledge approach, can improve the accuracy up to 11.8% in the two different offices and seasons.

FP-Deeplab: a segmentation model for fabric defect detection

Abstract Deep learning for defect detection has become prevalent to improve fabric production and efficiency and quality. However, fabric defect detection faces challenges such as low recognition ratio, suboptimal classification performance, poor detection inefficiency, and high model complexity. To address these issues, an end-to-end semantic segmentation network is proposed employing an efficient encoder-decoder structure, denoted as Feature Pyramid-Deeplab (FP-Deeplab). The improvements involves enhancing the backbone network by improving the MobileNetV3 network for superior performance, a novel Atrous Spatial Pyramid Pooling with Dilated Strip Pooling (ASPP-DSP) module which combines strip pooling, dilated convolution and ASPP, to ensure an expanded receptive field and the capability to gather distant contextual information. Additionally, a Feature Pyramid module (FP module) is proposed to integrate multiscale features at various stages more efficiently. The incorporating of depth-wise separable convolution in FP-Deeplab enables significant parameter and computational cost reduction, catering to online detection requirements. Experimental results certify the superiority of FP-Deeplab over classical and recent segmentation models. Comparative analysis demonstrates higher segmentation accuracy and reduced parameter quantity. Specifically, compared to the benchmark Deeplabv3+ model with MobileV2 as the backbone, FP-Deeplab achieves a notable increase in segmentation accuracy (F1 score and MIoU) by 4.26% and 5.81%, respectively. Moreover, the model parameters (params) are only one-fifth of the original model, indicating the efficiency and effectiveness of our proposed approach.