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Giant magnetostrictive material (GMM) is well known for its hysteresis behaviors related to multiple influencing factors, such as bias magnetic field and excitation level. This feature greatly challenges the loss prediction of giant magnetostrictive transducers. The existing loss prediction models need further improvement to cover the sensitive magnetic properties of GMM. In this paper, the variations of the iron losses with the operation conditions are interpreted by the variations of the Jiles–Atherton model parameters, and a concise parameter identification procedure with great generalizability is proposed. The iron losses of Terfenol-D under variable excitation levels and the DC biases are further investigated. With the proposed procedure, the introduced eight additional modification parameters for the original Jiles–Atherton model are easy to obtain, and merely a few hysteresis loops of Terfenol-D are involved. An experimental setup is established for further demonstration. Comparisons between the measured and calculated results indicate the effectiveness of the proposed approach. Throughout the bias-excitation space within the frequency range under investigation, the iron losses can be precisely estimated with an acceptable error of less than 5%. [ABSTRACT FROM AUTHOR]

Reverse transcription–digital PCR (RT–dPCR) is attracting attention as a method that enables SI-traceable RNA quantification without calibration, but its accuracy and bias have not been thoroughly studied. In this study, the accurate quantification of RNA by the RT–dPCR method was investigated using NMIJ CRM 6204-b, an RNA certified reference material whose certified value was assigned by orthogonal chemical measurement methods. Moreover, a two-step RT–dPCR method was adopted to examine in detail the conditions for the RT reaction process, which was expected to be the major uncertainty component in the RT–dPCR measurement. Optimization experiments revealed that the type of reverse transcriptase, the concentration of template RNA, and the type and concentration of primers in the RT reaction affected the value quantified by RT–dPCR. Under the optimal conditions, the value quantified by RT–dPCR, 76.4 ng/μL ± 6.7 ng/μL (the quantified value ± expanded uncertainty (k = 2)), was consistent with the certified value, 68.2 ng/μL ± 5.8 ng/μL, of NMIJ CRM 6204-b RNA 1000-A within the expanded uncertainty. From the results of the uncertainty evaluation, the relative combined uncertainty of the RT–dPCR method was 4.42%, and the major uncertainty components in the RT–dPCR method were the preparation of RT solution (3.68%), the inter-day difference (1.80%), and the RT reaction (1.30%). Together, the results suggested that the contribution of the RT reaction process to the total uncertainty was greater than that of the dPCR process. [ABSTRACT FROM AUTHOR]

Incorporating PEM electrolyzers (PEMElz) into microgrid systems presents a promising avenue for utilizing surplus renewable energy to produce hydrogen while meeting the system operation requirements. The accurate emulation of the behavior of PEMElz in such systems requires a detailed representation of their electrochemical and thermal characteristics. Yet modeling these characteristics optimally poses a challenge due to their inherent complexity, characterized by multivariate, non-linear, and multi-modal characteristics. To address this challenge, this paper proposes an advanced approach to model PEMElz by introducing general forms for the integrated electrochemical and thermal model based on a set of optimally identified parameters. The introduced general forms are generated using an n-dimensional polynomial fitting process for a set of optimally identified parameters, which are determined using the Partial Reinforcement Optimizer (PRO) under various temperature and pressure levels. These general forms provide a reliable PEMElz model to adapt to any temperature and pressure operating conditions. The proposed approach's effectiveness in capturing the static characteristics of PEMElz is verified by analyzing polarization and thermal curves across multiple operating conditions. Furthermore, the proposed general forms are implemented in Matlab/Simulink to emulate the PEMElz behavior when it is incorporated into a microgrid dominated by high penetration levels of variable generation resources such as solar photovoltaic (PV). This demonstration highlights the direct relevance and tangible impact of the proposed general-formulated model based on the optimal identified parameters approach in various operating scenarios. • Identified optimal parameters for a combined electrochemical and thermal PEMElz. • Tailored partial reinforcement optimizer (PRO) for PEMElz parameter identification. • Deriving general forms for the PEMElz model parameters. • Investigated proposed PEMElz in microgrid system. • The general forms are a computationally effective tool reflecting PEMElz performance. [ABSTRACT FROM AUTHOR]

To properly simulate the laser directed energy deposition (L-DED) process, the heat source model is crucial as it represents the absorbed heat energy, which controls the thermal phenomena associated with L-DED. Goldak's double-ellipsoidal heat source model has been broadly used to represent energy distribution in a wide range of laser-based simulation processes. It is therefore important to accurately determine Goldak's model parameters for correct L-DED simulation. In this work, an original inverse identification procedure was proposed to determine Goldak's heat source parameters by coupling a 3D finite element thermal model with a genetic algorithm. The multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used by considering two objective functions simultaneously. The temperatures at specific points in the melt pool were extracted from the 3D finite element simulations and then used to construct objective functions for the optimization procedure. By minimizing the differences between the simulated and reference temperatures, Goldak's heat source parameters were identified based on experimental L-DED fabricated single-track deposits for different process conditions. The model accurately estimated the penetration depth and slightly overestimated the heat-affected zone (HAZ). The relative errors are reliable enough to reasonably predict Goldak's heat source model parameters. The inverse identification procedure proposed in this study can help reduce time and experimental costs to estimate heat source parameters for the simulation of the L-DED process. [ABSTRACT FROM AUTHOR]

In the identification process of SSI–DATA method, the stabilization diagram is generally used to determine the order of the system. However, in the modal parameter identification of spatial grid structure, there are few stable points that meet the requirements, and the stability axis is not obvious. So, the location of the modal stability point cannot be determined. Furthermore, the QR decomposition of the Hankel matrix is computationally inefficient and difficult to use for online identification when analyzing the spatial structure. In order to solve the above problems, this paper proposes a time-saving random subspace method of spatial grid structure based on an improved stabilization diagram. On the basis of the traditional random subspace method, the projection of the Hankel matrix is transformed into the projection transformation of its R matrix, and then the subsequent calculation is carried out. Then, the frequency search domain is set and the auxiliary stability axis is made. According to the spectrum peak, stabilization diagram and the auxiliary stability axis, the position of the stability axis is comprehensively judged. The modal parameters of the structure can be identified more accurately, which improves the calculation efficiency and avoids modal omissions or misjudgments to a certain extent. Using a spatial structure model (upper chord 18 × 12m, lower chord 16 × 10m, grid 2 × 1.5m), the advantages of this method were compared and verified. The results indicate that the calculation time of the traditional SSI–DATA method is 29.077s, while the improved calculation time is 3.752s, resulting in an 87.1% improvement in calculation efficiency, while also preventing mode omission and misjudgment. By further applying this method to vibration monitoring of spatial grid structure hangars, its applicability, higher computational efficiency, and precision have been proven. [ABSTRACT FROM AUTHOR]

ABSTRACT This article is aimed to study the parameter identification of the ExpARX system. To overcome the computational complexity associated with a large number of feature parameters, a parameter separation scheme based on the different features of the identification model is introduced. In terms of the phenomenon that the coupling parameters lead to the inability of algorithms, a separable synchronous interactive estimation method is introduced to eliminate the coupling parameters and perform parameter estimation in accordance with the hierarchical principle. For the purpose of achieving high‐accuracy performance and reducing complexity, a separable synchronous gradient iterative algorithm is derived by means of gradient search. In order to improve the identification accuracy, a separable synchronous multi‐innovation gradient iterative algorithm is proposed by introducing the multi‐innovation identification theory. In order to improve the convergence speed, a separable synchronous multi‐innovation conjugate gradient iterative algorithm is proposed by introducing the conjugate gradient theory. Finally, a simulation example and a real‐life example of piezoelectric ceramics are used to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Objective: The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans. Methods: Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated. Results: The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p < 0.05), and the trends of parameter changes aligned with the seizure process. Additionally, excitatory parameters of epileptogenic contacts were higher than that of non-epileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%. Conclusion: The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses. [ABSTRACT FROM AUTHOR]

Introduction: Fuel cell technology is a harbinger of the future for generating electricity due to their high efficiency and low emissions achieved through the direct conversion of chemical energy into electrical energy without combustion. Methods: To optimize the design and performance, a fuel cell model is essential to predict its behaviour in different conditions. This technical note presents a novel physics-based approach, the Young's Double-slit Experiment Optimizer (YDEO), for identifying parameters in Proton Exchange Membrane Fuel Cells. A performance metric is established by formulating an objective function that relies on the summation of squared errors between experimental and estimated values. Results and discussion: The effectiveness of this approach is evaluated through the analysis of four benchmark test cases: Horizon 500 W, BCS500 W, NedstackPS6, and 250 W. The corresponding objective function values for these test cases are 0.011243, 2.065557, 0.011698, and 5.250849, respectively. The simulation results demonstrate the efficacy of the proposed YDEO algorithm when compared with other existing popular and contemporary algorithms in the literature. [ABSTRACT FROM AUTHOR]

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model's ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification. [ABSTRACT FROM AUTHOR]

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery's surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions. [ABSTRACT FROM AUTHOR]

The effect of preanalytical and analytical factors on the α-synuclein (α-syn) seed amplification assay's (SAA) performance has not been fully explored. Similarly, there is limited knowledge about the most suitable assay protocol and kinetic parameters for misfolded α-syn seed quantification. We studied the effect of centrifugation, repeated freeze-thaw cycles (up to seven), delayed freezing, detergent addition, and blood contamination on the performance of the cerebrospinal fluid (CSF) α-syn SAA real-time quaking-induced conversion (RT-QuIC). Moreover, we analysed the inter- and intra-plate variability, the recombinant protein batch effect, and the RT-QuIC parameters' variability when multiple samples were run in controlled conditions. Finally, we evaluated the assay potential of quantifying α-syn seed by assessing kinetic curves in serial CSF dilutions. Among tested preanalytical variables, a ≥0.01 % blood contamination and adding detergents significantly affected the RT-QuIC kinetic parameters and the number of positive replicates. Increasing the number of replicates improved result reproducibility. The number of positive replicates in serially diluted CSF samples improved discrimination between samples with high and low seeding activity, and the time to threshold (LAG) was the most reliable kinetic parameter in multiple experiment settings. Preanalytical variables affecting α-syn RT-QuIC performance are limited to blood contamination and detergent addition. The number of positive replicates and the LAG are the most reliable variables for quantifying α-syn seeding activity. Their consistent measurement in serial dilution experiments, especially when associated with an increased number of sample replicates, will help to develop the α-syn RT-QuIC assay further into a quantitative test. [ABSTRACT FROM AUTHOR]

Wire arc additive manufacturing enables the production of components with high deposition rates and the incorporation of multiple materials. However, the manufactured components possess a wavy surface, which is a major difficulty when it comes to simulating the mechanical behavior of wire arc additively manufactured components and evaluation of experimental full-field measurements. In this work, the wavy surface of a thick-walled tube is measured with a portable 3D scanning technique first. Then, the surface contour is considered numerically using the finite cell method. There, hierarchic shape functions based on integrated Legendre polynomials are combined with a fictitious domain approach to simplify the discretization process. This enables a hierarchic p-refinement process to study the convergence of the reaction quantities and the surface strains under tension–torsion load. Throughout all considerations, uncertainties arising from multiple sources are assessed. This includes the material parameter identification, the geometry measurement, and the experimental analysis. When comparing experiment and numerical simulation, the in-plane surface strains are computed based on displacement data using radial basis functions as ansatz for global surface interpolation. It turns out that the finite cell method is a suitable numerical technique to consider the wavy surface encountered for additively manufactured components. The numerical results of the mechanical response of thick-walled tubes subjected to tension–torsion load demonstrate good agreement with real experimental data, particularly when employing higher-order polynomials. This agreement persists even under the consideration of the inherent uncertainties stemming from multiple sources, which are determined by Gaussian error propagation. [ABSTRACT FROM AUTHOR]

This work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response. [ABSTRACT FROM AUTHOR]

Accurate and efficient identification of pollution sources is a key process that assists in the treatment of water pollution incidents. The ensemble Kalman filter (EnKF) has been proven to be an effective approach for identifying pollution source parameters (e.g., source location, release time, and mass released). In this paper, a method involving multiple observations of reconstruction (MOR) is proposed for reconstructing multidimensional state vectors for assimilation based on pollutant concentration monitoring techniques. The newly reconstructed state variables have dimensionless characteristics that decouple the source mass from the parameter group to be identified before assimilation is performed. This approach can mitigate the interference of assimilation caused by nonmain source parameters. As a result, the pollution sources and material dispersion coefficients can be simultaneously identified at limited observation sites. Then, a set of synthetic numerical examples with 7 scenarios is assembled to investigate and compare the unique characteristics of the derived state variables during assimilation. A laboratory experiment for unknown parameter identification based on monitoring the chemical oxygen demand (COD) concentration is carried out in an annular flume to verify the applicability of the method in real events. The results show that the EnKF combined with the MOR method based on the decoupling pattern performs well in identifying pollution sources and dispersion coefficients simultaneously. The method can still perform excellently in identifying parameters in practice when some data in the observation sequences are lost, with relative errors of pollution source parameters being controlled within 4%. The relative errors of the identified transverse and longitudinal dispersion coefficients are 39% and 12%, respectively. Overall, by evaluating the original data, reconstructing the dataset, and combining it with the EnKF method, it is proven that the MOR–EnKF method is an effective measure for identifying high-dimensional unknown parameter groups. [ABSTRACT FROM AUTHOR]

In this paper, a neural network guidance based on sample dimension reduction is proposed for a reusable launch vehicle. Different from previous works, the training samples of neural networks use Pearson correlation matrix analysis for dimension reduction, which significantly reduces the data volume of training samples and improves the efficiencies of sample construction, data storage, and neural network training. First, the analytical samples are generated by integrating dimensionless energy longitudinal motion equations. Then, the input parameters are processed by Pearson correlation matrix analysis, significantly reducing the data volume of training samples while ensuring the training effectiveness. Furthermore, the neural network is utilized to learn the mapping relationship between reduced-dimension flight states and predictive range-to-go, eliminating numerical integration calculations and improving the real-time performance of guidance. Finally, an extended Kalman filter (EKF) is implemented online for aerodynamic parameter identification, which dramatically enhances the adaptability to internal uncertainties and external disturbances. Compared with the traditional numerical guidance method, simulation results demonstrate that the proposed guidance exhibits superior real-time performance and faster computation speed while ensuring high accuracy and strong robustness to perturbations. [ABSTRACT FROM AUTHOR]

This paper studies the influence of different factors, such as polymer and fiber type and aging, on the internal friction and fractional parameter, which are determined for rubber concrete and fiber-reinforced rubber concrete by means of the Resonant Frequency and Damping Analyzer (RFDA Basic) implementing the Impulse Excitation Technique (IET). Both materials contained low molecular weight cis-polybutadiene rubber SKDN-N as the main binder. Another type of binding material has been used for further investigation of internal friction phenomena in polymer concrete. Namely, low molecular weight polybutadiene rubber (BR) of mixed microstructure was utilized to produce the samples for the experimental program. Two different types of fiber are used: metal and polymer fiber. It has been found that the addition of steel fiber reduces internal friction in the material, whereas propylene fiber acts as additional damping material and increases internal friction. BR-based concrete and fiber-reinforced concrete show rather high values of internal friction, i.e. behave more viscoelastically. BR-based concrete performs higher values of the fractional parameter, and, therefore, more obvious viscoelastic properties. Introduction of steel fiber in the mixture reduces the fractional parameter, since it plays the role of additional bonds in the material structure. However, addition of propylene fiber increases fractional parameter in both, SKDN-N and BR-based concretes. It can be explained by additional damping effect of the polymer fiber. The obtained results are compared with those for the reference specimens made of ordinary Portland cement concrete, and a certain influence of the selected parameters on the internal friction of the considered materials are established. [ABSTRACT FROM AUTHOR]

• The error model of a 5-DOF PEF is established based on the POE formula to satisfy completeness, continuity, and minimality. • An MPC-based parameter identification approach is proposed to address the challenge in the measurement of pose information. • A novel calibration method containing the POE-based error model and the MPC-based identification approach is formed. • The method makes advances in model accuracy and measurement difficulty, and can cost-effectively finish calibration tasks. Limb deformity is a common complaint in orthopedic surgery. Currently, gradual treatment using parallel external fixator (PEF) has become the preferred option for deformity correction. As the key medical apparatus with the special application properties of temporary assembly and direct utilization, the geometric errors generated during the manufacturing and assembly process of the PEF contribute to the post-correction residual and iatrogenic deformities. However, considering the complexity of clinical scenarios, there is a lack of an applicable solution to reduce the effect of the above error source of PEF. To overcome these problems, taking a 4-U P S/S P R PEF as the research object, this paper proposes a novel kinematic calibration method. First, the error model with the features of completeness, continuity, and minimality is first established based on the product of exponentials (POE) formula. Then, the identifiability analysis is conducted to eliminate the redundant error parameters and ensure the stability and accuracy of the iterative calculation in the process of parameter identification. Furthermore, a multiple plane constraint (MPC)-based parameter identification approach is introduced to the calibration task of the PEF to effectively reduce the difficulty of the pose information measurement in the current cost-effective constraint calibration method. Finally, the simulation and prototype experiments validate that the proposed calibration method (i.e., the combination of the POE-based error model and the MPC-based parameter identification approach) can accomplish the calibration tasks in clinical applications and meet the cost-effectiveness and efficiency requirements. [ABSTRACT FROM AUTHOR]

This paper takes a reaction-diffusion predator-prey system with ratio-dependent Holling III functional response function and Leslie-Gower term into consideration. First of all, the system model is proposed on the basis of basic biological assumptions and previous work, and the existence conditions of the equilibrium point of the system are discussed. Secondly, under the assumption of the existence of equilibrium point, the Turing instability necessary conditions induced by diffusion are investigated. Thirdly, optimal control theory is derived, and the adjoint system and the first-order optimization condition are established. Fourthly, parameter identification based on optimal control is studied, and the technique is extended to network structure. Finally, extensive numerical simulations, including Turing pattern, Normalized Population High Distribution Area (NPHDA) diagram and parameter identification, are carried out to illustrate and validate the analytical results. For the system dynamics phenomena, the results from different perspectives effectively demonstrate that the theoretical findings, numerical simulations and natural reality are identical. In terms of the parameter identification of continuous model and network model, the efficiency and accuracy of various algorithms are fully tested. • This paper proposes and investigates index normalized population height distribution area. • The optimal control problem of reaction-diffusion predator-prey system is formally derived. • The parameter identification research is extended to population network structure space. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous linear motor (PMSLM) parameters are susceptible to temperature changes and motor aging, which can result in inaccurate models and reduced control accuracy. Therefore, PMSLM parameters must be monitored accurately. In this study, a back-propagation (BP) neural network is used to identify the resistance, inductance, and flux linkage parameters of a PMSLM, and a genetic algorithm (GA) is employed to optimize the identification system. A full-rank model is established, and the BP neural network parameter identifier is used to realize the multiparameter discrimination, and the GA is employed to optimize the network parameters of the identifier model to improve its training speed and accuracy. A GABP-based multiparameter identification system for a PMSLM is built, and the experiment results show that the GABP parameter identifier can accurately identify the four parameter values of the PMSLM simultaneously. [ABSTRACT FROM AUTHOR]

Summary: In industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous‐time models. This article presents a stochastic gradient estimation algorithm and a recursive least squares estimation algorithm for identifying the parameters of continuous systems. It derives the parameter identification model of linear continuous‐time systems based on the Laplace transforms of the input and output of the systems. To prove that the techniques given here work, we have included a simulated example. [ABSTRACT FROM AUTHOR]

Summary: In practical applications, many processes have nonlinear characteristics that require nonlinear models for accurate description. However, constructing such models and determining their parameters are a challenging task. This article explores filtered identification methods for estimating the parameters of a particular type of nonlinear Hammerstein systems with ARMA noise. An auxiliary model‐based least squares algorithm is developed for such systems based on the auxiliary model identification idea. A hierarchical least squares algorithm that utilizes the hierarchical identification principle is proposed to enhance computational efficiency. Additionally, a key term separation technique is employed to express the system output as a linear combination of parameters, allowing the system to be decomposed into smaller subsystems for more efficient estimation of parameters. Simulation results demonstrate the effectiveness of these proposed algorithms. [ABSTRACT FROM AUTHOR]

Calyx cavity is a common physiological disorder in many persimmon cultivars. It is characterised by a separation between the calyx and the surrounding flesh. The presence of calyx cavity in export fruit can lead to phytosanitary risks and reduced storage potential due to more rapid softening and increased chilling injury. Manual detection of calyx cavity at packing is very time-consuming and uneconomic. This work investigated the ability of non-destructive evaluation, combined with modelling, to identify calyx cavity in 'Fuyu' persimmons. Fruit were evaluated at harvest, followed by a period of nine-week storage in modified atmosphere packaging at 0°C. The presence and severity of calyx cavity was related with higher red colouration and fresh weight before storage. Using colour index data and weight data, binary classification of calyx cavity by linear discriminant analysis (LDA) resulted 74.1% correct segregation. The model correctly identified 70.5% of calyx cavity fruit (29.5% false negatives) and 77.4% of no cavity fruit (22.6% false positives). The use of non-destructive calyx cavity classification based on the evaluation of quality parameters is a starting point in providing the ability to segregate healthy fruit before packaging and storage. Further work is necessary to improve model performance before implementation is viable. [ABSTRACT FROM AUTHOR]

The electro-pneumatic braking system with ON/OFF solenoid valves has been widely used in trains due to its advantages and superiority. The undesirable impact of the thermal effect on the electro-pneumatic braking system leads to frequent valve switching, degradation of the pressure tracking performance and sometimes instability. This article presents an adaptive model predictive control approach to solve the pressure control problem under temperature uncertainty based on a switched unscented Kalman filter. First, a nonlinear switched dynamical model with the uncertain temperature parameter is derived for the electro-pneumatic braking system by comprehensively integrating its nonlinear, discontinuous dynamics and thermal effect. Using a switched unscented Kalman filter on the presented model of the system, the temperature parameter is accurately estimated to improve the model's accuracy. Based on the corrected system model and the designed adaptive model predictive control method, the pressure tracking performance and the valves' switchings of the electro-pneumatic braking system are improved, and the stability is guaranteed. The simulations and the experiments conducted for a braking system prototype confirm the performance validity of the proposed method. • A switched UKF is designed to account for the hybrid characteristics of the braking system. • An adaptive hybrid MPC is proposed to achieve optimal pressure tracking performance while minimizing the number of valve switches. • The stability of the closed-loop system is rigorously analyzed using Lyapunov theory. [ABSTRACT FROM AUTHOR]

A novel single-sensor method for monitoring rotating blade vibration is proposed and utilized to identify vibration parameters under the non-stationary condition. By analyzing the pulse-signal waveform, the blade tip displacement and vibration velocity are extracted. Then, the motion equation under the non-stationary condition is further developed to provide a theoretical basis. Finally, the optimization technology is applied to extract vibration parameters. Compared with multiple-sensor methods, the proposed method has lower installation difficulty, less equipment cost, fewer sensors, and no strict sensor layout requirement. Numerical simulations and experiments are conducted to validate the effectiveness and robustness of the proposed method. The relative error in the natural frequency does not exceed 0.1 %. Additionally, errors in other parameters are less than 8 % in the experiment. • A novel single-sensor method is proposed to identify the vibration parameters of the blade under non-stationary condition. • A novel measurement approach is proposed, which is based on the blade tip vibration velocity (BTVV). • Dynamic characteristic of the non-stationary vibration is investigated by the theoretical analysis. And the formula expression of BTVV under non-stationary condition is first derived. • Simulations and the experiment are performed to validate the proposed method. [ABSTRACT FROM AUTHOR]

Subsynchronous oscillation (SSO) is the resonance between a new energy generator set and a weak power grid, and the resonance frequency is usually the sub-/super-synchronous frequency. The eigensystem realization algorithm (ERA) is a classic algorithm for extracting modal parameters based on matrix decomposition. By leveraging the ERA's simplicity and low computational cost, an enhanced methodology for identifying the key parameters of SSO is introduced. The enhanced algorithm realizes SSO angular frequency extraction by constructing an angular frequency fitting equation, enabling efficient identification of SSO parameters using only a 200 ms synchrophasor sequence. In the process of identification, the fitting-based ERA effectively addresses the limitation of the existing ERA. The accuracy of SSO parameter identification is improved, thereby realizing that SSO parameter identification can be carried out using a 200 ms data window. The fitting-based ERA is verified using synthetic and actual data from synchrophasor measurement terminals. The research results show that the proposed algorithm can accurately extract fundamental and subsynchronous or supersynchronous oscillation parameters, effectively realizing dynamic real-time monitoring of subsynchronous oscillations. [ABSTRACT FROM AUTHOR]

Due to the deformation of ships, it becomes difficult to ensure the accuracy of attitude measurement in typical areas on the deck, which seriously impacts the safety and operational efficiency of shipborne equipment. To address this issue, this paper presents a parameter identification method for dynamic deformation models based on angle increment differences and introduces the related vector machine (RVM) algorithm for online estimation of dynamic deformation model parameters. In view of the truncation error and non-Gaussian noise of the model, this article proposes a dynamic attitude measurement method based on model predictive filtering (MPF), constructs a dynamic measurement model using Rodrigues parameters in an inertial frame, and designs a maximum correlation entropy (MCE) robust filter to achieve robust estimation of deck dynamic deformation. The performance of the method is verified through simulation analysis and shipborne experiments. The comparative results indicate that, compared with existing methods, the proposed improved deck dynamic attitude measurement algorithm based on model prediction (IDAM) can substantially enhance the accuracy of attitude measurement in the presence of deck dynamic deformations. [ABSTRACT FROM AUTHOR]

Correctly torquing bone screws is important to achieve good patient outcomes. An automated system has been previously proposed that may allow more objective torquing of bone screws. Here the system is tested vs. a static torque limit in pig bone. 5 screws were first inserted until stripping to calibrate the dynamic and static limits. Then 6 were inserted limited to 80% of the mean stripping torque. Then 7 were inserted using 80% of the dynamic model-predicted stripping torque. The pull-out strength of the dynamic and static insertions were compared and no statistically significant difference was found. Future work should consider at using larger screws and different bone samples, with more repetitions to try and obtain a statistically significant result. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the current polarization code parameter recognition algorithm lacks the recognition for the starting point of code word and the information bit recognition algorithm is complicated, a polarization code parameter blind recognition algorithm based on the code matrix estimation in proposed. The proposed algorithm firstly multiples the intercepted code word matrix with the Kronecker matrix under the corresponding code length and the reverse rearrangement matrix to obtain the code matrix estimation, then identifies the code length and the code word starting point by the distribution characteristics of the code matrix, and finally uses the trained convolutional neural network to identify the information bits and freezing bits of the polarization code. The experimental results show that the proposed method not only realizes the recognition for the starting point of code word, but also realizes the code length recognition when the code word's starting point is unknown, and the code length recognition accuracy is better than the existing algorithm. When the bit error rate is 0. 19, the polarization code length recognition rate of parameter (32,12) can SÍII reach more than 90%. [ABSTRACT FROM AUTHOR]

Fast and accurate identification of structural modal parameters after an earthquake is crucial for assessing structural conditions and facilitating repair. With the development of modern earthquake observation techniques, the recorded ground motion can be leveraged as extra input information for modal identification, enabling the experimental modal analysis applicable. This study develops a Bayesian modal identification algorithm that aims at estimating the most probable value (MPV) of modal parameters and their identification uncertainty. Incorporating the recorded seismic input, the algorithm utilizes with the structural equation of motion in the frequency domain to formulate the likelihood function and adopts a constrained Laplace method for Bayesian posterior approximation of modal parameters. With the aid of complex matrix calculus, an iterative scheme is developed, allowing a fast search of the MPV of modal parameters and an analytical evaluation of the posterior covariance matrix. The performance of the proposed algorithm is validated by examples with synthetic, laboratory and field data, respectively. In addition, its effectiveness on predicting structural responses under a future earthquake is illustrated, showing its potential for various downstream applications in seismic structural health monitoring. [ABSTRACT FROM AUTHOR]

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In this paper, we present effective and efficient identification methods with a higher accuracy for fractional order system (FOS) with time delays and nonzero initial condition. We construct a new hybrid of orthonormal Bernoulli polynomials and block pulse functions (HOBPBPFs), and then derive the explicit representation for fractional integral operational matrix (FIOM) based on the HOBPBPFs via Laplace transformation. The orthogonality of polynomials gives us a higher accuracy of the proposed method. Based on it, new identification methods for FOS with input or state delay as well as nonzero initial condition are proposed. In particular, the variable least squares identification method for FOS with state delay using a hybrid seems to be the first within our knowledge. The effectiveness and efficiency of the proposed method is illustrated in several simulations via the comparison with methods using block pulse functions (BPFs) or a hybrid of Bernoulli polynomials and BPFs. [ABSTRACT FROM AUTHOR]

In order to solve the issues of large computation and control performance affected by motor parameters in the conventional model predictive torque control (MPTC) of permanent magnet synchronous motors (PMSMs), a robust model predictive torque control strategy with online parameter identification based on an improved differential evolution extended Kalman filter (IDEEKF-RMPTC) is proposed To begin with, and a steady-state voltage vector at the next time is obtained through a low-pass filter and used as the reference voltage vector to select the alternative voltage vector. The parameter robustness of the PMSM system is enhanced, and the computational effort is reduced. In addition, an improved differential evacuation algorithm for the extended Kalman filter (EKF) is designed, and the system noise matrix Q and measurement noise matrix R of the EKF are optimized. The estimation error is reduced; the stability of the system is enhanced; and the accuracy of the identification of the motor parameters is improved. Finally, the computational effort of the system is effectively reduced by the proposed IDEEKF-RMPTC strategy, and the parameter robustness of the PMSM drive system under parameter mismatch conditions is enhanced which are proved by the experimental results. [ABSTRACT FROM AUTHOR]

At present, a large number of fractional differential models of migration processes in soils are developed. Their practical application largely depends on the possibility to determine the values of their parameters. In this regard, we study the possibility of recovering the values of parameters for one such generalized model from noised data in order to assess the threshold of measurement accuracy, beyond which the complication of a model leads to an inability to distinguish its solutions from the solutions of simpler models. We consider the 1D fractional-order model of water head dissipation in water-saturated soil with linear deformation that includes the Caputo–Fabrizio derivative with respect to the time variable and the Riemann–Liouville derivative with respect to the space variable. Direct problems for this model are proposed to be solved by an optimized computational procedure based on a finite-difference scheme. Inverse problems of model's parameter identification are solved using a multi-threaded Particle Swarm Optimization technique. The results of computational experiments showed that the values of model parameters can be restored with less than 10 % relative error for the number of input water head values equal to 1000 and the level of noise less than 5 % . Our results also show that the order of the Riemann–Liouville derivative can be with an average relative error of less than 3 % restored even at 10 % level of noise and 40 input values, when the accuracy of other parameters' restoration drops significantly. [ABSTRACT FROM AUTHOR]

Radar direction finding is an essential cooperative monitoring method for aircraft, and the directional parameters of the direction-finding antenna determine the accuracy of direction finding. A modified amplitude comparison direction finding model is proposed based on the traditional principle of adjacent amplitude comparison direction finding and the error of amplitude comparison direction finding. Based on the modified amplitude comparison direction finding model, an error correction genetic simulated annealing (COR-GSA) algorithm is proposed to identify unknown directional antenna directionality parameters by determining the antenna directionality parameters that need to be identified. Identification experiments were conducted using a self-designed dual channel DF receiver, and 1000 sets of aircraft real azimuth data were used for verification. The results showed that using the COR-GSA algorithm to identify antenna directional parameters had the highest DF accuracy. Finally, the identified antenna directionality parameters were used to track the aircraft’s azimuth, and the tracking error was reduced by 18.3% compared to the tracking error without azimuth correction. [ABSTRACT FROM AUTHOR]

In this work we focus on the development of a numerical algorithm for the inverse elastography problem. The goal is to perform an efficient material parameter identification knowing the elastic displacement field induced by a mechanical load. We propose to define the inverse problem through a quadratic optimization program which uses the direct problem formulation to define the objective function. In this way, we end up with a convex minimization problem which attains its minimum at the solution of a linear system. The effectiveness of our method is illustrated through numeral examples. [ABSTRACT FROM AUTHOR]

Objective. In quantitative dynamic positron emission tomography (PET), time series of images, reflecting the tissue response to the arterial tracer supply, are reconstructed. This response is described by kinetic parameters, which are commonly determined on basis of the tracer concentration in tissue and the arterial input function. In clinical routine the latter is estimated by arterial blood sampling and analysis, which is a challenging process and thus, attempted to be derived directly from reconstructed PET images. However, a mathematical analysis about the necessity of measurements of the common arterial whole blood activity concentration, and the concentration of free non-metabolized tracer in the arterial plasma, for a successful kinetic parameter identification does not exist. Here we aim to address this problem mathematically. Approach. We consider the identification problem in simultaneous pharmacokinetic modeling of multiple regions of interests of dynamic PET data using the irreversible two-tissue compartment model analytically. In addition to this consideration, the situation of noisy measurements is addressed using Tikhonov regularization. Furthermore, numerical simulations with a regularization approach are carried out to illustrate the analytical results in a synthetic application example. Main results. We provide mathematical proofs showing that, under reasonable assumptions, all metabolic tissue parameters can be uniquely identified without requiring additional blood samples to measure the arterial input function. A connection to noisy measurement data is made via a consistency result, showing that exact reconstruction of the ground-truth tissue parameters is stably maintained in the vanishing noise limit. Furthermore, our numerical experiments suggest that an approximate reconstruction of kinetic parameters according to our analytic results is also possible in practice for moderate noise levels. Significance. The analytical result, which holds in the idealized, noiseless scenario, suggests that for irreversible tracers, fully quantitative dynamic PET imaging is in principle possible without costly arterial blood sampling and metabolite analysis. [ABSTRACT FROM AUTHOR]

Aiming at the current lithium-ion battery storage power station model, which cannot effectively reflect the battery characteristics, a proposed electro-thermal coupling modeling method for storage power stations considers the characteristics of the battery body by combining the equivalent circuit model and accounting for the effect of temperature on the battery. Based on the modeling of a single lithium-ion battery, the equivalent circuit model and thermal model are integrated to create the battery's electro-thermal coupling model. The parameters of this coupling model are determined using the particle swarm algorithm. On this basis, the battery compartment model of the energy storage station is analyzed and verified by utilizing the circuit series-parallel connection characteristics. Subsequently, the electro-thermal coupling model of the energy storage station is established. The dual Kalman filter algorithm is utilized to simulate and validate the electric-thermal coupling model of the energy storage power station, considering ontological factors such as battery voltage, current, and temperature. The results demonstrate that the established coupling model can accurately determine the SOC and temperature of the power station. This ability allows for a more precise reflection of the battery characteristics of the energy storage station. It also validates the accuracy and effectiveness of the electric-thermal coupling model of the energy storage station. This finding is crucial for assessing the state and ensuring the safe operation of the battery system in the energy storage station. [ABSTRACT FROM AUTHOR]

This work presents a complete workflow for the parameter identification of an MBS model representing a vibratory conveying system. First, the MBS model built within the multibody dynamics tool HOTINT is shown. Essential components, such as contact modeling and adaptive time step control, are explained in detail. The model includes a considerable amount of parameters that need to be identified. Altough, this model is fully deterministic, the complex contact dynamics can cause major effect on results by minor parameter variations. Thus, parameter variations usually show statistical variance, which is characteristic for real conveying processes too. For parameter identification it is important to detect parameters that significantly correlate with results. A method that allows to distinguish significant and non-significant parameters is presented. The application to the multibody system shows that stiffness, restitution and sliding friction of the contact model are the essential parameters. Next, the parameter identification is discussed with particular attention to multimodal distributions, that can occur in conveying systems. Furthermore, the integration of statistics into the deterministic simulation model is discussed. In order to demonstrate the applicability of the proposed methods, a parameter identification is performed based on measurement data of a single part on a real conveying system in the development site of STIWA Automation GmbH. Finally, the validity of the fully parametrized simulation model is shown by investigating geometric adjustments of the conveyor. In order to further verify the identified set of parameters, a conveying process with multiple parts is analyzed and compared in measurement and simulation. [ABSTRACT FROM AUTHOR]

Background: Loess is prone to large deformation and flow slide due to natural and artificial interfaces inside. The strength of these interfaces controls the mechanical properties of loess. Obtaining their mechanical parameters through in-situ testing is essential for evaluating the mechanical stability in loess engineering with interfaces. Methods: By developing a borehole micro static cone penetration system and creating various types of loess with interfaces, extensive borehole penetration model tests were conducted to observe changes in cone tip resistance during penetration. The response surface method was used to analyze the impact of various test conditions on the calculated resistance. A three-dimensional surface fitting method was employed to establish the relationship between penetration parameters and shear strength parameters, which was validated through in-situ testing. Results: The developed borehole micro static cone penetration system achieves overall miniaturization while providing significant penetration power and ensuring an effective penetration distance. Cone tip resistance development during penetration can be divided into three stages: initial, rapid increase, and slow increase. The transition times between these stages vary for different soils. Calculated resistance is positively correlated with dry density and normal stress and negatively correlated with water content. A quadratic positive correlation was established between calculated resistance and shear strength parameters during penetration. In composite soils, the interaction between water content and normal stress is strong. Compared to intact soil samples, the shear strength parameters of composite soils are more prominently influenced by water content. Conclusion: A system for testing interface mechanical parameters was innovatively developed, fulfilling the need to obtain interface shear strength parameters for deep soil. This study can provide support for ensuring the long-term stability of the loess slope or subgrade with interfaces. [ABSTRACT FROM AUTHOR]

Classical physical modeling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts of data (data-driven) are the two most common methods used for the mapping of complex physical processes. In recent years, the efficient combination of these approaches has become increasingly important. Continuum mechanics in the core consists of conservation equations that-in addition to the always-necessary specification of the process conditions-can be supplemented by phenomenological material models. The latter are an idealized image of the specific material behavior that can be determined experimentally, empirically, and based on a wealth of expert knowledge. The more complex the material, the more difficult the calibration is. This situation forms the starting point for this work's hybrid data-driven and model-based approach for mapping a complex physical process in continuum mechanics. Specifically, we use data generated from a classical physical model by the MESHFREE software (MESHFREE Team in Fraunhofer ITWM & SCAI: MESHFREE. https://www.meshfree.eu, 2023) to train a Principal Component Analysis-based neural network (PCA-NN) for the task of parameter identification of the material model parameters. The obtained results highlight the potential of deep-learning-based hybrid models for determining parameters, which are the key to characterizing materials occurring naturally such as sand, soil, mud, or snow. The motivation for our research is the simulation of the interaction of vehicles with sand. However, the applicability of the presented methodology is not limited to this industrial use case. In geosciences, when predicting the runout zones of landslides or avalanches and evaluating corresponding protective measures, the parameterization of the respective material model is essential. [ABSTRACT FROM AUTHOR]

Indentation is a versatile method to assess the hardness of different materials along with their elastic properties. Recently, powerful approaches have been developed to determine further material properties, like yield strength, ultimate tensile strength, work-hardening rate, and even cyclic plastic properties, by a combination of indentation testing and computer simulations. The basic idea of these approaches is to simulate the indentation with known process parameters and to iteratively optimize the initially unknown material properties until just a minimum error between numerical and experimental results is achieved. In this work, we have developed a protocol for instrumented indentation tests and a procedure for the inverse analysis of the experimental data to obtain material parameters for time-dependent viscoplastic material behavior and kinematic and isotropic work-hardening. We assume the elastic material properties and the initial yield strength to be known because these values can be determined independently from indentation tests. Two optimization strategies were performed and compared for identification of the material parameters. The new inverse method for spherical indentation has been successfully applied to martensitic steel. [ABSTRACT FROM AUTHOR]

Coffee, one of the most widely consumed commodities globally, embodies a sensory experience deeply rooted in social, cultural, and hedonic contexts. The cold brew (CB) method, characterized by cold extraction, is a refreshing and unique alternative to traditional coffee. Despite its growing popularity, CB lacks defined preparation parameters and comprehensive analysis of its aromatic composition. In this study, we aimed to obtain a representative extract of the volatile matrix of CB and characterize the aroma of sensory-active compounds using advanced techniques such as headspace–solid-phase Microextraction (HS-SPME) and headspace-solid-phase extraction (HS-SPE) for volatile compound extraction, followed by gas chromatography–olfactometry–mass Spectrometry (GC-O-MS) for compound identification. Optimization of the HS-SPME parameters resulted in the identification of 36 compounds, whereas HS-SPE identified 28 compounds, which included both complementary and similar compounds. In HS-SPME, 15 compounds exhibited sensory activity with descriptors such as floral, caramel, sweet, and almond, whereas seven exhibited sensory activity with descriptors such as chocolate, floral, coffee, and caramel. This comprehensive approach to HS-SPME and HS-SPE aroma extraction with GC-O-MS offers an efficient methodology for characterizing the aroma profile of CB, paving the way for future research and quality standards for this innovative coffee beverage. [ABSTRACT FROM AUTHOR]

The study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%. [ABSTRACT FROM AUTHOR]

Featured Application: Optimization of premium food can production. This research aims to propose a novel approach for evaluating and minimizing scraps in an industrial production of premium food cans with distortion printing. Beyond conventional formability criteria, a waving requirement is introduced to ensure aesthetic quality of the printed graphics. The research focuses on real production conditions, specifically involving double-cold-reduced (DR) low-carbon steel sheets and chromium-coated tin-free steel with a thickness of 0.16 mm. The sheets are laminated on both sides with a plastic film prior to undergoing distortion printing on the exterior. Subsequently, a blank is subjected to a drawing-redrawing process to form a food can. To address challenges associated with characterizing these thin sheets, a material parameter identification method is proposed and demonstrated. The thickness profile and flange length are identified as key criteria for this identification process. Measurements of thickness distribution and flange length are obtained using digital image correlation (DIC) and microscopy techniques. Within the manufacturing system, uncertainties related to material properties and forming processes can result in scraps or defects. To analyze these processes, finite element analysis (FEA) is employed and validated through experiments. For the evaluation of scrap rates, uncertainty propagation is conducted using a metamodeling technique, specifically employing radial basis function (RBF) neural networks. The study concludes by offering process optimization recommendations aimed at reducing the scrap rate. [ABSTRACT FROM AUTHOR]

This paper presents a new adaptive modification of active disturbance rejection control (ADRC) with parameter estimation based on a recursive least-squares (RLS) method. The common ADRC used in many applications relies on the simple approach, which assumes the simplification of the object into an integral chain form. However, this model-free ADRC does not guarantee the stability of a closed-loop system in the presence of noticeable modeling uncertainties, so it is compared in this paper to another approach, in which the linear part of the system is included in the ADRC framework (generalized ADRC). This incorporation of the model is examined in the paper for a wide range of model and controller parameters, considering also the presence of external disturbances as well as parameter uncertainties, pointing out the limitations of fixed-gain algorithms. Then, the adaptive modification of the model-based ADRC is proposed, which is equipped with a real-time estimation of model parameters by means of the RLS method in continuous time. The stability conditions of the proposed modification of the algorithm in the closed control loop are also analyzed. It can be concluded that, under appropriate conditions, the inclusion of information about known plant parameters into the ADRC can noticeably improve the conditions of the control system. The proposed adaptive model-based approach enables quality improvement during the control process even with initially unknown parameters, for time-varying parameters, and in the presence of parametric uncertainties and external disturbances. The tests were performed on a real plant—the task of controlling the angular velocity of the direct current (DC) motor was considered. [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve the parameter estimation accuracy of the system to be identified by using measurements from a known system. By introducing the transfer gain matrix and setting the effective identification criterion, a novel transfer sparse identification method is raised, which deals with the sparse issues more precise. Besides, the unbiased form is given in the parameter analysis and the recursion form can prevent the dimension catastrophe related problems. Moreover, in order to test the effects of the transfer and avoid bad performance, a negative transfer analysis condition is carried out. Finally, the simulation verifies the enhancements and benefits of the proposed transfer sparse identification method, confirming that the transfer performance outperforms better than that of no transfer, especially on the zero parameters identification. [ABSTRACT FROM AUTHOR]

Introduction: Nowadays, five-phase permanent magnet synchronous motors have been widely used in the industrial and transportation fields, and the existing sliding mode control methods for speed control systems can no longer meet the requirements such as fast response and good stability. Methods: In light of the aforementioned considerations, the study initially employs mathematical modeling to elucidate the five-phase permanent magnet synchronous motor. Secondly, on the basis of proportional-integralderivative sliding mode control, radial basis function and Takagi-Sugeno-Kang fuzzy model are introduced for parameter identification and optimization and regulation. Finally, a new neural network regulation algorithm and speed control strategy are proposed. Results and Discussion: The experimental results demonstrated that the expected parameter optimization rate of the regulation algorithm can reach 90%, and the overshooting amount under small inertia working condition is only 3%, and the adjustment time is 0.02 s. The new control algorithm can be used to control the motor speed with the lowest speed fluctuation and the fastest recovery time. In addition, when affected by the load torque, the motor speed controlled by the new strategy fluctuated the least, with a speed drop of only 1% and the fastest recovery time of 0.02 s. It exhibited the lowest control error of 3.7% and the lowest overshooting amount of 5.9%. Conclusion: In summary, the suggested approach has the potential to significantly enhance the speed control system’s control performance while maintaining strong resilience and anti-interference capabilities. The method has certain guiding significance for the practical application of five-phase permanent magnet synchronous motor speed control system. [ABSTRACT FROM AUTHOR]

This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range. As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Chemodivergent synthesis of benzofurans and 2,3‐dihydrobenzofurans has been realized. Under a reaction system consisting of DBDMH and K2CO3 as promotors, controlled conditions enabled the formation of two sets of valuable heterocycles from the tandem transformation of enaminones and salicylaldehydes. The key to success was the identification of the reaction parameters, in which the imine intermediate which was formed by transient halogenation coupling and substitution processes underwent either aldol condensation/annulation or imine hydrolysis/aldol condensation. The additives NH4Cl or Fe2(SO4)3 controlled the unique selectivity of this reaction. A broad substrate scope of enaminones and salicylaldehydes has been employed in this reaction, demonstrating excellent functional group tolerance and versatility. [ABSTRACT FROM AUTHOR]