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In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom). • Social changes influencing the contact rate transmission. • Phenomenological model and predictions for epidemic outbreaks. • Time-dependent parameters identification in epidemic models. • Environmental changes influencing the contact rate of transmission. • Public health policies and mutation dynamics changing the conditions of contagion. [ABSTRACT FROM AUTHOR]

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The growing electric vehicle (EV) sector, while tackling climate issues, also encounters obstacles concerning resource usage and a rise in EV disposal. This study examines the circularity framework in the EV sector to address these problems, emphasizing the importance of reusing and upcycling resources for sustainability. Moreover, numerous nations have implemented recycling and R&D policies to offer legal and policy backing for the advancement of recycling technology. This report will investigate and contrast various recycling technologies. Furthermore, it involves an examination of key figures in the EV sector to identify deficiencies in the EV materials supply chain and tactics for minimizing waste. Conversations with a variety of EV stakeholders will provide perspectives on the creative methods businesses are implementing to tackle these obstacles. By embracing the principles of a circular economy, the EV industry can act as a model for a sustainable future, decreasing its environmental footprint and encouraging a more efficient use of resources. [ABSTRACT FROM AUTHOR]

In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer's to the piezoelectric layer's thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials' electromechanical coupling coefficient squared, k 31 2 . It is also found that the upper-limit relative corrections are only related to k 31 2 , i.e., the higher k 31 2 is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field. [ABSTRACT FROM AUTHOR]

In order to reduce the influence of motor parameters variation on the current harmonic suppression and improve the accuracy of harmonic compensation voltage, a current harmonic suppression method considering parameters variation is proposed to suppress the current harmonic for interior permanent magnet synchronous motors (IPMSM) control system in this paper. Based on the current harmonic suppression method, a forgetting factor recursive least square method (FFRLS) is introduced to build an adaptive current harmonic suppression loop. The FFRLS can identify the IPMSM parameters, and the identification results of parameters variation are introduced into the steady-state harmonic voltage equation to dynamically update the harmonic compensation voltage. Therefore, the influence of motor parameters variation on the harmonic compensation voltage generation is reduced, and the effect of current harmonic suppression is improved. Finally, the effectiveness of the proposed method is verified by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

There is a strong dynamic coupling between the base of free-floating space robot and the space manipulator. In order to reduce the interference of the movement of the space manipulator on the robot's base, the trajectory of the space manipulator needs to be optimized. In this paper, the joint path of space manipulators is optimized by using the pseudo-spectral method to reduce the impact of the arm movement on the base. For space robots, parameters identification is the premise of trajectory planning. A parameters identification method is proposed, which only needs to measure the base's angular speed. Numerical simulation results verify the validity of the proposed method. The attitude angle of floating base returns to the initial state after maneuvers of space manipulators. [ABSTRACT FROM AUTHOR]

Inter-area frequency modes of electromechanical oscillations are decisive factors that influence the performance of electrical power systems. However, the accurate estimation of these frequencies remains a challenge due to the non-linear behavior of the response system following a disturbance and the strong coupling among the frequencies. This work describes a curve fitting and electromechanical frequency estimation algorithm, called Multi-method Piecewise Identification (MPI), based on a combination of the Matrix Pencil Method (MPM), the Prony method, and Subspace Identification (SSI), choosing the best approximation of an interval basis according to the model accuracy index (MAI) and the mean square error (MSE). The advantages and limitations of MPI were analyzed by computational analyses that were carried out with Python-based code that estimates the low frequencies for several ringdown signals, including oscillating signals from Australian and Brazilian systems that arise after large disturbances. To ensure robustness to random and noisy conditions, the method was tested with a synthetic signal with Gaussian white noise and time-variant frequencies, and compared to the methods mentioned above, the Hilbert–Huang Transform (HHT), and Eigensystem Realization Algorithm (ERA). The MPI has shown robust results and is capable of obtaining a better fitting for the signal than the other methods.

To describe materials deformation with viscoplastic characteristics, the Anand model is usually used. For a certain material, the classical method based on the saturation stress of material deformation is usually adopted to identify the parameters in its Anand model. However, for some materials, the saturation stress can not be obtained in the measurable strain range of general experiment equipment, and the classical method should be modified for this situation. Hence in this paper, a method used to identify the saturation stress of material from the insaturation state of deformation is proposed. For the proposed method, the identification of saturation stress is transferred to a problem of obtaining the solution of an objective function, which is derived from the stress-strain curves of insaturation state. By combining the proposed method, the classical Anand parameters identification method can be used in a wider range than before. [ABSTRACT FROM AUTHOR]

Since installing solid oxide fuel cells (SOFCs)-based systems suffers from high expenses, accurate and reliable modeling is heavily demanded to detect any design issue prior to the system establishment. However, such mathematical models comprise certain unknowns that should be properly estimated to effectively describe the actual operation of SOFCs. Accordingly, due to their recent promising achievements, a tremendous number of metaheuristic optimizers (MHOs) have been utilized to handle this task. Hence, this effort targets providing a novel thorough review of the most recent MHOs applied to define the ungiven parameters of SOFCs stacks. Specifically, among over 300 attempts, only 175 articles are reported, where thirty up-to-date MHOs from the last five years are comprehensively illustrated. Particularly, the discussed MHOs are classified according to their behavior into; evolutionary-based, physics-based, swarm-based, and nature-based algorithms. Each is touched with a brief of their inspiration, features, merits, and demerits, along with their results in SOFC parameters determination. Furthermore, an overall platform is constructed where the reader can easily investigate each algorithm individually in terms of its governing factors, besides, the simulation circumstances related to the studied SOFC test cases. Over and above, numerical simulations are also introduced for commercial SOFCs’ stacks to evaluate the proposed MHOs-based methodology. Moreover, the mathematical formulation of various assessment criteria is systematically presented. After all, some perspectives and observations are provided in the conclusion to pave the way for further analyses and innovations. [ABSTRACT FROM AUTHOR]

The growing electric vehicle (EV) sector, while tackling climate issues, also encounters obstacles concerning resource usage and a rise in EV disposal. This study examines the circularity framework in the EV sector to address these problems, emphasizing the importance of reusing and upcycling resources for sustainability. Moreover, numerous nations have implemented recycling and R&D policies to offer legal and policy backing for the advancement of recycling technology. This report will investigate and contrast various recycling technologies. Furthermore, it involves an examination of key figures in the EV sector to identify deficiencies in the EV materials supply chain and tactics for minimizing waste. Conversations with a variety of EV stakeholders will provide perspectives on the creative methods businesses are implementing to tackle these obstacles. By embracing the principles of a circular economy, the EV industry can act as a model for a sustainable future, decreasing its environmental footprint and encouraging a more efficient use of resources.

In recent years, the demand for photovoltaic (PV) energy has increased parallel to scientific research on PV cells, including electrical modeling, characterization, and extraction of unknown parameters. Moreover, our main contribution in this paper focuses on experimental investigation of the effect of solar radiation and temperature on the performance of a small photovoltaic plant years after its installation in the Ouargla region. As in many parts of the world, this studied area has experienced rising temperatures due to climate change, affecting system outputs. Therefore, in the first phase, we offer to characterize PV modules in various conditions and analyze the electrical parameters’ performance. The results indicate solar radiation and temperature influence PV modules’ electrical parameters. Moreover, the temperature influences the open-circuit voltage, while solar radiation positively impacts the short-circuit current. On the other side, we determine the optimal parameters of these modules and develop an accurate PV model using the bald eagle search algorithm (BES), gradian-based optimizer algorithm (GBO), and whale optimization Algorithm (WOA) based on a single diode model. The achievements show that the BES and GBO algorithms give good results for the optimum estimation of the PV model compared to WOA, where the best fitness was recorded at 0.015608 with the lowest deviation of 0.012565 and 0.039588, respectively. However, the BES has generated the minimum error values and with minimum iteration number, which indicates that this technique is more stable and robust for PV module parameter extraction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for identifying the parameters of the multispecies Lotka-Volterra model based on sampled data. The conditions (nonhyperplanarity, recurrence) under which the "Stripe" algorithm achieves the identification goals are found. The relation between nonhyperplanar recurrent trajectories and the persistent excitation condition for systems of general form is studied. [ABSTRACT FROM AUTHOR]

The vibration response of the plates attached with the free viscoelastic damping layer is investigated to predict the performance of free surface damping treatment. The frequency-dependent dynamic properties of the viscoelastic layer are expressed by fractional Kelvin-Voigt model. A simple and efficient identification approach based on the gradient-based optimization principle is employed to evaluate the viscoelastic model parameters from the experimental data. Both classical and fractional Kelvin-Voigt models are employed to describe the viscoelasticity, and the corresponding model parameters are identified. Further, the application of these identified parameters in the modeling and analyzing of real viscoelastic composite structures is demonstrated through quantitative examples. Step load-induced transient responses of the viscoelastic structures modeled using the classical and fractional model parameters are investigated and compared, to demonstrate the consequences of unknowingly using the classical Kelvin-Voigt model. The effects of the viscoelastic layer thickness on the transient response of the two-layered plate are also studied. [ABSTRACT FROM AUTHOR]

In order to solve the friction and wear problem of the sliding electric contact of the pantograph of electrified railway, the wear characteristics of the pantograph slide were studied by using the self-made sliding electric contact tester of the pantograph slide, and the variation law of the wear rate with the contact current, sliding speed, pressure fluctuation amplitude and fluctuation frequency was obtained. Based on this, the mathematical model expression of the wear rate is constructed through the study of the proportional relationship between no-current-carrying wear, pressure load fluctuation wear, total wear rate under current-carrying friction and wear rate under pure mechanical friction by using Archard wear model. The combination of general global optimization algorithm and McQuart algorithm is used to optimize the parameter identification of the wear rate model expression, and the accuracy of the wear rate model is proved. The qualitative analysis of the wear rate is transformed into the quantitative analysis of the wear rate, which has certain guiding significance for further research on the wear of the arch mesh system. [ABSTRACT FROM AUTHOR]

The precision and stability of the Robot-Assisted Percutaneous Puncture (RAPP) system have become increasingly crucial with the widespread integration of robotic technology in the field of medicine. The accurate calibration of the RAPP system prior to surgery significantly influences target positioning performance. This study proposes a novel system calibration method that simultaneously addresses system hand–eye calibration and robot kinematic parameters calibration, thereby enhancing the surgery success rate and ensuring patient safety. Initially, a Closed-loop Hand–eye Calibration (CHC) method is employed to rapidly establish transformation relationships among system components. These CHC results are then integrated with nominal robot kinematic parameters to preliminarily determine the system calibration parameters. Subsequently, a hybrid algorithm, combining the regularized Levenberg–Marquardt (LM) algorithm and a particle filtering algorithm, is utilized to accurately estimate the system calibration parameters in stages. Numerical simulations and puncture experiments were conducted using the proposed system calibration method and other comparative methods. The experimental results revealed that, among several comparative methods, the approach presented in this paper yields the greatest improvement in the puncture accuracy of the RAPP system, demonstrating the accuracy and effectiveness of this method. In conclusion, this calibration method significantly contributes to enhancing the precision, operational capability, and safety of the RAPP system in practical applications. [ABSTRACT FROM AUTHOR]

An approach to identifying the parameters of the motion model for a surface ship subject to external disturbances is proposed. It uses the measurements of heading, yaw rate, speed through the water, and global satellite navigation system (GNSS) data. The model structure is set in the state space. We use the criterion of how close is the real vehicle response to a given control input to its motion model response under the same disturbances. It is proposed to apply the Kalman filter with the state vector including the disturbances, and an iterated procedure for estimating parameters by minimizing the criterion. It is shown that this ensures stable identification of model parameters under different disturbances. Simulation results are presented to evaluate the quality of identification. The approach was validated in full-scale tests of a high-speed boat. [ABSTRACT FROM AUTHOR]

Unknown constant parameters estimation problem for a nonlinear time-varying system with delayed measurements is considered. The objective of this work is to design an adaptive observer for a nonlinear time-varying system. The observer must provide asymptotic convergence of the unknown constant parameters estimates to their true values. The main idea behind the method is to perform the parametrization of initial dynamical system based on GPEBO (Generalized Parameter Estimation Based Observer) technology and to build a linear regression model. The identification of linear regression model unknown parameters is performed using least square method with forgetting factor. This work develops the previously published approach for the case of nonlinear time-varying systems with delayed measurements. New parameters estimation algorithm can be applied for technical tasks, such as technical condition control and automatic control systems design.

In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer’s to the piezoelectric layer’s thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials’ electromechanical coupling coefficient squared, k312. It is also found that the upper-limit relative corrections are only related to k312, i.e., the higher k312 is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field.

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, ��-axis inductance, ��-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, ��-axis inductance, ��-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified. Keywords: parameters identification, teaching–learning-based optimization, hub motor, temperature rise.

We present an inertia-augmented relaxed micromorphic model that enriches the relaxed micromorphic model previously introduced by the authors via a term Curl P · in the kinetic energy density. This enriched model allows us to obtain a good overall fitting of the dispersion curves while introducing the new possibility of describing modes with negative group velocity that are known to trigger negative refraction effects. The inertia-augmented model also allows for more freedom on the values of the asymptotes corresponding to the cut-offs. In the previous version of the relaxed micromorphic model, the asymptote of one curve (pressure or shear) is always bounded by the cut-off of the following curve of the same type. This constraint does not hold anymore in the enhanced version of the model. While the obtained curves' fitting is of good quality overall, a perfect quantitative agreement must still be reached for very small wavelengths that are close to the size of the unit cell. [ABSTRACT FROM AUTHOR]

Ultra-precise actuation at extremely low speeds over a broad range is a major challenge for advanced manufacturing. A novel two-axis differential micro-feed system (TDMS) has been proposed recently to overcome the low-speed crawling of the worktable. However, due to the diversity of the force states of the TDMS, the methods for identification identifyingof friction parameters traditionally (like the all -components identification method, ACIM) didn't did not perform well. And many studies on the performance of the pre-sliding phase of the TDMS are missing. Therefore, a novel whole-system identification method (WSIM) based on the TDMS was proposed in this paper to precisely identify the friction parameters under different states of motion. The generalized Maxwell sliding (GMS) friction model was also applied to improve the accurate description of the pre-sliding. A novel corrected Stribeck curve based on the TDMS (TDMSSC) was proposed under the uniqueness of the TDMS structure. Control experiments showedn that the WSIM has higher precision and stability rather thancompared torather than the ACIM, and the correction of the Stribeck curve for the TDMS makes a contribution to the performance. This method significantly improves the accuracy and stability of the machine tool drive system. [ABSTRACT FROM AUTHOR]

Selective catalytic reduction (SCR) technology is the most widely used denitrification method in industry. However, the calibration of its kinetic model is very problematic due to the large number of parameters involved and the nonlinear characteristics, especially for the undesired reactions such as formation-decomposition of ammonium nitrate (AN), ammonium sulfate (AS), ammonium bisulfate (ABS), etc. In this paper, the synthetic gas test bench and the SCR system of WD10 marine diesel engine burning high-sulfur fuel oil (HSFO) and low-sulfur fuel oil (LSFO) are taken as the research objects. Firstly, based on the experimental data obtained from the synthetic gas test bench, the pre-exponential multiplier and activation energy of each reaction were identified using the Discrete-Grid algorithm. In this work, considering that the production of N2O, SO3, AN, AS, and ABS in the undesired reactions is very low, not on the same order of magnitude as that of NO and NH3, it is easy to overlook their reaction characteristics in the optimization process. We have adopted an approach that amplifies the error between the predicted concentration and the observed concentration of the species to solve this problem. Secondly, a detailed and complete SCR kinetic model was constructed. Finally, with the parameter identification results obtained from the synthesis gas bench as initial values, all the kinetic parameters of the WD10 marine engine SCR model was calibrated simultaneously using the Discrete-Grid algorithm. The results showed that the SCR kinetic model derived from the synthetic gas test bench was suitable for the real marine engine SCR system when the range of temperature and space velocity (SV) of the synthetic gas bench test is similar to that of the marine SCR system. [ABSTRACT FROM AUTHOR]

Double casing joints are flexible pipe joints used for connecting aircraft fuel pipelines, which can compensate for the displacement and corner of the connected pipes and have complex mechanical characteristics. However, it is difficult to use sensors to directly measure the mechanical connection parameters of flexible joints. In this paper, we construct a coupling dynamics model and parameter identification of a double casing joint. Firstly, we analyze the structure and working principle of double-layer casing joints and establish the dynamics model of a single-layer flexible joint based on the transfer matrix method. Then, we deduce the coupling matrix of the inner and outer pipeline according to the deformation coordination conditions combined with matrix dimension extension. We establish the coupling dynamics model of flow–solid coupling of double casing joints. Furthermore, parameters such as equivalent stiffness and damping of each motion of the double casing joint in the casing unit are identified using the force-state mapping (FSM) method, and an analytical solution in the frequency domain under hammering excitation is given by the dynamics model. Finally, the dynamics test bench of the double casing joint for aircraft fuel is set up, and the free mode test of the double casing joint assembly is carried out. The results show that under free boundary hammering excitation, the theoretical and experimental frequency-domain response results are well matched, both obtaining seven main resonance peaks, and the maximum error is 9.45%, which shows the validity of the pipeline dynamics modeling method with a double casing joint. [ABSTRACT FROM AUTHOR]

It is of great significance to have research on the deformation characteristics and stress distribution of aortic wall. Reliable prediction of constitutive parameters requires an inverse process, which possesses challenges. This work proposes an inverse procedure to identify the constitutive parameters of aortic walls, which integrates nonlinear finite element method (FEM), random forest (RF) model and hybrid Random Search (RS) and Grid Search (GS) algorithm. FEM models are first established to simulate nonlinear deformation of aortic walls subjected to uniaxial tension tests. A dataset of nonlinear relationship between the engineering stress and main stretch of aortic walls is created using FEM models and the nonlinear relationship is learned through RF model. The hybrid RS&GS algorithms are used to adjust the major hyperparameters in RF. Then the optimized RF is utilized to predict constitutive parameters of aortic walls with the help of uniaxial tension tests. The prediction results show that the RF optimized by hybrid RS&GS (RF-RS&GS) approach is an effective and accurate approach to identify the constitutive parameters of aortic walls. The present RF-RS&GS model can be further extended for the predictions of constitutive parameters of other types of nonlinear soft materials. Additionally, the relative importance of constitutive parameters of aortic walls in Gasser–Ogden–Holzapfel (GOH) strain energy function is investigated. It is found that the parameters C 1 0 and κ in GOH are most intensive to the engineering stress of aortic walls. [ABSTRACT FROM AUTHOR]

With the characteristics of high energy conversion efficiency, high energy density and low operating temperature, the proton exchange membrane fuel cells (PEMFC) have become one of the green energy sources with broad prospects. The establishment of accurate mathematical model of the PEMFC is the basis of simulation and control strategy. At present, some intelligent algorithms have certain drawbacks, and can hardly find the balanced point between precision and computational time. In this study, a novel parameter identification approach combining the hybrid particle swarm optimization (PSO) algorithm with differential evolution, i.e. hybrid DEPSO, is proposed to obtain the unknown parameters in the PEMFC mathematical model and solve the problems of premature convergence of PSO and poor global search ability of differential evolution. Six benchmark functions are applied to verify the performance of the algorithm. The results prove that the hybrid DEPSO can evade local optimum preferably while having swifter convergence rate. Two PEMFC stacks are investigated and modeled. In order to evaluate the accuracy of model, the sum of squared errors between the measured voltage and the estimated output voltage are examined. Numerical results show higher accuracy of the hybrid DEPSO-based model comparing with other recently published optimization approaches. Furthermore, the simulation results indicate that the accuracy of the PEMFC model optimized by the hybrid DEPSO algorithm improves 0.19-1.86%, which can provide a new solution the multi-objective optimization problem and promote the practical application of the PEMFC. [ABSTRACT FROM AUTHOR]

Stand-alone operation of electrical power generators is of increasing interest, because of geographical or financial limitations and reliability issues. This paper presents a simple and innovative approach for sensor-less vector control of stand-alone Doubly Fed Induction generators (DFIG). Unlike grid-connected DFIG, accurate estimation of speed/position of the rotor is crucial for frequency control of stand-alone DFIG. As a new work, an open-loop flux and speed estimator have been developed for the DFIG application. The advantage of a developed speed/angle estimator is its independency of rotor current sensors, the feature that enhances system robustness. Such as other off-line observers, developed estimators are sensitive to machine parameters. To overcome this problem, particle-swarm-optimization has been practically implemented in a digital-signal-processor for parameter identification. Both simulation and experimental results prove the acceptable performance of the proposed approach, especially in preserving the amplitude and frequency of sinusoidal load voltage. [ABSTRACT FROM AUTHOR]

This manuscript presents the remote monitoring of the main parameters in the 18O isotope separation technological process. It proposes to monitor the operation of the five cracking reactors in the isotope production system, respectively, the temperature in the preheating furnaces, the converter reactors and the cracking reactors. In addition, it performs the monitoring of the two separation columns from the separation cascade structure, respectively, the concentrations of the produced 18O isotope and the input nitric oxides flows. Even if the production process is continuously monitored by teams of operators, the professionals who designed the technical process and those who can monitor it remotely have the possibility to intervene with the view of making the necessary adjustments. Based on the processing of experimental data, which was gathered from the actual plant, the proposed original model of the separation cascade functioning was developed. The process computer from the monitoring system structure runs the proposed mathematical model in parallel with the real plant and estimates several signal values, which are essential to be known by the operators in order to make the appropriate decisions regarding the plant operation. The separation process associated with the final separation column from the separation cascade structure is modeled as a fractional-order process with variable and adjustable differentiation order, which represents another original aspect. Neural networks have been employed in order to implement the proposed mathematical model. The accuracy, validity and efficiency in the operation of the proposed mathematical model is demonstrated through the simulation results presented in the final part of the manuscript. [ABSTRACT FROM AUTHOR]

In this work and in the context of PDE constrained optimization problems, we are interested in identification of a parameter in the diffusion equation proposed in [1]. We propose to identify this parameter automatically by a gradient descent algorithm to improve the restoration of a noisy image. Finally, we give numerical results to illustrate the performance of the automatic selection of this parameter and compare our numerical results with other image denoising approaches or algorithms.

Model parameters identification of the fuel cells has several applications in practice. However, due to its nonlinear dynamic and its complicated nature, solving it based on most of the classic methods is too hard. Also, using any kind of methods based on soft computing does not also provide a satisfying result which can be proved based on the no free lunch theorem. In the present study, a new methodology has been proposed for optimum parameters identification of the Proton exchange membrane fuel cell (PEMFC) stacks. Here, an improved version of African vulture optimizer (IAVO) is organized for this purpose. To prove the accuracy of the proposed method, it is applied to three standard test cases and the results have been compared with some five latest techniques. The results showed that the proposed IAVO algorithm with 1.98(±0.49), 0.03(±0.02), and 1.08(±0.47) parameter fitting value for NedStackPS6, BCS 500 W, and SR-12 500 W, respectively, provides the best results with minimum error value. This shows that the efficiency of the proposed IAVO algorithm is too better than the other methods in parameter identification of the PEMFC stacks.

The first stage in designing an efficient solid oxide fuel cell (SOFC) model is to accurately determine its unknown parameters. The modeling and analysis of the steady-state and dynamic responses of SOFC models require precise and reliable parameter identification. As a result, this paper proposes four novel metaheuristic optimization algorithms to extract the parameters of a reliable SOFC and build an accurate model whose response matches the actual measured response. The paper considers the following optimization algorithms: Gorilla troops optimizer (GTO), Honey badger algorithm (HBA), African vultures optimization algorithm (AVOA), and Chimp optimization algorithm (ChOA). These recent algorithms have the benefits of providing a suitable balance between the global exploration and the local phases of exploitation that improve the population variety and efficiently reach a global optimal solution. These algorithms are used to determine the optimal parameters of the SOFC steady-state model using a set of a Siemens cell experimental dataset at various operating temperatures. Moreover, a Proportional-Integral-Derivative (PID) controller is proposed in the dynamic model to improve the transient response of a Siemens 100 kW stack voltage and current at different power levels. Furthermore, extensive comparisons based on statistical analysis are used to validate the results of the proposed optimizers. The proposed optimizers are then compared with other optimization techniques presented in previous literature. The results show that the GTO and HBA optimizers outperform other algorithms in determining the best SOFC parameters that produce accurate voltage-current and power-current characteristic curves.

This paper considers the identification of the Hammerstein system with immeasurable process noise. The complexity of the Hammerstein system makes it difficult to obtain accurate mathematical expressions of the parameters, or even impossible to obtain accurate mathematical expressions at all. In this contribution, we cast the Hammerstein system parameter identification problem as a posterior parameter estimation problem and take a sampling and Stein variational inference viewpoint to solve it. Improved Stein variational gradient descent(ISVGD)algorithm is proposed in posterior parameter calculation. Compared with other methods, not only the prior distribution of parameters but also the proposal distribution of parameters is considered. In other words, The posterior information is enriched and the parameter identification accuracy is improved. At the same time, ISVGD and reversible jump markov chain monte carlo (RJMCMC) algorithms are used (called ISVGD-RJMCMC algorithm) in the structure detection problem. In the algorithm, the correct basis function number k can be found in the parameter estimation. It tends to be accurate but is slow to converge. Three simulation examples were given to demonstrate the proposed algorithm's effectiveness. Furthermore, the performances of these approaches were analyzed, including parameter estimation accuracy and error, system order estimation and parameter convergence analysis. • Stein variational inference is used in Hammerstein system identification. • Improved Stein variational gradient descent(ISVGD)algorithm is proposed. • ISVGD-RJMCMC algorithm is used in the structure detection problem. [ABSTRACT FROM AUTHOR]

For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model's accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed. [ABSTRACT FROM AUTHOR]

Bald eagle search algorithm (BES) is a recent metaheuristic algorithm based on bald eagle hunting behavior. Like other metaheuristic algorithms, the BES algorithm is prone to entangle in local optimums due to limited diversity, sluggish convergence rate, or improper equilibrium between exploitation and exploration. Thus, adaptive parameters are injected into the original BES to overcome these shortcomings. These parameters are a function of the current and the max number of iterations. They provide the eagle with more diversity during the exploration and exploitation phases. The modified BES is tested on test functions provided by Congress on Evolutionary Computation 2020 and Congress on Evolutionary Computation 2022. The obtained results are compared to that of other reliable and recent algorithms. In addition, analysis of variance and Tuckey tests are utilized to confirm the results' significance. Due to its benefits, lithium-ion batteries are employed in more and more applications. However, extracting its parameters is challenging due to its complex model. Hence, the proposed algorithm will handle this task to approve its performance in complex problems. The significant benefit of this extraction method is its excellent precision, with fitness value declining (root mean square error) to 0.89 × 10−3 compared to the original BES (1.013 × 10−3) with a standard deviation of 1.12 × 10−3. To confirm the performance of mBES, a second battery was tested with the New European Driving Cycle profile. The mBES has the lowest fitness values (0.058896) and the least standard deviation (5.89 × 10−7). • A new MA named mBES is created based on the standard BES algorithm. • Estimation of a Li-ion battery model to validate the performance of the proposed mBES. • Compared to its competitors, mBES has demonstrated more confident and dependable conduct. • The proposed method's superiority is demonstrated. [ABSTRACT FROM AUTHOR]

To realize the stable tracking control of the optoelectronic stabilized platform system under nonlinear friction and external disturbance, an active disturbance rejection controller (ADRC) with friction compensation is proposed to improve the target tracking ability and anti-disturbance performance. First, a nonlinear LuGre observer is designed to estimate friction behavior and preliminarily suppress the interference of friction torque on the system. Then, an ADRC is introduced to further suppress the residual disturbance after friction compensation, and the stability of the ADRC system is also proved. The effectiveness of this scheme is proved by simulation experiments, and this scheme is compared with conventional ADRC and LuGre friction feedforward compensation. The simulation results show that an ADRC with LuGre friction compensation is better with trajectory tracking performance, which suppresses the influence of disturbance and improves the stability of the optoelectronic stabilized platform system. [ABSTRACT FROM AUTHOR]