Search

A novel time-dependent deterministic SEIRS model, extended with vaccination, hospitalization, and vital dynamics, is introduced. Time-varying basic and effective reproduction numbers associated with this model are defined, which are crucial metrics in understanding epidemic dynamics. Furthermore, a parameter identification approach has been used to develop a numerical method to compute these numbers for long-term epidemics. We analyze the actual COVID-19 data from the USA, Italy, and Bulgaria to solve appropriate inverse problems and gain an understanding of the time evolution behavior of the basic and effective reproduction numbers. Moreover, an insightful comparison of key coronavirus data and epidemiological parameters across these countries has been conducted. For this purpose, while the basic and effective reproduction numbers provide insights into the virus transmission potential, we propose data-driven criteria for assessing the actual realization of the transmission potential of the SARS-CoV-2 virus and the effectiveness of the applied restrictive measures. To obtain these results, we conduct a mathematical analysis to demonstrate various biological properties of the new differential model, including non-negativity, boundedness, existence, and uniqueness of the solution. The new model and the associated numerical simulation tools proposed herein could be applied to COVID-19 data in any country worldwide and hold a promising potential for the transmission capacity and impact of the virus. [ABSTRACT FROM AUTHOR]

Machines with highly saturated magnetic circuits are utilized to maximize drive efficiency. However, their control is non-trivial due to highly non-linear characteristics, and therefore, an accurate parameter identification procedure is crucial. This paper presents and validates a comprehensive flux linkage identification procedure. Several steps needed for accurate parameter identification and their influence on the estimation results are described. The impact of all main parts of the electrical drive, i.e., dead times introduced between switching off and switching on the power transistors, phase delay from the current low-pass filters, delay from the control system, and misalignment of the d – axis angle due to the machine asymmetries is considered and partially eliminated. An improved identification procedure with the look-up table-based dead time compensation and estimation of equivalent circuit resistance is applied to estimate the parameters of the highly saturated PMSM traction machine from the fourth generation of the Toyota Prius, and the obtained results are compared with a Finite Element Method-based model of this machine. Finally, dynamic test results are conducted to prove the proposed approach's accuracy. [ABSTRACT FROM AUTHOR]

Accurate parameters identification of photovoltaic(PV) models is essential for state assessment of PV systems, as well as for supporting maximum power point tracking and system control, thus holding significant importance. To precisely identify parameters of different PV models, this paper proposes an improved JAYA algorithm based on self-adaptive method, termed Sjaya. Sjaya incorporates three position update strategies, all utilizing adaptive factors, automatically transitioning from explorative to exploitative behaviors, enhancing the population’s ability to escape local optima in the solution space and avoiding premature convergence. The first strategy involves learning towards the best and worst individuals in the population, with the individual iteration direction perturbed by adaptive and normal distribution probability factors to enhance population exploration. The second strategy entails learning towards superior and inferior subgroups, effectively leveraging information from the population, with the ranges of these two subgroups continuously evolving throughout the iteration process. In the third strategy, a novel individual selection mechanism is devised, allocating selection probabilities to individuals based on the exploration phase. Individual updates entail learning from three selected individuals within the population, thereby enhancing population diversity. The proposed Sjaya method is employed to address the parameters identification problem of single diode, double diode, and photovoltaic module models of various photovoltaic types. In numerical experiments, each algorithm was tested 30 times. The average root mean square error (RMSE) of Sjaya for the single diode model and double diode model of RTC France were 9.86022E-04 and 9.849674E-04, respectively. In addition, we use three PV modules to detect Sjaya and competing algorithms. The RMSE of Sjaya on the Photo Watt-PWP 201 module, STM6-40/36 module and STP6-120/36 module is 2.431177E-03, 1.772275E-03 and 1.568231E-02 respectively. The synthesis of experimental findings and analysis indicates that Sjaya outperforms other methods in terms of competitiveness, while also demonstrating high effectiveness and robustness. [ABSTRACT FROM AUTHOR]

This paper deals with the parameter identification of induction machine rotor and stator windings. These two magnetically coupled circuits are described and analyzed to explain the novel method that can identify the set of substitute parameters (not equivalent circuit parameters) which can be used for some analysis of transient processes in a stationary induction machine. The parameters are: inductive leakage factor, substitute rotor resistance, substitute rotor inductance, and substitute mutual inductance. The method belongs to the group of so-called off-line methods, i.e., it is performed on a machine in a standstill, and it assumes a star-connected induction motor. The correctness of the method is verified by calculation as well as by measurement. Moreover, the practical use of the method is shown in an example of how to determine the rotor winding temperature rise, and the results of the measurement on an induction motor with a power of 0.75 kW are presented. The method can be performed even when the rotor circuit is hidden inside the completely closed rotor construction, as in the case of a squirrel cage induction motor, i.e., without access to the rotor circuit. [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]

Purpose: The purpose of this study is to identify the dynamic parameters of journal bearings in asymmetric rotor systems without additional test runs or excitations. Design/methodology/approach: An asymmetric rotor-bearing test rig was set up for the identification experiment. Comparations were made between the measured response of the asymmetric rotor and the symmetric rotor. The mathematical model of the asymmetric rotor is established by the finite element method. The identification algorithm is based on the model of the rotor and the measured vibration response to identify bearing parameters. The influence of modeling error and measurement noise on the identification results are numerically analyzed. The dynamic parameters of the journal bearings under different rotational speeds are identified and compared with the theoretical values calculated by the perturbation method. Findings: The experiment results show that the vibration characteristics of the asymmetric rotor and the symmetric rotor are different. The numerical evaluation of the identification algorithm shows that the algorithm is accurate and has good robustness to modeling error and measurement noise. The identified dynamic parameters agree reasonably well with the parameters derived from the theoretical bearing model. Originality/value: The proposed identification method uses the unique vibration characteristics of asymmetric rotors to identify the bearing dynamic parameters. As the method does not require excitations or additional test runs, it is suitable for the field test. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0096/ [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]

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]

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]

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]

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 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]

In this paper, the mechanical behavior of 10 and 14 vol.% SiCp/Al composites was studied with both experiments and numerical analysis. A 3D macrostructure model based on modified Gurson–Tvergaard–Needleman (GTN) model was developed to predict the elasto‐plastic response and fracture behavior of particle reinforced aluminum matrix composites (PRAMCs). The particle size effects on the plastic strengthening induced by geometrically necessary dislocations (GNDs) were introduced, and the size distribution of the SiC particles was obtained according to the statistical analysis of the microstructure. The material parameters for GTN model were determined with a parameter identification process, and the validity of the numerical results was validated by the agreement of the experimental stress–strain curve. Results reveal that particle size randomness plays an important role in the fracture morphologies. Highlights: Establish a macro‐mechanical model to predict failure behavior of PRAMCs.Modify the GTN damage model to introduce the effect of particle failure and decohesion.Identify the parameters for the modified GTN model and validate the model by comparing with experimental results.Find that particle size randomness plays an important role in fracture morphologies. [ABSTRACT FROM AUTHOR]

Accurate equivalent orthotropic material parameters (OMPs) of the stator core and windings are crucial for calculating the natural frequencies of the electrical machines (EMs), which is essential for the vibration and noise prediction. To perform a precise modal analysis of the EMs, an analytical method (AM) is proposed to identify equivalent OMPs of the stator core and windings for electrical machines in this study. Firstly, the AM for modal analysis of the stator core and assembly is derived using the first-order shear deformation theory. Furthermore, an identification method for the equivalent OMPs is proposed by employing the particle swarm optimization algorithm. Then, the method is implemented on an EM for the OMP identification, and the results demonstrate that the method not only achieves high accuracy but also requires less computation time. Finally, utilizing the identified OMPs, the modal frequencies of the entire EM are computed with a theoretical model, which is verified through finite element simulations and modal tests. The virtual springs technology is used to consider the actual boundaries, and the influence of different boundary conditions on the modal frequencies of the EM are compared. This study significantly contributes to the efficient and precise identification of OMPs in EMs, as well as facilitating rapid computation of modal frequencies. It also holds a pivotal position in the vibration and noise prediction of the EMs. [ABSTRACT FROM AUTHOR]

This paper presents the modelling of a novel underactuated underwater vehicle called Blucy. It is a modular vehicle used in various underwater monitoring missions. A benchmark model is essential for developing robust controllers, navigation systems, path following, and path generation algorithms to improve the vehicle's capabilities. In this context, a workflow that balances experimental and numerical simulation methods is implemented, where physical parameters such as mass and inertial properties are computed using a well-designed CAD model. The hydrodynamic coefficients are calculated using CFD simulations, while the added mass coefficients are determined using the AMCOMP. A four-quadrant thrust model is built using existing data in the literature and later refined based on the experimental result as well as the simulation data. Finally, based on the estimated parameters, the developed model is validated against the data gathered during a mission. • This paper proposes an end-to-end UUV benchmark model, validated with mission data. • A balanced workflow for parameter estimation, blending experiments and simulations. • Application of AMCOMP to a complex underwater vehicle for the first time. [ABSTRACT FROM AUTHOR]

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, 1-axis inductance, 1-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, 1-axis inductance, 1-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. [ABSTRACT FROM AUTHOR]

In this paper, synchronization analysis and parameters identification issues are explored for uncertain delayed fractional-order BAM neural networks. By designing pertinent state feedback control strategies and parameters updated laws, some ample criteria are procured for ensuring the finite-time synchronization and the Mittag-Leffler synchronization of the considered networks via exploiting the Lyapunov function theory, fractional calculus theory and inequality analysis techniques, meanwhile, the settling time of finite-time synchronization is given, which relates to the initial values. Moreover, parameters identification is actualized triumphantly for uncertain or unknown parameters. Finally, numerical examples are provided to show the availability of the theoretical results. [ABSTRACT FROM AUTHOR]

The present study proposes a new parameter estimation technique for undefined parameters of proton exchange membrane fuel cell (PEMFC) stack models. To provide an efficient model identifier, an objective function by the Sum of Squared Error (SSE) between the empirical data and the predicted date of the PEM fuel cell stack has been employed, and the aim is to minimize this objective function. Here, a new improved design of Remora Optimizer is designed and used for the minimization purpose. The improved method has been improved by self-adaptive weighting for improving the convergence rate and for providing promising balance searching in both initial and next steps, and chaos mechanism for increasing the optimization process while keeping the exploration term based on pseudorandom values. The technique is used in two test cases and then a comparison of the results with several newest techniques is performed. Simulation results showed that the proposed IROA method with 2.05% and 0.019% error ratio for NedSstack PS6 and Horizon 500W PEMFC stack models, respectively, provides the best results compared with other comparative methods. Simulation achievements indicate that the presented approach has higher efficiency than the other compared methods. [ABSTRACT FROM AUTHOR]

The current work uses a novel modified algorithm to propose a new method for identifying parameters in solid oxide fuel cells. The method advocates using an improved design of a newly defined cat and mouse optimizer to find unknown parameters with the minimum sum of squared error (SSE) between experimental data and the results obtained from our constructed model. The technique is then applied to a studied case, and sensitivity evaluation based on pressure and temperature changes is used for its authentication. The method is then put in comparison with several other latest techniques to show its better effectiveness to deliver the highest confirmation with the experimental data. Sensitivity analysis showed that during temperature variation in 562.71°C, 650.81°C, 697.79°C, 747.87°C, and 780.80°C, the proposed method provides the minimum SSE, which are 9.53e-3, 6.95e-3, 5.53e-3, 5.08e-4, and 4.29e-4, respectively. Also, pressure variations from 1 atm to 5 atm by the proposed method, with 4.05e-3, 3.37e-3, 2.76e-3, 1.95e-3, and 1.62e-3, respectively, provides the minimum SSE. [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. [ABSTRACT FROM AUTHOR]

The fuel cell is vital in electrical distribution networks as a distributed generation in today's world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies' transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current–voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer's side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model. [ABSTRACT FROM AUTHOR]

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual's judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control. [ABSTRACT FROM AUTHOR]

Data from the World Health Organization indicate that Bulgaria has the second-highest COVID-19 mortality rate in the world and the lowest vaccination rate in the European Union. In this context, to find the crucial epidemiological parameters that characterize the ongoing pandemic in Bulgaria, we introduce an extended SEIRS model with time-dependent coefficients. In addition to this, vaccination and vital dynamics are included in the model. We construct an appropriate Cauchy problem for a system of nonlinear ordinary differential equations and prove that its unique solution possesses some biologically reasonable features. Furthermore, we propose a numerical scheme and give an algorithm for the parameters identification in the obtained discrete problem. We show that the found values are close to the parameters values in the original differential problem. Based on the presented analysis, we develop a strategy for short-term prediction of the spread of the pandemic among the host population. The proposed model, as well as the methods and algorithms for parameters identification and forecasting, could be applied to COVID-19 data in every single country in the world. [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. • The MPI makes it possible to follow the inter-area frequencies over time. • The MPI can identify periodic and aperiodic signals, even in the presence of noise. • The proposed method can be implemented in any free transient software. • The MPI combines three existing methods to get the best approximation for a signal. [ABSTRACT FROM AUTHOR]

• A two-stage method through restart analysis algorithm highly improves efficiency of biaxial wheel fatigue simulation. • Tire model parameters are identified based on tire stiffness properties obtained by biaxial test machine with inner drum. • The approach ensures high accuracy in predicting disc stresses, while effectively reduces errors in the rim region. To improve accuracy and convergence of biaxial wheel fatigue simulation with coupled nonlinearity, we propose a two-stage approach based on a composite tire model. The tire model is calibrated through an identification procedure, wherein the actual tire stiffness characteristics are matched, effectively addressing the difficulty in lack of tire structure and materials information. Based on the identified tire model, restart analysis algorithm is employed to decouple the biaxial simulation into a two-stage analysis, where wheel deformability is sequentially considered. At the first stage, large deformation of the loaded tire is calculated by modeling the wheel as a rigid part. Then the deformation and stress states of tire are maintained at the second stage, and the wheel elasticity is recovered for stress calculation. Compared to a single-stage direct method, the proposed method significantly reduces computational costs, while exhibiting only a minor stress discrepancy on the wheel rim. Finally, experimental results show that the present method not only ensures high accuracy in predicting stresses of the wheel disc, but also effectively reduces errors on the wheel rim region. It is convinced that the proposed method provides an efficient and reliable means for the comprehensive evaluation of wheel strength in biaxial fatigue tests. [ABSTRACT FROM AUTHOR]

The problem of identifying the parameters of a dynamic system that describes the oscillations of coupled van der Pol oscillators is considered. A method for constructing a nonlinear identifier is proposed to asymptotically estimate unknown input signals (phase vector of the system) in real time. The method of invariant relations is used to synthesize additional relations between the known and unknown quantities in control problems on the trajectories of the system under consideration. Numerical modeling confirms the applicability of the proposed procedure of solving the identification problem. [ABSTRACT FROM AUTHOR]

In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive. [ABSTRACT FROM AUTHOR]

Identifying accurate dynamic parameters is of great significance to improving the control accuracy of industrial robots, but this area is relatively unexplored in the research. In this paper, a new algorithm for accurately identifying the dynamic parameters of a 6-degrees-of-freedom (DOF) robot is proposed by establishing a dynamic model. First, a multibody dynamic model of the robot is established, which can decouple the dynamic parameters of the rigid bodies that make up the robot. Decoupling is the basis of parameters identification. In order to ensure that the model is suitable for large-angle range motion and has good real-time performance, quaternion is used as the angle coordinate, and the model established thereby eliminates the singularity and improves the calculation efficiency. Second, the dynamic model is rewritten, and the dynamic parameters are separated as the parameters to be identified; thus, the parameters identification model is obtained. Furthermore, an identification algorithm based on the least-squares method is proposed, which can realize the accurate identification of dynamic parameters. The algorithm is verified by a simulation example. The results show that the value of the maximum absolute error of the identified parameters is −0.0264, and the maximum relative error is 0.031%, which proves the correctness and accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

Aluminum foam/polyurethane (AF/PU) interpenetrating phase composites damper is a multi-phased hysteretic friction damper, which has a high potential for application to mitigating the seismic responses of building structures. In this paper, the hysteretic responses of the AF/PU composites damper subjected to different loading displacement amplitudes and loading cycles were experimentally investigated, and the main characteristics of the multi-phased hysteretic friction responses are assessed. According to the hysteretic responses of the AF/PU composites damper, a new model that is featured by a hysteresis mechanism, a nonlinear spring and a damping dashpot in parallel is constructed. Through the sensitivity analysis of relevant parameters, the optimum model is obtained and its main basic parameters (E , K e , C) are identified using the Universal Global Algorithm of the First Optimization software. The relationships of the main basic parameters with the loading displacement amplitude and loading cycle are then established. By the comparisons with the experimental results, it is validated that the proposed optimum model has an adequate capacity to capture and predict the important features of the multi-phased hysteretic friction characteristics under the various loading displacement amplitudes and loading cycles. [ABSTRACT FROM AUTHOR]

Simple Summary: This article aims to study the COVID-19 data for New York City. We use both the daily number of second dose vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results. In this article we study the efficacy of vaccination in epidemiological reconstructions of COVID-19 epidemics from reported cases data. Given an epidemiological model, we developed in previous studies a method that allowed the computation of an instantaneous transmission rate that produced an exact fit of reported cases data of the COVID-19 outbreak. In this article, we improve the method by incorporating vaccination data. More precisely, we develop a model in which vaccination is variable in its effectiveness. We develop a new technique to compute the transmission rate in this model, which produces an exact fit to reported cases data, while quantifying the efficacy of the vaccine and the daily number of vaccinated. We apply our method to the reported cases data and vaccination data of New York City. [ABSTRACT FROM AUTHOR]

A thorough understanding of an industrial robot’s dynamic model is critical for practical robotic applications. An effective dynamic model is required for optimal controller design and trajectory planning. Robot manufacturers only provide kinematic data, which can only guarantee a certain level of positioner accuracy. The design of the trajectory-planning scheme, on the other hand, necessitates a thorough understanding of its dynamic features. The identification of dynamic parameters involves several procedures. This study used the Euler-Lagrangian equation to derive the robot dynamic model in its canonical form. To excite each link of the irb1600 robot industrial robot while avoiding displacement, velocity, and acceleration discontinuities at the start and endpoints, a Freudenstein 1-3-5 trajectory based on Fourier series expansion was used. The dynamic parameters were determined using the nonlinear least-squares approach based on the Levenberg-Marquart equation. The Savitzky-Golay smoothing filters improved the identification method by decreasing system noise. [ABSTRACT FROM AUTHOR]

In this article, novel approaches for the identification of synchronous generator (SG) parameters are presented. The proposed approaches are based on the usage of field current, armature current and both of them together measured during the sudden short-circuit test. The identification test method consists of fitting experimentally obtained mentioned currents waveforms with corresponding ones obtained by analytical expressions. The parameters are extracted using a novel hybrid metaheuristic algorithm developed in this article. In this way, the geometric method presented in IEEE standards, which can be inaccurate due to susceptibility to human error, is replaced with modern artificial intelligence techniques. Furthermore, unlike existing literature approaches, field and armature current expressions are not simplified and there is no neglecting of the parameters. As a result, the proposed approaches provide the determination of a larger number of parameters and more accurate fitting of analytical with experimental current waveforms. The applicability and efficiency of using proposed approaches are demonstrated on short-circuit current waveforms measured on 109.6 MVA SG from hydropower plant Bajina Basta in Serbia. The results obtained by using proposed approaches are compared with corresponding results provided by manufacturers, as well as with the results determined using literature-known methods and standardized procedures. It is shown that the proposed approaches are effective, small time consuming, and more accurate than literature known numerical methods and standardized procedures. [ABSTRACT FROM AUTHOR]

The aim of the present work is to compare the interlaminar shear strength and fracture toughness of glued carbon fiber rods obtained using different experimental approaches and provide the effective way to characterise the interlaminar properties for reliable simulation of the delamination. Five different test methods (tension, single shear test, and double shear test, mode I and mode II delamination tests) were performed. Using the explicit LS-DYNA code the finite element model capable of simulating the damage process of bonded connection was developed. The interlaminar connection and delamination criteria were calibrated using the parameter identification methodology implemented in LS-OPT optimization tool. [ABSTRACT FROM AUTHOR]

Modeling and optimization of the proton exchange membrane fuel cells (PEMFCs) raise a crucial challenge due to their characteristics of multi-variability and nonlinearity natures. To ensure an accurate and reliable model for PEMFCs, best values of their uncertain parameters should be defined carefully. A conventional artificial ecosystem optimizer (AEO) and an improved and developed AEO (called IAEO) are used to realize the later aim. In the proposed IAEO, a dynamic crossover pattern is presented to enable the algorithm to achieve better solution, and also prevent the stuck in local optima. Sum of squared errors (SSE) defines the fitness function subjected to set of practical constraints. The proposed IAEO-based algorithm is analyzed and demonstrated on different typical benchmarking PEMFCs modules widely used in the literature. Comprehensive simulations and performance assessments are carried out on the PEMFCs models to affirm the efficacy and robustness of the proposed IAEO based on methodology while simulating the commercial PEMFC stacks behavior in regards to the experimental data. In this context, best values of SSE resulted by IAEO are 0.0116, 0.3359, and 2.1459, for BCS 500-W, 250 W stack, and NedStack PS6, respectively that are very competitive values among other challenging methodologies. This noticeably indicates that the developed IAEO-based method gives better efficiency with the highest robustness and convergence speed compared with the other methods. At a later stage, dynamic performance of PEMFCs stacks are carried out. It can be established that the reported outcomes affirm the superiority and reliability of the IAEO algorithm over the conventional AEO and the other competitors. • IAEO algorithm is developed to accurately identify PEMFC model parameters. • Dynamic crossover pattern is presented to enrich the diversification of the population. • Mutation based-Gaussian strategy is proposed to enhance the quality of solution. • Three types of commercial PEMFC module are investigated in this work. • The statistical tests confirm the superiority of IAEO compared to other challenging algorithms. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are becoming increasingly significant because they can convert solar energy into electricity. The conversion efficiency is related to the PV models' parameters, so it is crucial to identify the parameters of PV models. Recently, various metaheuristic methods have been proposed to identify the parameters, but they cannot provide sufficient accurate and reliable performance. To address this problem, this paper proposes a spiral-based chaos chicken swarm optimization algorithm (SCCSO) including three strategies: (1) the information-sharing strategy provides the latest information of the roosters for searching global optimal solution, beneficial to improve the exploitation ability; (2) the spiral motion strategy can enable hens and chicks to move toward their corresponding targets with a spiral trajectory, improving the exploration ability; and (3) a self-adaptive-based chaotic disturbance mechanism is introduced around the global optimal solution to generate a promising solution for the worst chick at each iteration, thereby improving the convergence speed of the chicken flock. Besides, SCCSO is used for identifying different PV models such as the single-diode, the double-diode, and PV module models. Comprehensive analysis and experimental results show that SCCSO provides better robustness and accuracy than other advanced metaheuristic methods. [ABSTRACT FROM AUTHOR]

The harsh working environment of compressor threats the integrality of bladed disk. Crack is one of the most fatal faults of rotating blades. Detecting the crack of blades at early stage could ensure the safe operation of gas turbine and save economic costs. Blade tip timing (BTT) is a noncontact blade vibration measurement technique, which is widely used in recent years. In this article, a procedure of crack detection using BTT data is proposed. First, some typical parameters are taken into consideration, including resonance frequency, amplitude change of natural frequency, amplitude of blade tip, phase, and interblade spacing. It has been found that the frequency of the blade will decrease with the increment of crack length and reduction of crack height. In addition, the resonance frequency of the bladed disk will split when the blade cracks. The amplitude of different orders of resonance frequency varies with cracks. The amplitude of the first order changes very slightly, whereas the second and third orders are relatively obvious. The blade tip vibration amplitude and phase change with cracks without obvious regularity. The interblade distance will change due to the appearance of cracks. According to the above rules, the frequency and the interblade distance are adopted as crack diagnosis indicators. First, we use the Lomb–Scargle periodogram to quickly determine whether the crack appears and then use the blade frequency and interblade distance to locate the number of the blade having crack. In the experimental test, BTT data measured on a titanium bladed disk is used to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Summary: The scope of this research is a problem of parameters identification of a linear time‐invariant plant, which (1) input signal is not frequency‐rich, (2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the above‐stated problem. Such a filter allows us to obtain a bounded regressor value, for which a condition of the initial excitation (IE) is met. Using the MRE scheme, the recursive least‐squares method with the forgetting factor is used to derive an adaptation law. The following properties have been proved for the proposed approach. If the IE condition is met, then: (1) the parameter error of identification is bounded and converges to zero exponentially (if there are no external disturbances) or to a set (in the case of them) with an adjustable rate, (2) the parameters adaptation rate is a finite value. The above‐mentioned properties are mathematically proved and demonstrated via simulation experiments. [ABSTRACT FROM AUTHOR]

In order to reduce the dimension of wind farm model and improve the simulation efficiency under the premise of ensuring the accuracy, this article proposes a dynamic equivalent method for permanent magnet synchronous generator (PMSG) based wind farm cluster utilizing the adaptive extended Kalman filters. Combining the dynamic model of PMSG, the equivalent wind turbine parameters that need to be identified are incorporated into the extended state variables, and an online parameters identification method based on extended Kalman filter (EKF) theory is put forward. To eliminate the influence of random noise on the accuracy of identification, the adaptive technology is introduced into EKF to estimate the process and measurement noise covariance in real time through the Sage–Husa estimator. Thus, a wind farm cluster equivalent model is obtained. Finally, the effectiveness and robustness of the proposed method are verified by time-domain simulation and frequency-domain simulation in IEEE 39-bus system. [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion battery accidents occur frequently, and thermal runaway accidents are common. Lithium plating may occur in batteries at different temperatures, state of charge (SOCs), charging rates, etc., and lithium plating at the anode is one of the important incentives for thermal runaway of batteries. The plating of lithium metal is closely related to the anode potential of the battery. In this paper, based on the anode potential sensor, the anode potential of the battery under various working conditions is detected in real‐time, and the direct anode equivalent circuit model (DAECM) and the indirect anode equivalent circuit model (IDAECM) are proposed. The DAECM directly estimates the anode potential based on identifying anode parameters. Therefore, the IDAECM is proposed to estimate the cathode potential directly based on cathode parameter identification, to estimate the anode potential indirectly. First, the high‐precision P2D model is used to simulate the cathode, anode, and full cell potentials under various working conditions. According to the 1/50C discharge rate test, the open circuit voltage (OCV)‐SOC curve of the cell is obtained. The NEDC conditions are used to identify the parameters of the model, and the values of each parameter of the two models under different SOCs are obtained. According to the parameter identification results above, the anode potential under multirate constant current charging (MRCC) test conditions is estimated. Based on the method above, the IDAECM model is suitable for estimating the anode potential in the early stage of fast charging with large rate constant current, while the DAECM model is suitable for the final stage of fast charging with small rate constant current. [ABSTRACT FROM AUTHOR]

For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot. [ABSTRACT FROM AUTHOR]

Summary: State of charge (SOC) is a vital parameter which helps make full use of battery capacity and improve battery safety control. In this paper, an improved adaptive dual unscented Kalman filter (ADUKF) algorithm is adopted to realize co‐estimation of the battery model parameters and SOC. Notably, the covariance matching method that can adapt the system noise covariance and the measurement noise covariance is used to improve the estimation accuracy. Besides, singular value decomposition (SVD) is utilized to deal with the non‐positive error covariance matrix in both unscented Kalman filters, further enhancing the stability of estimation algorithm. Verification results under Dynamic Stress test and Federal Urban Driving Schedule test indicate that improved ADUKF can achieve more accurate SOC estimates with error band controlled within 2.8%, while that of traditional dual unscented Kalman filter (DUKF) can only be controlled within 5%. Moreover, robustness analysis is also conducted and the validation results present that the proposed algorithm can still provide precise SOC prediction results under some disturbances, such as erroneous initial SOC, inaccurate battery capacity, and various ambient temperatures. [ABSTRACT FROM AUTHOR]

In this article, an improved deadbeat predictive current control (DPCC) method with parameters identification for surface-mounted permanent magnet synchronous machines (SPMSMs) is proposed. With the proposed DPCC method, zero steady-state current error and deadbeat dynamic current response could be achieved, even with inaccurate initial motor parameters. On basis of the conventional DPCC method, a novel parameters identification for the stator resistance and inductance is developed, which is the main contribution of this article. The proposed parameters identification method works based on a reconstructed characteristic vector from the disturbance observer with current injection. Compared with traditional recursive-least-square methods, the proposed method can be implemented with greatly reduced computation burden. Additionally, since the design is established based on the fully discretized model, the effectiveness will be guaranteed on both low-frequency and high-frequency motors, which is a significant advantage of the proposed method. [ABSTRACT FROM AUTHOR]

A new approach was used to characterise the hardening, instability and fracture behaviour of ultra-thin (0.1 mm) stainless steel sheets under stress triaxiality ranging from 0.37 to 0.66. The Swift and the linear hardening laws were used to capture the stress–strain relationship for the complete level of plastic deformation that exceeds that achievable by a uniaxial tensile test. For this, the Virtual Field Method (VFM) was applied, which uses the strain field measured on the surface of the notched samples, to output the material hardening parameters. To determine one set of hardening parameters that fits all conditions of stress triaxiality, an upper bound of major strain was selected for the VFM fitting that optimises a single set of hardening parameters across all of the stress triaxiality conditions. The void coalescence parameter in a Gurson-Tvergaard-Needleman (GTN) fracture model was calibrated and the model results for fracture initiation were validated experimentally with a quasi-biaxial stretching test. [ABSTRACT FROM AUTHOR]