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The objective of this paper is to recommend an adjusted Susceptible-Exposed-Infectious-Removed (SEIR) model that characterizes the temporal patterns of various individuals affected by the omicron variant in an epidemic. This model considers factors such as vaccination, asymptomatic cases, indoor and outdoor air, hospitalizations, and deaths, as well as the impact of immunization and memory trace. While many recent studies overlook the complexities of multiple strains, including their diverse transmission rates and reaction to vaccines, this study introduces a new fractional derivative model to examine the spread of the omicron variant of COVID-19 and the implementation of a vaccination campaign. A thorough theoretical analysis is conducted, and the critical factor (R c r n z) is calculated using the model equations. It is demonstrated that when R c r n z is less than 1, the disease-free state is globally asymptotically stable, meaning that the epidemic diminishes. Moreover, the stability of both global and local equilibrium points is examined. Numerical simulations are employed to demonstrate the alignment between the numerical findings and theoretical characteristics. The model is adjusted to experimental data that reflect the progression of the omicron variant of COVID-19 in Guangzhou, exhibiting a satisfactory performance in predicting the number of infected individuals, thereby suggesting its capability to accurately estimate asymptomatic cases. Furthermore, to emphasize the benefits of employing fractional differential equations, the paper provides examples related to memory trace and hereditary characteristics. Moreover, the examined models are expected to be applied and expanded upon in order to contribute to the formulation of policies for disease control during times of limited vaccine availability. To summarize, the model appears to be a sufficient tool for researching and managing infectious diseases. It is projected that as the Omicron variant's prevalence declines, there will be a reduced need for respiratory-focused precautions. • An SVEIAOPHRD model is put forward to mathematically analyze the spread of the omicron variation of COVID-19. • Mathematical criteria for determining the stability of the equilibria are established. • An efficient nonstandard approach for resolving the continuous problem is suggested and examined. • The numerical simulations validate the analytical and numerical outcomes depicted in this study. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive super-twisting global integral terminal sliding mode control algorithm for the trajectory tracking of autonomous robotic manipulators with uncertain parameters, unknown disturbances, and actuator faults. Firstly, a novel global integral terminal sliding mode surface is designed to ensure that the tracking errors of autonomous robotic manipulators converge to zero in finite time and the global robustness of the system is also enhanced. Then a new adaptive method is devised to deal with the adverse effect of nonlinear uncertainty. To suppress the chattering phenomenon, the adaptive super-twisting algorithm is used in this paper, which can ensure that the control torque is a continuous input signal. Based on the adaptive mechanism, the adaptive super-twisting global integral terminal sliding mode controller is developed to provide superior control performance. The stability analysis of the system is demonstrated by using the Lyapunov method. Ultimately, the effectiveness of the control scheme is confirmed by a simulation study. • A novel adaptive finite-time global integral terminal sliding mode control scheme is designed. • A novel global integral terminal sliding mode surface is proposed to ensure faster convergence and better global robustness. • A novel adaptive law is designed to adjust the estimation parameters according to the change of system state to avoid overestimation. • The actuator fault is considered to make the proposed control logic more suitable for practical applications. [ABSTRACT FROM AUTHOR]

This paper presents an inverse finite element (FE) approach aimed at estimating multi-layered human penile tissues. The inverse FE approach integrates experimental force–displacement and boundary deformation data of penile tissues with a developed FE model and uses new experimental data on human penile tissue. The experimental study encompasses whole organ plate-compression tests and individual layer tensile and compression tests, providing comprehensive insights into the tissue's mechanical behaviour. The biomechanical characterisation of penile tissue is of crucial significance for understanding its mechanical behaviour under various physiological and pathological conditions. The FE model is constructed using the realistic geometry of the penile segment and appropriate constitutive models for each tissue layer to leverage the accuracy and consistency of the model. Through systematic variation of tissue parameters in the inverse FE algorithm, simulations achieve the best match with both force–displacement and deformed boundary results obtained from the whole organ plate-compression tests. Test results from individual tissue layers are also utilised to assess the estimated parameters. The proposed inverse FE approach allows for the estimation of penile tissue parameters with high precision and reliability, shedding light on the mechanical properties of this complex biological organ. This work has applications not only in urology but also for researchers in various disciplines of biomechanics. As a result, our study contributes to advancing the understanding of human penile tissue mechanics whilst the methodology could also be applied to a range of other soft biological tissues. This research uses a multi-target inverse finite element (FE) approach for estimating the material parameters of human penile tissues. By integrating experimental data and a realistic FE model, this study achieves high-precision constitutive model parameter estimation, offering key insights into penile tissue mechanics under various loading conditions. The significance of this work lies in the use of this inverse FE approach for fresh-frozen human penile tissues, to identify the mechanical properties and constitutive models for both segregated tunica albuginea and corpus cavernosum as well as intact penile tissue segments. The study's scientific impact lies in its advancement of the understanding of human urological tissue mechanics, impacting researchers and clinicians alike. [Display omitted] [ABSTRACT FROM AUTHOR]

• A new Weibull log-logistic mixture distribution accumulation generation operator is established. • A novel grey model with multiple interaction effects is established. • The robustness and stability of the proposed method is proved and validated. • A new parameter estimation method is designed. • The proposed method is superior to other seven benchmark models in four case studies. Forecasting the failure of hard disk drives is important in server operation and has attracted increasing attention. However, current disk drive warning systems suffer from high false positive rates and high resource consumption when dealing with hard disk drive overall failure. Therefore, to accurately and stably predict hard disk drive overall failure, this paper develops a new Weibull Log-Logistic grey forecasting model with multiple interaction effects. Firstly, to capture and fit the trend of hard disk failure data flexibly and reduce the volatility, a new accumulation generation operator is established by introducing the Weibull Log-Logistic mixture distribution. Secondly, the proposed model constructs a multiple interaction term to describe the nonlinear relationship of the dependent variables on the system behavior series and the multiple interaction effects between the independent variables. Then, the parameter estimation method is designed to improve the fitting accuracy of the new model, in which linear estimation method, nonlinear estimation method, and meta-heuristic optimization algorithm are used to calculate the parameter values according to their categories. Finally, four varieties of hard disk drive data sets from BackBlaze are selected as study cases to validate the effectiveness of the proposed model. The results show that the mean MAPE of the proposed method is 2.1181 %, 2.2306 %, 8.1712 %, and 4.3417 %, respectively, corresponding to (hard disk drives ST8000NM0055, ST12000NM0007, ST10000NM0086, and ST14000NM0138), and the average value of the evaluation metrics are optimal in all competing models. Furthermore, it is observed that the number of reallocated sectors has the greatest influence in causing the failure of hard disk drives. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

The widespread use of wheeled mobile robots (WMRs) in many fields has created new challenges. A critical issue is wheel slip, which, if not accurately determined and controlled, causes instability and deviation from the robot's path. In this paper, an intelligent approach for estimating the longitudinal and lateral slip of wheels is proposed that can effectively compensate for the negative effects of slippage. The proposed algorithm relies on three regression networks to estimate the longitudinal slip ratio of the right and left wheels and sideslip angle on terrains with different friction coefficients. The datasets collected during tests on different surfaces with various maneuvers are used to train the artificial neural networks (ANNs). A developed dynamic model of a WMR considering wheel slip and modified traction force is presented. The adaptive robust controller, based on sliding mode control (SMC), is introduced to deal with the problems related to slipping, unknown uncertainties, and disturbances. The simulation results demonstrate that the presented controller has better performance than SMC in handling external disturbances and uncertainties, which leads to reduction in tracking error and faster convergence to zero. The proposed controller with an intelligent slip estimator, has been applied to a four-wheel mobile robot to demonstrate its effectiveness and feasibility. The high accuracy of slip estimation in the mentioned intelligent algorithm has resulted in the presented method being on average 26% more effective in reducing the tracking error than the control method without slip compensation in each test for circular trajectory. • Developing a dynamic model of WMR using modified traction force. • Proposing slip estimator based on an intelligent learning for both longitudinal and lateral slip. • Eliminating the time delay in slip estimation and compensating for its effect in the same time step by predicting the slip amount. • Improved robot performance in trajectory tracking on slippery surfaces. [ABSTRACT FROM AUTHOR]

This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state–space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input–output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples. • Present a state estimator to estimate the states of bilinear state space systems. • Apply the hierarchical principle to handle the associated terms among sub-algorithms. • Present a data filtering-based multi-innovation gradient-based iterative algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the novel leader-following tracking control method is proposed for mobile robots, which consists estimation technique of the speed of the leader robot (LR), and a parameter-dependent controller for the follower robot (FR). To estimate the speed of LR, a novel Physics Informed Machine Learning (PIML) is proposed to learn the dynamics of the state observer via the error state model. The dynamics of the state observer in PIML play a significant role for stable learning and state estimation of uncertain models. The gain of the parameter-dependent controller is determined by the convex combination of the robust control technique via the polytopic model. Finally, the tracking performance of the proposed method is verified through the simulation and experiment. • A novel PIML with the state observer is proposed to learn physical equations. • A parameter estimation method is developed for the time-varying uncertain systems. • The performance of the parameter-dependent controller is verified by the experiment. [ABSTRACT FROM AUTHOR]

In this paper, the Cramér-Rao Lower Bound (CRLB) for estimating the rotation parameters of pulsars using X-ray pulsar observation data is deduced, and the calculation equation is presented. In order to verify the correctness of the deduced equation, we use the X-ray pulsar observation data to estimate pulsar rotation parameters, and obtain the root mean square error (RMSE) of the estimated pulsar rotation parameters through conducting repeated experiments. The experimental results suggest that when the observation time increases, the RMSE gradually approaches the estimated CRLB, and that when the observation time is 2.4 × 106 s, the error between the RMSE of pulsar frequency estimation and the CRLB stays at 10−11 order of magnitude. This verifies that the CRLB expression deduced in this paper is the theoretical lower bound for estimating pulsar rotation parameters. The deduced CRLB in this paper helps determine the minimum variance estimator for pulsar rotation parameter estimation using X-ray pulsar data, providing a benchmark for the comparison between the minimum variance estimator and other estimators. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved enhancement space-variant anisotropic PDE-constrained for image denoising, based on a learning optimization procedure. Since the tensor structure of the Weickert-type operators encodes three critical parameters: λ 1 , λ 2 and θ , which define the local direction geometry in the image and also controls smoothing intensity along image features. We have then adopted an automatic estimation of these parameters based on a PDE-constrained optimization with a learning step of the additional information about the clean image. For the numerical solution of the computed free-noise image, we introduce a non-smooth Alternating Direction Method of Multipliers (ADMM) algorithm. The numerous tests in the experiments part incense, visually and quantitatively, the incomes of this new spatial tensor for PDE smoothing over the other denoising approaches. • An improved enhancement space-variant anisotropic PDE-constrained for image denoising is introduced. • An automatic estimation of the diffusion-tensor parameters based on a bilevel optimization with a learning step is involved. • A non-smooth ADMM algorithm is also performed to compute the solution of the optimization-constrained problem. • The comparative results demonstrate the performance of the proposed algorithm against complex noise structures. [ABSTRACT FROM AUTHOR]

This paper proposes a partially periodic oscillation model, which is motivated by time series modelling for COVID-19 daily confirmed cases, in particular, to represent more accurately the dynamic features of the 7-day periodicity. In order to express the phenomenon of the partial 7-day cycle in the COVID-19 data, some partial periodic part is added to a heterogeneous autoregression model. Estimation algorithm based on the least squares errors and regression analysis is provided and parameter estimation consistency is given along with its proof. A Monte-Carlo simulation study is carried out to investigate the finite-sample performance. The proposed model is applied to the COVID-19 daily confirmed cases of the most affected eight countries which posses the partially periodic oscillation. Model criteria such as RMSE, MAE, HMAE, AIC and BIC are compared with other existing models. Efficiency of the model, relative to the benchmark, is evaluated to reveal its better accuracy performance. Out-of-sample forecasting analysis is conducted as well. The novelty is that this work is a challenging trial to identify the partially periodic oscillation of COVID-19 data, without smoothing, as well as the proposed model outperforms the existing time series models in the empirical analysis of the worldwide COVID-19. • A partially periodic oscillation model combined with heterogeneous autoregression is proposed. • The model is applied to the COVID-19 daily confirmed cases in the top eight countries with partially periodic oscillation. • A simple and robust estimation method is developed. • Model criteria measures such as RMSE, MAE, HMAE, AIC and BIC are evaluated and compared with existing models. • Efficiency of the proposed model, relative to benchmark models, is reported as 126%-163% improvement in some countries. [ABSTRACT FROM AUTHOR]

Strong relation between reliability studies and product quality. And reliability analysis deals with failure time of units and there are many well-known lifetime distributions. In this paper, we adopt the statistical inference of one more flexible one parameter distribution called Suja lifetime distribution. We primarily take into account four methods to estimate the distributional parameter other than the maximum likelihood estimation. The approximate confidence interval and two bootstrap confidence intervals have also been determined. The point and interval estimators of the Suja' parameter are assessed and compared throughout the Monte Carlo simulation study. Finally, the Suja distribution is used to modeled and analyzed three real data sets. [ABSTRACT FROM AUTHOR]

This paper focuses on development of a three-stage iterative identification algorithm for parameter estimation of a Wiener model with autoregressive moving average (ARMA) noise entering between linear and nonlinear part. The proposed three-stage algorithm is developed based on generalized extended gradient iterative (GEGI) algorithm to increase the convergence rate at a low number of iterations. To increase the convergence rate, a second algorithm is derived based on generalized extended least squares algorithm (GELSI). The proposed 3-stage generalized extended least square iterative (3S-GELSI) algorithm will also decrease the computational burden compared to the single-stage algorithm. Finally, to show that the discussed methods can identify the considered system effectively, simulation results of three examples are provided. The results demonstrate that the 3-stage GEGI (3S-GEGI) algorithm outperforms its basic single-stage counterpart in terms of convergence rate. Moreover, the proposed 3S-GELSI algorithm exhibits even greater superiority in terms of convergence rate compared to other existing methods. Additionally, it will be demonstrated that the computational burden decreases in the 3S-GELSI algorithm, as indicated by the reduced number of total flops. • Identification of output nonlinear-Box Jenkins Wiener models with colored noise. • 3-stage algorithm is developed based on GEGI algorithm. • To increase the convergence rate, an algorithm is derived based on GELSI algorithm. • 3S-GELSI algorithm decrease the computational burden compared to the single-stage algorithm. • The unmeasurable signals and the unknown system parameters are estimated effectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The distributed state and parameter estimation problem is investigated in this paper for discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances over a wireless sensor network. A novel architecture for distributed state estimator is introduced that incorporates adaptive coupling gains to govern the information exchange between the sensor nodes under event-triggering mechanism. The aim of this paper is to provide a scalable structure for unknown parameter identification independent of sensor networks' complexity. The boundedness of estimation error is ensured in the framework of uniformly ultimately bounded stability by developing an algebraic connectivity based criterion. The estimator gains including proposed coupling gains are then presented as solution to matrix inequalities. Finally, two simulation examples are presented to demonstrate the effectiveness of proposed estimation architecture. • A novel distributed state estimation structure is introduced. • Both unknown parameters and sensor nonlinearities have been considered. • Event-triggered observer conditions with improved feasibility are developed. • A method for determining coupling gains for complex design problem is revealed. [ABSTRACT FROM AUTHOR]

This paper describes an accurate model of the Electrical Power Subsystem (EPS) of the Thermal Analysis Support and Environment Characterization Laboratory (TASEC-Lab), a university experiment developed for using in high-altitude balloon missions to measure and analyze the convection heat transfer. A Li-ion battery and two high efficient DC–DC converters have been characterized through laboratory tests and fitting to the experimental results. The developed mathematical models described in this paper are used to carry out accurate EPS simulations and are validated by comparison with the experimental data. • Design and modeling of the electrical power subsystem of a stratospheric balloon experiment. • New Li-lion battery discharging model that describes the three main parts: initial decay, linear, and exponential decay. • Formulation of a new DC/DC converter efficiency model. • Simulation and model validation of the electrical power subsystem. [ABSTRACT FROM AUTHOR]

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive for automatic parameter estimation. In this paper, we present a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm finds near-optimal set of parameters to explain observed gauged data. This framework, although applied in a poorly-gauged urban catchment, is adapted for catchments with more detailed observations. The results of the automatic calibration indicate NSE = 0.99 for the V-Tilted catchment, RMSE = 830 mg L-1 for salt concentration pollutograph in a wooden-plane (i.e., 8.3% of the event mean concentration), and NSE = 0.89 in a urban real-world catchment. This paper also explores the issue of equifinality (i.e., multiple parameters giving the same calibration performance) in model calibration indicating the performance variation of calibrating only with an outlet gauge or with multiple gauges within the catchment. [Display omitted] • An automatic calibration algorithm for distributed flood and water quality modeling is developed. • It uses HydroPol2D model and calibrate water quantity and quality parameters globally. • Data from observed gauges such as discharges, depths, and concentration is used for calibration. • Poorly placed gauges and low runoff events can increase equifinality during calibration. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems require bypass diodes to protect PV modules from operating at negative voltages, thus avoiding the degradation of a PV array under partial-shading conditions. However, the presence of bypass diodes produces changes in the power production of the PV array; thus, an accurate bypass diode model is essential to estimate the power production of the PV array, which is required to analyze the viability of a PV installation. Therefore, this paper proposes a modified model for the bypass diodes present in commercial PV arrays; such a proposed model is based on the Schottky equation, including a series resistance. This paper also proposes a non-invasive and non-destructive method for estimating the model parameters, which avoids the requirement of opening the module junction box; thus, it applies to commercial PV arrays. Simulation and experimental results confirm the improved accuracy of the proposed solution over the classical bypass diode model usually adopted for estimating the power production of PV arrays. [ABSTRACT FROM AUTHOR]

This paper gives an overview of parameter estimation and system identification for quantum input–output systems by continuous observation of the output field. We present recent results on the quantum Fisher information of the output with respect to unknown dynamical parameters. We discuss the structure of continuous-time measurements as solutions of the quantum Zakai equation, and their relationship to parameter estimation methods. Proceeding beyond parameter estimation, the paper also gives an overview of the emerging topic of quantum system identification for black-box modelling of quantum systems by continuous observation of a travelling wave probe, for the case of ergodic quantum input–output systems and linear quantum systems. Empirical methods for such black-box modelling are also discussed. [ABSTRACT FROM AUTHOR]

In a life testing experiments, accelerated life tests (ALTs) model has provided a significant decrease for the cost and time. The problem of statistical inference of constant-stress ALTs based on censored data is discussed in this paper. So, we implement partially constant-stress ALTs model to test units have two parameter Kumaraswamy lifetime population under adaptive Type-II progressive censoring scheme. The population parameters as well as acceleration factor are estimated by using maximum likelihood method for point and interval estimation. Two different confidence intervals are obtained under bootstrap technique. Also, Bayesian approach under different loss functions is used to contract the point and interval estimates of the model parameters with the help of Markov chain Monte Carlo method (MCMC). For illustrative purpose a simulate data set are analyzed. Different developed results discussed in this paper are compared through Monte Carlo simulation study. [ABSTRACT FROM AUTHOR]

Despite the simplicity of the Duffing oscillator, its dynamical behaviour is extremely rich. Hence, the Duffing equations are used to describe the dynamic behaviour of many real-world nonlinear systems for a wide range of frequency bands and amplitude of the excitation signal in basic sciences and engineering. For example, the Duffing oscillator has been successfully used to model a variety of physical processes such as stiffening springs, beam buckling, nonlinear electronic circuits, superconducting Josephson parametric amplifiers, and ionisation waves in plasmas etc. Therefore, the identification of the Duffing oscillator model/parameters directly from the measured input–output data is a topic of active research in many scientific fields In this paper, we use the concept of stochastic differential equations (SDEs) to identify a model of the Duffing oscillator. SDE-based grey-box models allow us to capture the underlying mathematical structure describing the physics of the system (e.g. the original Duffing equations) using the drift term and explicitly handling of model uncertainty (or the process noise) using the diffusion term whereas the measurement uncertainty is modelled using the measurement noise term respectively. In this paper, we propose a slight variation of the maximum likelihood estimation framework used for the identification of SDEs based grey-box models yielding improved performance for long-term predictions. The proposed framework is combined with an iterative residual analysis to develop a grey-box model of the forced Duffing oscillator. The benchmark data from the so-called Brussels "Silverbox system", which is an electrical circuit mimicking the forced Duffing oscillator dynamics is used for the identification purpose. Finally, the identified model performance (the simulation errors) is compared with the existing results available in the literature. • The likelihood function of the SDE is modified for long-term predictions. • An iterative residual-based method to develop physical-stochastic models is presented. • Modified likelihood function is used for modelling the Duffing Oscillator. • Competitive results are obtained on the "Silverbox"-benchmark. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to improve the identification efficiency of non-linear systems using the Kalman filter (KF), which is optimised with the Artificial Electric Field (AEF) algorithm. The conventional KF suffers from the proper tuning of its parameters, which leads to a divergence problem. This issue has been solved to a great extent by the meta-heuristic AEF algorithm assisted Kalman filter (AEF-KF). This paper proposes three steps for the identification of the systems while solving the problem as mentioned above. Firstly, it converts the identification model to a measurement problem. Next, the AEF algorithm optimises the KF parameters by considering the fitness function with the KF equations. The third step is to identify the model using conventional KF algorithm with the optimised KF parameters. To evaluate the performance of the proposed method, parameter estimation error, mean squared error (MSE), fitness (FIT) percentage, statistical information and percentage improvement are considered as the performance metrics. To validate the performance of the proposed method, five distinct non-linear models are identified with the Volterra model using KF and the AEF-KF techniques under various noisy input conditions. Besides, the practical applicability of the proposed approach is also tested on two non-linear benchmark systems using experimental data sets. The obtained simulation results confirm the efficacy and robustness of the proposed identification method in terms of the convergence speed, computational time and various performance metrics as compared to KF, Kalman smoother (KS) which is optimised using different state-of-the-art evolutionary algorithms and also other existing recently reported similar types of stochastic algorithms based approaches. • The proposed AEF-KF method resolves the proper tuning problem of Kalman filter. • The role of AEF algorithm is to obtain the optimised set of all the KF parameters. • The proposed method is insensitive to various additive noise input levels. • Simulations carried out on five Volterra models along with two benchmark systems. • AEF-KF outperforms KF and all other reported results with higher convergence speed. [ABSTRACT FROM AUTHOR]

This paper considers problems related to output error models for output data estimation and parameter identification in missing output data systems. In this regard, a new sequentially parallel distributed adaptive signal processing method with the implementation of a low complexity least squares algorithm is introduced to estimate the parameters of an auxiliary model relative to the original system, as well as handling irregularly missing output data in a stochastic framework. The validation of the proposed distributed architecture using the low complexity least squares algorithm is presented in terms of computational complexity and processing time. Measurement results show that the proposed architecture using the low complexity least squares approach provides fast convergence, parallel linear computational complexity, and significantly reduced processing time compared to the sequentially operated recursive least square (RLS) algorithm for parameter identification in missing output data systems. Finally, the effectiveness of the low complexity least squares algorithm is tested with a system example [ABSTRACT FROM AUTHOR]

Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities. • Oscillatory mode assessment in power systems using real world synchrophasor measurement. • Time Varying Filter based EMD (TVF-EMD) approach is used to identify the oscillatory modes assisted with Teager Kaiser Energy Operator (TKEO). • The proposed method is compared with the existing nonstationary algorithms like Variational mode decomposition and Empirical mode decomposition. • Verified with synthetic data, simulation data from a IEEE 16 machine 68 bus standard system and recorded PMU measurement from Indian Power Grid. [ABSTRACT FROM AUTHOR]

• Fractional-order grey neural network is proposed for the first time. • Generalized weighted fractional order accumulation generation operation and its inverse operation is proposed for the first time. • Several numerical examples are used to prove the validity of the proposed model. Recently, grey system, neural network, and fractional order calculus theory have become popular research areas, and an increasing number of scholars have joined these studies, conducted illuminating research, and produced a number of significant results. Numerous research studies have demonstrated that these three strategies are crucial to solving a wide range of practical problems. In this paper, we present a fractional order neural grey system model with a three-layer structure in which the input of the network is a fractional order cumulative sequence, and the output is a predicted value in order to maximize the benefits of each of the three elements. The purpose of this research is to present a strategy for reducing the number of conditions in order to improve the stability of parameter estimation by using QR decomposition. The order of the models is determined by an intelligent optimization algorithm. Finally, real-world examples are used to validate the model's validity, and experimental results indicate that the newly presented model is more accurate than previous models. [ABSTRACT FROM AUTHOR]

This paper presents an experimental activity aimed at assessing performance of techniques for inertia and attitude parameters estimation of an uncooperative but known space target. The adopted experimental set-up includes a scaled-down 3D printed satellite mock-up, a spherical air bearing and a low-cost solid-state LIDAR. The experimental facility also comprises a motion capture system to obtain a benchmark of the pose (position and attitude) parameters and an ad-hoc designed passive balancing system to keep the centre of mass as close as possible to the centre of rotation. The LIDAR-based 3D point clouds, collected while the target rotates on the spherical air-bearing to reproduce the rotational dynamics of an almost-free rigid body, are processed to obtain pose and angular velocity estimate. Those data are used to determine the inertia properties of the target by solving a linear system based on the integral form of Euler equations. Performance and robustness of the algorithm for attitude and inertia properties estimation are assessed considering both the inertia benchmark provided by a high-fidelity CAD model of the target and the pose solution obtained from the motion tracking system and its extrinsic calibration with respect to the LIDAR. • The estimation of attitude and inertia parameters of an uncooperative space target is addressed. • A LIDAR-based algorithmic architecture is experimentally validated. • The design and calibration of the experimental set-up is described. • Errors below 10° and 20% are obtained for attitude and inertia parameter, respectively. [ABSTRACT FROM AUTHOR]

Wind farm (WF) is gradually required to provide inertial support during frequency events nowadays. Existing WF inertial control methods have exhibited limitations in parameter estimation and optimal control parameter tuning under variable operating conditions. Moreover, traditional model-based methods suffer from computational efficiency problems due to their reliance on repeated iterations or derivative computations in optimization process. Thus, this paper proposes an inertial control method for large-scale WF based on hierarchical distributed hybrid deep-reinforcement learning (HDH-DRL). Firstly, the control objectives for the inertial control are defined, and are decomposed on the basis of wind turbines (WTs) division. Next, this paper presents a HDH-DRL based inertial control framework consisting of two levels. The upper-level control is achieved by the hybrid multi-agent DRL (HMA-DRL) algorithm with hybrid action exploration mechanism and multi-agent coordination. The consensus based lower-level control aims to achieve the consensus convergence of the control through the locally distributed interaction among WTs. Finally, the inertial control processes of the model are exhibited; the influences of DRL algorithms on the control are discussed; the computational speed and solution accuracy of the proposed method are compared with the model-based methods; the multi-scenario applicability and performance in local communication failures of the model are analyzed. The results demonstrate that the proposed method significantly improves the active power response and frequency stability, achieving rapid consensus convergence with a power deviation below 0.3%, surpassing traditional control methods by at least 0.2%; also, it demonstrates superior performance across various scenarios, including transient and steady-state conditions, with frequency enhancements up to 0.30% and exceptional stability under different wind conditions and communication disruptions. • Introduces HDH-DRL for improved WF inertial control and frequency stability. • Develops a bi-level HDH-DRL framework for faster, accurate control actions. • Utilizes HMA-DRL with a novel exploration mechanism for complex WF operations. • Achieves better accuracy and quicker computation than model-based methods. • Ensures control robustness across varied wind conditions and communication breaks. [ABSTRACT FROM AUTHOR]

In recent decades, computer vision-based methods have been introduced into engineering structures to provide full-field, non-contact vibration measurement. However, the vision-based vibration measurement method is challenging to measure the targetless structures. This paper proposed a target-free out-of-plane vibration measurement method using projection speckle and camera self-calibration technology. In this method, the camera extrinsic parameter estimation result based on the direct method is used as the initial value in the proposed method. Then, a nonlinear inequality constraint on the extrinsic parameters is established regarding the epipolar condition to minimize the reprojection error. Furthermore, the projection relationship between the projection speckle displacement and the actual displacement of the structure is studied so that the deviation of the out-of-plane vibration displacement is corrected. In the experimental verification, 3D reconstruction and rigid translation experiments are designed, respectively. The results show that the self-calibration accuracy of the binocular camera proposed in this paper is notably improved compared with the existing methods. At the same time, the deviation correction effect of the vibration displacement is significant. Finally, the typical flat plate structure and composite wing structure are taken as vibration measurement cases to demonstrate that the proposed approach is reliable and accurate in measuring the dynamic responses of engineering structures. • A target-free vision-based method using projection speckle and camera self-calibration is proposed for structural vibration measurement. • To increase the accuracy of camera self-calibration, a nonlinear inequality constraint is implemented to minimize reprojection errors. • The correlation between speckle and actual displacement is established, correcting the out-of-plane deviation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast time-frequency mask technique that relies on the sparseness of source signals for blind source separation (BSS) to separate a mixture of two input sounds in a single signal automatically. Due to the sparseness of source signals, the signal can be distinguished when it is transformed into the time-frequency domain. Most previous methods did not mention the effect of different angles on accuracy. To overcome such problems, we first define two features which are normalized level-ratio and phase-difference. Next, we use our method to decrease the variance of Direction of Arrival (DOA). This can reduce the variance of features so that it can reduce the iterations of k -means. Finally, a mask is generated according to the clustered features. Our method does not require any prior information or parameter estimation. The motivation of the proposed design is to incorporate the BSS system with some smart voice appliances. In the application scenario, all the non-human voices may appear and regard as interference. We use Signal to Distortion Ratio (SDR) and Signal to Interference Ratio (SIR) to make some comparison. Based on the proposed system, then we present a hardware design. We use the TSMC 90-nm CMOS process. As a cost-effective result, it consumes about 120 K gates and executes with a frequency of 10 MHz. The power consumption is only 2.92 mW with low power design considerations. • This paper presents the algorithm and hardware design for blind source separation. • The experimental results with various mixtures are simulated in real environments to verify the effectiveness. • Real demonstration is constructed in the embedded system. • VLSI design of the proposed chip is performed. [ABSTRACT FROM AUTHOR]

Parameters for defining photovoltaic models using measured voltage–current​ characteristics are essential for simulation, control, and evaluation of photovoltaic-based systems. This paper proposes an enhanced chaotic JAYA algorithm to classify the parameters of various photovoltaic models, such as the single-diode and double-diode models, accurately and reliably. The proposed algorithm introduces a self-adaptive weight to regulate the trend to reach the optimal solution and avoid the worst solution in various phases of the search space. The self-adaptive weight capability also allows the proposed technique to reach the best solution at the earliest phase, and later, the local search process starts, which also increase the ability to explore. A three different chaotic process, including sine, logistics and tent map, is proposed to optimize the consistency of each generation's best solution. The proposed algorithm and its variants proposed are used to solve the parameter estimation problem of various PV models. To show the proficiency of the suggested algorithm and its variants, an extensive simulation is carried out using MATLAB/Simulink software. Two statistical tests are conducted and compared with the latest techniques for validating the performance of the suggested algorithm and its variants. Comprehensive analysis and experimental results display that the suggested algorithm can achieve highly competitive efficiency in terms of accuracy and reliability compared to other algorithms in the literature. This research will be backed up with extra online service and guidance for the paper's source code at https://premkumarmanoharan.wixsite.com/mysite. • A new CJAYA algorithm is proposed to estimate the parameters of SDM and DDM models. • Chaotic and self-adaptive weights are combined with JAYA to get the best solution. • The performance of CJAYA and its variants are tested on 23 benchmark functions. • Four PV models are considered to validate the performance of CJAYA and its variants. • Detailed analysis, results, and statistical tests show that the CJAYA is superior. [ABSTRACT FROM AUTHOR]

The existing model-based impedance learning control methods can provide variable impedance regulation for physical human–robot interaction (PHRI) in repetitive tasks without interactive force sensing, however, these methods require the completion of the repetitive tasks with constant time, which restricts their applications. For PHRI in repetitive tasks with different completion time, this paper proposes a spatial hybrid adaptive impedance learning control (SHAILC) strategy by using the spatial periodic characteristics of the tasks. In the spatial hybrid adaptation, spatial periodic adaptation is used for estimating time-varying human impedance and differential adaptation is designed for estimating robotic constant unknown parameters. The use of deadzone modifications in hybrid adaptation maintains the accuracy of the parameter estimation when the tracking error is small relative to the modeling error. The control stability is analyzed by a Lyapunov-based analysis in the spatial domain, and the control effectiveness and superiority is illustrated on a parallel robot in repetitive tasks with different task completion time. • Robot spatial dynamics is developed from robot temporal dynamics based on a defined spatial operator. • Spatial periodic adaptation and differential adaptation for estimating human impedance and robotic parameters,respectively. • The human impedance is disturbed spatial periodic. • Deadzone modifications in the hybrid adaptation maintains the accuracy of the parameter estimate. [ABSTRACT FROM AUTHOR]

• The epistemic uncertainty in the process of space object tracking is properly modeled by OPMs. • A refined PAR+ algorithm is developed to handle different levels of prior knowledge in IOD. • A rigorous parameter estimation method of Gaussian possibility functions is proposed to initiate the proposed possibilistic filter. • Improved accuracy of orbital state estimation can be achieved by the fusion of TLEs with real radar measurements. Bayesian filtering is a popular class of estimation algorithms for addressing the space object tracking problem. Bayesian filters assume a random physical system with known statistics of various uncertainty sources. The major challenge is that the exact knowledge of some random process may not be available for analysis, preventing us from performing a probabilistic characterization of the epistemic uncertainty components. In this paper, we explore the use of the Outer Probability Measures (OPMs) to achieve a faithful uncertainty representation derived from all available yet imperfect information in the process of space object tracking. Leveraging the concepts of OPMs, a refined Possibilistic Admissible Region approach is proposed, in which the initial orbital state is modeled using a novel parameter estimation method. The OPM filter is employed to integrate different types of data sources in the presence of assumed ignorance. The efficacy of the developed method is validated by several space object tracking scenarios using real radar measurements and two-line elements data. [ABSTRACT FROM AUTHOR]

• A new class of non-stationary binomial AR(1) models with change point. • Parameter estimation with change point known or unknown cases. • Change point testing and forecasting method. • Two real data applications in the field of epidemiology. In this paper, we conduct the statistical inference for a new class of non-stationary binomial AR(1) models with change point. The parameter estimation problem is considered, as well as the testing problem for the possible change point. For this task, a residual-based cumulative sum (CUSUM) test is employed, and we show that the limiting null distributions take the form of the functions of Brownian bridge. In the simulation study, we evaluate the performance of the estimation methods and the respective CUSUM test. We furthermore propose a method for the forecasting of the model. Finally, two real data applications were provided in the field of epidemiology. [ABSTRACT FROM AUTHOR]

In this paper, the development and implementation of a novel approach for fault detection of an aircraft auxiliary power unit (APU) has been demonstrated. The developed approach aims to target the proactive identification of faults, in order to streamline the required maintenance and maximize the aircraft's operational availability. The existing techniques rely heavily on the installation of multiple types of intrusive sensors throughout the APU and therefore present a limited potential for deployment on an actual aircraft due to space constraints, accessibility issues as well as associated development and certification requirements. To overcome these challenges, an innovative approach based on non-intrusive sensors i.e., microphones in conjunction with appropriate feature extraction, classification, and regression techniques, has been successfully demonstrated for online fault detection of an APU. The overall approach has been implemented and validated based on the experimental test data acquired from Cranfield University's Boeing 737-400 aircraft, including the quantification of sensor location sensitivities on the efficacy of the acquired models. The findings of the overall analysis suggest that the acoustic-based models can accurately enable near real-time detection of faulty conditions i.e., Inlet Guide Vane malfunction, reduced mass flows through the Load Compressor and Bleed Valve malfunction, using only two microphones installed in the periphery of the APU. This study constitutes an enabling technology for robust, cost-effective, and efficient in-situ monitoring of an aircraft APU and potentially other associated thermal systems i.e., environmental control system, fuel system, and engines. • Novel online monitoring system for aircraft auxiliary power unit. • Acoustic-to-thermodynamic parameter estimation model proposed for fault diagnostics. • Acoustic feature extraction and regression techniques evaluated on near-field data. • Multiple faults can be detected using the proposed scheme with high accuracy. • The proposed system can estimate the parameters in real time using two microphones. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of determining the parameter values of the agent-based model of social campaign message adoption relying on Bass's classical innovation diffusion model. The problem concerns the parameters that reflect the influence of advertising and social communication on the adoption of the idea conveyed in the social campaign. Due to the fact that the factors influencing the behavior of message recipients are conditioned by their personality, circumstances or reaction to the environment, the conventional methods for estimating these parameters may not deliver a model reproducing reality with the required accuracy. Therefore, this paper proposes a procedure for determining the values of agent-based model parameters that relies on an experimental data acquisition procedure using a combination of cognitive neuroscience techniques and a survey method. The presented research examines a social campaign promoting road safety. The obtained results prove the suitability of the suggested solution for estimating the parameters of the agent-based model of social campaign message adoption. The proposed approach contributes to the methodology of data collection and parameter estimation in building agent-based models, although it is not without some limitations. [ABSTRACT FROM AUTHOR]

Accurate state-of-charge (SOC) estimation is crucial for ensuring the safe and reliable operation of battery management systems (BMS). Among the various algorithms used for SOC estimation in real-vehicle BMS, the extended Kalman filter (EKF) algorithm holds significance due to its adherence to optimal estimation principles and its property of insensitivity to initial values. By studying the relationship between error sources and SOC estimation errors, it becomes possible to develop targeted measures for enhancing the accuracy of SOC estimation based on the EKF. From a probabilistic perspective, this paper derives theoretical equations that establish the connection between SOC estimation errors and various error sources, including measured voltage, measured current, open circuit voltage curve, capacity, ohmic internal resistance, and polarization resistance. Furthermore, the paper analyzes the relationship among multiple error sources in generating SOC estimation errors. Building upon the outcomes of this theoretical analysis, a joint SOC estimation method that combines the EKF with Ampere-hour counting (AHC) is employed to identify errors. In scenarios where simultaneous faults occur in the current and voltage sensors, they are identified based on the slope and Euclidean distance of the two SOC trajectories, respectively. Subsequently, sensor faults correction and SOC compensation are implemented by leveraging simplified equations involving capacity and SOC increments and measured voltage and SOC estimation errors. In addition to addressing sensor faults, the paper also considers battery model parameter errors. By incorporating customized current pulse profile and theoretical equations relating to error sources and SOC estimation errors, a comprehensive error estimation of model parameters is achieved, capable of handling single and multiple errors. The derived simplification bridges the gap between error sources and SOC estimation errors, offering a novel approach for parameter sensitivity analysis and a theoretical foundation for quantifying these errors. The experimental results demonstrate the effectiveness and rapidity of the proposed method for identifying and correcting sensor faults and model parameter errors. • Derive error equations of signal error sources and SOC errors. • Analysis the relationship of multiple error sources in generating SOC errors. • Achieve the identification-correction of current and voltage sensor faults. • A model parameter errors estimation method is demonstrated. • A new method for parameter sensitivity analysis. [ABSTRACT FROM AUTHOR]

Achieving the objectives of sustainable development in water and agri-food systems requires the utilisation of decision-support tools in stakeholder-driven processes to construct and simulate various scenarios and evaluate the outcomes of associated policy interventions. While it is common practice to involve stakeholders in participatory modelling processes, their comprehensive documentation and the lessons learned remain scarce. In this paper, we share our experience of engaging stakeholders throughout the entire system dynamics modelling process. We draw on two projects implemented in the Volta River Basin, West Africa, to understand the dynamics of water and agri-food systems under changing environmental and socioeconomic conditions. We outline eight key insights and lessons as practical guides derived from each stage of the participatory modelling process, including the pre-workshop stage, problem definition, model conceptualization, simulation model formulation, model testing and verification, and policy design and evaluation. Our findings demonstrate that stakeholders can actively contribute to all phases of the system dynamics modelling process, including parameter estimation, sensitivity analysis, and numerical simulation experiments. However, we encountered notable challenges, including the time-intensive nature of the process, the struggle to reach a consensus on the modelled problem, and the difficulty of translating the conceptual model into a simulation model using stock and flow diagrams – all of which were addressed through a structured facilitation process. While the projects were anchored in the specific context of West Africa, the key lessons and insights highlighted have broader significance, particularly for researchers employing PSDM in regions characterised by multifaceted human-environmental systems and where stakeholder involvement is crucial for holistic understanding and effective policy interventions. This paper contributes practical guidance for future efforts with participatory modelling, particularly in regions worldwide grappling with sustainable development challenges in water and agri-food systems, and where stakeholder involvement is crucial for holistic understanding of the multiple challenges and for designing effective policy interventions. [Display omitted] [ABSTRACT FROM AUTHOR]

Inevitable model uncertainty could render active yaw control (AYC) to make suboptimal or even irrational decisions. To this end, this paper quantitatively analyzes the influential mechanisms of uncertainty on optimal yaw policy in terms of the wake model and power model. In order to address power model uncertainty that has received little attention in the AYC field, this paper proposes an efficient adaptive economic predictive control. This framework begins with the construction of a neural network-based compensator to provide online adaptive estimation of parameter uncertainty. On this basis, an adaptive economic predictive control with the economic objective of maximizing the total power is designed to increase the confidence of yaw policy in the presence of generation uncertainty, and a parallel heuristic solver is developed to allow real-time control of large-scale wind farms. Finally, extensive results based on three wind farm cases demonstrate the effectiveness and advantages of the proposed control strategy. • Quantitative uncertainty analysis of wake model and power model on AYC is conducted. • Adaptive economic predictive control is proposed to hedge against generation uncertainty. • Enhanced parallel SSA is developed to enable real-time operation. [ABSTRACT FROM AUTHOR]

In this paper we provide the first solution to the challenging problem of designing a globally convergent estimator for the parameters of the standard model of a continuous stirred tank reactor. Because of the presence of non-separable exponential nonlinearities in the system dynamics that appear in Arrhenius law, none of the existing parameter estimators is able to deal with them in an efficient way and, in spite of many attempts, the problem was open for many years. To establish our result we propose a novel procedure to obtain a suitable nonlinearly parameterized regression equation and introduce a radically new estimation algorithm—derived applying the Immersion and Invariance methodology—that is applicable to these regression equations. A further contribution of the paper is that parameter convergence is exponential and is guaranteed with weak excitation requirements. To achieve this remarkable property we rely on the utilization of a recently introduced parameter estimator that seamlessly combines a classical least-squares search with the dynamic regressor extension and mixing estimation procedure. • A globally convergent estimator for the parameters of a CSTR. • A novel procedure to obtain a suitable nonlinearly parameterized regression equation of the CSTR. • Parameter convergence is guaranteed with extremely weak excitation requirements. [ABSTRACT FROM AUTHOR]

In this paper, the robust bipartite tracking consensus problem for second-order multi-agent systems has been addressed in the presence of lumped disturbances and unknown velocity information. The leader's dynamics are modeled by uncertain fractional-order equality based on the neural network (NN), and the followers can only obtain the position information of the leader under the signed networks. A novel robust bipartite tracking consensus control scheme is developed by combining the NN approximators, the continuous sliding mode control (SMC) strategy, and the improved extended high-gain observer (EHGO). The improved EHGO is used to compensate for and estimate each agent's lumped disturbances and unknown velocity information in the controller design process. Moreover, NN is constructed to approximate the velocity and acceleration of the uncertain leader's dynamics for generating the feedforward signal of controllers, and the adaptive update laws of estimation parameters are generated online based on the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified by some numerical simulations. • A robust SMC-based bipartite consensus tracking controller is designed. • Velocity-free measurements, uncertainties, and disturbances are considered. • The NN-EHGO scheme is designed to enhance the robustness of the system. • The unknown leader's dynamics are approximated by the designed NN. • Parameters of update laws are generated online based on the Lyapunov function. [ABSTRACT FROM AUTHOR]

• A partial grey prediction model with control matrix (CMPGM(1, M)) is proposed. • The matrices sequence and partial derivatives of mean sequence are used to replace the points sequence. • The novel model can predict traffic flow with better accuracy amongst the comparison models. Short-term traffic flow analysis is the core part of the intelligent transportation system, and also the critical basis for traffic management and control system to guide traffic flow. Real-time and accurate short-term traffic flow prediction can be effectively applied to traffic control and guidance, providing reliable and effective information for traffic managers and travelers, and improving travel efficiency. Based on the spatiotemporal characteristics of traffic flow and periodicity of traffic flow, this paper uses partial differential equation variables to reflect the spatiotemporal characteristics of traffic flow, combines the modelling mechanism of partial differential and the grey prediction model organically, and establishes a partial grey prediction model with control matrix by using the partial derivatives of the mean sequence. At the same time, the parameters estimation and modelling steps of the novel model are obtained by matrix analysis and recursion. A set of data is used from two perspectives to verify the proposed model's effectiveness. Experimental results show that the proposed model is stable and effective. Finally, the novel model is applied to the short-time traffic flow simulation and prediction of the highway. The research results show that the novel model is better than other comparison models. The novel model mainly aims at the matrix data form of traffic flow and is applied to general short-term traffic flow prediction. On the one hand, the prediction results of the novel model can provide theoretical basis and basic methods for the traffic management system, traffic data for traffic information system, and new ideas for the traffic flow prediction research. On the other hand, it can help traffic managers solve accurate real-time traffic flow information, provide real-time traffic information for travelers, and help them plan and adjust routes in advance. [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]

The core idea of traditional adaptive control is to reconstruct parameter estimation errors with known signals and damping injection based on tracking error, while the formulation of desired damping in controller designs is usually a nontrivial task. The main contribution of this paper lies in the development of the classic RISE result and a constructive damping injection procedure for the adaptive tracking control of Euler–Lagrange mechanical systems. By utilizing generalized dynamic scaling function, scalar filtering, the improved RISE method and analyzing the existence of finite escape time of the closed-loop system, a globally asymptotically stable result is obtained with facilitative damping injection, significant order reduction and improved design efficiency when compared with the existing results. Simulations on a fully actuated 2-DOF planar robot manipulator model demonstrate the effectiveness of the proposed methods. • Order reduction. The extended dynamic orders of the proposed adaptive controller is less than composite adaptive RISE controller. • Performance improvement. The proposed controller achieves the global asymptotic stability with the non-CE-alike adaptive estimation performance. • Design simplification. There are many control parameters in the controller that can be adjusted to obtain the desired performance, and the computational difficulties are avoided such as analytical solution of PDE in I&I. [ABSTRACT FROM AUTHOR]

• Attitude and inertial parameter are estimated without 3D model. • Ill-posed point cloud is detected and avoided. • Combination of MEKF and pose graph reduces error of estimate. Determining the attitude and inertial parameters of a noncooperative target is essential in an on-orbit servicing mission. Various methods based on machine vision have been proposed, but most of them require the 3D model of the target. This paper proposes a model-free method through sequentially registering point clouds captured by a depth camera. Our main contributions are the avoidance of the ambiguity in registration, and the combination of the multiplicative extended Kalman filter and the pose graph optimization to reduce the effect of measurement noise and drift error. A hardware experiment was performed to generate the sequence of point clouds of a three-axis free-floating target and validate our method. The result shows that the proposed method outperforms existing methods and effectively identifies the inertial parameters, including the normalized principal moments of inertia and the orientation of principal axes. [ABSTRACT FROM AUTHOR]

In this paper, an offline tuning strategy and an online parameter estimation method are exploited to calibrate the solid oxide fuel cell mathematical model. Different to existing offline tuning strategy, the developed strategy is designed in order to tune the model under various operation conditions. First, the particle swarm optimization method combined with the gradient-based search method is applied to tune unknown parameters in the state-space model and the steady-state model for each operation condition. Then, the sensitive parameters are expanded to the polynomial equations. Moreover, the reconstructed model including coefficients in the polynomial equations are determined by using the particle swarm optimization method with gradient-based search method for whole operation conditions. To show the slowly time-varying performance of a solid oxide fuel cell, an adaptive optimal learning law based on the optimization technology is proposed to online minimize a cost function with the information of the estimation error. The estimation error is extracted through several low-pass filters and simple algebraic calculation. Finally, the proposed offline tuning strategy and the developed online adaptive estimation method are verified by conducting experiments on a practical solid oxide fuel cell test bench. • State-space and steady-state models of a SOFC system are simultaneously calibrated. • Time-varying parameters are estimated by an adaptive parameter estimation method. • Comparison of offline and online parameter estimation methods is provided. [ABSTRACT FROM AUTHOR]

In aerial load transportation applications, knowing the mass of the load in advance is not always possible. The load dynamics depend on its mass and using a high-performance model-based controller with an inaccurate mass will introduce unmodeled disturbances to the system that will negatively affect the closed-loop performance. This paper addresses the design of a trajectory tracking controller for a quadrotor with a slung-load that has an unknown mass. The proposed solution is an adaptive controller with online estimation designed using the backstepping technique. Nonlinear control laws for thrust and angular velocity, and an adaption law for mass estimation are proposed, which guarantee the convergence of the trajectory tracking and the estimation errors to zero, and are robust to variations in load mass. Simulation and experimental results are presented to assess the validity and performance of the proposed controller. • A rigorous stability proof shows that the trajectory tracking and estimation errors converge asymptotically to zero. • The adaption law is updated online, compensating the unknown mass while the load tracks a reference trajectory. • The swing motion of the load is allowed to follow naturally the aggressiveness of the reference trajectory. • High-performance experimental results are showcased, including trajectory tracking and aggressive maneuvers with varying load mass and large load swings. [ABSTRACT FROM AUTHOR]

• A particular output error model is proposed via polynomial transformation, in which an extra filter is not required [18,36]. • A loss function is introduced using initial parameter and output error items, which gives a perspective for scheme design. • A recursive identification framework is derived with variable modified gain, which facilitates online operation [14,31]. In this paper, an adaptive recursive estimation scheme based on a novel recursive framework is proposed for a controlled autoregressive autoregressive moving average (CARARMA) system. A common loss function is established using the prediction error scheme, which has two shortcomings in case of interference, namely, biased estimation and minima problems. To overcome the two shortcomings, a recursive estimation scheme is proposed by using output error data with a discount factor and initial error data with a penalty operator. The former data do not involve the noise information of system data, so the biased estimation issue can be improved. The latter data include initial value information, such that the minima problem can be resolved. To achieve the target, polynomial transformation is applied to transform the CARARMA system into a particular model, then the loss function is introduced. Based on the loss function and recursive structure, a recursive estimator is developed. Moreover, the convergence of the proposed identification scheme is strictly analyzed. The advantage and practicality of the proposed estimator are evaluated by using a numerical example and real-world process. [ABSTRACT FROM AUTHOR]

• Multivariate rates and proportions • Multivariate fractional variables • Univariate GSB distribution to the multivariate setting • Parametric family of multivariate distributions for vectors of proportions or rates This paper introduces a general class of multivariate distributions for rates and proportions on the basis of the multivariate elliptical distribution. We derive various general properties of this multivariate distribution. The model parameters are estimated using the maximum likelihood estimation method. In particular, we have considered parameter estimation in detail for two special cases of this multivariate family, using multivariate normal kernel and multivariate Student- t kernel. Hypothesis testing inference for the correlation matrix on the basis of the likelihood ratio test statistic is also proposed. Empirical applications that employ real data are considered to illustrate the usefulness of the new class of multivariate distributions for rates and proportions in practice. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive parameter estimation scheme is investigated for a class of multi-player pursuit–evasion games subject to unknown objectives with linear feedback and cross terms in control strategy. In virtue of the adaptive method, each player updates controller strategies to estimate opponent's control parameters before getting the Nash equilibrium solution. Moreover, the sufficient condition is provided to guarantee stability of the proposed adaptive procedure. Meanwhile, the stability behavior of closed-loop Nash strategy with adaptive estimator is guaranteed by the contraction mapping method. Finally, a simulation of an all-against-one game with one escaper and three pursuers is carried out to demonstrate the effectiveness of the proposed methodology. • Adaptive parameter estimators are designed for both sides of pursuers and escapers. • The sufficient condition is provided to guarantee the convergence of the estimators. • Sufficient conditions are obtained to stabilize the closed-loop system under Nash equilibrium solution. [ABSTRACT FROM AUTHOR]

Dietary restriction (DR) interventions have demonstrated their efficacy in extending lifespan; however, the association between lifespan extension and health span remains unclear. This article aims to analyze the relationship between DR-induced lifespan and health span in Caenorhabditis elegans (C. elegans) , a widely used animal model in lifespan studies. By examining various parameters such as lipofuscin accumulation (an aging marker) and locomotor and feeding capacities (indicators of muscle degradation rate), we have compiled papers that investigate and report on these DR-induced effects.The majority of the papers reviewed consistently demonstrate that DR improves both lifespan and health span in C. elegans. Worms subjected to DR exhibit slower lipofuscin accumulation compared to those fed ad libitum, indicating a reduction in age-related cellular damage. Additionally, DR-treated worms display a higher locomotion capacity, suggesting a slower rate of muscle degradation. However, it is worth noting that there are some discrepancies among the papers regarding feeding capacity. These contradictions can be attributed to the different methods employed to initiate DR. While many approaches slow muscle degeneration and enhance pumping rates through adaptation to limited food sources, other methods, such as using eat-2 mutant worms or interventions that mimic the effects of eat-2 , reduce feeding capacity and consequently restrict food intake. In conclusion, the findings suggest a strong correlation between DR-induced longevity and the extension of health span in C. elegans , as evidenced by improvements in various health span parameters. DR interventions not only extend lifespan but also mitigate age-related markers and preserve locomotor capacity. Although conflicting results are observed regarding feeding capacity, the overall evidence supports the notion that DR promotes healthier aging in this animal model. • Rigorous systematic search: We implemented Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) design to ensure the accuracy and reliability of our results. • Novel Insights: We identified several health indices that are modulated by dietary restriction, shedding light on the biological mechanisms involved. • Robust Results: Data extraction according to the PRISMA guideline, demonstrated a clear relationship between dietary restriction and lifespan extension but lacked consistency in health span. • Practical Implications: This review provides valuable insights for researchers, clinicians, and individuals interested in optimizing their health and lifespan. [ABSTRACT FROM AUTHOR]