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

The indirect method for bridge modal identification based on the response of moving vehicles has attracted widespread attention in recent years. However, most existing studies only focus on the simply supported bridge, while continuous girder bridges are widely used in practical engineering. Therefore, this study proposes an indirect frequency identification method for continuous girder bridges. First, the mode shape formula of the equal-span continuous beam is deduced using the three-moment equations, and the analytical solution of the vehicle vibration response is deduced via the vehicle–bridge coupled vibration theory. Second, the derived analytical solution is verified through numerical analysis results and field test results. Finally, the effects of bridge and vehicle parameters on the frequency identification accuracy are analyzed. The results show that a reasonable frequency of the trailer should be selected to avoid the resonance between the vehicle and the bridge for better performance. The research findings can provide a reference for the indirect identification of continuous girder bridges based on the response of moving vehicles. [ABSTRACT FROM AUTHOR]

Recent advances in origami science and engineering have particularly focused on the challenges of dynamics. While research has primarily focused on statics and kinematics, the need for effective and processable dynamic models has become apparent. This paper evaluates various dynamic modelling techniques for rigid-foldable origami, particularly focusing on their ability to capture nonlinear dynamic behaviours. Two primary methods, the lumped mass–spring–damper approach and the energy-based method, are examined using a bistable stacked Miura-origami (SMO) structure as a case study. Through systematic dynamic experiments, we analyse the effectiveness of these models in predicting bistable dynamic responses, including intra- and interwell oscillations, in different loading conditions. Our findings reveal that the energy-based approach, which considers the structure's inertia and utilizes dynamic experimental data for parameter identification, outperforms other models in terms of validity and accuracy. This model effectively predicts the dynamic response types, the rich and complex nonlinear characteristics and the critical frequency where interwell oscillations occur. Despite its relatively increased complexity in model derivation, it maintains computational efficiency and shows promise for broader applications in origami dynamics. By comparing model predictions with experimental results, this study enhances our understanding of origami dynamics and contributes valuable insights for future research and applications. This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'. [ABSTRACT FROM AUTHOR]

Rationale: Ion‐mobility (IM)–derived collision cross‐section (CCS) values can serve as a valuable additional identification parameter within suspect and non‐target screening studies of environmental contaminants. However, these applications require to assess the reproducibility of CCS calculations between different IM set‐ups. Especially for the comparison of trapped and drift‐tube IM (TIMS/DTIM) derived CCS values, data for environmental applications is lacking. Methods: The presented study assessed the bias of TIMS derived CCSN2 (TIMSCCSN2) values of 48 environmental contaminants from three classes in comparison to a previously established DTIM database. Based on two sets of isomeric bisphenols, the resolving power of both systems was compared, addressing the instrumental settings which influence the resolution of TIMS measurements. Results: For 91% of the datapoints, bias between TIMSCCSN2 and DTCCSN2 values (latter set as reference) were < 2%, indicating a good inter‐platform reproducibility. TIMS resolving power was dependent on the selected mobility window and ramping times whereby a resolution of up to 116 was achieved. Similar resolving power was observed for multiplexed DTIMS data if a high‐resolution post‐processing step was implemented. Conclusions: These results provide valuable insights in CCSN2 reproducibility facilitating database transfer in future TIMS based studies. Knowledge on the influence of acquisition settings on robustness of TIMSCCSN2 calculations and resolving power can ease method development supporting efficient development and reliable identifications of emerging environmental contaminants. [ABSTRACT FROM AUTHOR]

This article is concerned with the trajectory tracking control problem for the nonlinear systems in the sense of the predefined settling time and accuracy. In contrast with the existing works, we focus on the cases where the system dynamics, its bounding functions, the unmatched disturbances, and the time‐varying parameters are totally unknown; the derivatives of the desired trajectory are not required to be available. They significantly challenge the identification and/or approximation‐based control solutions. To overcome this obstacle, a novel robust prescribed performance control approach via state feedback is put forward in this article. It not only ensures the natural satisfaction of the specific initial condition but also realizes a full‐time performance specification for trajectory tracking. Furthermore, for the case of unmeasured state variables, an output‐feedback control approach is further derived by adopting an input‐driven filter and conducting trivial changes on the design procedure. Moreover, both approaches exhibit significant simplicity, without the needs for parameter identification, function approximation, disturbance estimation, derivative calculation, or command filtering. Three simulation studies are conducted to clarify and verify the above theoretical findings. [ABSTRACT FROM AUTHOR]

Weave and drainage marks on paper surfaces derived from forming fabrics installed in paper machines were examined. Non-destructive differentiation of copy papers from various manufacturers was performed by a 2D Lab Formation Sensor. Individual characteristics such as angles and wavelengths formed by the fabric were selected as parameters for the analysis. These parameters were sampled from a total of 500 papers: 50 sheets of paper from each of the 10 groups by copy paper manufacturers. Using machine learning algorithms such as partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), and naive bayes (NB) models to analyze these parameters allows for the identification of copy paper groups by manufacturers. The NB classifier demonstrated superior performance, achieving an accuracy of 0.973 even with a 50 % reduction in input variables. This study shows that periodic marks on paper surfaces can act as important indicators of the forming fabric and the distinct features of a paper-making machine. In general, this paper proposes that the 2D Lab Formation Sensor along with machine learning models can be utilized for non-destructive copy paper categorization. This identification method could be broadened to determine information regarding suspicious documents for forensic identification purposes. [ABSTRACT FROM AUTHOR]

Non-viscous damping parameters can be identified approximately based on the frequency response function of structures. However, it is difficult or expensive to perform vibration test and layout enough amount measured points for structures. The stochastic subspace method can identify modal damping parameters only using responses data from partial measured points. This paper extends the stochastic subspace method from the viscous damping system to non-viscous damping system. In addition, the eigenvalues of the second-order form equation of motion are proved equally to the state space formulation. The identified procedure of the proposed method is given in detail. Finally, the method is validated using three numerical examples for assessing its accuracy. [ABSTRACT FROM AUTHOR]

Micro-milling technology is widely used in the manufacturing of micro three-dimensional complex parts in aerospace, mechanical equipment, biomedical devices, electronic components, and other fields due to its advantages of high machining accuracy, small size, and complex surface. Consequently, understanding its machining process is essential. This study proposes a time-varying dynamic micro-milling process model with the effects of tool runout, tool wear, and cutting status and explores the stable cutting domain of real-time machining. Considering the time-varying characteristics of the cutting process, combined with measured data and particle filtering method, the cutting force coefficients and tool wear parameters in the dynamic model at different times are identified. The dynamic equation of micro-milling is derived using a discrete method, and the stability of the cutting process is evaluated. Multiple cutting experiments are conducted on the Al6061, and the test data are compared with simulated values. The comparison results show that the accuracy of the proposed model in predicting the cutting state can reach 100%, and the average error of cutting force prediction is 7.72%. The research results can provide theoretical guidance for the safety evaluation of micro-milling and the selection of machining conditions. [ABSTRACT FROM AUTHOR]

Lime wood, spruce and pine are investigated with regard to its hygro-mechanical long-term behaviour. Experiments are conducted for an identification of model parameters and for model validation. Swelling and shrinkage coefficients, dry density, sorption characteristics and parameters for visco-elasticity, visco-plasticity and mechano-sorption are determined for the main material directions. Supplemented by literature values, a complete set of parameters for long-term hygro-mechanical modelling of wood species is found. Constrained swelling and shrinkage are analysed and the origin of the stress development is investigated. It is demonstrated, that creep phenomena lead to significant stress reduction by relaxation, in case of moisture changes especially due to mechano-sorption. The influence of different model parts is investigated. A numerical parameter study shows the influence of several material parameters on the stress evolution. Experimental material investigations such as those presented here are essential for the application of numerical simulation methods for the prediction of material behaviour and for the assessment of deformations, stresses and damage potential of climatically loaded timber structures. [ABSTRACT FROM AUTHOR]

Starting from the seminal works of Toupin, Mindlin and Germain, a wide class of generalized elastic models have been proposed via the principle of virtual work, by postulating expressions of the elastic energy enriched by additional kinematic descriptors or by higher gradients of the placement. More recently, such models have been adopted to describe phenomena which are not consistent with the Cauchy-Born continuum, namely the size dependence of apparent elastic moduli observed for micro and nano-objects, wave dispersion, optical modes and band gaps in the dynamics of heterogeneous media. For those structures the mechanical response is affected by surface effects which are predominant with respect to the bulk, and the scale of the external actions interferes with the characteristic size of the heterogeneities. Generalized continua are very often referred to as media with microstructure although a rigorous deduction is lacking between the specific microstructural features and the constitutive equations. While in the forward modelling predictions of the observations are provided, the actual observations at multiple scales can be used inversely to integrate some lack of information about the model. In this review paper, generalized continua are investigated from the standpoint of inverse problems, focusing onto three topics, tightly connected and located at the border between multiscale modelling and the experimental assessment, namely: (i) parameter identification of generalized elastic models, including asymptotic methods and homogenization strategies; (ii) design of non-conventional tests, possibly integrated with full field measurements and advanced modelling; (iii) the synthesis of meta-materials, namely the identification of the microstructures which fit a target behaviour at the macroscale. The scientific literature on generalized elastic media, with the focus on the higher gradient models, is fathomed in search of questions and methods which are typical of inverse problems theory and issues related to parameter estimation, providing hints and perspectives for future research. [ABSTRACT FROM AUTHOR]

It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of identification of a Robin coefficient (also known as impedance coefficient) in a parabolic PDE from terminal observations of the temperature distributions. The problem is ill-posed in the sense that small perturbation in the observation may lead to a large deviation in the solution. Thus, in order to obtain stable approximations, we employ the Tikhonov-regularization. We propose a weak source condition motivated by the work of Engl and Zou (2000) and obtain a convergence rate of O (δ 1 2) , where δ is the noise level of the observed data. The obtained rate is better than some of the previous known rates. Moreover, the advantage of the proposed source condition is that the above mentioned convergence rate is obtained without the need for characterizing the range space of modelling operator and its Fréchet derivative, which is in contrast to the general convergence theory of Tikhonov-regularization for non linear operators. [ABSTRACT FROM AUTHOR]

The future power of photovoltaic systems (PVS) is gaining significant attention due to its rising potential. This has resulted in a substantial amount of research emphasizing the importance of optimizing the PVS efficiency. However, the identification of PV cell model parameters remains a challenging task, mainly due to the characteristics of PV cells and their dependence on varying meteorological conditions. In this work, we present a novel methodology based on an improved new multi objective particle swarm optimization (NMOPSO) algorithm for the PV cell parameters identification. The main goal is to minimize the root mean square error (RMSE) and to calculate the series resistance (Rs) by means of its non-linear equation form. The applied algorithm uses an evolving and adaptive search strategy to enhance both speed of convergence for the parameter identification process precision. Through extensive simulations, we demonstrate that proposed approach outperforms current methods in terms of accuracy, precision, and PV parameters extraction. [ABSTRACT FROM AUTHOR]

This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation. [ABSTRACT FROM AUTHOR]

Structural damping, which has been widely used to quantify the energy dissipation of engineering structures, plays an important role in dynamic analysis during the design phase and the assessment of existing structures. However, the development of an accurate damping model remains an open question due to the unclear mechanism. In this study, structural damping was identified based on loss factors for beam structures, which were used for presenting the damping behavior during vibrations. The equation of motion for the bending deformation of a Timoshenko beam was derived, accounting for the material damping. To identify the loss factor of the beam from the response signals at a limited number of measurement points, an inverse algorithm based on wave coefficient estimation was developed, considering the numerical stability for data interpretation. Based on the proposed loss factor identification method, several beam models with different shapes of cross-sections were investigated, focusing on the feasibility of the method of varying cross-section cases. Comparing the spectral element model with the loss factor and the finite element model with Rayleigh damping, it was confirmed that the loss factor can better describe the actual energy dissipation behavior of the structure. It provides a more accurate theoretical model for estimating structural damping in practical engineering. [ABSTRACT FROM AUTHOR]

ABSTRACT This article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional‐order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient‐based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi‐innovation gradient‐based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations. [ABSTRACT FROM AUTHOR]

Energy harvesting technology, as an emerging energy technology, has contributed outstandingly to the application of online monitoring sensors in power systems. The accurate parameter identification of the harvesters is crucial for their power supply applications. This work aims to investigate the parameter identification of harvesters with complex equivalent circuit models, here taking a current transformer as an example. However, previous studies focused on optimizing algorithms rather than data sources. This paper demonstrates a vector impedance quantum genetic algorithm (QGA) parameter identification method to identify the amplitude and phase information of the impedance responses. By comparing the results of the proposed method with the genetic algorithm and PSO based on impedance responses and QGA based on load resistance responses, it is proved that the proposed vector impedance QGA identification method is optimal in terms of accuracy, speed, and robustness. The root mean square errors of the output voltage and the phase difference with the primary current for the proposed method are 5.884 × 10−5 V and 4.473 × 10−4 ms. Moreover, the practical applicability of this method is validated, demonstrating its effectiveness in real‐life and industrial settings. The proposed identification method in this paper changes the source data so that the sample data are small in volume and extensive in information, which enables faster and more accurate parameter identification. This study provides a new idea for parameter identification researches. [ABSTRACT FROM AUTHOR]

This paper presents a height control approach employing real‐time parameter identification for supersonic sea‐skimming missiles. Firstly, an integrated identification technique based on the Unscented Kalman filter was established for supersonic cruise missiles. The method facilitates the online identification of aerodynamic and thrust coefficients while simultaneously estimating state variables such as height and angle of attack. Secondly, a backstepping control method was designed to regulate the height of sea‐skimming missiles. Control commands were generated based on online identification and state estimation, as well as the additional angle of attack caused by wind being compensated. The simulation results show that the proposed height control method achieved a high level of precision when operating in the challenging conditions of ultralow‐height sea skimming, despite the presence of parameter uncertainties, measurement inaccuracies, sea wave disturbances, and wind effects. [ABSTRACT FROM AUTHOR]

Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder. [ABSTRACT FROM AUTHOR]

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation. [ABSTRACT FROM AUTHOR]

The acquisition of bridge frequency by using the ubiquitous ordinary passenger vehicles and smartphones in cities has attracted an increasing amount of attention. This study proposes a bridge monitoring method and develops a vehicle–bridge crowdsourcing monitoring (VBCM) platform for public participation. It takes smartphones carried by vehicles as monitoring terminals for a long-term bridge monitoring task. To validate the feasibility of available smartphone brands as a signal collector, a small shake table test is carried out and the processing scheme for the smartphone sampling data is investigated. A smartphone sensor frequency gain technique is proposed to satisfy the fusion of multisensor data in different smartphones. Furthermore, a magnetic target identification technique is proposed to pick up and extract the bridge response signal segment corresponding to the vehicle’s entering and leaving, as well as to eliminate redundant data. To this end, the network of multiple smartphones is synchronized. For the sake of applicability and feasibility, a series of scaled experiments are conducted in the laboratory considering an adapted toy car driving over a simply supported steel bridge. The results demonstrated the practicality of the proposed methodology. The combination of easily accessible smartphones oriented to a crowdsourcing model and a driving vehicle lowers the threshold for the bridge monitoring process, which also pinpoints a potential for future structural health monitoring development. [ABSTRACT FROM AUTHOR]

The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack. [ABSTRACT FROM AUTHOR]

In adaptive control, error models use system output error and adaptive laws to update controller parameters for control or identification tasks. Fractional-order calculus, involving non-integer-order derivatives and integrals, is increasingly important for modeling, estimation, and control due to its ability to generalize classical methods and offer improved robustness to disturbances. This paper addresses the gap in the literature where fractional-order gradient methods have not yet been extensively applied in identification and adaptive control schemes. We introduce a fractional-order error model with fractional-order gradient (FOEM1-FG), which integrates fractional gradient operators based on the Caputo fractional derivative. By using theoretical analysis and simulations, we confirm that FOEM1-FG maintains stability and ensures bounded output errors across a variety of input signals. Notably, the fractional gradient's performance improves as the order, β , increases with β > 1 , leading to faster convergence. Compared to existing integer-order methods, the proposed approach provides a more flexible and efficient solution in adaptive identification and control schemes. Our results show that FOEM1-FG offers superior stability and convergence characteristics, contributing new insights to the field of fractional calculus in adaptive systems. [ABSTRACT FROM AUTHOR]

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings. [ABSTRACT FROM AUTHOR]

Recently, Migórski‐Dudek  investigated a steady Oseen flow for a generalized Newtonian incompressible fluid with unilateral and frictional‐type boundary conditions. They established an existence theorem for the steady Oseen model when the divergence‐free convection field b$\mathbf {b}$ and acting volume force f$\mathbf {f}$ are known. However, prior knowledge of b$\mathbf {b}$ and f$\mathbf {f}$ is often impossible in practical engineering applications. This leads to the question of whether the divergence‐free convection field b$\mathbf {b}$ and acting volume force f$\mathbf {f}$ can be determined. The main objective of this paper is to provide a positive response to this challenging and intriguing question. Specifically, we aim to formulate an inverse problem for identifying b$\mathbf {b}$ and f$\mathbf {f}$, characterized by a nonlinear and nonsmooth regularized optimization problem. Initially, we introduce a variational selection mapping for the steady Oseen model. We then establish the boundedness and generalized continuity of this variational selection. Finally, by leveraging optimization theory, convex analysis, and nonsmooth analysis, we develop an abstract regularization framework for the considered inverse problem and establish the existence of a solution.  [ABSTRACT FROM AUTHOR]

The development and validation of lateral control strategies for railway running gears with independently rotating driven wheels (IRDWs) are an active research area due to their potential to enhance straight-track centering, curve steering performance, and reduce noise and wheel–rail wear. This paper focuses on the practical application of theoretical models to a 1:5 scaled test rig developed by the German Aerospace Center (DLR), addressing the challenges posed by unmodeled phenomena such as hysteresis, varying damping and parameter identification. The theoretical model from prior work is adapted based on empirical measurements from the test rig, incorporating the varying open-loop stability of the front and rear wheel carriers, hysteresis effects, and other dynamic properties typically neglected in literature. A transparent procedure for identifying dynamic parameters is developed, validated through closed- and open-loop measurements. The refined model informs the design and tuning of a cascaded PI and PD controller, enhancing system stabilization by compensating for hysteresis and damping variations. The proposed approach demonstrates improved robustness and performance in controlling the lateral displacement of IRDWs, contributing to the advancement of safety-critical railway technologies. [ABSTRACT FROM AUTHOR]

In the quest for sustainable and efficient energy solutions, hydrogen fuel cells emerge as a beacon of hope, offering a promising pathway towards a greener future. Accurate Identification of the ungiven parameters of proton exchange membrane fuel cell (PEMFC) mathematical models is indispensable for designing, managing, and simulating the practical PEMFC. In order to identify the parameters of PEMFC punctually, this paper presents a modified version of the slime mould algorithm (MSMA). In order to increase capability of the MSMA in the exploitation phase, both locally and globally, the sine-cosine technique has been utilized to boost the search capabilities. To assess the performance of MSMA, MSMA is first utilized to address ten well-known benchmark functions. The obtained results confirm that MSMA outperforms SMA on all benchmark functions. Then, MSMA is employed to solve the optimization problem of different mechanical design problems and also the MSMA provides superior performance over the standard SMA. Finally, the MSMA is used to identify the unknown parameters of four typical PEMFCs: 250W PEMFC, BCS 500W PEMFC, AVISTA SR-12 model, and the Temasek 1 kW PEMFC model. Experimental results boost the supremacy of MSMA in the PEMFC parameters extraction by comparing it with the original SMA and well-known potent optimization techniques. Furthermore, MATLAB/Simulink is employed for advanced dynamic PEMFC modeling, facilitating a comprehensive assessment of fuel cell parameters. The validation of this dynamic PEMFC model, using MSMA-optimized parameters, establishes its practical utility in system analysis and real-world fuel cell operation, marking a significant advancement in PEMFC technology management and simulation. [ABSTRACT FROM AUTHOR]

The precise control of centrifugal hydrogen compressor (CHC) driven by high-speed permanent magnet synchronous motor (PMSM) is the key to the stable and efficient operation of hydrogen fuel cell (HFC). In this paper, an adaptive current deviation decoupling control (CDDC) strategy based on variable step size affine projection algorithm (VSS-APAs) is proposed to solve the high-speed PMSM control problem caused by current cross-coupling and parameter perturbation under complex operating conditions. Firstly, the step size is dynamically adjusted by VSS-APA through a normalized gradient descent to respond to system fluctuations, achieving rapid parameter identification during dynamic changes and accurate tracking in steady states. Secondly, stator inductance, magnetic flux linkage, stator resistance, and disturbance torque are identified in two-time-scale based on the characteristics of parameter changes, overcoming the rank deficiency in motor mathematical equations. Finally, utilizing online-identified motor parameters, the gains of the CDDC are adjusted, and compensation is applied for back electromotive force (BEMF) and disturbance torque, thereby ensuring the control accuracy of the motor under parameter perturbations and system disturbances. Experimental results demonstrate that the proposed method enhances the responsiveness and accuracy of the control strategy through efficient parameters identification. Consequently, it guarantees high-efficiency and stable performance of the CHC across different working conditions, markedly advancing the overall functionality and dependability of hydrogen energy system. • An adaptive current decoupling control scheme for high-speed permanent magnet synchronous motor is proposed. • A two-time-scale VSS-APAs for online identification of motor parameters is designed. • The effectiveness of this method is verified by experiments. [ABSTRACT FROM AUTHOR]

This paper is devoted to analyzing the regularity in time and polynomial decay of solutions for a class of fractional reaction–diffusion equations (FrRDEs) involving delays and nonlinear perturbations in a bounded domain of Rd$\operatorname{\mathbf {R}}^{d}$. By establishing some regularity estimates in both time and space variables of the resolvent operator, we present results on the Hölder and C1$C^{1}$‐regularity in time of solutions for both time‐delayed linear and semilinear FrRDEs. Based on the aforementioned results, we study the existence, uniqueness, and regularity of solutions to an identification problem subjected to the delay FrRDE and the additional observations given at final time. Furthermore, under quite reasonable assumptions on nonlinear perturbations and the technique of measure of noncompactness, the existence of decay solutions with polynomial rates for the problem under consideration is shown. [ABSTRACT FROM AUTHOR]

Inverse problems are commonly used in many fields as they enable the estimation of parameters that cannot be experimentally measured. However, the complex nature of inverse problems requires a strong background in data and signal processing. Moreover, ill-posed problems yield parameters that have a strong linear dependence on the problem. The ill-posed nature of these problems lead to many errors in numerical computations that can make parameter identification nearly impossible. In this paper, a new data processing tool is proposed to maximize the sensitivity of the model to the parameters of interest, while reducing the correlation between them. The effectiveness of the toll is demonstrated through a given inverse problem example using Periodically Pulsed Photothermal Radiometry (PPTR). [ABSTRACT FROM AUTHOR]

Due to its unique magnetic circuit structure, the synchronous reluctance motor (SynRM) exhibits significant cross-saturation effects during operation, posing challenges for accurate control and performance optimization of the motor. This paper presents a method for identifying cross-saturation characteristic parameters and an improved mathematical modeling method for the nonlinear changes in SynRM parameters caused by magnetic circuit self-saturation and cross-saturation in the static state of the motor. Offline stand still tests are conducted to acquire characteristic data of the motor under different saturation levels. Subsequently, neural networks learn the complex relationships between input currents and output flux linkages, while numerical optimization techniques refine the extracted parameters to minimize model discrepancies. These identified parameters are then integrated into an enhanced mathematical model that effectively incorporates cross-saturation effects. The accuracy and effectiveness of the proposed model are rigorously validated through comprehensive comparisons with finite element analysis (FEA) results and further simulations. [ABSTRACT FROM AUTHOR]

The paper presents an approach to solving the problem of unknown motion parameters Bayesian identification for the stochastic dynamic system model with randomly delayed observations. The system identification and the object tracking tasks obtain solutions in the form of recurrent Bayesian relations for a posteriori probability density. These relations are not practically applicable due to the computational challenges they present. For practical implementation, we propose a conditionally minimax nonlinear filter that implements the concept of conditionally optimal estimation. The random delays model source is the area of autonomous underwater vehicle control. The paper discusses in detail a computational experiment based on a model that is closely aligned with this practical need. The discussion includes both a description of the filter synthesis features based on the geometric interpretation of the simulated measurements and an impact analysis of the effectiveness of model special factors, such as time delays and model unknown parameters. Furthermore, the paper puts forth a novel approach to the identification problem statement, positing a random jumping change in the motion parameters values. [ABSTRACT FROM AUTHOR]

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the A S M D I damage index, A S M D I exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems. [ABSTRACT FROM AUTHOR]

Due to their ease of use and low cost, passive damping methods are a preferred mean for the reduction of noise in many engineering applications. This applies in particular to foam materials, which exhibit good acoustic and mechanical damping properties. The selection of suitable materials is usually carried out experimentally and can be very labor‐intensive and time‐consuming. For this reason, it is helpful to develop qualified numerical methodsthat can be used for material design and selection. Hence, foam materials must be characterized experimentally in order to enable a later comparison with vibroacoustic simulations. This contribution, therefore, aims at providing suitable parameters for the use in numerical analyses and their validation. Firstly, the microstructure of a foam specimen is captured by means of a CT scan. In addition to providing the geometry for the multi‐physics simulations, this measurement is also used to determine characteristic foam features, for example, strut thickness and pore size distribution. In the second step, the frequency‐dependent stiffness and damping properties of the material are determined by a special experimental setup utilizing an electrodynamic shaker. Here, the dynamic system is approximated as a single‐mass oscillator, which is sufficiently accurate for low frequencies. These properties will later be used in the numerical model to evaluate different parameter identification approaches. In the third and last step of the experimental campaign, measurements with an impedance tube are conducted to obtain the coefficient of absorption. This material parameter is particularly suitable for comparing experiments and simulations. Finally, the correlation between the experimental results is examined to provide a deeper understanding of the foam materials. [ABSTRACT FROM AUTHOR]

To improve the precision of the dynamic modeling of bolted joints, a novel method is presented to model the dynamics by using a radially segmented annular virtual material equivalent bolted joints considering the thickness in this work. Firstly, on the basis of obtaining the contact pressure distribution at bolted joints, the division criterion of the virtual material layer along the radial segments is proposed, and the finite element (FE) model of the equivalent bolted joints of the radially segmented annular virtual material considering the thickness is established. Secondly, the characteristic parameters (elastic modulus, poisson's ratio, density, and thickness) of the radially segmented annular virtual material are used as inputs to calculate the natural frequencies of the bolted joints system, and the deep neural network (DNN) model is constructed and trained on this data sample. Finally, the cuckoo search (CS) algorithm is used to optimally identify the characteristic parameters of radially segmented annular virtual material. The bolted joints of the self-designed solid model of the bolted joints system are studied and analyzed as an example, and the maximum relative error of the first six orders natural frequencies lowers than 1.5 %. [ABSTRACT FROM AUTHOR]

The study investigates flutter boundaries and auto-regressive moving average model (ARMA) stability analysis through numerical simulation cases. Two wind tunnel flutter tests were conducted to analyze the influence of parameter identification errors on boundary prediction accuracy. The research findings indicate: (a) Damping errors have smaller impact than frequency errors on the criterion of flutter boundaries. The criterion in the flutter boundary method is more insensitive to damping errors than the criterion in the ARMA stability analysis method. (b) For wing bending-torsion coupled mode flutter, the criteria of these two methods exhibit similar decreasing trend, which helps to predict flutter boundaries in advance. (c) For planar bending flutter, the ARMA stability analysis method may be more suitable than the flutter boundary method. [ABSTRACT FROM AUTHOR]

The failure of bonded composite materials is accompanied by specific failure modes. These are specifically Mode I, Mode II, Mode III, and their combination (so-called mixed mode). These modes depend on the direction and type of loading. The mechanical properties describing the damage initiation and the damage evolution are unique according to the type of adhesive and present mode of failure. However, a few research studies have focused on an adhesive thicknesses greater than 0.2 mm. The main objective of this research is to investigate the mechanical properties of a bonded joint with large adhesive thickness loaded according to Modes I and II. The observed failure parameters, the cohesive and damage parameters, are identified by minimizing the difference between the force–displacement diagram obtained from the experimental data for both Mode I and Mode II. The finite element model is confronted with these parameters and is evaluated based on their agreement. Compared to other studies with a small adhesive layer thickness, the values of failure parameters are lower. The results show that the adhesive thickness has an influence on the values of cohesive and damage parameters and that these parameter values decrease significantly compared to a small adhesive thickness. The obtained parameters can be further used to predict the fracture toughness of other bonded joints loaded in any direction. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a novel method for identifying the hydrodynamic parameters of a deep‐sea mining vehicle during deployment and retrieval. The proposed approach combines numerical simulation with a nonlinear filter. Initially, a dedicated hydrodynamic model for the deployment and retrieval of the mining vehicle is constructed. The identification process commences with simulations based on computational fluid dynamics (CFD). This approach utilizes CFD to simulate the motion of the deep‐sea mining vehicle during deployment and retrieval, employing an implicit solution approach to analyze its motion in Heave and Yaw degrees of freedom under periodic external forces. Consequently, this provides hydrodynamic performance data. Subsequently, the unscented Kalman filter (UKF) estimator is applied to optimally solve an augmented matrix that incorporates both motion data and hydrodynamic parameters, yielding numerical values for the hydrodynamic parameters. Simulation results demonstrate that, in comparison to motion performance obtained by the CFD method, the hydrodynamic model derived from UKF enables an effective prediction of the motion of the deep‐sea mining vehicle, with prediction errors consistently below 6%. [ABSTRACT FROM AUTHOR]

BACKGROUND: Early blight is one of the main diseases that causes serious pepper yield and quality reduction. The identification of the presymptomatic features during the incubation period is of great research significance, in that scientific prevention measures in the incubation period may effectively reduce the loss of peppers. RESULTS: The present study confirms the feasibility of the identification of presymptomatic features in pepper early blight by infrared thermography during the incubation period. The infrared thermal images of pepper blades were captured during the whole incubation period in our experiment. Evaluation parameters such as temperature uniformity (U) and the variation coefficient (C) were established by infrared thermal images for the blade temperature field. Evaluation parameters such as temperature uniformity U and variation coefficient C of pepper blades change during the time from being inoculated to significant morbidity. For cases of stabbing inoculation, there were no relatively obvious symptoms in visible light images until 96 h after inoculation, whereas some presymptomatic features appeared in infrared thermal images at 60 h after inoculation. Based on the uniformity changes (δU) and the variation coefficient changes (δC) in the temperature field distribution, the characteristic polynomial (CP)‐GBDT model has been established by optimizing the gradient boosting decision tree (GBDT) model. Compared with the GBDT mode, the CP‐GBDT model accuracy increased from 87.5% to 91.7% on the test set. CONCLUSION: Evaluation parameters such as temperature uniformity U and variation coefficient C can be used to effectively characterize the presymptomatic features of pepper early blight by infrared thermography during the incubation period. The results of the present study can provide a reference for the detection of crop diseases in the incubation period. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This study proposes an integrated dimension-reduction representation strategy for simulating multivariate mainshock-aftershock stochastic ground motions based on measured motion records. The strategy includes a parameter identification method for modeling evolutionary power spectrum density (EPSD) of mainshock-aftershocks, a coherence function describing the spectrum correlation between mainshock and aftershock, and the EPSD function matrix establishment for mainshock-aftershock field combining with complex spatial coherence function. Furthermore, the dimension-reduction representation for simulating the ground motions considering the sequence and spatial variability simultaneously is presented with merely two elementary random variables. Numerical results effectively demonstrate the accuracy, efficiency, and engineering applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Machining different types of complex parts requires changing speed of the motorized spindle to accommodate the various processes, resulting in temperature rise or decline and then causing thermal deformation of machine tool, which plays a significant impact on the machining accuracy of the parts. According to the Law of Conservation of Energy and Fourier's Law of Heat Conduction, the heat transfer mechanism of the motorized spindle is investigated. Based on the thermo-solid structure coupling theory, the analytic models of the temperature field and thermal deformation for the motorized spindle are established, and the thermal hysteresis phenomenon is explored by means of the case studies. The characteristic experiments were carried out under variable working conditions and the model parameters were identified using Genetic Algorithm (GA). The accuracy and effectiveness of the proposed models for predicting temperature and thermal deformation of the spindle are validated which is applicable to compensate the thermal errors. Moreover, compared with data-driven modeling approach, our study needs lesser data for parameter identification, greatly saving the computation time in modeling. [ABSTRACT FROM AUTHOR]

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

The paper deals with a source identification problem of the anomalous diffusion equations from nonlocal final data observations where the nonlinearity probably takes values in Hilbert scales. The existence and uniqueness results are proved by establishing some estimates for resolvent operators and using the embedding theorems. We also study regularity results for this equation in terms of the Hölder continuity of mild solutions. Finally, the multi-term fractional diffusion equations with polynomial nonlinearities and the ultra-slow diffusions are considered as illustrative applications. [ABSTRACT FROM AUTHOR]

Soft materials, with the sensitivity to various external stimuli, exhibit high flexibility and stretchability. Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters. However, identifying multiple parameters under complex deformations remains a challenge, especially with limited observed data. In this study, we develop a physics-informed neural network (PINN) framework to identify material parameters and predict mechanical fields, focusing on compressible Neo-Hookean materials and hydrogels. To improve accuracy, we utilize scaling techniques to normalize network outputs and material parameters. This framework effectively solves forward and inverse problems, extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical properties. We explore different approaches for imposing boundary conditions (BCs) to assess their impacts on accuracy. To enhance efficiency and generalization, we propose a transfer learning enhanced PINN (TL-PINN), allowing pre-trained networks to quickly adapt to new scenarios. The TL-PINN significantly reduces computational costs while maintaining accuracy. This work holds promise in addressing practical challenges in soft material science, and provides insights into soft material mechanics with state-of-the-art experimental methods. [ABSTRACT FROM AUTHOR]

Lightweight electric vehicles (LEVs) possess great advantages in the viewpoint of fuel consumption, environment protection, and traffic mobility. However, due to the drastic reduction of vehicle weights and body size, the effects of inertial parameter variation in LEV control system become much more pronounced and have to be systematically estimated. This paper presents a dual adaptive unscented Kalman filter (AUKF) where two Kalman filters run in parallel to synchronously estimate vehicle inertial parameters and additional dynamic states such as vehicle mass, vehicle yaw moment of inertia, the distance from front axle to centre of gravity and vehicle sideslip angle. The proposed estimation only integrates and utilizes real-time measurements of in-wheel-motor information and other standard in-vehicle sensors in LEV. The LEV dynamics estimation model including vehicle payload parameter analysis, Pacejka model, wheel and motor dynamics model is developed, the observability of the observer is analysed and derived via Lie derivative and differential geometry theory. To address nonlinearities and undesirable noise oscillation in estimation system, the dual noise adaptive unscented Kalman filter (DNAUKF) and dual unscented Kalman filter (DUKF)are also investigated and compared. Simulation with various manoeuvres are carried out to verify the effectiveness of the proposed method using MATLAB/Simulink-Carsim®. The simulation results show that the proposed DNAUKF method can effectively estimate vehicle inertial parameters and dynamic states despite the existence of payload variations. [ABSTRACT FROM AUTHOR]

A complete kinematic error model that contains all potential error sources, is one of the fundamentals to ensure the best result of kinematic calibration. In the case of parallel manipulators, the complete error model includes items of passive joint motions. These motions usually cannot be accurately observed during the identification process but can be derived through kinematic analysis. However, due to kinematic errors, the obtained passive joint motions deviate from their actual values, causing motion errors of passive joints. In other words, the passive joints introduce input errors to the error model, which are of the same order of magnitude as kinematic errors and much larger than other measurement and random noises. This issue can significantly affect the stability and accuracy of parameter identification, but it has not been clearly recognized in the literature. To address this problem, this paper systematically analyzes the influence of passive joints' motion errors on the kinematic calibration of parallel manipulators. It is found that when the complete error model including the passive joints' motion errors is used for kinematic calibration of parallel manipulators, the variance of kinematic error parameters solved by the least squares (LS) method greatly increases, leading to iterative divergence even when the identification matrix is column full rank. To improve the stability and accuracy of parameter identification, this study employs the total least squares (TLS) method, which is a dedicated approach for handling input errors, in the identification process. Numerical simulations and experiments of kinematic calibration are conducted on several parallel manipulators. The results validate the correctness and effectiveness of the analysis of the influence of passive joints' motion errors on the kinematic calibration of parallel manipulators. Furthermore, the results indicate that the TLS method can efficiently and accurately accomplish the identification of kinematic parameters. • Input errors which drawn little attention in kinematic calibration, are analyzed. • Influence of passive joints' motion errors on kinematic calibration is revealed. • TLS method is employed, ensuring the stability of parameter identification. • Results of numerical simulations and experiments verify the analysis in this paper. [ABSTRACT FROM AUTHOR]

Benezediols are widely used in different areas of industry, thus identification and quantification of benzenediols is of utmost importance due to their toxicity and high environmental abundance. In this work, benzenediol isomers (pyrocatechol, resorcinol, and hydroquinone) were investigated by using the Bray–Liebhafsky (BL) oscillatory reaction. All three isomers exhibit different behavior in the BL reaction, which renders the BL system applicable as a chemosensor. The period between the fifth and sixth oscillation, the amplitude of the sixth oscillation and in the case of hydroquinone, the emergence of a new oscillation in the BL reaction were selected as the parameters used for the identification and quantification of these isomers. Furthermore, electron paramagnetic resonance spectroscopy and DFT calculations were performed in order to provide insights into the mechanism of benzenediols reactions with the BL system. [ABSTRACT FROM AUTHOR]

The measurement of deep water gravity wave elevations using in situ devices, such as wave gauges, typically yields spatially sparse data due to the deployment of a limited number of costly devices. This sparsity complicates the reconstruction of the spatio-temporal extent of surface elevation and presents an ill-posed data assimilation problem, which is challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN) to reconstruct physically consistent wave fields between two elevation time series measured at distinct locations within a numerical wave tank. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schrödinger equation (NLSE) into the PINN's loss function. We first showcase a data assimilation task by employing constant NLSE coefficients predetermined from spectral wave properties. However, due to the relatively short duration of these measurements and their possible deviation from the narrow-band assumptions inherent in the NLSE, using constant coefficients occasionally leads to poor reconstructions. To enhance this reconstruction quality, we introduce the base variables of frequency and wavenumber, from which the NLSE coefficients are determined, as additional neural network parameters that are fine tuned during PINN training. Overall, the results demonstrate the potential for real-world applications of the PINN method and represent a step toward improving the initialization of deterministic wave prediction methods. [ABSTRACT FROM AUTHOR]

Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely applied in the form of new energy electric vehicles and large-scale battery energy storage systems to improve the cleanliness and greenness of energy supply systems. Accurately estimating the state of power (SOP) of lithium-ion batteries ensures long-term, efficient, safe and reliable battery operation. Considering the influence of the parameter identification accuracy on the results of state of power estimation, this paper presents a systematic review of model parameter identification and state of power estimation methods for lithium-ion batteries. The parameter identification methods include the voltage response curve analysis method, the least squares method and so on. On this basis, the methods used for modeling and estimating the SOP of battery cells and battery packs are classified and elaborated, focusing on summarizing the research progress observed regarding the joint estimation method for multiple states of battery cells. In conclusion, future methods for estimating the SOP of lithium-ion batteries and their improvement targets are envisioned based on the application requirements for the safe management of lithium-ion batteries. [ABSTRACT FROM AUTHOR]