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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]

With the great opportunities created by the new advances in Industry 4.0, many manufacturers are testing and investing in new equipment and infrastructure to deploy these technologies. However, there are a huge number of small and medium-sized manufacturers (SMMs) that are lagging behind due to the lack of in-house R&D capabilities and workforce shortage and/or financial constraints to afford such investment. Additionally, application of theoretical production research in SMMs often confront challenges such as low data availability and data quality, etc. In this paper, we describe a case study at a local medium-sized manufacturer of electromechanical devices for industrial, consumer, and medical applications, who was struggling to meet ever-growing market demand, and apply a novel approach of production system modelling to overcome the challenge of unavailability of the operation up-and downtime data. Specifically, the parametric model of the production system is identified using several system performance metrics derived based on the parts flow data of the in-process buffer. With the mathematical model constructed, the system bottleneck is analysed anda number of improvement scenarios are explored that can potentially enhance the system throughput. Finally, model sensitivity is analysed by calculating the deviation of the model-predicted performance metrics to those produced by a reference nominal model. This analysis demonstrates that the model constructed using our proposed approach is robust even when the system parameters vary from the baseline ones. [ABSTRACT FROM AUTHOR]

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate. Author summary: Computational cognitive models occupy an important position in cognitive science research, as they offer a simple way of quantifying cognitive processes (such as how fast someone learns, or how noisy they are in choice selection), and testing which cognitive theories offer a better explanation of the behavior. To relate cognitive models to the behavioral data, researchers rely on statistical tools that require estimating the likelihood of observed data under the assumptions of the cognitive model. This is, however, not possible to do for all models as some models present significant challenges to likelihood computation. In this work, we use artificial neural networks (ANNs) to bypass likelihood computation and approximation altogether, and demonstrate the success of this approach applied to model parameter estimation and model comparison. The proposed method is a contribution to ongoing development of modeling tools which will enable cognitive researchers to test a broader range of theories of cognition. [ABSTRACT FROM AUTHOR]

Hysteresis nonlinearity widely exists in the piezoelectric actuator (PEA), and the hysteresis nonlinearity has strong dynamic characteristics that lead to deterioration of tracking performance. To decrease the positioning error caused by hysteresis nonlinearity, a generalized Bouc-Wen (GBW) hysteresis model and its compensation method are proposed in this paper. First, based on the Bouc-Wen hysteresis model, two asymmetric terms and a second-order IIR filter are applied to describe the asymmetric hysteresis and high-frequency phase lag characteristics of PEA. Then, the model parameters with strong relevance to frequency variation are modified as frequency-dependent parameters. Meanwhile, based on the particle swarm optimization (PSO) algorithm, a novel parameter identification algorithm is designed for identifying the parameters of GBW hysteresis model. Then, an inverse feedforward controller is constructed based on the GBW hysteresis model, and a composite compensation control algorithm combining PID controller and repetitive controller is developed to reduce the unmodeled dynamics errors and unknown external disturbances. Finally, the comparison experiment results show that the accuracy and performance of the GBW model proposed in this paper are much better than the classical Bouc-Wen (CBW) model and the enhanced Bouc-Wen (EBW) model, and the developed compensation controller has excellent control performance and robustness. [ABSTRACT FROM AUTHOR]

The conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the kth‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics. [ABSTRACT FROM AUTHOR]

In the field of material modeling, thermoplastic polymers are often studied because of their complex material behavior and their prevalence in industry applications due to their low cost and wide range of applications. Nowadays, where reusability becomes more and more important, materials which can undergo reversible thermomechanical deformations are appealing for, e.g., the construction of car body components. To predict such complex forming processes with multiple influencing factors, such as temperature, strain rate or underlying material morphology, model formulations are needed that account for these influences simultaneously and are validated against experimental data. Unfortunately, up to now only a few contributions are available which consider all these phenomena. In addition, the range of process parameters considered is often narrow due to the experimental effort required for testing. This usually results in limited predictive capabilities of the model. To overcome these limitations, in this work, a thermo-mechanically coupled material model is developed that accounts for the underlying morphology in terms of the degree of crystallinity (DOC). The model formulation is derived in a thermodynamically consistent manner, incorporating coupled nonlinear visco-elastic and elasto-plastic material behavior at finite strains. To characterize and further validate the model, mechanical as well as thermal experiments are conducted for polyamide 6 (PA6). Here, a blending strategy of PA6 together with an amorphous co-polymer is introduced during specimen production to achieve a wider range of stable DOCs(approximately 15%). The model formulation is successfully applied to experimental results and its predictions are in good agreement with experimental observations. [ABSTRACT FROM AUTHOR]

State of charge (SOC) is a key state variable in lithium‑ion battery management system. The battery model and estimation algorithm are important factors that affect the accuracy of SOC estimation. In this paper, the optimization battery model is created by optimizing the hybrid power pulse characteristic (HPPC) parameter combination through orthogonal analysis. The simulation results demonstrate that optimizing the HPPC parameter combinations can improve battery modeling accuracy. Then, an extended Kalman particle filter (EKPF) algorithm is proposed by using the extended Kalman filter (EKF) algorithm as the density function of the particle filter (PF) algorithm. The EKPF algorithm is verified under the dynamic stress test and Beijing bus dynamic stress test conditions, the root mean absolute errors and root mean square errors in all cases are less than 1.5%. The experimental results show that the EKPF algorithm can combine the advantages of EKF and PF to estimate lithium‐ion battery SOC accurately. [ABSTRACT FROM AUTHOR]

The utilization of machine learning advantages in solving engineering problems has proven to be beneficial and accurate. Dealing with partial differential equations (PDEs), on the other hand, PINN could be enhanced using additional information from the physical mathematics of the problems. In this regard, a physics-informed neural network (PINN) is exploited in the current study to extract phase and group velocities of the multi-scale hybrid composite plate structure. In doing so, the exact differential equations of homogenized plate structure are extracted using Hamilton's principle based on modified couple stress theory and shear deformation displacement field. The boundary conditions along with initial conditions are also considered in the equations. The PINN tries to minimize the loss function which is defined using extracted PDEs, boundary conditions, and initial conditions. Moreover, the results of PINN are further compared with the analytical solution using harmonic expansion of the displacement variables. The results show highly accurate and reliable results obtained using PINN in comparison to analytical results. [ABSTRACT FROM AUTHOR]

This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input–output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achieved, while satisfying the underlying dynamic equilibrium. Numerical examples on recorded subject data demonstrate the effectiveness of the proposed framework in generating a physics-informed forward-dynamics surrogate, which yields higher accuracy in motion predictions of elbow flexion–extension of an MSK system compared to the case with single-scale training. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject's kinematics data. [ABSTRACT FROM AUTHOR]

Aleurites moluccanus (candlenut) and Bertholletia excelsa (Brazil nut) are marketed as dietary supplements for weight loss. These dietary supplements have been found to sometimes be adulterated with toxic nuts/seeds from Cascabela thevetia, commonly known as yellow oleander or lucky nut. This study emphasizes the key identification parameters to differentiate the genuine and adulterated nuts. Samples were obtained from authenticated sources of the nuts and from commercial sources of dietary supplements. This study examined 38 samples, including voucher and commercial samples. All eight commercial candlenut dietary supplement samples were adulterated. Additionally, two samples sold as Brazil nuts were also found to be adulterated. Other nuts were screened for the presence of Cardiac Glycosides, but none were found to be positive. The presence of yellow oleander was confirmed in all commercial dietary supplement samples marketed as candlenut as well as in commercial samples of Brazil nut. This study provides simple key identification characters using micro-morphology and histochemical localization of cardiac glycosides in the commercial nuts, HPTLC fingerprints, and LC-DAD-Q-ToF analytical parameters to detect and identify adulteration in commercial products. [ABSTRACT FROM AUTHOR]

Diazo compounds are gathering interest for their potential in promoting greener synthesis routes. We investigate, at a lab‐scale, the continuous synthesis of diazo acetonitrile (DAN) using a micro‐structured flow reactor and a flow reaction calorimeter. Data concerning DAN formation in the former, and relative to reaction heat and gas flow rate in the latter, are collected. We present a physical and a grey‐box model, both of which are calibrated to our data. Compared to the physical model, the grey‐box approach allows to better incorporate the complex chemical reaction pathways of DAN. [ABSTRACT FROM AUTHOR]

Flexible barriers have been recently proposed as a promising alternative for trapping woody debris driven by the flow in torrents and rivers before they reach elements at risks. Small-scale experiments in similitude with the real-scale have been conducted in view of addressing the interaction between the flow and the barrier. A particular attention was paid to the identification of the parameters with influence on the loading experienced by the barrier, varying the woody debris mixtures characteristics, water discharge, flume inclination and woody debris supply mode. This investigation emphasised the intricacy of the relation between the barrier loading and the characteristics of the trapped logs and of the logs accumulation. The barrier loading revealed inversely proportional to the woody debris accumulation permittivity, which quantifies its capacity to let the water seep through. Permittivity depended on the way the accumulation built up and on the evolution of its characteristics with increasing discharge and trapped logs volume. Finally, the loading exerted by the flow on the barrier was derived from the barrier elongation, revealing that it could be modelled as a hydrostatic load with a reduction factor of 0.5. [ABSTRACT FROM AUTHOR]

In order to accurately predict the minimum creep rate under the actual working conditions of compacted graphite cast iron cylinder head, this paper conducted creep experiments in the temperature range of 723.15–823.15 K and the stress range of 100–300 MPa. The creep damage evolution and damage mechanisms under test conditions were also analyzed. Then the data fitting performances were analyzed and compared from three aspects: the optimization‐seeking algorithm, the form of the objective function and the construction of the creep constitutive model. Finally, the chosen optimization algorithm combined with the proposed objective function and creep constitutive model resulted in 100% of the predicted values within the 3 times error band and 26.67% of the predicted values within the 1.5 times error band. This improves the prediction accuracy while reducing the effect of data overfitting. [ABSTRACT FROM AUTHOR]

Non-destructive evaluation of the spatially resolved residual stresses from limited measurement data is crucial for a comprehensive assessment of structural integrity and performance reliability of engineering components. Generally, residual stresses are caused by incompatible internal eigenstrains and along this line, a novel sensitivity-based eigenstrain reconstruction approach is developed in this work for residual stress identification in thin plates using vibrational modal data. There are two key ingredients in establishing the proposed approach. At first, residual stresses are parameterised through direct eigenstrain analysis and thereafter, residual stress reconstruction is recast as a parameter identification problem whose goal function is just the least-squares of the misfit between the measured and calculated data. Second, to minimise the nonlinear least-squares goal function, the modal sensitivity analysis is called to linearise the misfit and the trust-region constraint is invoked in conjunction with the Tikhonov regularisation to enhance the convergence. Numerical examples are investigated to verify the robustness, accuracy and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Hardware-in-the-loop simulation is an important way to test the complex contact process of two spacecraft docking in space. Hardware-in-the-loop simulations are known to be unstable and divergent because of various unexpected dynamics, including delays in the system. Existing force compensators for the force-sensing delay, dynamic response delay, and structural deformation of the upper and lower platforms require real-time identification of the contact parameters of the docking mechanisms (DMs), which is difficult because of the unknown contact model structure. However, the existing force- and position-based compensation methods do not consider the structural deformation of the upper platform. In this study, a force- and position-based compensation method is proposed to compensate for the force-sensing delay, actuation-response delay, and structural dynamic position error of the lower and upper platforms. It is not necessary to identify the contact parameters of the DMs. Many verifications by software and experiments show that by using the proposed method, a stable simulation with satisfactory accuracy can be achieved under different simulation conditions. Capture experiments on a space manipulator are provided to demonstrate the applications. [ABSTRACT FROM AUTHOR]

In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function, making the extreme point of the fitness function more prominent and improving the algorithm's search ability while reducing the algorithm's computational burden. Secondly, on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions, it is merged with the filled function to improve the algorithm's capacity to skip out of the local optimal solution. This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current, voltage, and speed data. The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor, and it has superior global convergence performance and robustness. [ABSTRACT FROM AUTHOR]

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Depending on cutting conditions, unacceptable machining errors are caused by tool deflection in end milling operations. Many studies have proposed methods for predicting the machining error owing to the tool deflection to achieve the theoretical optimization of the cutting conditions. However, the conventional machining error simulation is not practically utilized to determine the optimal cutting conditions. Tool system stiffness parameters and cutting coefficients must be identified in advance to simulate machining errors. However, dynamometers and displacement sensors are required for parameter identification. Therefore, it is impossible to identify the required parameters in typical factories, which do not possess such special equipment. In this study, a practical method was developed to identify the stiffness parameters that can be determined in factories. The proposed method employs on-machine measurement and sensor-less cutting force monitoring to achieve practical parameter identification. In the proposed method, the profile milling is first conducted. During the milling operation, the cutting force and cutting torque are monitored through a controller based on the sensor-less monitoring technique. After the operation, the machining error distribution on the machined surface is measured on machine using a touch probe. The required parameters are identified by minimizing the differences between the measured and theoretical forces, torques, and machining error distributions. [ABSTRACT FROM AUTHOR]

The Bouc–Wen nonlinear hysteretic model is widely used in nonlinear systems, and it is difficult to identify the parameters of Bouc–Wen model because of its high nonlinearity. In the paper, a hybrid genetic algorithm combined with sequential quadratic programming is proposed to solve the problem about the parameter identification of Bouc–Wen model. Sequential quadratic programming method has good local convergence and superlinear convergence rate. Therefore, compared with the genetic algorithm, the proposed identification method can accelerate the convergence speed in parameter recognition of Bouc–Wen model and achieve excellent recognition accuracy. Through the function test and the numerical simulation of the shear structure, it is proved that the proposed method has high convergence speed and accurate identification. In addition, the theory and method proposed in this paper are combined with Bouc–Wen model to identify parameters of overall restoring force model of the energy-dissipation structures, the identification results are in good agreement with the experimental results, verifying reliability and accuracy of the model and robustness of the proposed identification method. [ABSTRACT FROM AUTHOR]

Parameter identification is of great significance for the postearthquake performance evaluation of structure equipped with tuned mass damper (TMD). However, the soil-structure interaction (SSI) effects have not been considered in the parameter identification of structure with TMD yet, which influence the dynamic characteristics and seismic responses of structures. This paper aims at proposing a framework for identifying the physical parameters of soil-structure-TMD system. Firstly, the accelerated particle swarm optimization (APSO) algorithm is combined with the search space reduction (SSR) method. Then, the frequency response function and transmissibility function are adopted for output-input and input-only cases, respectively, and a simplified mechanical model for soil-structure-TMD system is employed. Next, the measured responses are used to identify the physical parameters of structure with TMD considering SSI effects. Finally, the effectiveness of the proposed identification method is investigated, and the influences of frequency band and noise pollution on the identification performance are discussed. The results show that the proposed strategy can identify the system physical parameters accurately and quickly. It is worth noting that high frequency bands and noise pollution may lead to estimation error, especially for output-only case. [ABSTRACT FROM AUTHOR]

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive dynamic surface sliding mode control technique to address the issue of system parameter changes in permanent magnet synchronous motor (PMSM) position servo systems. The proposed method involves adopting a linear parameter varying (LPV) observer-based parameter identification algorithm and adaptive control technique. Initially, a mathematical model of the PMSM is established, and the system parameters are divided into nominal and perturbation values. This allows for the reconstruction of the system model into a state space equation that incorporates the unknown perturbation parameters. To accurately estimate these unknown parameters, an LPV observer is designed based on the reconstructed model. Additionally, an adaptive dynamic surface sliding mode control technique is explored to achieve the desired tracking performance. Meanwhile, an exponential reaching law is introduced to expedite the dynamic behavior of the system and mitigate chattering. Finally, a suitable Lyapunov function is selected to ensure the overall stability of the system. The simulation results demonstrate the effectiveness of the parameter identification and control algorithm in achieving good identification and tracking control ability for PMSM systems. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the impact of production parameters on the reproducibility of kombucha fermentation over several production cycles based on backslopping. Six conditions with varying oxygen accessibility (specific interface surface) and initial acidity (through the inoculation rate) of the cultures were carried out and compared to an original kombucha consortium and a synthetic consortium assembled from yeasts and bacteria isolated from the original culture. Output parameters monitored were microbial populations, biofilm weight, key physico-chemical parameters and metabolites. Results highlighted the existence of phases in microbial dynamics as backslopping cycles progressed. The transitions between phases occurred faster for the synthetic consortium compared to the original kombucha. This led to microbial dynamics and fermentative kinetics that were reproducible over several cycles but that could also deviate and shift abruptly to different behaviors. These changes were mainly induced by an increase in the Saccharomyces cerevisiae population, associated with an intensification of sucrose hydrolysis, sugar consumption and an increase in ethanol content, without any significant acceleration in the rate of acidification. The study suggests that the reproducibility of kombucha fermentations relies on high biodiversity to slow down the modulations of microbial dynamics induced by the sustained rhythm of backslopping cycles. [ABSTRACT FROM AUTHOR]

Applications involving hygro-thermo-mechanical models are found in engineering fields as diverse as electronic packaging, concrete structures, composite materials and wood structures. Within the framework of wood processing, hygro-thermo-mechanical problems account for the cross-dependence of moisture content, temperature and dimensional changes of wooden components. Numerical simulation plays an important role in predicting the behaviour of wooden structures and, therefore, use of appropriate process and material parameters is essential for a successful prediction. The present work is inserted in this context and addresses application of optimisation techniques to identification of heat transfer, moisture diffusion and moisture-dependent swelling/shrinkage parameters. The direct problem is formulated based on a fully coupled transient solution of the energy, moisture and momentum transfer conservation laws. A global–local hybrid optimisation technique is proposed combining swarm intelligence and deterministic approaches, respectively based on particle swarm optimisation and the Nelder–Mead (NM) technique. The first stage aims at reducing the search space, so that the NM algorithm is able to determine the global minimum with acceptable accuracy. The strategy is both robust and efficient, being able to avoid local minima with reduced number of fitness computations. An illustrative example featuring a moisture-gaining process is also discussed, in which special attention is placed on the elastic and material swelling coupled effects caused by moisture transfer. [ABSTRACT FROM AUTHOR]

With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of "illness", if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%. [ABSTRACT FROM AUTHOR]

A total of 45 alkylbenzenes were detected and identified in crude oils with different depositional environments and thermal maturities from the Tarim Basin, Beibuwan Basin, and Songliao Basin using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC x GCTOFMS). By analyzing the distribution characteristics of C0eC5 alkylbenzenes, it is found that the content of some alkylbenzenes varies greatly in crude oils. Based on the distribution characteristics of 1,2,4,5- tetramethylbenzene (TeMB) and 1,2,3,4-TeMB, the ratio of 1,2,4,5-TeMB to 1,2,3,4-TeMB is proposed to indicate the organic matter origin and depositional environment of ancient sediments. Oil samples originated mainly from lower hydrobiont, algae, bacteria and source rocks deposited under reducing/ anoxic conditions have low 1,2,4,5-/1,2,3,4-TeMB values (less than 0.6), while oil samples originated mainly from terrestrial higher plants and source rocks deposited under oxic/sub-oxic conditions have higher 1,2,4,5-/1,2,3,4-TeMB values (greater than 1.0). The significant difference of 1,2,4,5-/1,2,3,4-TeMB values is controlled by 1,2,4,5-TeMB content. 1,2,4,5-TeMB content in oils derived from source rocks deposited in oxidized sedimentary environment (greater than 1.0 mg/g whole oil) is higher than that in oils from source rocks deposited in reduced sedimentary environment (less than 1.0 mg/g whole oil). 1,2,4,5-/1,2,3,4-TeMB ratio might not or slightly be affected by evaporative fractionation, biodegradation and thermal maturity. 1,2,4,5-/1,2,3,4-TeMB ratio and 1,2,4,5-TeMB content can be used as supplementary parameter for the identification of sedimentary environment and organic matter input. It should be noted that compared to the identification of organic matter sources, the 1,2,4,5-/1,2,3,4-TeMB parameter is more effective in identifying sedimentary environments. [ABSTRACT FROM AUTHOR]

The elastic hydraulic isolator is a novel liquid-gas mixture isolator applied to floating slab track structure. The dynamic parameters of isolators are the key to the attenuation effect of ground vibration. Therefore, a nonlinear dynamic model and parameter identification method for vibration isolation supports of floating slab track structure is proposed. The changes in stiffness and damping parameters of the elastic hydraulic isolator are studied by changing its damping components and comparing them with those of the steel spring isolator and rubber isolator. The results indicate that the accuracy of the nonlinear dynamic model is verified by comparing the experimental curve with the reconstructed curve. The dynamic parameters of the elastic hydraulic isolator are dependent on the excitation frequency and amplitude and are influenced by the liquid viscosity and the damping plate. The stiffness and damping parameters of the elastic hydraulic isolator are similar to those of the rubber isolator, but the variation law with excitation frequency is different. [ABSTRACT FROM AUTHOR]

Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells. [ABSTRACT FROM AUTHOR]

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness. [ABSTRACT FROM AUTHOR]

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

The implementation of a progressive rehabilitation training model to promote patients' motivation efforts can greatly restore damaged central nervous system function in patients. Patients' active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient's healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human–robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient's movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient's healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of parameter identification for discrete-time stochastic systems with unknown exogenous inputs. These systems form an important class of dynamic stochastic system models used to describe objects and processes under a high level of a priori uncertainty, when it is not possible to make any assumptions about the evolution of the unknown input signal or its statistical properties. The main purpose of this paper is to construct a new SVD-based modification of the existing Gillijns and De Moor filtering algorithm for linear discrete-time stochastic systems with unknown exogenous inputs. Using the theoretical results obtained, we demonstrate how this modified algorithm can be applied to solve the problem of parameter identification. The results of our numerical experiments conducted in MATLAB confirm the effectiveness of the SVD-based parameter identification method that was developed, under conditions of unknown exogenous inputs, compared to maximum likelihood parameter identification when exogenous inputs are known. [ABSTRACT FROM AUTHOR]

This paper provides new experimental data regarding inelastic strain accumulation in structural steel samples. The samples are subjected to stress‐controlled cyclic loading with positive mean stress within each cycle; the loading blocks are combined in a complex pattern to study the effect of load history. A large‐strain ratcheting model is constructed based on a material law with a nonlinear kinematic and isotropic hardening. A new rule of ductile damage accumulation is suggested and a class of models with hardening stagnation is introduced. We show that models which include the hardening stagnation describe the experimental data more accurately. Unfortunately, due to a large spread of mechanical properties from sample to sample, it is impossible to describe the entire set of experiments using a single set of parameters. To address this challenge, we have developed novel mathematical tools for parameter identification in the context of stochastic variations in mechanical properties. These tools include the concept of a collar of model responses and the notion of protrusion. We propose an extended parameter identification problem that rethinks the standard error functional. We demonstrate that the extended identification problem effectively accounts for the large spread observed in experimental data when applied to the developed ratcheting model. [ABSTRACT FROM AUTHOR]

Recently in [V. Markaki, D. Kourounis and A. Charalambopoulos, A dual self-monitored reconstruction scheme on the TV -regularized inverse conductivity problem, IMA J. Appl. Math. 86 2021, 3, 604–630], a novel reconstruction scheme has been developed for the solution of the inclusion problem in the inverse conductivity problem on the basis of a weighted self-guided regularization process generalizing the total variation approach. The present work extends this concept by implementing and investigating its applicability in the two-dimensional elasticity setting. To this end, we employ the framework of the reconstruction of linear and isotropic elastic structures described by their Lamé parameters. Numerical examples of increasingly challenging geometric complexities illustrate the enhanced accuracy and efficiency of the method. [ABSTRACT FROM AUTHOR]

Summary: By using the collected batch data and the iterative search, and based on the filtering identification idea, this article investigates and proposes a filtered multi‐innovation generalized projection‐based iterative identification method, a filtered generalized gradient‐based iterative identification method, a filtered generalized least squares‐based iterative identification method, a filtered multi‐innovation generalized gradient‐based iterative identification method and a filtered multi‐innovation generalized least squares‐based iterative identification method for equation‐error autoregressive systems described by the equation‐error autoregressive models. These filtered generalized iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises. [ABSTRACT FROM AUTHOR]

In this paper, a new dynamic photogrammetry method using an unmanned aerial vehicle (UAV) is proposed and the UAV-based operational modal analysis method is established. The presented dynamic photogrammetry method can measure the dynamic displacements of structure using one or two cameras. However, it is difficult to use fixed cameras or handheld cameras to measure the dynamic responses for some outdoor structures, because it is hard to reach for measurement or maintenance sometimes. Therefore, UAV is an effective alternative and the homography-based perspective rectification method can be used to correct the UAV images. However, the presented methods use only four calibration points. Additionally, there is still obvious signal drift in the extracted dynamic responses. In this paper, a new time varying homography matrix, calculated using n calibration points, is proposed here for the first time to correct the perspective distortion for image at different time and the relative displacements are used to eliminate the influence of UAV in-plane motion. The influence analysis of UAV vibration and the method to eliminate the UAV vibration are the novelty of this paper. And then, the UAV-based operational modal analysis method is proposed. The proposed method is verified using a plane frame structure in laboratory. The results show that the proposed method can effectively eliminate the vibration of UAV and identify the structural modal parameters. Under the impulse excitation and white noise excitation, when the calibration points n is larger than 60, the relative errors of natural frequency and damping ratio are less than 0.4% and 5.45%, respectively. And the MAC values are all greater than 0.99. Meanwhile, the influence of the number of adopted calibration points on modal parameters identification is considered in the experiment and the advised value is given. [ABSTRACT FROM AUTHOR]

To overcome the inaccurate frequency band division in empirical wavelet transform (EWT) induced by high noise and modulation edge band, this study proposes an improved EWT (IEWT) and applies it to operational modal analysis (OMA) in civil structures. The IEWT segments the frequency band using spectral trend determined with removing the high-frequency components of the Fourier spectrum. The synchroextracting transform is employed to denoise the frequency components obtained from IEWT combined with the random decrement technique and Hilbert transform to identify modal parameters. The effectiveness of IEWT and the proposed modal analysis method is comprehensively verified by a full-scale cable-stayed footbridge. The results demonstrate that the proposed IEWT, random decrement technique, and Hilbert transform can accurately identify the modal parameters from multi-setup ambient vibration measurements. [ABSTRACT FROM AUTHOR]

The track irregularities affect the safety and comfort of running vehicle and increase the dynamic responses of vehicle and track. There is the rail irregularity at the welded region, and the irregularity can be regarded as the superposition of two cosine waves with the long wavelength about 1 m and the short wavelength of 0.1–0.2 m. The welded location has both short-wavelength irregularity and long-wavelength irregularity. A combined method is presented to identify the rail irregularity at the welded region using the acceleration time history curve of wheelset. Firstly, the accelerations of wheelset are transformed into the different frequencies domain signals by wavelet transformation, and the rail irregularity with the short wavelength and high-frequency is determined. Secondly, the rail irregularity with the long wavelength and low-frequency is identified by the Wigner–Ville distribution or the short-time energy method. Finally, the rail irregularity at the welded region is determined by both of the above two steps met. The correctness of the proposed method is validated by numerical example. The proposed method can accurately and efficiently identify the high-frequency and low-frequency characteristics of the rail irregularity at the welded region. [ABSTRACT FROM AUTHOR]

Quadrotor unmanned aerial vehicles (UAVs) are small, agile four-rotor systems suitable for various applications, from surveillance to disaster support missions. Hence, achieving high-precision trajectory tracking is crucial for their successful deployment. This research focuses on modelling, parameter identification, and Linear Quadratic Regulator (LQR) control design for quadrotors, aiming to enhance their trajectory following capabilities. The quadrotor dynamics are a sixth degree-of-freedom (6DOF) equation of motion derived from Newton's second law, encompassing moment of inertia, centre of gravity, weight, and thrust of propeller parameters. Experimental measurements are conducted to accurately determine these parameters, ensuring a realistic representation of the quadrotor system. Subsequently, a linearized model is constructed to provide a suitable plant for control system development. The LQR control design is intended to improve the trajectory tracking performance. This control strategy is validated through simulation and practical experiments, demonstrating its effectiveness in achieving high-precision trajectory following capabilites. The proposed approach demonstrates that LQR control effectively guides the quadrotor to resemble a predefined trajectory, experiencing only 3 % overshoot observed during the initial phase of flight. [ABSTRACT FROM AUTHOR]