Parameter Identification Research Articles

An enhanced indirect modal identification procedure for bridges based on the dynamic response of moving vehicles

Indirect dynamic structural identification, referred to as Vehicle Scanning Method (VSM), pertains to the estimation of bridge modal parameters by using data recorded from sensors directly mounted on a moving vehicle. This procedure has recently gained increasing attention due to its low cost and simple implementation. Nevertheless, the non-stationary and complex nature of the vehicle–bridge interaction phenomenon poses some limitations to the applicability of the method, especially in terms of accuracy. In this regard, in this paper, an enhanced procedure for the dynamic identification of bridges modal parameters based on the VSM is introduced, taking into account the effects of vehicle/bridge damping and road pavement roughness. Specifically, based on the Variational Mode Decomposition (VMD) method, the relevant Intrinsic Mode Functions (IMFs) and corresponding modal frequencies are determined from the recorded signal. Further, the Natural Excitation Technique (NExT) is adopted in conjunction with a noise-robust area ratio-based approach, for modal damping ratios estimation. Finally, mode shapes are evaluated by properly correcting the instantaneous amplitudes of each IMF considering the influence of the corresponding estimated modal damping ratio. Several numerical simulations are presented to show the validity of the proposed procedure and comparisons with the classical Hilbert Spectrum-based identification approach are employed to assess its reliability and improved accuracy.

Implementation of inverse method for identification of process parameters in a laser heating process

Changes in process parameters can alter the temperature data of a solid body during the laser heating process. This paper proposes an inverse method to determine the laser heating parameters, namely, sheet width, sheet thickness, laser power, and scan speed of the work specimen for the given temperature response which is assigned by the user. The method uses an analytical tool on moving heat source as a forward model capable of real-time temperature prediction of the sheet under the laser heating process. The effectiveness of the present forward model that incorporated the temperature-dependent material properties is tested by experimental findings performed on Al 6061-T6 sheets. Next, an iterative search process based on heuristic method is carried out for satisfying a prescribed temperature response by the optimization of unknown parameters. To illustrate the implementation and assess the practicality of the inverse method, two examples based on laser heating applications are used. The method proposed herein recover the unknowns for the prescribed temperature response after a few iterations, provides an efficient way to optimize the laser heating process. Additionally, the effect of measurement error on the findings of the inverse problem is addressed.

Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

Abstract. ​​​​​​​Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.

A Building Point Cloud Extraction Algorithm in Complex Scenes

Buildings are significant components of digital cities, and their precise extraction is essential for the three-dimensional modeling of cities. However, it is difficult to accurately extract building features effectively in complex scenes, especially where trees and buildings are tightly adhered. This paper proposes a highly accurate building point cloud extraction method based solely on the geometric information of points in two stages. The coarsely extracted building point cloud in the first stage is iteratively refined with the help of mask polygons and the region growing algorithm in the second stage. To enhance accuracy, this paper combines the Alpha Shape algorithm with the neighborhood expansion method to generate mask polygons, which help fill in missing boundary points caused by the region growing algorithm. In addition, this paper performs mask extraction on the original points rather than non-ground points to solve the problem of incorrect identification of facade points near the ground using the cloth simulation filtering algorithm. The proposed method has shown excellent extraction accuracy on the Urban-LiDAR and Vaihingen datasets. Specifically, the proposed method outperforms the PointNet network by 20.73% in precision for roof extraction of the Vaihingen dataset and achieves comparable performance with the state-of-the-art HDL-JME-GGO network. Additionally, the proposed method demonstrated high accuracy in extracting building points, even in scenes where buildings were closely adjacent to trees.

Linear Identification Problems for Equations with the Dzhrbashyan–Nersesyan Derivative

Linear Identification Problems for Equations with the Dzhrbashyan–Nersesyan Derivative

Identifying the plane position of the receiver coil in a multi‐transmitter IPT system through the identification of parameters

AbstractThe study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%.

An adaptive backstepping control strategy based on radial basis function neural networks for the magnetorheological semi-active suspension

Owing to nonlinear issues such as external disturbances and uncertain parameters within the semi-active suspension system (SASS), the vibration amplitude of the suspension system tends to increase, and the time required for the suspension system to reach a steady-state response is prolonged. Hence, this paper proposes an adaptive backstepping control strategy based on radial basis function neural networks (RBF-NNs). Firstly, the damping force characteristics of the magnetorheological (MR) damper are tested, and the experimental data are utilized for parameters identification and fitting of the Bouc-Wen model. To establish a connection between the controller and the forward model of the MR damper, the forward model of the MR damper, the inverse model of the MR damper, and the model of the MR-SASS are constructed. Secondly, the backstepping controller and the adaptive backstepping controller based on RBF-NNs are designed. The stability and reliability of the closed-loop suspension system are verified through stability analysis using Lyapunov function. Finally, the dynamic characteristics of the passive control, backstepping control, and adaptive backstepping control strategies based on RBF-NNs applied to MR-SASS are analyzed under B-Class road excitation and speed bump road excitation. The acceleration, suspension dynamic deflection, and tire dynamic load are selected as the evaluation indices. The results demonstrate that the adaptive backstepping controller based on RBF-NNs significantly enhances the ride comfort of the SASS.

On the experimental determination and prediction of damage evolution in composites via cyclic testing

This study introduces a novel approach to obtaining and predicting damage evolution laws using quasi-static cyclic testing within the framework of continuum damage mechanics. To achieve this, a comprehensive set of characterization and parameter identification tests was performed. Carbon-epoxy specimens were manufactured using the filament-winding technique, and these laminates were tested using a universal testing machine. Digital image correlation was employed in all experiments to capture strain fields, and an alternative method utilizing the combined loading compression device is presented to obtain mechanical properties. Once the material was characterized, cyclic tests were conducted, including [Formula: see text], [Formula: see text], and [Formula: see text] tensile tests, and shear v-notch tests. These aim to determine damage evolution laws for both conditions, pure and coupled stress states. From these tests, values for damage onset and threshold were obtained and used to define four new parameters. These parameters permit the estimation of degradation relationships for any arbitrary orientation without requiring additional tests. This approach was tested as the proof-of-concept within the positive range of transverse tension and in-plane shear stress domain. The obtained results are promising, justifying its extension to other failure mechanisms. While acknowledging its limitations, this new approach holds potential for implementation in the analysis of progressive failure in fiber-reinforced composite materials, possibly in conjunction with established failure criteria and computational tools, mainly in the finite element method domain.

Dimension-Reduction Representation of Multivariate Mainshock-Aftershock Ground Motions Based on Measured Records

ABSTRACT 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.

Asymptotically efficient Quasi-Newton type identification with quantized observations under bounded persistent excitations

This paper is concerned with the optimal identification problem of dynamical systems in which only quantized output observations are available under the assumption of fixed thresholds and bounded persistent excitations. Based on a time-varying projection, a weighted Quasi-Newton type projection (WQNP) algorithm is proposed. With some mild conditions on the weight coefficients, the algorithm is proved to be mean square and almost surely convergent, and the convergence rate can be the reciprocal of the number of observations, which is the same order as the optimal estimate under accurate measurements. Furthermore, inspired by the structure of the Cramér–Rao lower bound, an information-based identification (IBID) algorithm is constructed with an adaptive design about weight coefficients of the WQNP algorithm, where the weight coefficients are related to the parameter estimates which leads to the essential difficulty of algorithm analysis. Beyond the convergence properties, this paper demonstrates that the IBID algorithm tends asymptotically to the Cramér–Rao lower bound, and hence is asymptotically efficient. A numerical example is simulated to show the effectiveness of the proposed algorithms.

Using active cooling/heating for 1C1R grey-box model parameter identification in actual environment: A proof-of-concept study

A model predictive control (MPC) has been important method for managing both comfort and energy saving in heating, ventilation, and air conditioning (HVAC) problems. However, recent data-driven models for MPC, relying on extensive training data, pose challenges for practical implementation in real-world thermal environments due to their time-consuming nature. The grey-box approach, utilizing a simple RC model as a thermal environment proxy, is well known for its data efficiency. Nevertheless, numerical instability arising from the multi-collinearity of observed data can degrade the model’s time efficiency. To deal with this issue, we propose and implement a rapid and stable parameter identification method that integrates active cooling-heating within the actual thermal environment. Our method requires only a single day of data to estimate basic RC model parameters, allowing for immediate use of the RC model in MPC as soon as an air conditioner is installed. Practical Application Using a grey-box model for MPC in HVAC systems is efficient, but the multicollinearity problem of observed data undermines its time efficiency. Our method deals with this challenge by enabling immediate model parameter identification with just 1 day’s data, incorporating active cooling-heating. We demonstrate it in an actual thermal environment. The high level of time and data efficiency afforded by our method is particularly beneficial for IoT applications like malfunction detection and adaptive room reparameterization associated with room rearrangement.

Detection and Analysis of Abnormal High-Current Discharge of Cylindrical Lithium-Ion Battery Based on Acoustic Characteristics Research

The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of abnormal conditions. In this study, acoustic data from batteries under two discharge rates, 0.5 C and 3 C, were collected using a specially designed battery acoustic test system. By analyzing selected acoustic parameters in the time domain, the acoustic signals exhibited noticeable differences with the change in discharge current, highlighting the potential of acoustic signals for current anomaly detection. In the frequency domain analysis, distinct variations in the frequency domain parameters of the acoustic response signal were observed at different discharge currents. The identification of acoustic characteristic parameters demonstrates a robust capability to detect short-term high-current discharges, which reflects the sensitivity of the battery’s internal structure to varying operational stresses. Acoustic emission (AE) technology, coupled with electrode measurements, effectively tracks unusually high discharge currents. The acoustic signals show a clear correlation with discharge currents, indicating that selecting key acoustic parameters can reveal the battery structure’s response to high currents. This approach could serve as a crucial diagnostic tool for identifying battery abnormalities.

Parameter identification of Yoshida–Uemori combined hardening model by using a variable step size firefly algorithm

Abstract The material behavior under cyclic loading is more complex than under monotonic loading and the usage of the sophisticated constitutive models is required to accurately define the elastoplastic behaviors of the advanced high-strength steels and aluminum alloys. These models involve the numerous material parameters that are determined from cyclic tests and accurate calibration of the variables has a great influence on the description of the material response. Therefore, the development of a precise and robust identification method is needed to obtain reliable results. In this study, a systematic methodology depending upon the firefly algorithm (FA) with variable step size has been developed and Yoshida–Uemori combined hardening model parameters of a dual-phase steel (DP980) and an aluminum alloy (AA6XXX-T4) are determined. The identified parameters are verified based on comparisons between the finite element simulations of the cyclic uniaxial tension-compression tests and experimental data and also the search performance of the variable FA is evaluated by comparing it with the standard FA. It is seen from these comparisons that variable FA can easily find and rapidly converge to the global optimum solutions.

Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response

For system identification problems associated with long-length impulse responses, the recently developed decomposition-based technique that relies on a third-order tensor (TOT) framework represents a reliable choice. It is based on a combination of three shorter filters, which merge their estimates in tandem with the Kronecker product. In this way, the global impulse response is modeled in a more efficient manner, with a significantly reduced parameter space (i.e., fewer coefficients). In this paper, we further develop a Kalman filter based on the TOT decomposition method. As compared to the recently designed recursive least-squares (RLS) counterpart, the proposed Kalman filter achieves superior performance in terms of the main criteria (e.g., tracking and accuracy). In addition, it significantly outperforms the conventional Kalman filter, while also having a lower computational complexity. Simulation results obtained in the context of echo cancellation support the theoretical framework and the related advantages.

Dynamic modeling and energy demand analysis of chain heavy load transfer system on marine mobile platform

ABSTRACT The demand for transferring the heavy load on marine mobile platform is steadily increasing, surpassing the capabilities of the current system. This paper introduces a chain heavy load transfer system and develops its dynamic model. This paper studies the sensitivity laws of the system’s energy demand to unknown parameters and proposes an adaptive mutation particle swarm optimization (AMPSO), whose identification accuracy is verified through comparative analysis. Experimental verification confirms the reliability of the system model while studying the influence of environmental loads on the system’s energy demand. The results demonstrate that the chain transfer system meets the requirements of marine mobile platforms, and the system model incorporates the primary influencing factors. Unknown parameters significantly influence the system’s energy demand, and the displacement error after parameter identification using AMPSO is 0.00056 rad, substantially enhancing the accuracy of model calculations. The model calculations of the system closely align with experimental measurements, with a maximum displacement error not exceeding 0.004 rad. Sway exerts a greater influence on the system’s energy demand compared to tilt. When tilt and sway are superimposed, the friction torque between the load and the track results in increased input torque and power for the vertical arrangement of the system, with a maximum torque of 328.6kN·m and maximum power of 400 kW.

Sensorless fuzzy control algorithm for permanent magnet synchronous motor based on particle swarm optimization parameter identification and harmonic extraction

ABSTRACT In sensorless control of permanent magnet synchronous motor (IPMSM) with sliding mode observer, the problem of parameter robustness and poor stability seriously affects the control effect of sensorless operation. Therefore, a fuzzy sliding mode observer (SMO) phaselocked loop (PLL) combining particle swarm optimization (PSO) parameter identification and recursive least squares adaptive linear (RLS-Adaline) harmonic extractor is proposed. First, the fuzzy controller is used to process the control parameters of the sliding mode observer and the phaselocked loop. Secondly, the RLS-Adaline harmonic extractor is used to effectively filter the higher harmonic component of the back electromotive force (EMF), thus significantly improving the sensorless control effect. Thirdly, the PSO algorithm is used to identify the parameters of the motor, and the effect of parameter identification is judged by the performance index. The experimental results show that the proposed control method can effectively reduce the speed error and rotor position error.

#2031 Exploring the utility of serum proteomics for outcome prediction of autosomal polycystic kidney disease (ADPKD)

Abstract Background and Aims ADPKD is an inherited disorder characterized by the development of cysts in both kidneys, leading to renal enlargement and a gradual decline in renal function. It is the leading cause of end stage renal disease (ESRD) with an estimated prevalence of 1:1000. The estimated glomerular filtration rate (eGFR) is the calculated estimation of kidney function by using different equations. The eGFR equation used in this study is Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI, 2009) equation, which includes serum creatinine level, age, and gender. eGFR is also frequently used for assessing overall health of ADPKD patients, classifying the disease stages, and determining appropriate therapeutic interventions. As in ADPKD, clinical symptoms manifest typically in later disease stages, early diagnosis is crucial to slow the disease progression and improve patients’ life quality. This study has two main objectives: (1) the identification of molecular and clinical parameters associated with the annual eGFR decline and (2) the development of a predictive model for future eGFR in patients with ADPKD using serum proteomics. This research aims to advance more effective strategies for the misdiagnosis, stratification, and treatment of ADPKD by combining proteomic and clinical parameters. Method Utilizing retrospective eGFR values (n = 7187) of patients with ADPKD (n = 1291) recruited from the AD(H)PKD patient cohort, the annual decline in eGFR (slope) was calculated. A minimum of three eGFR values and the absence of tolvaptan use (a medication that inhibits the mechanisms underlying eGFR decline) were inclusion criteria. In order to determine the slope, robust linear regression was applied. Prior to the analyses, patients whose slopes were clinically extreme (−10 & >5 mL/min/1.73 m2 per year) were excluded. Total of 2469 eGFR values from 219 ADPKD patients were included in this study. To derive a feature set (FS) suitable for slope prediction, the least absolute shrinkage and selection operator (LASSO) was applied independently to two subsets of the serum proteome (SP) data: (i) SP alone and (ii) SP in conjunction with key clinical variables: age, gender, eGFR, and MAYO class (SPMC). Linear regression (LR) models were constructed using the acquired FSs individually (SP and SPMC), and cross-validation (CV) was incorporated to prevent overfitting. Following that, prediction models were generated and compared in terms of their ability to predict slopes and were visualized for an overview. Results Using feature selection, four features associated with slope were identified in both SP and SPMC data. Three of those features were proteomic-based and common to both feature sets (SC and SPMC). LR models exhibited stability in R2s in test sets of CV, indicating no sign of overfitting. Therefore, the LR slope prediction models were built with SP and SPMC feature sets by using all available data. The adjusted R2 of SP and SCMP LR models were 0.274 (R2 = 0.287, F(4,213) = 21.48, p = 6.74 × 10−15) and 0.301 (R2 = 0.317, F(5,208) = 19.31, p = 8.94 × 10−16). This step was followed by the slope prediction of the same patients (internal validation) to examine models’ prediction capacity by comparing the predicted to observed slopes. According to comparisons, our LR models predict closer to actual slope if slope is greater than −5 and lower than 0 mL/min/1.73 m2 per year. Most of the observed slopes were in between the specified thresholds, which explains why the model is working well. However, the deviations between predicted and the observed slopes increases as the slopes exceed this range. Conclusion Identified biomarkers could be used for how the disease will progress, however, fine-tuning of the models might be necessary. Even though there were patients with positive and extremely negative eGFR slopes, our model accurately predicts slopes in the previously specified range. These extreme cases might also be responsible for models’ reduced accuracy outside of that range. One possible solution could be to use weighted lasso to reduce the effect of “extreme” slopes on the models.

Development of a Digital Model for Predicting the Variation in Bearing Preload and Dynamic Characteristics of a Milling Spindle under Thermal Effects

The spindle tool is an important module of the machine tool. Its dynamic characteristics directly affect the machining performance, but it could also be affected by thermal deformation and bearing preload. However, it is difficult to detect the change in the bearing preload through sensory instruments. Therefore, this study aimed to establish a digital thermal–mechanical model to investigate the thermal-induced effects on the spindle tool system. The technologies involved include the following: Run-in experiments of the milling spindle at different speeds, the establishment of the thermal–mechanical model, identification of the thermal parameters, and prediction of the thermal-induced preload of bearings in the spindle. The speed-dependent thermal parameters were identified from thermal analysis through comparisons with transient temperature history, which were further used to model the thermal effects on the bearing preload and dynamic compliance of the milling spindle under different operating speeds. Current results of thermal–mechanical analysis also indicate that the internal temperature of the bearing can reach 40 °C, and the thermal elongation of the spindle tool is about 27 µm. At the steady state temperature of 15,000 rpm, the bearing preload is reduced by 40%, which yields a decrease in the bearing rigidity by approximately 16%. This, in turn, increases the dynamic compliance of the spindle tool by 22%. Comparisons of the experimental measurements and modeling data show that the variation in bearing preload substantially affects the modal frequency and stiffness of the spindle. These findings demonstrated that the proposed digital spindle model accurately mirrors real spindle characteristics, offering a foundation for monitoring performance changes and refining design, especially in bearing configuration and cooling systems.

Phenomenon of Early Upper Palaeolithic Bladelet Assemblages in the Altai Mountains

Purpose. The present study is devoted to the analysis, systematization, verification and generalization of data on “Aurignacian-like” bladelet assemblages of the Early Upper Palaeolithic of the Altai. In the regional context, this luminous phenomenon is considered the main component of the technocomplex of the Ust-Karakolian cultural tradition and even a cultural marker.Results. The study was based on the analysis of archaeological material from known Early Upper Palaeolithic sites in the region, published and archival data containing information on the chronology, stratigraphy and planography of the industries. As a result, the data on the bladelet component of the Ust-Karakolian complexes from Altai, their industrial and spatial contexts have been updated and supplemented. Materials from the Ust-Karakol-1 (excavation 1, horizon B) and Anui-2 (excavation 2, horizon B) sites were found to be the reference for preservation and information of this industrial group. In both cases, the bladelet strategy dominates the assemblage, clearly standing out against the back-ground of non-specialized blade and flake production. The age of the industry was determined to be around 37–34 thousand cal. BP using previously published and new radiocarbon dating data.Conclusion. According to the obtained data, the main problems of identification and research of the Ust-Karakolian sites in Altai are related to the peculiarities of the conditions of their formation. They were determined by the processes of active slope formation, which led to deformation, fragmentation or complete destruction of the original archaeological structures.

Sentinels of nth-order insensitivity

This study generalizes the definition of the sentinel function, which was introduced by J.L. Lions to study identification problems, to a more insensitive kind of sentinel which is applied to identify pollution terms of the polynomial form w. r. t. the real λ. The main idea is to reconstruct the sentinel function to be nth-order independent of the incomplete data. Contrary to the original definition, information about the pollution term with an error of order n+1 is given by the sentinel of nth-order insensitivity.