Model-based Parameter Estimation Research Articles

Production and Reliability Oriented SOFC Cell and Stack Design

This paper reports the results from the European project PROSOFC (funded by FCH-JU, FP7). Within the project an innovative methodology for a production and reliability oriented SOFC cell and stack design is developed. In particular the PROSOFC project aims at improving the robustness, manufacturability, efficiency and cost of SOLIDpowers state-of-the-art SOFC stacks so as to reach market entry requirements. The key issues are the mechanical robustness of solid oxide fuel cells (SOFCs), and the delicate interplay between cell properties, stack design, and operating conditions of the SOFC stack. The novelty of the project lies in combining state of the art methodologies for cost-optimal reliability-based design (COPRD) with actual production optimization. To achieve the COPRD beyond state of the art multi-physical modeling concepts were developed and validated for significantly improved understanding of the production and operation of SOFC stacks. The most critical failure modes for stack operation were identified in order to align the experimental investigations to generate suitable data for modelling these failures. The multi-physics model allows a probabilistic approach to consider statistical variations in production, material and operating parameters for the optimization phase. The project provides a methodology for 3D description of spatial distribution of material properties based on a random field models. The key for the whole methodology are validating experiments, homogenized models on multiple levels of the SOFC system (cell, interconnect, sealings) and introduction of extensive test programs specified by the COPRD methodology. The probabilistic models were related to the experimentally obtained properties of base materials to establish a statistical relationship between the material properties and the most relevant load effects in view of potential damage/failure based on multi-physics simulation can be established. Software algorithms for meta models that allow the detection of relationships between input parameters (materials, loading, etc.) and output parameters and to perform a sensitivity analysis were developed and implemented. The capabilities of the methodology for COPRD being developed is illustrated on two practical cases. A procedure based on meta models has been developed to estimate elastic, primary and secondary creep model parameters from 4-point bending tests. The goal is to (i) account for the effects of the friction between the sample and the rollers, anticlastic curvature, contact point shift and large deflection, which can affect the accuracy of the measurements, and (ii) measure properties which cannot be obtained using closed-form analytical solutions. This could be achieved by model-based parameter estimation with a 3-D continuum finite-element (FE) model, at the cost of very high running time. The proposed approach is computationally much more efficient. It starts with a sensitivity analysis to generate distributed meta models of the displacement at the inner rollers during loading-unloading cycles and creep measurements simulated by the 3-D FE model. The optimization problem for parameter estimation is then solved using the meta models. The accuracy of the approach was tested using a set of numerical experiments and comparison with results from 3-D imaging and computational homogenization. The experiments on Ni-YSZ samples were then analyzed. For the stochastic investigation of the thermo-electrochemical behavior of the stack a multi-physics stack model implemented in gPROMS and Fluent was linked and controlled automatically by optiSlang to perform a sensitivity analysis. The outcome is (i) the identification of the most relevant production and operating parameters which can have a significant impact on the stack lifetime and (ii) meta models of the efficiency and of the 3-D temperature distribution generated in the view of optimization. The tests show for instance that the accuracy of the 3-D meta model of the temperature distribution is sufficient to identify the minimum of variations in the spatial distribution of the temperature during cycling between full and part load by using sets of operating conditions for optimization.

Aerodynamic Parameter Identification for an Airborne Wind Energy Pumping System

Airborne Wind Energy refers to systems capable of harvesting energy from the wind by flying crosswind patterns with a tethered aircraft. Tuning and validation of flight controllers for AWE systems depends on the availability of reasonable a priori models. In this paper, aerodynamic coefficients are estimated from data gathered from flight test campaign using an efficient multiple experiments model based parameter estimation algorithm. Data fitting is performed using mathematical models based on full six degree of freedom aircraft equations of motion. Several theoretical and practical aspects as well as limitations are highlighted. Finally, both model selection and estimation results are assessed by means of R-squared value and confidence ellipsoids.

Pole-zero analysis and wavelength scaling of carbon nanotube antennas

Carbon nanotube (CN) antennas have applications in the THz electromagnetic spectrum. Nanotubes have a highly dispersive and frequency dependent conductivity model. In this article, we compare the poles and zeros in the input impedance of CN antennas at different lengths. We used model-based parameter estimation to approximate the input impedance of the antenna with a rational function in the complex frequency domain. Despite dispersive conductivity of CN, the imaginary part of the poles and zeros are respectively the integer multiples and odd multiples of the imaginary part of the first pole and zero. However, the real part of poles is almost constant, while the pattern was not observed for the real part of zeros. We also show that CN dipoles operating between 43 and 53 GHz are well matched if the source impedance is much higher than 50 ohms, and even higher than 12.9 kΩ. The fundamental resonances (f0) of CN dipoles plotted versus their inverse-half-length (1/L) are linearly related, but the intercept of the fitted straight line is non-zero unlike that for perfect electric conductor (PEC) dipoles. This leads to non-linear variation in wavelength scaling of CN dipoles. The resonant CN antennas are relatively much shorter than PEC dipoles.

Ultrasound-contrast-agent dispersion and velocity imaging for prostate cancer localization.

Prostate cancer (PCa) is the second-leading cause of cancer death in men; however, reliable tools for detection and localization are still lacking. Dynamic Contrast Enhanced UltraSound (DCE-US) is a diagnostic tool that is suitable for analysis of vascularization, by imaging an intravenously injected microbubble bolus. The localization of angiogenic vascularization associated with the development of tumors is of particular interest. Recently, methods for the analysis of the bolus convective dispersion process have shown promise to localize angiogenesis. However, independent estimation of dispersion was not possible due to the ambiguity between convection and dispersion. Therefore, in this study we propose a new method that considers the vascular network as a dynamic linear system, whose impulse response can be locally identified. To this end, model-based parameter estimation is employed, that permits extraction of the apparent dispersion coefficient (D), velocity (v), and Péclet number (Pe) of the system. Clinical evaluation using data recorded from 25 patients shows that the proposed method can be applied effectively to DCE-US, and is able to locally characterize the hemodynamics, yielding promising results (receiver-operating-characteristic curve area of 0.84) for prostate cancer localization.

Data preprocessing for a vehicle-based localization system used in road traffic applications

Abstract. This paper presents a fixed-point implementation of the preprocessing using a field programmable gate array (FPGA), which is required for a multipath joint angle and delay estimation (JADE) used in road traffic applications. This paper lays the foundation for many model-based parameter estimation methods. Here, a simulation of a vehicle-based localization system application for protecting vulnerable road users, which were equipped with appropriate transponders, is considered. For such safety critical applications, the robustness and real-time capability of the localization is particularly important. Additionally, a motivation to use a fixed-point implementation for the data preprocessing is a limited computing power of the head unit of a vehicle. This study aims to process the raw data provided by the localization system used in this paper. The data preprocessing applied includes a wideband calibration of the physical localization system, separation of relevant information from the received sampled signal, and preparation of the incoming data via further processing. Further, a channel matrix estimation was implemented to complete the data preprocessing, which contains information on channel parameters, e.g., the positions of the objects to be located. In the presented case of a vehicle-based localization system application we assume an urban environment, in which multipath propagation occurs. Since most methods for localization are based on uncorrelated signals, this fact must be addressed. Hence, a decorrelation of incoming data stream in terms of a further localization is required. This decorrelation was accomplished by considering several snapshots in different time slots. As a final aspect of the use of fixed-point arithmetic, quantization errors are considered. In addition, the resources and runtime of the presented implementation are discussed; these factors are strongly linked to a practical implementation.

Gaussian mixture model: An application to parameter estimation and medical image classification

Gaussian mixture model based parameter estimation and classification has recently received great attention in modelling and processin g data. Gaussian Mixture Model (GMM) is the probabilistic model for representing the presence of subpopulations and it works well with the classification and parameter estimation strategy. Here in this work Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques. The performance of the classification strategy of both the classifiers used has a better proficiency when compared to the other classifiers. By combining the SVM and GMM we co uld be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques. In this the GMM classifier and the SVM classifier are trained usin g the parameters and they are to be compared.

Adaptive Sparse Sampling to Estimate Radiation and Scattering Patterns to a Specified Uncertainty with Model-Based Parameter Estimation: Compute patterns using as few as two to four samples per lobe

Analyzing antennas and scatterers in electromagnetics usually involves evaluating far-field radiation and scattering patterns. For problems involving objects, a few wavelengths to an extent, the computing time needed to evaluate a far field is usually much less than that needed to find the source distribution on that object. As the object size increases, however, the time required to obtain the far field can become significant, especially for a monostatic scattering pattern where a new source distribution occurs for each incidence angle or for the radiation and receiving patterns of large complex objects. This is especially the case if the far field is to be sampled finely enough in angle to ensure that important features of the pattern are not missed.

Resonant Frequency Estimation of Reradiation Interference at MF from Power Transmission Lines Based on Generalized Resonance Theory

The resonant mechanism of reradiation interference (RRI) over 1.7MHz from power transmission lines cannot be obtained from IEEE standards, which are based on researches of field intensity. Hence, the resonance is ignored in National Standards of protecting distance between UHV power lines and radio stations in China, which would result in an excessive redundancy of protecting distance. Therefore, based on the generalized resonance theory, we proposed the idea of applying model-based parameter estimation (MBPE) to estimate the generalized resonance frequency of electrically large scattering objects. We also deduced equation expressions of the generalized resonance frequency and its quality factor Q in a lossy open electromagnetic system, i.e. an antennatransmission line system in this paper. Taking the frequency band studied by IEEE and the frequency band over 1.7 MHz as object, we established three models of the RRI from transmission lines, namely the simplified line model, the tower line model considering cross arms and the line-surface mixed model. With the models, we calculated the scattering field of sampling points with equal intervals using method of moments, and then inferred expressions of Pade rational function. After calculating the zero-pole points of the Pade rational function, we eventually got the estimation of the RRI’s generalized resonant frequency. Our case studies indicate that the proposed estimation method is effective for predicting the generalized resonant frequency of RRI in medium frequency (MF, 0.3~3 MHz) band over 1.7 MHz, which expands the frequency band studied by IEEE.

Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design

Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This contribution provides a tutorial review on methods for identifiability analysis, regularization techniques and optimal experimental design. A guideline for the analysis and classification of nonlinear ill-posed problems to detect practical identifiability problems is given. Techniques for the regularization of experimental design problems resulting from ill-posed parameter estimations are discussed. Applications are presented for three different case studies of increasing complexity.

Sensorimotor synchronization with tempo-changing auditory sequences: Modeling temporal adaptation and anticipation

The current study investigated the human ability to synchronize movements with event sequences containing continuous tempo changes. This capacity is evident, for example, in ensemble musicians who maintain precise interpersonal coordination while modulating the performance tempo for expressive purposes. Here we tested an ADaptation and Anticipation Model (ADAM) that was developed to account for such behavior by combining error correction processes (adaptation) with a predictive temporal extrapolation process (anticipation). While previous computational models of synchronization incorporate error correction, they do not account for prediction during tempo-changing behavior. The fit between behavioral data and computer simulations based on four versions of ADAM was assessed. These versions included a model with adaptation only, one in which adaptation and anticipation act in combination (error correction is applied on the basis of predicted tempo changes), and two models in which adaptation and anticipation were linked in a joint module that corrects for predicted discrepancies between the outcomes of adaptive and anticipatory processes. The behavioral experiment required participants to tap their finger in time with three auditory pacing sequences containing tempo changes that differed in the rate of change and the number of turning points. Behavioral results indicated that sensorimotor synchronization accuracy and precision, while generally high, decreased with increases in the rate of tempo change and number of turning points. Simulations and model-based parameter estimates showed that adaptation mechanisms alone could not fully explain the observed precision of sensorimotor synchronization. Including anticipation in the model increased the precision of simulated sensorimotor synchronization and improved the fit of model to behavioral data, especially when adaptation and anticipation mechanisms were linked via a joint module based on the notion of joint internal models. Overall results suggest that adaptation and anticipation mechanisms both play an important role during sensorimotor synchronization with tempo-changing sequences.This article is part of a Special Issue entitled SI: Prediction and Attention.

Input design and parameter estimation with open source tools

In this paper we present free tools for model-based optimal input design and parameter estimation. The discussed tool-chain is tailored for the needs of small- and medium sized companies. Its programming core is based on Scilab and the JModelica platform and features input design (DOE), optimal control problems (OCP), and parameter estimation. Finally, the entire tool-chain concept is tested via simulation of a cart and pendulum system.

The Simultaneous Interpolation of Target Radar Cross Section in Both the Spatial and Frequency Domains by Means of Legendre Wavelets Model-Based Parameter Estimation

The understanding of the target radar cross section (RCS) is significant for target identification and for radar designing and optimization. In this paper, a numerical algorithm for calculating target RCS is presented which is based on Legendre wavelet model-based parameter estimation (LW-MBPE). The Padé rational function fitting model applied for MBPE in the frequency domain is enhanced to include spatial dependence on the numerator and denominator coefficients. This allows the function to interpolate target RCS in both the frequency and spatial domains simultaneously. The combination of Legendre wavelets guarantees the convergence of the algorithm. The method is convergent by increasing the sampling frequency and spatial points. Numerical results are provided to demonstrate the validity and applicability of the new technique.

Accelerating the frequency-domain response calculation of complex grounding system using wavelet based MBPE technique

The transient response of grounding systems is essential for their designs and related electromagnetic compatibility problems in power systems. Although the method of moments (MoM) is a popular way to analyze the responses of grounding systems, it is prohibitively slow. In this paper, the model-based parameter estimation (MBPE) technique and wavelet matrix transform (WMT) are combined and applied to the method of moments (MOM). It is expanded on two-steps technique to accelerate solution of electric field integral equations (EFIE). In a first step, the WMT is used to get a sparse matrix equation in wavelet-domain. Then in a second step, the adaptive model-based parameter estimation is used to reduce the number of frequency-domain calculation points required for the evaluation of space–time current distribution along a grounding grid. The accuracy of the proposed model is confirmed by comparing the simulation results of several case studies with those obtained using the direct MoM method combined with inverse fast Fourier transform (IFFT). It is shown that considerable computation efficiency is achieved in terms of CPU time without losing accuracy.

Application of AIM and MBPE Techniques to Accelerate Modeling of 3-D Doubly Periodic Structures with Nonorthogonal Lattices Composed of Bianisotropic Media

An efficient methodology is introduced for rapid analysis and design of three-dimensional (3-D) doubly periodic structures over a wide frequency range based on hybrid finite element boundary integral (FEBI) methods. The 3-D doubly periodic structures can be represented as nonorthogonal lattices composed of general inhomogeneous bianisotropic media with arbitrarily-shaped metallic patches. Based on Floquet theory and periodic boundary conditions, the original stated problem that involves infinite periodic structures can be converted into a single unit cell. Using the equivalence principle, the derived BI equation formulation is applied to the top and bottom surfaces of the unit cell, which results in a perfectly reflectionless boundary condition for the FE-based approach. Then, the unit cell was meshed using triangular prismatic volume elements, which provide a great deal of flexibility in modeling complex planar geometries with arbitrary shapes in the transverse direction. The adaptive integral method (AIM) was employed to accelerate the calculation of the matrix-vector product for the BI portion within the iterative solver. Furthermore, a model-based parameter estimation (MBPE) technique was proposed for the wide-band interpolation of the required impedance matrix elements in the BI part for near field components that were used in the AIM procedure. The accuracy and efficiency of the proposed hybrid algorithms are demonstrated by the presented numerical results (e.g., in comparison with analytical solutions). Several simulation results are presented to illustrate the flexibility of the proposed methods for analysis of frequency selective surfaces with arbitrarily-shaped metallic patches, bianisotropic materials, and nonorthogonal lattice configurations.

Understanding the need of ventricular pressure for the estimation of diastolic biomarkers

The diastolic function (i.e., blood filling) of the left ventricle (LV) is determined by its capacity for relaxation, or the decay in residual active tension (AT) generated during systole, and its constitutive material properties, or myocardial stiffness. The clinical determination of these two factors (diastolic residual AT and stiffness) is thus essential for assessing LV diastolic function. To quantify these two factors, in our previous work, a novel model-based parameter estimation approach was proposed and successfully applied to multiple cases using clinically acquired motion and invasively measured ventricular pressure data. However, the need to invasively acquire LV pressure limits the wide application of this approach. In this study, we address this issue by analyzing the feasibility of using two kinds of non-invasively available pressure measurements for the purpose of inverse mechanical parameter estimation. The prescription of pressure based on a generic pressure–volume (P–V) relationship reported in literature is first evaluated in a set of 18 clinical cases (10 healthy and 8 diseased), finding reasonable results for stiffness but not for residual active tension. We then investigate the use of non-invasive pressure measures, now available through imaging techniques and limited by unknown or biased offset values. Specifically, three sets of physiologically realistic synthetic data with three levels of diastolic residual active tension (i.e., impaired relaxation capability) are designed to quantify the percentage error in the parameter estimation against the possible pressure offsets within the physiological limits. Maximum errors are quantified as 11 % for the magnitude of stiffness and 22 % for AT, with averaged 0.17 kPa error in pressure measurement offset using the state-of-the-art non-invasive pressure estimation method. The main cause for these errors is the limited temporal resolution of clinical imaging data currently available. These results demonstrate the potential feasibility of the estimation diastolic biomarkers with non-invasive assessment of pressure through medical imaging data.

Enhancement of Collision Mitigation Braking System Performance Through Real-Time Estimation of Tire-road Friction Coefficient by Means of Smart Tires

In the case of modern day vehicle control systems employing a feedback control structure, a real-time estimate of the tire-road contact parameters is invaluable for enhancing the performance of the chassis control systems such as anti-lock braking systems (ABS) and electronic stability control (ESC) systems. However, at present, the commercially available tire monitoring systems are not equipped to sense and transmit high speed dynamic variables used for real-time active safety control systems. Consequently, under the circumstances of sudden changes to the road conditions, the driver's ability to maintain control of the vehicle maybe at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road friction coefficient is available. Existing tire-road friction estimation approaches often require certain levels of vehicle longitudinal and/or lateral motion to satisfy the persistence of excitation condition for reliable estimations. Such excitations may undesirably interfere with vehicle motion controls. This paper presents a novel development and implementation of a real-time tire-road contact parameter estimation methodology using acceleration signals from a smart/intelligent tire. The proposed method characterizes the terrain using the measured frequency response of the tire vibrations and provides the capability to estimate the tire road friction coefficient under extremely lower levels of force utilization. Under higher levels of force excitation (high slip conditions), the increased vibration levels due to the stick/slip phenomenon linked to the tread block vibration modes make the proposed tire vibrations based method unsuitable. Therefore for high slip conditions, a tire-road friction model-based parameter estimation approach is proposed. Hence an integrated approach using the smart/intelligent tire based friction estimator and the model based estimator gives us the capability to reliably estimate friction for a wider range of excitations. Considering the strong interdependence between the operating road surface condition and the instantaneous forces and moments generated; this real time estimate of the tire-road friction coefficient is expected to play a pivotal role in improving the performance of a number of vehicle control systems. In particular, this paper focuses on the possibility of enhancing the performance of collision mitigation braking systems. Language: en

Auxiliary model based parameter estimation for dual-rate output error systems with colored noise

The dual-rate sampled-data systems can offer better quality of control than the systems with single sampling rate in practice. However, the conventional identification methods run in the single-rate scheme. This paper focuses on the parameter estimation problems of the dual-rate output error systems with colored noises. Based on the dual-rate sampled and noise-contaminated data, two direct estimation algorithms are addressed: the auxiliary model based recursive extended least squares algorithm and the recursive prediction error method. The auxiliary model is employed to estimate the noise-free system output. An example is given to test and illustrate the proposed algorithms.

Optimisation method on conformal array element positions for low sidelobe pattern synthesis

An optimisation method for antenna element positions in conformal arrays is proposed in this study. Owing to the platform effect, the element pattern in a conformal array varies with its location. It is usually very time consuming to simulate the element patterns at all possible locations. In this proposed method, the patterns of an element located at an arbitrary position on the platform are obtained by interpolating the element patterns at several sampled locations using the model-based parameter estimation (MBPE) method. This interpolation strategy consists of two steps. Firstly, it interpolates the Fourier coefficients of the given element patterns at the sampled locations to acquire the Fourier coefficients of the element patterns at the desired location. Then the inverse discrete Fourier transform (IDFT) or the inverse fast Fourier transform (IFFT) algorithm is applied to obtain the element pattern. Compared with the conventional interpolation methods, this interpolation needs much fewer samples in the spatial domain and a smaller number of interpolation parameters. The element layout is optimised by the particle swarm optimisation (PSO) method. For each layout, the array excitations are obtained by the robust convex optimisation method. This proposed method is demonstrated through an optimisation on a cylindrical-parabolic conformal array

Wavelet Based MBPE Technique for Fast RCS Computation of DNG Metamaterials

Described by the drude model, the DNG metamaterials is essentially a dispersive media, the scattering analysis of it over broad band by asymptotic waveform evaluation (AWE) technique is difficult since the computation of high order differential coefficients for the impedance matrix elements, to reduce the differential order, the model-based parameter estimation (MBPE) technique based on two expanded points is applied to analysis the scattering characters of double-negative (DNG) metamaterials. A wavelet matrix transform (WMT) is used to get a sparse matrix equation in wavelet-domain, which is solved over a broad frequency band by the MBPE. As compared with AWE, the MBPE technique obtained more accurate results over a broader frequency band.

Precise recognition of warhead and decoy based on components of micro-Doppler frequency curves

From the view of electromagnetic scattering, it is indicated that the micro-Doppler (m-D) characteristics of an extended target undergoing micro-motions are actually induced by the change of incident directions of radar pulses. Different micro-motions may lead to similar change of incident directions, consequently inducing similar m-D characteristics. To tackle this problem, rather than distinguish warhead and decoy directly from m-D characteristics, the frequency components of m-D frequency curves are used as a new characteristic for recognition in this paper. To get high precision of frequency components estimation, model-based parameter estimation (MBPE) is utilized to extract the m-D frequency curves from TFR. To obtain high accurate simulation results, the backscattered signal simulation is conducted by full-wave numerical method. The simulation results validate the theoretical analysis and the high performance of the proposed method.