Preliminary Parameter Estimation Research Articles

Enhancing battery electrochemical-thermal model accuracy through a hybrid parameter estimation framework

Electrochemical-thermal models offer profound insight into the internal state of batteries, demonstrating significant potential in energy storage. However, model complexity makes accurate parameter acquisition challenging. This study proposes a novel hybrid parameter estimation framework for effective parameterisation of battery electrochemical-thermal models. A high-fidelity reduced-order electrochemical-thermal model was established to accelerate the simulation of the entire time-domain process. A global sensitivity analysis of the model parameters was conducted, comprehensively evaluating their impact on simulated voltage and temperature. Highly sensitive parameters were categorised into static and time-dependent dynamic estimation groups for hierarchical estimation. A deep learning method was applied to generate preliminary parameter estimates to accelerate the convergence of estimation process. Subsequently, a genetic algorithm was employed to optimise the preliminary estimates of static parameters. A full temporal domain estimation was conducted using Markov Chain Monte Carlo methods for time-dependent dynamic parameters. The experimental data covers six dynamic and nine constant current conditions to validate the proposed method's feasibility. After parameter estimation, the proposed method reduces the comprehensive normalised root mean square error of voltage and temperature by up to 80.36% compared to four conventional methods. The minimum average absolute errors of voltage and temperature under constant and dynamic current conditions are 10.63 mV and 0.10 °C, respectively. The proposed method provides new insights for the parameterisation of battery models.

ГЕНЕТИЧЕСКИЙ АЛГОРИТМ В ЗАДАЧЕ ОЦЕНИВАНИЯ ПАРАМЕТРОВ АСИНХРОННОГО ДВИГАТЕЛЯ С КОРОТКОЗАМКНУТОЙ ОБМОТКОЙ РОТОРА НА ОСНОВЕ БАЛАНСА МГНОВЕННОЙ ПОЛНОЙ ПОТРЕБЛЯЕМОЙ МОЩНОСТИ

Currently, four methods are mainly used to evaluate the parameters of an asynchronous motor with a short-circuited rotor(AD with a short-circuited rotor): according to catalog data, according to test results, preliminary parameter estimation and dynamic identification. Each of the above methods has its advantages and disadvantages, however, the only method that allows you to evaluate the parameters of the AD model with a short-circuited rotor without stopping the process is the method of dynamic parameter identification. The advantages of the method of dynamic identification of parameters of the asynchronous motor replacement circuit (AD), in addition to the absence of the need to stop the technological process, include the ability to evaluate the parameters of the AD model taking into account its dynamically changing temperature state. In turn, the disadvantage of this method of parameter estimation is the need to build a complex identification procedure, which requires taking into account many factors and does not have a generally recognized mathematical description. In connection with the identified problems, it can be argued that expanding the arsenal of tools that allow determining blood pressure parameters without stopping the technological process is an urgent and timely task

Robust and Efficient Parametric Spectral Density Estimation for High-Throughput Data

Modern scientific instruments readily record various dynamical phenomena at high frequency and for extended durations. Spanning timescales across several orders of magnitude, such “high-throughput” (HTP) data are routinely analyzed with parametric models in the frequency domain. However, the large size of HTP datasets can render maximum likelihood estimation prohibitively expensive. Moreover, HTP recording devices are operated by extensive electronic circuitry, producing periodic noise to which parameter estimates are highly sensitive. This article proposes to address these issues with a two-stage approach. Preliminary parameter estimates are first obtained by a periodogram variance-stabilizing procedure, for which data compression greatly reduces computational costs with minimal impact to statistical efficiency. Next, a novel test with false discovery rate control eliminates most periodic outliers, to which the second-stage estimator becomes more robust. Extensive simulations and experimental results indicate that for a widely used model in HTP data analysis, a substantial reduction in mean squared error can be expected by applying our methodology.

Selection of Optimal Parameters for the Jiles–Atherton Magnetic Hysteresis Model

A method is proposed for implementation of the most popular hysteresis model, the Jiles–Atherton model, which has a number of advantages over other models. A technique for optimization of the parameters of the hysteresis model based on a real coded genetic algorithm is presented. The method is implemented in two stages. The first stage involves preliminary estimation of the model parameters and the range of their variation. The second stage is the direct implementation of the genetic algorithm. The criterion of convergence is based on the achievement of a preset value of the standard deviation and the maximum permissible number of generations. The genetic algorithm was implemented with 50 individuals. Each individual is associated with four variables that correspond to the hysteresis model parameters. The maximum number of generations was set to 50 and 100. The initial probabilities of the crossover and mutations were set to 90 and 5%, respectively. A specific feature of the proposed implementation of the genetic algorithm consists in internal optimization of the fifth parameter for each individual of the population. The computer code was developed using the Delphi environment. Comparison of the experimental and simulated curves showed good agreement. A method that involves preliminary estimation of the parameters and further application of the genetic algorithm yields rather accurate results, is easy to implement, and provides a high data-processing speed.

Analytical template for gravitational-wave echoes: Signal characterization and prospects of detection with current and future interferometers

Gravitational-wave echoes in the post-merger ringdown phase are under intense scrutiny as probes of near-horizon quantum structures and as signatures of exotic states of matter in ultracompact stars. We present an analytical template that describes the ringdown and the echo signal for nonspinning objects in terms of two physical parameters: the reflectivity and the redshift at the surface of the object. We characterize the properties of the template and adopt it in a preliminary parameter estimation with current (aLIGO) and future (Cosmic Explorer, Einstein Telescope, LISA) gravitational-wave detectors. For fixed signal-to-noise ratio in the post-merger phase, the constraints on the model parameters depend only mildly on the details of the detector sensitivity curve, but depend strongly on the reflectivity. Our analysis suggests that it might be possible to detect or rule out Planckian corrections at the horizon scale for perfectly-reflecting ultracompact objects at $5\sigma$ confidence level with Advanced LIGO/Virgo. On the other hand, signal-to-noise ratios in the ringdown phase equal to $\approx 100$ (as achievable with future interferometers) might allow us to probe near-horizon quantum structures with reflectivity $\gtrsim30\%$ ($\gtrsim85\%$) at $2\sigma$ ($3\sigma$) level.

Latent class model with conditional dependency per modes to cluster categorical data

We propose a parsimonious extension of the classical latent class model to cluster categorical data by relaxing the conditional independence assumption. Under this new mixture model, named conditional modes model (CMM), variables are grouped into conditionally independent blocks. Each block follows a parsimonious multinomial distribution where the few free parameters model the probabilities of the most likely levels, while the remaining probability mass is uniformly spread over the other levels of the block. Thus, when the conditional independence assumption holds, this model defines parsimonious versions of the standard latent class model. Moreover, when this assumption is violated, the proposed model brings out the main intra-class dependencies between variables, summarizing thus each class with relatively few characteristic levels. The model selection is carried out by an hybrid MCMC algorithm that does not require preliminary parameter estimation. Then, the maximum likelihood estimation is performed via an EM algorithm only for the best model. The model properties are illustrated on simulated data and on three real data sets by using the associated R package CoModes. The results show that this model allows to reduce biases involved by the conditional independence assumption while providing meaningful parameters.

Contribution of HIV-1 genomes that do not integrate to the basic reproductive ratio of the virus.

Recent experimental data indicate that HIV-1 DNA that fails to integrate (from now on called uDNA) can by itself successfully produce infectious offspring virions in resting T cells that become activated after infection. This scenario is likely important at the initial stages of the infection. We use mathematical models to calculate the relative contribution of unintegrated and integrated viral DNA to the basic reproductive ratio of the virus, R0, and the models are parameterized with preliminary data. This is done in the context of both free virus spread and transmission of the virus through virological synapses. For free virus transmission, we find that under preliminary parameter estimates, uDNA might contribute about 20% to the total R0. This requires that a single copy of uDNA can successfully replicate. If the presence of more than one uDNA copy is required for replication, uDNA does not contribute to R0. For synaptic transmission, uDNA can contribute to R0 regardless of the number of uDNA copies required for replication. The larger the number of viruses that are successfully transmitted per synapse, however, the lower the contribution of uDNA to R0 because this increases the chances that at least one virus integrates. Using available parameter values, uDNA can maximally contribute 20% to R0 in this case. We argue that the contribution of uDNA to virus reproduction might also be important for continued low level replication of HIV-1 in the presence of integrase inhibitor therapy. Assuming a 20% contribution of uDNA to the overall R0, our calculations suggest that R0=1.6 in the absence of virus integration. While these are rough estimates based on preliminary data that are currently available, this analysis provides a framework for future experimental work which should directly measure key parameters.

Building a stochastic template bank for detecting massive black hole binaries

The coalescence of pairs of massive black holes are the strongest and most promising sources for LISA. In fact, the gravitational wave signal from the final inspiral and merger will be detectable throughout the universe. In this paper we describe the first step in a two-step hierarchical search for the gravitational wave signal from inspiraling massive BH binaries. It is based on a method routinely used in ground-based gravitational wave astronomy, namely filtering the data through a bank of templates. However we use a novel, Monte Carlo based (stochastic), method for laying a grid in the parameter space, and we use the likelihood maximized analytically over some parameters, known as the -statistic, as a detection statistic. We build a coarse template bank to detect gravitational wave signals and to make preliminary parameter estimation. The best candidates will be followed up using a Metropolis–Hasting stochastic search to refine the parameter estimates. We demonstrate the performance of the method by applying it to the Mock LISA data challenge 1B (training data set).

Development of a Kinetic Model for Thermal Crosslinking of Rubbery Polymers

AbstractSummary: A kinetic model for the thermal crosslinking of rubbery polymers is presented. The reaction mechanism used to develop the model includes thermal radical generation producing a polymeric radical and a primary radical, crosslinking from attack of a polymer radical to any inactive polymer molecule, bimolecular radical termination among chains of any degree of branching, and radical termination between a polymer radical and a primary radical from the thermal radical generation. The overall polymer population is divided into “generations”, which are defined in terms of the number of primary (linear) chains of original rubbery polymer attached to the polymer molecule. Mass balances for each generation are written down, and expressions to calculate fraction of branched molecules, gel fraction, and number and weight average chain lengths are derived. Model simulations, parameter sensitivity analyses and preliminary parameter estimation studies are presented, taking the thermal crosslinking of linear polybutadiene as a case study.Development of a kinetic model for thermal crosslinking of rubbery polymers: reaction mechanism, model equations and parameter sensitivity analyses.magnified imageDevelopment of a kinetic model for thermal crosslinking of rubbery polymers: reaction mechanism, model equations and parameter sensitivity analyses.

A new, mechanistic model for organic carbon fluxes in the ocean based on the quantitative association of POC with ballast minerals

Abstract In simulation studies of the ocean's role in the global carbon cycle, predicting the depth-distribution for remineralization of particulate organic carbon (POC) is of particular importance. Following Sarmiento et al. (Global Biogeochemical Cycles 7 (1993) 417), most simulation models have the power-law curve of Martin et al. (Deep-Sea Research 34 (1987) 267) for this purpose. The Martin et al. curve is an empirical fit to data, most of which is from shallow floating sediment traps. Using such a fit implies that all the information necessary for prediction is contained in the carbon flux itself, so that the organic-carbon flux F OC ( z ) at any depth z can be predicted from the flux of organic carbon F OC ( z 0 ) at some near-surface depth z 0 . Here, we challenge this basic premise, arguing that fluxes of ballast minerals (silicate and carbonate biominerals, and dust) determine deep-water POC fluxes, so that a mechanism-based model of POC flux must simultaneously predict fluxes of both POC and ballast minerals. This assertion is based on the empirical observation that POC fluxes are tightly linked quantitatively to fluxes of ballast minerals in the deep ocean. Here, we develop a model structure that incorporates this observation, and fit this model to US JGOFS EqPac data. This model structure, plus the preliminary parameter estimates we have obtained, can be used to explore the implications of our model in studies of the ocean carbon cycle.

Cybernetic structured modeling of the production of polyhydroxyalkanoates by Alcaligenes Eutrophus

This paper presents a cybernetic structured mathematical model developed for the fermentation step of the process of production of the copolymer of polyhydroxyalkanoates by the bacteria Alcaligenes eutrophus. This process is performed in two different fermentation stages. The first emphasizes the growth of the microorganism in a batch operation without substrates limitations, while in the second, the focus is on copolymer production by a fed-batch operation in the absence of the nitrogen source. This paper presents the results of the treatment of experimental data and of preliminary parameter estimation. The fitting of the proposed model to the experimental data of a standard experiment showed a good agreement.

New Approach for Solving an Ecological Problems Caused by Manure—Modelling and Optimisation

ABSTRACTThis article deals with an approach for solving ecological problems caused by manure. Medium formulation design, full statistical analysis and unstructured dynamic model synthesis have been discussed. The described biotechnological process is used as an integrated approach for solving an ecological problem caused by manure and in the same time efficient production of L-lysine. The presented approach includes following steps: experimental design of medium formulation, response analysis of the basic kinetic variables, analysis of variance, regression analysis, discriminant analysis, duster analysis, synthesis a models of specific rates with preliminary estimation of parameters, optimisation of dynamic model of fed-batch process for L—lysine production. The adequacy of the proposed models is proved through simulation researches.

Computer-Aided Visual Inspection in Assembly

Some of the more critical aspects for the diffusion of vision systems in assembly plants are the skill required for the system set-up, the definition of algorithms and the programming phase. In this paper a new methodology is proposed to reduce the implementation time and cost by means of a computer-aided system working off-line. The designed system named CAVIS (Computer Aided Visual Inspection System) integrates several modules as product and algorithm databases, expert system for decision support, CAD modeller to generate synthetic images and software design. Some of the modules are still at a development stage. The output are the vision devices configuration and the inspection software. CAVIS has been tested on an industrial application for error detection in assembly: a new general-purpose algorithm for visual inspection is presented and results are discussed. The main features of the algorithm are suitable with the described approach: easy programming, unnecessary vision operator's experience, and off-line preliminary estimation of parameters.

Pollination and fruit growth models for studying the management of kiwifruit orchards. II. Models behaviour

The behaviour of two models representing pollination and fruit growth in kiwifruit orchards was studied, mainly by means of graphical analyses. Preliminary parameter estimates were proposed using mainly literature data. Simulation results were compared to experimental data found in the literature. The agreement was found satisfactory. Different versions of the pollination model were compared. Sensitivity to parameters and to key inputs were studied, through log-normalized coefficients and by investigating the model response over ranges of interest, respectively. The models were strongly sensitive to most parameters, and the analysis could be useful to plan experiments for parameter estimation. Sensitivity to key inputs was observed and suggested further research.

Preliminary estimation of parameters of beta distributions as statistical models of measurement series

Graph-analytic methods based on quantiles and moments are provided for preliminary estimation of parameters of beta-distributions as statistical models of measurement series.

Nonparametric Kernel Estimation for Semiparametric Models

This paper presents a number of consistency results for nonparametric kernel estimators of density and regression functions and their derivatives. These results are particularly useful in semiparametric estimation and testing problems that rely on preliminary nonparametric estimators, as in Andrews (1994, Econometrica 62, 43–72). The results allow for near-epoch dependent, nonidentically distributed random variables, data-dependent bandwidth sequences, preliminary estimation of parameters (e.g., nonparametric regression based on residuals), and nonparametric regression on index functions.

Precision passive ranging

The problem of passive ranging is complex, yet important. This paper formulates it as a nonlinear least squares problem which is solved via the Newton-Raphson technique. We use the FEED method for rapid prototyping and the automatic evaluation of partial derivatives. The paper presents two significant results. 1. The approach leads to rapidly convergent and accurate estimates of position for a variety of different noise models. 2. The use of FEED has led to a new and exact solution to the question of evaluating the effect of noise on parameter estimates without the need to perform Monte Carlo computational experiments. Nonlinear methods such as this require preliminary parameter estimates, for which we suggest associative memory neural networks.

Linear and nonlinear associative memories for parameter estimation

This paper proposes the use of associative memories for obtaining preliminary parameter estimates for nonlinear systems. For each parameter vector r i in a selected training set, the system equations are used to determine a vector s i of system outputs. An associative memory matrix M̂ is then constructed which optimally, in the least squares sense, associates each system output vector s i with its corresponding parameter vector r i . Given any observed system output vector s ∗ , an estimate r̂ for the system parameters is obtained by setting r ̂ = M ̂ s ∗ . Numerical experiments are reported which indicate the effectiveness of this approach, especially for the nonlinear associative memory case in which the training vectors s i include not only the system output levels but also products of these levels. Training with noisy output vectors is shown to improve the accuracy of the parameter estimates when the observation vectors s ∗ are noisy. If experimental data are available for use as the training set, the estimation procedure can be carried out without knowing the system equations.

Ionic-strength-dependent substrate inhibition of the lysis of Micrococcus luteus by hen egg-white lysozyme.

The kinetics of lysis of Micrococcus luteus by hen egg-white lysozyme in dilute buffer media is characterized by pronounced substrate inhibition. This effect occurs within the complete pH range where lysozyme activity is detectable. The electrostatic potential of the negatively charged cell-wall proteoglycan increases with decreasing ionic strength, resulting in an enhanced affinity between proteoglycan and lysozyme and probably favouring multipoint substrate attachment. For the lysozyme-catalyzed hydrolysis of cell-wall proteoglycan three plausible mechanisms of substrate inhibition can be postulated. Two out of the three models fit our experimental data, the simplest of the two providing the most rigorous information on the kinetic parameters Km, V and Ki. Three graphical methods consistent with the chosen model were applied for preliminary parameter estimation and the constants obtained were compared to those from nonlinear least-squares analysis. If substrate inhibition is neglected it is shown that serious bias is imposed upon the parameters.

Functional adaptation in long bones: Establishing in vivo values for surface remodeling rate coefficients

In this paper we describe a computational means, based on beam theory, for application of the theory of adaptive elasticity to examples of real bone geometries. The results of the animal experiments were taken from the literature, and each documented the temporal evolution of a change in bone shape after a significant change in the mechanical loading environment of the bone. For each of these studies, we establish preliminary estimates of the in vivo values of the surface remodeling rate coefficients—the key parameters in the theory of surface remodeling. Our preliminary parameter estimates are established by comparison of published animal experimental results with surface remodeling theory predictions generated by the computational method.