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Several papers have proven that advanced process controller (APC) systems can save more energy in the process than proportional-integral-differential (PID) controller systems. Therefore, implementing an APC system is ultimately beneficial for saving energy in the plant. In a typical APC system deployment, the APC model parameters are calculated from dynamic data intervals obtained through the plant test. However, depending on the proficiency of the APC engineer, the results of the plant test and the APC model parameters are implemented differently. To minimize the influence of the APC engineer and calculate universal APC model parameters, a technique is needed to obtain dynamic data without a plant test. In this study, we utilize time-series data from a real petrochemical plant to determine dynamic intervals and estimate APC model parameters, which have not been investigated in previous studies. This involves extracting the data of the dynamic intervals with the smallest mean absolute error (MAE) by utilizing statistical techniques such as pruned exact linear time, linear kernel, and radial basis function kernel of change-point detection (CPD). After that, we fix the hyper parameters at the minimum MAE value and estimate the APC model parameters by training with the data from the dynamic intervals. The estimated APC model parameters are applied to the APC program to compare the APC model fitting rate and verify the accuracy of the APC model parameters in the dynamic intervals obtained through CPD. The final validation of the model fitting rates demonstrates that the identification of the dynamic intervals and the estimation of the APC model parameters through CPD show high accuracy. We show that it is possible to estimate APC model parameters from dynamic intervals determined by CPD without a plant test. [ABSTRACT FROM AUTHOR]

The major challenge is to validate software failure dataset by finding unknown model parameters used. For software assurance, previously many attempts were made based using classical classifiers as decision tree, Naïve Bayes, and k‐nearest neighbor for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect‐free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on particle swarm optimization and modified genetic algorithm for feature selection and bagging for effective classification of defective or nondefective modules in a dataset. This paper presents an empirical study on National Aeronautics and Space Administration Metric Data Program datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods. [ABSTRACT FROM AUTHOR]

Using a mathematical model of concrete carbonation that describes the variation in porosity as a consequence of the involved chemical reactions, we both validated and calibrated the related numerical algorithm of degradation. Once calibrated, a simulation algorithm was used as a forecasting tool for predicting the effects on the porosity of concrete exposed to increasing levels of CO2 emissions, as well as to rising temperatures. Taking into account future projections of environmental modifications deriving from climate changes, some scenarios were produced numerically by the mathematical algorithm that showed the effects of different pollution levels and global warming on the porosity of Portland cement in a time window of years. Finally, a theoretical study on the effects of pollution levels on the carbonation constant determining the advancement in the carbonation front was carried out for the analyzed scenarios.

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57 × 10 − 7 . [ABSTRACT FROM AUTHOR]

Signal processing of flutter flight test data enables verification of aircraft flutter design, and the signal from a flutter flight test excited by atmospheric turbulence is a particularly important form of the flutter test. Owing to the randomness of atmospheric turbulence excitation, multi-channel analysis of turbulence responses at various positions in the same component can improve the analytical accuracy of flutter signal processing. The relationship between the maximum singular value of the multi-channel turbulence response power spectral density matrix and the system self-power spectral density function is elucidated herein using a frequency domain decomposition method. However, there is a contradiction in the power spectral density function between the spectral line density and the spectral line smoothing calculated based on the periodogram of the frequency domain decomposition. By applying an autoregressive spectral model, the power spectral density function of the turbulence response is calculated to achieve spectral line smoothing and sufficient spectral line density. Additionally, the power spectral density function is then used to construct the power spectral density function matrix of the multi-channel turbulence response, and the maximum singular value curve is calculated based on the singular value decomposition of each spectral pin. Finally, the modal parameters of the turbulence response signal are estimated via multi-modal frequency domain fitting. The developed approach is validated based on simulations and flutter flight test turbulence response signals. [ABSTRACT FROM AUTHOR]

Using a mathematical model of concrete carbonation that describes the variation in porosity as a consequence of the involved chemical reactions, we both validated and calibrated the related numerical algorithm of degradation. Once calibrated, a simulation algorithm was used as a forecasting tool for predicting the effects on the porosity of concrete exposed to increasing levels of C O 2 emissions, as well as to rising temperatures. Taking into account future projections of environmental modifications deriving from climate changes, some scenarios were produced numerically by the mathematical algorithm that showed the effects of different pollution levels and global warming on the porosity of Portland cement in a time window of years. Finally, a theoretical study on the effects of pollution levels on the carbonation constant determining the advancement in the carbonation front was carried out for the analyzed scenarios. [ABSTRACT FROM AUTHOR]

The stable and sustainable operation of anaerobic digestion (AD 1 1 Anaerobic digestion.) plants is crucial for their feasible long-term operation. Due to the high non-linearity of the AD process, the main contribution of this paper is to investigate the performance of a robust unscented Kalman filter (UKF) for dynamic process state estimation for the organic fraction of municipal solid waste (OFMSW 2 2 Organic fraction of municipal solid waste.). A traditional UKF performance degrades in case of dynamical model uncertainties or the presence of unknown Gaussian and non-Gaussian noises. The utilized robust UKF proposed by Zhao and Mili (2019) addresses this issue by improving the accuracy of state estimation using the H-infinity filtering theory. The performance of the utilized estimator is tested for the case of uncertainties within the AD model and the substrate feed, as well as unknown Gaussian and non-Gaussian process and measurement noise. Results show that the developed robust UKF estimator achieves higher accuracy and presents higher robustness against uncertainties. Besides, the mathematical model considered for the simulation and the state estimator both rely on the extended version of the AD model No. 2 (E-AM2 3 3 Extended version of the anaerobic digestion model No. 2 including a hydrolysis step.) including a hydrolysis step. This model is a simplified AD model, which is a suitable choice for control and estimation purposes providing a reasonable trade-off between complexity and accuracy. In this paper, the developed E-AM2 model is adapted for the AD process of OFMSW. • Adaptation of a simple dynamic model for anaerobic digestion (AD) of organic waste. • Development of a robust state estimation method using the adapted AD model. • Performance of the developed state estimator in presence of uncertainties. [ABSTRACT FROM AUTHOR]

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57×10−7.

In this paper we introduce a mathematical model of concrete carbonation Portland cement specimens. The main novelty of this work is to describe the intermediate chemical reactions, occurring in the carbonation process of concrete, involving the interplay of carbon dioxide with the water present into the pores. Indeed, the model here proposed, besides describing transport and diffusion processes inside the porous medium, takes into account both fast and slow phenomena as intermediate reactions of the carbonation process. As a model validation, by using the mathematical based simulation algorithm we are able to describe the effects of the interaction between concrete and CO 2 on the porosity of material as shown by the numerical results in substantial accordance with experimental results of accelerated carbonation taken from literature. We also considered a further reaction: the dissolution of calcium carbonate under an acid environment. As a result, a trend inversion in the evolution of porosity can be observed for long exposure times. Such an increase in porosity results in the accessibility of solutions and pollutants within the concrete leading to an higher permeability and diffusivity thus significantly affecting its durability. [ABSTRACT FROM AUTHOR]

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one. Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions. • Transfer coe_cients and exchange currents estimation in HTPEMFCs using simulations. • Estimation of the radius of agglomerates and the thickness of the ionomer in HTPEMFCs. • Characterization of a HTPEMFC using 3D numerical simulations with genetic algorithms. • Using measurements, electrochemical parameters are found that fit a 3D model. [ABSTRACT FROM AUTHOR]

In this paper, a new approach has been introduced for optimal parameter estimation of a proton exchange membrane fuel cell (PEMFC) model. The main purpose is to minimize the total error between the empirical data and the proposed method by optimal parameter selection of the model. The methodology is based on using a newly introduced developed version of the Coyote Optimization Algorithm (DCOA) for determining the value of the unknown parameters in the model. Two different PEMFC models including 2 kW Nexa FC and 6kW NedSstack PS6 FC are adopted for validation and the results are compared with the empirical data and some well-known methods including conventional COA, Seagull Optimization Algorithm, and (N + λ) - ES algorithm to show the proposed method’s superiority toward the literature methods. The final results declared a satisfying agreement between the proposed DCOA and the empirical data. The results also declared the excellence of the presented method toward the other compared methods.

The current pandemic is a great challenge for several research areas. In addition to virology research, mathematical models and simulations can be a valuable contribution to the understanding of the dynamics of the pandemic and can give recommendations to both physicians and politicians. In this paper we give an overview about mathematical models to describe the pandemic by differential equations. As a matter of principle the historic origin of the epidemic growth models will be remembered. Moreover we discuss models for the actual pandemic of 2020/2021. This will be done based on actual data of people infected with COVID-19 from the European Centre for Disease Prevention and Control (ECDC), input parameters of mathematical models will be determined and applied. These parameters will be estimated for the UK, Italy, Spain, and Germany and used in a SIR-type model. As a basis for the model's calibration, the initial exponential growth phase of the COVID-19 pandemic in the named countries is used. Strategies for the commencing and ending of social and economic shutdown measures are discussed. To respect heterogeneity of the people density in the different federal states of Germany diffusion effects are considered. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of determining the parameter values of the agent-based model of social campaign message adoption relying on Bass's classical innovation diffusion model. The problem concerns the parameters that reflect the influence of advertising and social communication on the adoption of the idea conveyed in the social campaign. Due to the fact that the factors influencing the behavior of message recipients are conditioned by their personality, circumstances or reaction to the environment, the conventional methods for estimating these parameters may not deliver a model reproducing reality with the required accuracy. Therefore, this paper proposes a procedure for determining the values of agent-based model parameters that relies on an experimental data acquisition procedure using a combination of cognitive neuroscience techniques and a survey method. The presented research examines a social campaign promoting road safety. The obtained results prove the suitability of the suggested solution for estimating the parameters of the agent-based model of social campaign message adoption. The proposed approach contributes to the methodology of data collection and parameter estimation in building agent-based models, although it is not without some limitations. [ABSTRACT FROM AUTHOR]

In this study, we aimed to describe a modern, efficient, and reproducible methodology to optimize respiration rate parameters coupled with transport properties in mass balances describing Modified atmosphere packaging (MAP) systems. We considered mass balances for three different respiration rate j film (exponential, competitive and uncompetitive Michaelis–Menten kinetics) coupled with transport properties for two different packaging films. Experiments were conducted to validate the methodology using grapes placed in a polypropylene container opened on the top and sealed with packaging films. The methodology relies on a numerical optimization procedure called the Trust-Region-Reflective algorithm. We determined the predictive capability of models using goodness-of-fit criteria and assessed parameter uncertainty through standard errors. We also calculated the first-order optimality measure and the relative change in the sum of squares to verify the convergence of the implemented algorithm. Results showed that the respiration rate parameters obtained with this methodology for the exponential model provided a better fit than for the other two models. The fitting for the kinetic models is not very suitable since we found that the normalized standard errors were rather high. In conclusion, the methodology is robust, and we expect that it serves as a tool for assessing MAP technology design. [ABSTRACT FROM AUTHOR]

The major challenge is to validate software failure dataset by finding unknown model parameters used. For software assurance, previously many attempts were made based using classical classifiers as Decision Tree, Naïve Bayes, and k-NN for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect-free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on particle swarm optimization and modified genetic algorithm for feature selection and bagging for effective classification of defective or non-defective modules in a dataset. This paper presents an empirical study on NASA metric data program datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods. [ABSTRACT FROM AUTHOR]

Temperature is an important determinant of bacterial growth. While the dependence of bacterial growth on different temperatures has been well studied for many bacterial species, prediction of bacterial growth rate for dynamic temperature changes is relatively unclear. Here, the authors address this issue using a combination of experimental measurements of the growth, at the resolution of 5 min, of Escherichia coli and mathematical models. They measure growth curves at different temperatures and estimate model parameters to predict bacterial growth profiles subject to dynamic temperature changes. They compared these predicted growth profiles for various step‐like temperature changes with experimental measurements using the coefficient of determination and mean square error and based on this comparison, ranked the different growth models, finding that the generalised logistic growth model gave the smallest error. They note that as the maximum specific growth increases the duration of this growth predominantly decreases. These results provide a basis to compute the dependence of the growth rate parameter in biomolecular circuits on dynamic temperatures and may be useful for designing biomolecular circuits that are robust to temperature. [ABSTRACT FROM AUTHOR]

Modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clutter modelling, the actual data should be used for model parameter estimation. However, in some special situations, the actual data sample is very small. Existing methods cannot estimate the parameters of G0 distribution efficiently. To solve this problem, an improved recursive expectation–maximisation (EM) method is proposed to estimate the parameters of clutter in this article. This method combines the expectation step and maximisation step in one equation. Through recursive method and simplification of the positive definite matrix, this proposed method can obtain maximum likelihood estimation more efficiently than the conventional EM method and recursive EM method. Simulation results show that the performance of the proposed method is better than that of the conventional methods for a small data sample.

The major challenge in designing an Intelligent Information Software System for fault detection is to detect faults at an early stage unless it becomes a failure. This can be achieved by using feature selection and effective classification applied on failure datasets. Support Vector Machines (SVM) are used for efficient and accurate feature selection by finding unknown model parameters using local and global kernel parameter optimization. Research shows that previously many attempts were made using classical classifiers as decision tree, naïve bayes, and k-NN for software fault prediction. In earlier research, class imbalance problems in software fault datasets were not addressed. In this paper, we propose an intelligent hybrid algorithm that is based on feature selection hybrid kernel function SVM and entropy-based bagging for efficient classification to reduce the class imbalance problem. The proposed model is compared with traditional approaches. The improved hybrid algorithm based on entropy-based bagging and mixed kernel SVM can effectively improve the classification accuracy of NASA Metric Data Program (MDP) faulty datasets. This paper presents an empirical study on using the proposed hybrid algorithm and results showed that our proposed approach enhances the classification accuracy when compared with existing methods. [ABSTRACT FROM AUTHOR]

Modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clutter modelling, the actual data should be used for model parameter estimation. However, in some special situations, the actual data sample is very small. Existing methods cannot estimate the parameters of G0 distribution efficiently. To solve this problem, an improved recursive expectation–maximisation (EM) method is proposed to estimate the parameters of clutter in this article. This method combines the expectation step and maximisation step in one equation. Through recursive method and simplification of the positive definite matrix, this proposed method can obtain maximum likelihood estimation more efficiently than the conventional EM method and recursive EM method. Simulation results show that the performance of the proposed method is better than that of the conventional methods for a small data sample. [ABSTRACT FROM AUTHOR]

The major challenge is to validate software failure dataset by finding unknown model parameters used. Previously, many attempts for software assurance were made using classical classifiers as Decision Tree, Naïve Bayes, and k-NN for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect-free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on Particle Swarm Optimization (PSO) & Modified Genetic Algorithm (MGA) for feature selection and Bagging for effective classification of defective or non-defective modules in a dataset. This paper presents an empirical study on NASA Metric Data Program (MDP) datasets, using the proposed hybrid algorithm. Results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for characterizing electrochemical parameters in non-isothermal three-dimensional (3 D) simulation models of fuel cell stacks. The proposed methodology involves utilizing only easily measurable construction and non-invasive operational data. In order to achieve a reasonable computational cost, an iterative method developed on various 3 D computational domains is combined with a genetic algorithm optimization technique. The effectiveness of the methodology is demonstrated through its application on a real scale 40-cell high temperature PEM fuel cell (HTPEMFC) stack, with the results indicating good agreement between the model and experimental data. This approach has the potential to significantly reduce the computational cost of optimizing fuel cell designs while still maintaining accuracy. [Display omitted] [ABSTRACT FROM AUTHOR]

The current pandemic is a great challenge for several research areas. In addition to virology research, mathematical models and simulations can be a valuable contribution to the understanding of the dynamics of the pandemic and can give recommendations to physicians and politicians. Based on actual data of people infected with COVID-19 from the European Center for Disease Prevention and Control (ECDC), input parameters of mathematical models will be determined and applied. These parameters will be estimated for the UK, Italy, Spain, and Germany and used in an S I R -type model. As a basis for the model’s calibration, the initial exponential growth phase of the COVID-19 pandemic in the named countries is used. Strategies for the commencing and ending of social and economic shutdown measures are discussed.

A parallel Jaya algorithm implemented on the graphics processing unit (GPU-Jaya) is proposed to estimate parameters of the Li-ion battery model in this paper. Similar to the generic Jaya algorithm (G-Jaya), the GPU-Jaya is free of tuning algorithm-specific parameters. Compared with the G-Jaya algorithm, three main procedures of the GPU-Jaya, the solution update, fitness value computation, and the best/worst solution selection are all computed in parallel on GPU via a compute unified device architecture (CUDA). Two types of memories of CUDA, the global memory and the shared memory are utilized in the execution. The effectiveness of the proposed GPU-Jaya algorithm in estimating model parameters of two Li-ion batteries is validated via real experiments while its high efficiency is demonstrated by comparing with the G-Jaya and other considered benchmarking algorithms. The experimental results reflect that the GPU-Jaya algorithm can accurately estimate battery model parameters while tremendously reduce the execution time using both entry-level and professional GPUs. [ABSTRACT FROM AUTHOR]

In this paper, we model the recycling process for solid waste as performed in a material recovery facility. The intent is to inform the design and evaluation of a material recovery facility (MRF) in order to increase its profit, efficiency and recovery rate. We model the MRF as a multi-stage material separation process and develop a network flow model that evaluates the performance of the MRF through a system of linear equations. We estimate the parameters of the network flow model from historical data to find the best fit. We validate the model using a case-study of a light-packaging recovery section of an MRF in Spain. Additionally, we examine how uncertainty in the input material composition propagates through the system, and conduct a sensitivity analysis on the model parameters. [ABSTRACT FROM AUTHOR]

In this work, a generalized method for the estimation of biokinetic parameters in anaerobic digestion (AD) models is proposed. The method consists of a correlation-based approach to estimate specific groups of parameters mechanistically, followed by a sensitivity-based hierarchical and sequential single parameter optimisation (SHSSPO) calibration method for the remaining groups of parameters. The method was evaluated to estimate and calibrate the parameter values for sulfate reduction processes when included into the IWA Anaerobic Digestion Model No. 1 (ADM1) and simulations were compared with experimental data from literature. Under the proposed method, a large number of biokinetic parameters, namely biomass yields, maximum specific uptake rates, and half saturation constants, can first be estimated using mechanistic correlations. This achieves a significant reduction in the number of parameters to be fitted to data. For the remaining parameters, a method is proposed based on the overall sensitivity and degree of ubiquity of each parameter to establish a hierarchy in a sequential single parameter optimisation against the experimental data. This approach aims at eliminating the uncertainty on optimality (and therefore parameter identification) associated to multivariable parameter calibration problems. The method was applied to the sulfate reduction related parameters and led to the hydrogen sulfide inhibition parameters as the only ones requiring optimisation against experimental data. Comparison of the proposed SHSSPO performance with that of multi-dimensional parameter optimisation methods shows a superior performance in terms of overall error and computation times. Also, final simulation results led to model predictions of similar, if not better, quality than those achieved by multivariable parameter optimisation methods. The experimental variables optimized for included liquid effluent concentrations of sulfur species and volatile fatty acids as well as effluent methane gas flow. Overall, the proposed parameter estimation and calibration method provides a deterministic step-by-step approach to parameter estimation that decreases identifiability uncertainty at a very low computational effort. The results obtained suggest that the method could be generically applied with similar success to other biokinetic models frequently used in wastewater treatment. [ABSTRACT FROM AUTHOR]

The modeling of strains and deformations in salt mine areas encounters considerable difficulties because of the varying strength properties of salt, the complex morphological build of dome deposits and the rheological properties of salt. These properties have impacted the development of salt extraction for hundreds of years and the fact that the accurate determining of strains in a given specified moment and place are burdened with high uncertainty. Numerical modeling is useful when the model is reduced to one or several salt chambers. A broader range of underground post mining void considerably lowers the accuracy and efficiency of the calculations of such models. Stochastic models allow for a 3D modeling of the entire mining complex deposit, provided the model has been parametrized in detail. The methods of strains and deformations modeling were presented on the example of one of the biggest salt mines in Europe, where a volume of over 21 million m³ of salt was extracted. The stochastic model could be parametrized thanks to the documented results of measurements of convergence of the underground mining panels and leveling on the surface. The use of land subsidence inversion in the least squares method allowed for estimating the optimum values of parameters of the model. Ground deformation modeling was performed using the two-parameter time function, which allows for a simulation to be carried out in time. In the simulation, the convergence of underground excavations and the transition in time the effects of convergence into ground subsidence was taken into account. The detailed analysis of the geological conditions lead to modeling deviation of the subsidence trough. The accuracy of the modeling results was qualitatively and quantitatively confirmed by a comparison of the modeled to measured values of the vertical ground movement. The scaled model can be applied in future mining extraction projects in order to predict the strains and deformations for an arbitrary moment in time. [ABSTRACT FROM AUTHOR]

We consider the deterministic modeling, calibration, and model parameter estimation of two commonly employed inertial measurement units based on real test data acquired from a flight motion simulator. Each unit comprises three tri-axial devices: an accelerometer, a gyroscope, and a magnetometer. We perform the deterministic error modeling and calibration of accelerometers based on an improved measurement model, and the technique we propose for gyroscopes lowers costs by eliminating the need for additional sensors and relaxing the test bed requirement. We present an extended measurement model for magnetometers that reduces calibration errors by modeling orientation-dependent hard-iron errors in a gimbaled angular position-control machine. While we employ the model-based Levenberg-Marquardt optimization algorithm for the parameter estimation of accelerometers and magnetometers, we use a model-free evolutionary optimization algorithm (particle swarm optimization) for estimating the calibration parameters of gyroscopes. Errors are considerably reduced as a result of proper modeling and calibration. [ABSTRACT FROM AUTHOR]

Understanding time‐course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta‐analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time‐course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time‐course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time‐course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity. [ABSTRACT FROM AUTHOR]

The Lotka-Volterra model of predator-prey dynamics was used for approximation of the wellknown empirical time series on the lynx-hare system in Canada that was collected by the Hudson Bay Company in 1845-1935. The model was assumed to demonstrate satisfactory data approximation if the sets of deviations of the model and empirical data for both time series satisfied a number of statistical criteria (for the selected significance level). The frequency distributions of deviations between the theoretical (model) trajectories and empirical datasets were tested for symmetry (with respect to the Y-axis; the Kolmogorov-Smirnov and Lehmann-Rosenblatt tests) and the presence or absence of serial correlation (the Swed-Eisenhart and 'jumps up-jumps down' tests). The numerical calculations show that the set of points of the space of model parameters, when the deviations satisfy the statistical criteria, is not empty and, consequently, the model is suitable for describing empirical data. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a robust adaptive algorithm for frequency estimation in three‐phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's transformed three‐phase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's transformed signal where the frequency of system is related to the parameters of this model. To estimate the model parameters with noisy voltage reading, they utilise the notions of maximum correntropy criterion and gradient‐ascent optimisation. The proposed algorithm has the computational complexity of the popular complex least‐mean‐squares (CLMS) algorithm, along with the robustness that is obtained by using higher‐order moments beyond just second‐order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in different conditions, including the voltage sags and presence of impulsive noises and and higher‐order harmonics. Their simulation results demonstrate that the proposed algorithm provides improved frequency estimation performance compared with ACLMS especially when the measured voltages are corrupted by impulsive noise. [ABSTRACT FROM AUTHOR]

The Verhulst and Gompertz models were used for approximation of some well-known time series of Paramecia caudatum population dynamics (G.F. Gause, 'The Struggle for Existence,' 1934). The parameters were estimated for each of the models in two different ways: with the least-squares method (global fitting) and a non-traditional approach (the method of extreme points). The results were compared with those presented by Gause. Deviations of theoretical (model) trajectories from experimental time series were tested using various non-parametric statistical tests. It was shown that the estimates by the least-squares method lead to results that do not always meet the requirements that are imposed on a 'fine' model. However, in some cases a small modification of the least-squares-method estimates is possible that allows satisfactory representations of an experimental data set for approximation. [ABSTRACT FROM AUTHOR]

In this paper, we report on the performance of a semiempirical algorithm for the retrieval of soil moisture (SM) under dense tropical forests using ultrahigh frequency (UHF) polarimetric synthetic aperture radar (SAR) data. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the distorted Born approximation (DBA). The simplified model reduces the number of parameters and preserves the three dominant scattering mechanisms of volume, volume-surface, and surface for three polarized backscattering coefficients, i.e., σHH, σHV, and σVV, at UHF frequencies. The inversion process uses the Levenberg-Marquardt nonlinear least squares method to estimate the three model parameters: vegetation aboveground biomass, integrated SM up to a certain depth, and surface roughness. The performance of the inversion process is examined by first using simulation data where the initial values of the inversion process vary randomly and then using airborne UHF SAR data acquired in Costa Rica over La Selva Biological Station. The results with simulated data show that the inversion process is not significantly sensitive to initial values considering they are in the range of ±50% of the true value. A root-mean-square error (RMSE) of less than 4% can be achieved in retrieving the SM. The use of an alternate inversion approach without initial conditions using a genetic algorithm is less efficient (> 120 times longer time) and produces larger error with simulated data (RMSE = 11%) than the Levenberg-Marquardt estimation method. The inversion model simultaneously produces a biomass and SM distribution at 100-m spatial resolution. The RMSE of biomass estimation is 38 Mg/ha (15% relative error) when compared with 28 field plots. Over the plots where SM ground measurements are available, but not at the exact same day as the radar flight occurred, the total volumetric RMSE is 13.6%. However, only two ground measurements were very close to the flight day (three days apart), and for those, the SM estimate has about 3% absolute volumetric error. At the P-band, the SM sensing depth is inversely correlated with the SM allowing to map the spatial variations of SM close to the average root zone or hydrological active horizon of soils in tropical ecosystems. [ABSTRACT FROM PUBLISHER]

The identification of non‐linear stochastic spatio‐temporal dynamical systems given by stochastic partial differential equations is of great significance to engineering practice, since it can always provide useful insight into the mechanism and physical characteristics of the underlying dynamics. In this study, based on the difference method for stochastic partial differential equations, a novel state‐space model named multi‐input–multi‐output extended partially linear model for stochastic spatio‐temporal dynamical system is proposed. A new Reproducing Kernel Hilbert Space‐based algorithm named extended partially linear least square ridge regression is thus particularly developed for the identification of the extended partially linear model. Compared with existing identification methods available for spatio‐temporal dynamics, the advantages of the proposed identification method include that (i) it can make full use of the partially linear structural information of physical models, (ii) it can achieve more accurate estimation results for system non‐linear dynamics and (iii) the resulting estimated model parameters have clear physical meaning or properties closely related to the underlying dynamical system. Moreover, the proposed extended partially linear model also provide a convenient state‐space model for system analysis and design (e.g. controller or filter design) of the class of non‐linear stochastic partial differential dynamical systems. [ABSTRACT FROM AUTHOR]