Parameter Estimation Scheme Research Articles

An enabling framework for automated extraction of signals from market information in real time

This paper proposes a framework for a financial technology that characteristically embeds an automated parameter estimation scheme. Such a scheme ensures that market signals from the most recent observations are reflected readily in trading and decision making. This capitalises on filtering recursions that support a data-analytics scheme in the predictive domain using observed prices revealed in real time. The resulting technology facilitates and strengthens a futures trading environment that evolves dynamically and enhances market liquidity. In the implementation of our framework’s algorithms, we choose the fish futures market data in the context of providing the seafood market stability and sustainability. We develop a knowledge-based related mechanism, via a higher-order Markov chain and online filtering, that brings about price discovery and serves an important risk management tool for fish producers and buyers. Our results demonstrate the computational efficiency, flexibility in capturing price dynamics with complex features and adaptability to other financial markets of our proposed automation infrastructure.

Non-invasive surface profile measurement using a unitary transformation subspace approach in digital holography

Fast and accurate measurements of surface profiles is of vital importance in the field of non-destructive material testing. In the article, we propose the application of a robust phase retrieval method in digital holographic microscopy for nanoscale surface profile measurement. The proposed method is based on a parameter estimation strategy using a unitary transformation procedure applied over a windowed region of the numerically reconstructed wavefield signal. The proposed method is suitable for phase extraction even in the presence of noise and non-uniform amplitude variations. The applicability of the proposed method is demonstrated using both simulation and experimental results.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure

Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison

Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes.

Fitting Volume Models for Parana Pine With a Nonlinear Regression, Genetic Algorithm and Simulated Annealing

Improving volumetric quantification of Parana pine (Araucaria angustifolia) in Mixed Ombrophilous Forest is a constant need in order to provide accurate and timely information on current and future growing stock to ensure forest management. Thus, the present study aimed to evaluate and compare the volume estimates obtained through Nonlinear Regression (NR), Genetic Algorithm (GA) and Simulated Annealing (SA) in order to generate accurate volume estimates. Volumetric equations were developed including the independent variables diameter at breast height (dbh), total height (h) and crown rate (cr) and from the fit through the NR, GA and SA approaches. The GA and SA approaches evaluated proved to be a reliable optimization strategy for parameter estimation in Parana pine volumetric modelling, however, no significant differences were found in comparison with the NR approach. This study therefore contributes through the generation of robust equations that could be used for accurate estimates of the volume of the Parana pine in southern Brazil, thus supporting the planning and establishment of management and conservation actions.

Analysis of terrestrial water storage changes in the Shaan-Gan-Ning Region using GPS and GRACE/GFO

Both the Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GFO) provide effective tools to infer surface mass changes. In this paper, we combined GPS, GRACE/GFO spherical harmonic (SH) solutions and GRACE/GFO mascon solutions to analyze the total surface mass changes and terrestrial water storage (TWS) changes in the Shaan-Gan-Ning Region (SGNR) over the period from December 2010 to February 2021. To improve the reliability of GPS inversion results, an improved regularization Laplace matrix and monthly optimal regularization parameter estimation strategy were employed to solve the ill-posed problem. The results show that the improved Laplace matrix can suppress the edge effects better than that of the traditional Laplace matrix, and the correlation coefficient and standard deviation (STD) between the original signal and inversion results from the traditional and improved Laplace matrix are 0.84 and 0.88, and 17.49 mm and 15.16 mm, respectively. The spatial distributions of annual amplitudes and time series changes for total surface mass changes derived from GPS agree well with GRACE/GFO SH solutions and mascon solutions, and the correlation coefficients of total surface mass change time series between GPS and GRACE/GFO SH solutions, GPS and GRACE/GFO mascon solutions are 0.80 and 0.77. However, the obvious differences still exist in local regions. In addition, the seasonal characteristics, increasing and decreasing rate of TWS change time series derived from GPS, GRACE/GFO SH and mascon solutions agree well with the Global Land Data Assimilation System (GLDAS) hydrological model in the studied area, and generally consistent with the precipitation data. Meanwhile, TWS changes derived from GPS and GRACE mascon solutions in the SGNR are more reliable than those of GRACE SH solutions over the period from January 2016 to June 2017 (the final operation phase of the GRACE mission).

Sparse Parameter Estimation and Imaging in mmWave MIMO Radar Systems With Multiple Stationary and Mobile Targets

This work conceives novel target detection and parameter estimation schemes in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) radar (mMR) systems for both stationary and mobile targets/radar platform. Initially, the orthogonal matching pursuit (OMP)-based mmR (OmMR) algorithm is proposed for stationary targets to estimate their radar cross-section (RCS) coefficients, angle, range locations together with the number of targets. Next, mMR systems with mobile targets and platform are considered, followed by development of the simultaneous OMP (SOMP)-based mMR (SmMR) algorithm for RCS, angle/range estimation together with their Doppler velocities. The proposed algorithms lead to a significant improvement in performance since they exploit the inherent sparsity of the mMR scattering scene in contrast to the conventional schemes. Two-dimensional (2D) mMR imaging procedures are also presented for both scenarios in the angle, range, and Doppler dimensions. Analytical expressions are derived for the Cramér-Rao bounds (CRBs) for the mean-squared error (MSE) of joint estimation of the RCS coefficients and Doppler velocities. Simulation results demonstrate that proposed schemes perform well even in low signal-to-noise ratio (SNR) scenarios with a few snapshots of the scattering environment and yield improved performance in comparison to existing sparse as well as non-sparse schemes.

An Improved Scheme of Azimuthally Anisotropic Seismic Inversion for Fracture Prediction in Volcanic Gas Reservoirs

Seismic prediction of natural fractures is essential for the characterization of unconventional reservoirs because fractures provide seepage paths for fluid migration and storage space for hydrocarbon accumulation. However, it remains challenging to robustly estimate anisotropic parameters for fracture characterization based on seismic inversion methods. We propose an improved inversion scheme for the robust and accurate estimation of anisotropic parameters using PP-wave azimuthal amplitude differences incorporated with a hybrid optimization algorithm. Modeling analysis indicates that by removing the effect of isotropic terms, azimuthal amplitude differences are more sensitive to anisotropic parameters than the traditional azimuthal reflection coefficient; this can avoid possible instabilities in anisotropic parameter estimates that may occur in conventional methods owing to unbalanced weighting coefficients between isotropic and anisotropic terms. Meanwhile, the proposed hybrid algorithm takes advantage of the global optimization ability of the simulated annealing algorithm and the fast convergence characteristics of the particle swarm optimization algorithm. Synthetic data tests indicate that the hybrid algorithm achieves reliable and stable estimates of anisotropic parameters with higher computational accuracy and efficiency than traditional methods. The improved inversion method is applied to characterize fractures in volcanic gas reservoirs. Based on azimuthal amplitude differences obtained after estimating fracture orientation using the Fourier series method, anisotropic parameters are computed and converted to weakness properties, which is more meaningful in terms of the fracture properties. Our results indicate that the obtained fracture tangential weakness exhibits an apparent correspondence with well-log permeability, justifying the applicability of the proposed method for fracture prediction.

Approximate Message Passing With Parameter Estimation for Heavily Quantized Measurements

Designing efficient sparse recovery algorithms that could handle noisy quantized measurements is important in a variety of applications -- from radar to source localization, spectrum sensing and wireless networking. We take advantage of the approximate message passing (AMP) framework to achieve this goal given its high computational efficiency and state-of-the-art performance. In AMP, the signal of interest is assumed to follow certain prior distribution with unknown parameters. Previous works focused on finding the parameters that maximize the measurement likelihood via expectation maximization -- an increasingly difficult problem to solve in cases involving complicated probability models. In this paper, we treat the parameters as unknown variables and compute their posteriors via AMP. The parameters and signal of interest can then be jointly recovered. Compared to previous methods, the proposed approach leads to a simple and elegant parameter estimation scheme, allowing us to directly work with 1-bit quantization noise model. We then further extend our approach to general multi-bit quantization noise model. Experimental results show that the proposed framework provides significant improvement over state-of-the-art methods across a wide range of sparsity and noise levels.

A Robust Half-Cycle Single-Phase Prefiltered Open-Loop Frequency Estimator for Fast Tracking of Amplitude and Phase

This article presents a fast phase-locking and amplitude detection scheme for single-phase applications. The proposed scheme employs a pseudo-rectification process in order to generate even harmonics from the grid signal. The dc-offset component present in the grid signal increases the sensitivity of the pseudo-rectification process, and the design of the prefiltering stage must, therefore, be completely focused on the rejection of dc-offset and the even-harmonics. The application of a half-cycle (HC) prefilter would consequently appear to be a highly attractive option as regards improving the dynamic response time and even harmonic rejection abilities. However, the elimination of the dc-offset component and the estimation of phase and amplitude information is still a very challenging task. The aforementioned issues are addressed by employing an HC comb filter and an HC nonadaptive Lyapunov orthogonal signal generator. The estimation of single-phase grid parameters is thereby made simpler by employing an improved open-loop frequency estimation, which is capable of providing accurate frequency deviation information with good precision. The research shown herein also reveals that the estimation of the amplitude and phase information differs slightly from the usual mathematical notations owing to the pseudo-rectification method adopted in the proposed work. Note that the prefiltering stage is nonadaptive in nature, which may lead to steady-state errors in the amplitude and phase information under off-nominal frequency conditions. An online mathematical correction approach dependent on frequency deviation is, therefore, employed in order to obtain the error-free amplitude and phase information. Finally, the robustness of the current proposal is validated by experimentally comparing several known grid parameter estimation schemes using a real-time controller.

Development of Block Oriented Recursive and Constrained Parameter Estimation Schemes for ARMAX Models

In the process industry, black box linear perturbation models are often used for the development of model predictive controllers. Maintaining a high-quality model so as to achieve good control performance in the face of changing operating conditions is a difcult task. In adaptive control schemes, the model parameters are updated online using recursive least square schemes. These recursive schemes typically update the model parameters at every sampling instant. Since in many chemical/biological processes, the model parameters change at a relatively slow rate compared to state variables, it is beneficial to update the model parameters using blocks of data points instead of updating at each sampling instant. In this work, a constrained update scheme based on blocks of data is proposed for updating ARMAX model parameters online. The inclusion of constraints ensures that the noise model is stable and inversely stable. The constrained formulation is further simplifed to arrive at two unconstrained recursive parameter update schemes. The efficacy of the proposed schemes is demonstrated using a simulation study on an artificial system and experimental data obtained from a temperature control system and benchmark quadruple tank system.

Simultaneous Semiparametric Estimation of Clustering and Regression

ABSTRACT We investigate the parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. This problem involves clustering to infer the missing group variable based on the group-related variables, and regression to build a model on the target variable given the group and eventually some additional variables. Thus, this problem can be formulated as the joint distribution modeling of the target and of the group-related variables. The usual parameter estimation strategy for this joint model is a two-step approach starting by learning the group variable (clustering step) and then plugging in its estimator for fitting the regression model (regression step). However, this approach is suboptimal (providing in particular biased regression estimates) since it does not make use of the target variable for clustering. Thus, we advise the use of a simultaneous estimation approach of both clustering and regression, in a semiparametric framework. Numerical experiments illustrate the benefits of our proposition by considering wide ranges of distributions and regression models. The relevance of our new method is illustrated on real data dealing with problems associated with high blood pressure prevention. The proposed approach is implemented in the R package ClusPred available on CRAN. Supplementary materials containing the technical details and the R codes are available online.

Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter

Online estimation of internal states and parameters is often required for process monitoring, fault diagnosis and real time optimization. The conventional approach to tracking drifting parameters is to model their variations as a random walk process and estimate them simultaneously with the states. However, tuning of the random walk model is not a trivial exercise. Recently, moving window based simultaneous state and parameter estimation schemes have been proposed under the assumption that the parameters change slowly and remain constant within the window. These formulations make use of the conventional recursive Bayesian estimators or moving horizon estimator (MHE) for state estimation. While simultaneous smoothing of states along with filtering gives an edge to MHE over the conventional recursive estimators, MHE is computation intensive scheme. Recently, developed Receding-horizon Nonlinear Kalman (RNK) filter combines advantages of MHE (simultaneous smoothing) and recursive estimators (less computation time). In this work, we extend the RNK formulation to carry out simultaneous state and parameter estimation schemes under the assumption that parameters change at a slow rate and remain constant within the RNK window. Two maximum likelihood (ML) based parameter estimation schemes are developed by constructing partial likelihood and complete likelihood functions. Both formulations are further extended under the Bayesian framework to derive their maximum a-posteriori (MAP) versions by incorporating prior information of the parameters. The only tuning parameter in the proposed parameter estimation schemes is the window size, which is relatively easy to select. The efficacies of the proposed formulations are evaluated using simulation studies on two reactor systems and experimental data obtained from the benchmark quadruple tank system. Simulation studies reveal that the complete likelihood based estimation schemes have an edge over the partial likelihood based estimation schemes. Also, the proposed estimation schemes generate reasonably accurate state and parameter estimates with a significant reduction in the average computation time.

Novel Machine Parameter Estimation Scheme Toward Accurate Maximum Torque Production for Dual Three-Phase PMSMs

This article proposes a hybrid signal injection-based maximum torque per ampere (MTPA) control for dual three-phase permanent magnet synchronous machines (PMSMs). The proposed method consists of a real dc current injection in the harmonic subspace and a virtual ac current injection in the fundamental subspace. The real dc current injection in the harmonic subspace does not impact the average torque production, and induced losses are small enough to be neglected. In the meantime, to guarantee the accuracy of the proposed MTPA method, a novel machine parameter estimation scheme considering voltage source inverter nonlinearity effects is proposed in this article, which can track the variation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q_{1}$ </tex-math></inline-formula> -axis inductance and resistance accurately. Therefore, the proposed method only requires command voltages and currents in the torque model, and the variations of machine parameters are compensated by the proposed parameter estimation scheme. As the command voltages have correspondence with the variations of machine parameters, the magnetic saturation and temperature effects have been taken into account. The proposed method is validated by simulations and experimental results under various torque, speed, and temperature conditions. Additionally, the accuracy of the proposed method is validated, and its performance is proven to be superior through comparison of the derived results with those obtained from various existing methods.

Multioutput Gaussian Process Modulated Poisson Processes for Event Prediction

Prediction of events such as part replacement and failure events plays a critical role in reliability engineering. Event stream data are commonly observed in manufacturing and teleservice systems. Designing predictive models for individual units based on such event streams is challenging and an underexplored problem. In this work, we propose a nonparametric prognostic framework for individualized event prediction based on the inhomogeneous Poisson processes with a multivariate Gaussian convolution process (MGCP) prior on the intensity functions. The MGCP prior on the intensity functions of the inhomogeneous Poisson processes maps data from similar historical units to the current unit under study which facilitates sharing of information and allows for analysis of flexible event patterns. To facilitate inference, we derive a variational inference scheme for learning and estimation of parameters in the resulting MGCP modulated Poisson process model. Experimental results are shown on both synthetic data as well as real-world data for fleet-based event prediction.

Growth Equations in Forest Research: Mathematical Basis and Model Similarities

Purpose of ReviewGrowth equations have been widely used in forest research, commonly to assess ecosystem-level behavior and forest management. Nevertheless, the large number of growth equations has obscured the growth-rate behavior of each of these equations and several different terms for referring to common phenomena. This review presents a unified mathematical treatment of growth-rates besides several well-known growth equations by giving their mathematical basis and representing their behavior using tree growth data as an example.Recent FindingsWe highlight the mathematical differences among several growth equations that can be better understood by using their differential equations forms rather than their integrated forms. Moreover, the assumed-and-claimed biological basis of these growth-rate models has been taken too seriously in forest research. The focus should be on using a plausible equation for the organism being modelled. We point out that more attention should be drawn to parameter estimation strategies and behavior analysis of the proposed models. Thus, it is difficult for a single model to capture all possible shapes and rates that such a complex biological process as tree growth can depict in nature.SummaryWe pointed out misleading concepts attributed to some growth equations; however, the differences come from their mathematical properties rather than pure biological reasoning. Using the tree growth data, we depict those differences. Thus, comparisons of some functional forms (at least simple ones) must be carried out before selecting a function for drawing scientific findings.

PARAMETER ESTIMATION OF THE CLASSICAL FRACTAL MAP BASED ON A GIVEN JULIA SET’S SHAPE

Julia set is one of the most important branches in fractal theory, and has achieved much progress on both theoretical analysis and application research. Nevertheless, most of the existing investigations relied on the systems with known parameters. Few works have been done on the inverse problem about how to determine the system parameter based on a given Julia set’s shape. This work aims to address this issue by starting with the most classical polynomial map. First, by constructing a proper fitness function measuring the Julia sets’ area error, the Julia sets’ parameter estimation is formulated as a kind of optimization problem. According to the known conditions of Julia set to be estimated, the optimization problem is classified into two cases. For one case, when both the shape and size of Julia set are given, we only need to estimate the system’s own parameter [Formula: see text]. For the other case, when the Julia set has only a shape, we redesign the problem into three dimensions by adding a new scaling factor parameter [Formula: see text] and applying the partial coverage principle. Second, a particle swarm optimization (PSO) approach including dynamically adjustment strategy is adopted to solve the proposed optimization problem. At last, we present seven groups of simulations in which both randomly generated Julia sets and those in existing literature (or on the internet) are included. The simulation results verify the effectiveness of the proposed parameter estimation scheme.

Time-frequency representation with variant array of frequency-domain Prony estimators.

The frequency-domain Prony method (FDPM) [Ando, IEEE Trans. Signal Process. 68, 3461-3470 (2020)] provides a novel and exact short-time, frequency-decomposed scheme for autoregressive model identification and sinusoidal parameter estimation with a superior statistical performance. By using it as localized estimation elements, we construct the time-frequency representation (TFR) of signals as the frequency-reassigned map of the damped sinusoidal parameters of their components. The FDPM for both single and multiple sinusoids is based on a small number of windowless Fourier coefficients of sampled sequence. Thus, a unified and flexible construction of resolution and decomposition structures including linear and log-linear frequency arrays and their combination is possible, and dense analysis along the time axis can be implemented without a significant increase in computational cost. Conditions for constructing the frequency-variant arrays are formulated. Two cooperative behaviors in the TFR are considered to find stable traces of frequencies and rapidly time-varying incidences and components. Several experiments are shown to confirm extended features and performances of the proposed TFR using musical, speech, and natural sound signals.

Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction

Parameter estimation plays an important role in reducing model error and thus is of great significance to improve the simulation and prediction capabilities of the model. However, due to filtering divergence, parameter estimation by ensemble-based filters still faces great challenges. Previous studies have shown that a covariance inflation scheme could alleviate the filtering divergence problem by increasing the signal-to-noise ratio of the state-parameter covariance. In this study, we proposed a new inflation scheme based on a local ensemble transform Kalman filter (LETKF). With the new scheme, the Zebiak–Cane (Z-C) model parameters were estimated by assimilating the sea surface temperature anomaly (SSTA) data. The effectiveness of the parameter estimation and its influence on El Niño–Southern Oscillation (ENSO) prediction were evaluated in an observation system simulation experiments (OSSE) framework and real-world scenario, respectively. With the utilization of the OSSE framework, the results showed that the model parameters were successfully estimated. Parameter estimation reduced the model error when compared with only state estimation (onlySE); however, multiple parameter estimation (MPE) further improved the ENSO prediction skill by providing better initial conditions and parameter values than the single parameter estimation (SPE). Parameter estimation could thus alleviate the spring prediction barrier (SPB) phenomenon of ENSO to a certain extent. In real-world experiments, the optimized parameters significantly improved the ENSO forecasting skill, primarily in prediction of warm events. This study provides an effective parameter estimation strategy to improve climate models and further climate predictions in the real world.

Recycled two-stage estimation in nonlinear mixed effects regression models

We consider a re-sampling scheme for estimation of the population parameters in the mixed-effects nonlinear regression models of the type used, for example, in clinical pharmacokinetics. We provide a two-stage estimation procedure which resamples (or recycles), via random weightings, the various parameter's estimates to construct consistent estimates of their respective sampling distributions. In particular, we establish under rather general distribution-free assumptions, the asymptotic normality and consistency of the standard two-stage estimates and of their resampled version and demonstrate the applicability of our proposed resampling methodology in a small simulation study. A detailed example based on real clinical pharmacokinetic data is also provided.