Iterative Parameter Estimation Research Articles

Estimation of Multiple Contact Conductances in a Silicon-Indium-Silicon Stack

Abstract This report documents evaluation of simultaneous estimation of multiple interfacial heat transfer coefficients (HTCs) using transient measurements from an experiment designed for steady-state operation. The design of a mirror system for directing X-rays under cryogenic conditions requires knowledge of the interfacial HTC (contact conductance) between silicon and indium. An experimental apparatus was constructed to measure temperatures in a stack of five 7.62-mm thick pucks of silicon separated by 0.1-mm thick sheets of indium, which is operated under cryogenic temperatures in vacuum. Multiple pucks and interfaces are incorporated into the apparatus to allow evaluation of HTCs for surfaces of different roughness from a single experiment. Analysis of the sensitivity of each of the measured temperatures to each of the unknown HTCs reveals lack of linear independence of these sensitivities and suggests the recovery of the HTCs will be challenging. Artificially noised “data” were created from two different computational models by solving for temperatures and adding random Gaussian noise with a specified standard deviation. These data are subsequently analyzed using two different iterative parameter estimation methods: a Levenberg scheme and a Tikhonov iterative scheme. The required sensitivity matrix is computed using forward finite difference approximations. The results for the heat transfer coefficients for this model problem suggest that coefficients cannot be estimated independently, but the ratios relative to one of the unknowns can be recovered.

Torque Converter Dynamic Characterization Using Torque Transmissibility Frequency Response Functions: Locked Clutch Operation

<div>A unique torque converter test setup was used to measure the torque transmissibility frequency response function of four torque converter clutch dampers using a stepped, multi-sine-tone, excitation technique. The four torque converter clutch dampers were modeled using a lumped parameter technique, and the damper parameters of stiffness, damping, and friction were estimated using a manual, iterative parameter estimation process. The final damper parameters were selected such that the natural frequency and damping ratio of the simulated torque transmissibility frequency response functions were within 10% and 20% error, respectively, of the experimental modal parameters. This target was achieved for all but one of the tested dampers. The damper models include stiffness nonlinearities, and a speed-dependent friction torque due to centrifugal loading of the damper springs. Recommendations include further testing to separate the coulomb friction mechanism from the viscous damping mechanism, testing with the torque converter operating in open mode, and tests on a series of customized dampers with centrifugal pendulum absorber hardware.</div>

Effect of crossover from placebo to darolutamide on overall survival in men with non-metastatic prostate cancer: sensitivity analyses from the randomised phase 3 ARAMIS study

BackgroundIn the phase 3 ARAMIS study (NCT02200614), darolutamide significantly improved metastasis-free survival in patients with non-metastatic castration-resistant prostate cancer (nmCRPC). Following the primary analysis, the study was unblinded, and placebo recipients were permitted to cross over to open-label darolutamide. Despite crossover, darolutamide significantly improved overall survival (OS). We conducted sensitivity analyses to estimate the effect of placebo–darolutamide crossover on OS. MethodsPatients with nmCRPC were randomised to oral darolutamide 600 mg twice daily (n = 955) or placebo (n = 554). Prespecified (rank-preserving structural failure time [RPSFT] and iterative parameter estimation [IPE]) and post hoc (OS-adjusted censoring and inverse probability of censoring weighting [IPCW], with weightings for baseline testosterone and prostate-specific antigen) sensitivity analyses were conducted. ResultsAfter unblinding, 170 of 554 placebo recipients (30.7%) crossed over to darolutamide. At the final OS intention-to-treat analysis (median 11.2 months after unblinding), darolutamide significantly improved OS by 31% versus placebo (hazard ratio [HR] 0.69, 95% confidence interval [CI] 0.53–0.88; P = 0.003). The benefit increased in the analyses adjusting for crossover is as follows: RPSFT HR 0.68, 95% CI 0.51–0.90; P = 0.007; IPE HR 0.66, 95% CI 0.51–0.84; P < 0.001; OS-adjusted censoring HR 0.59, 95% CI 0.45–0.76; IPCW HR 0.63, 95% CI 0.48–0.81. The favourable safety profile of darolutamide was maintained, including in crossover patients. ConclusionsAfter adjusting for crossover, darolutamide reduced the risk of death by up to 41% in patients with nmCRPC. The effect of darolutamide on OS may have been underestimated in the original intention-to-treat analysis.

Predictive compensation based quantization iterative learning control for nonlinear nonaffine discrete‐time systems

AbstractIn this work, the problems of predictive compensation, unknown nonlinearity, and nonaffine structure are considered simultaneously for a quantized iterative learning control (QILC) design and analysis under a data‐driven framework. The compensation strategy can avoid deteriorated data transmission quality owing to limited channel capacities. First, a dynamic linearization methodology is employed to transform the nonlinear plant into a virtual iterative linear data model (iLDM) which includes all the input signals over a time‐window from the initial time instant to the current one. The iLDM is also used as a predictive model to estimate the unavailable information caused by the encoding–decoding mechanism. Then, a predictive compensation‐based QILC is proposed by optimizing quadratic functions, which includes an output prediction mechanism, a quantized iterative learning updating law, a quantized iterative parameter estimation law, and a resetting algorithm. The result is also extended to a class of MIMO nonlinear nonaffine discrete‐time systems. The developed control laws are data‐driven and independent of any system information. The theoretical results are proved by the use of contraction mapping principle and induction method. Examples are provided to verify the effectiveness of the proposed methods.

Speech Signal Analysis With a Refined Iterative Adaptive Method

A new speech signal analysis method referred to as a refined iterative adaptive method (RIAM) is introduced in this paper. Based on time-varying adaptive sinusoidal modeling, the RIAM method tries to determine in an iterative adaptive manner the instantaneous components of time-varying quasi-periodic multi-component signals such as voiced speech. The proposed method can adjust the current analysis parameters to the time-varying characteristics of the speech signal. This is done using a refined iterative sinusoidal parameter estimation algorithm based on a frequency correction mechanism combined with an adaptive scheme. The experiments on voiced speech demonstrate that the proposed RIAM algorithm outperforms some well-known state-of-the-art approaches. The RIAM algorithm provides a higher signal to reconstruction error ratio (SRER) of 42.267 dB with an improvement of 19.752 dB, 5.054 dB, and 2.552 dB compared to the conventional sinusoidal model (SM), adaptive harmonic model (aHM), and extended adaptive quasi harmonic model (eaQHM), respectively.

Joint parameter estimation and decoding in a distributed receiver

SummaryThis paper presents an algorithm for iterative joint channel parameter (carrier phase, Doppler shift, and Doppler rate) estimation and decoding of transmission over channels affected by Doppler shift and Doppler rate using a distributed receiver. This algorithm is derived by applying the sum‐product algorithm (SPA) to a factor graph representing the joint a posteriori distribution of the information symbols and channel parameters given the channel output. In this paper, we present two methods for dealing with intractable messages of the SPA. In the first approach, we use particle filtering with sequential importance sampling for the estimation of the unknown parameters. We also propose a method for fine‐tuning of particles for improved convergence. In the second approach, we approximate our model with a random walk phase model, followed by a phase tracking algorithm and polynomial regression algorithm to estimate the unknown parameters. We derive the Weighted Bayesian Cramer‐Rao Bounds for joint carrier phase, Doppler shift, and Doppler rate estimation, which take into account the prior distribution of the estimation parameters and are accurate lower bounds for all considered signal‐to‐noise ratio values. Numerical results (of bit error rate and the mean‐square error of parameter estimation) suggest that phase tracking with the random walk model slightly outperforms particle filtering. However, particle filtering has a lower computational cost than the random walk model‐based method.

Data-assimilation-based parameter estimation of bathymetry and bottom friction coefficient to improve coastal accuracy in a global tide model

Abstract. Global tide and surge models play a major role in forecasting coastal flooding due to extreme events or climate change. The model performance is strongly affected by parameters such as bathymetry and bottom friction. In this study, we propose a method that estimates bathymetry globally and the bottom friction coefficient in shallow waters for a global tide and surge model (GTSMv4.1). However, the estimation effect is limited by the scarcity of available tide gauges. We propose complementing sparse tide gauges with tide time series generated using FES2014. The FES2014 dataset outperforms the GTSM in most areas and is used as observations for the deep ocean and some coastal areas, such as Hudson Bay and Labrador, where tide gauges are scarce but energy dissipation is large. The experiment is performed with a computation- and memory-efficient iterative parameter estimation scheme (time–POD-based coarse incremental parameter estimation; POD: proper orthogonal decomposition) applied to the Global Tide and Surge Model (GTSMv4.1). Estimation results show that model performance is significantly improved for the deep ocean and shallow waters, especially in the European shelf, directly using the CMEMS tide gauge data in the estimation. The GTSM is also validated by comparing to tide gauges from UHSLC, CMEMS, and some Arctic stations in the year 2014.

Fully Integrated Temporary Persistent Scatterer Interferometry

The persistent scatterer interferometry (PSI) is a powerful remote sensing technique to monitor displacements of the Earth&#x2019;s surface. It is based on identifying and analyzing phase-stable scatterers undergoing marginal decorrelation over time. Most PSI approaches constrain the analysis to scatterers which are coherent over the whole considered time series (PS) and neglect scatterers which are only temporary persistent (TPS). Here, we propose a method to fully integrate TPS into an existing PSI approach which is characterized by an iterative parameter estimation with subsequent phase unwrapping. We build on a previously published method to identify TPS and estimate their coherent lifetime based on amplitude statistics. A phase-based likelihood ratio test is proposed to iteratively refine the appearing/fading dates of TPS during the parameter estimation. Finally, we jointly unwrap the phase observations of PS and TPS to receive displacement time series. The temporal datum of TPS is re-defined if their lifetime does not cover the selected master scene. Experimental results based on Sentinel-1 data in the Vietnamese city of Ca Mau show that the change date refinement significantly increases the average coherence and number of identified TPS. The densification of the observation point network by incorporating TPS helps to better detect and understand displacement phenomenons. The displacement time series of TPS are beneficial to analyze nonlinear motion in connection with urban development, like initial settlement of newly constructed buildings.

Robust identification for input non‐uniformly sampled Wiener model by the expectation‐maximisation algorithm

The problems of inconsistent data sampling frequency, outliers, and coloured noise often exist in system identification, resulting in unsatisfactory identification results. In this study, a novel identification method of input non-uniform sampling Wiener model with a coloured heavy-tailed noise is proposed. The lifted Wiener model with coloured noise and outlier value disturbed is constructed. Under the expectation-maximisation (EM) algorithm framework, the student's t-distribution is introduced to model the contaminated output data. The variance scale is regarded as a unique latent variable, and the iterative parameter estimation formula of the non-uniform sampling Wiener model is derived. The idea of the auxiliary model is applied to acquire the unmeasured middle variable and handle the coloured noise variable in the non-uniformly sampled Wiener model. The Differential Evolution algorithm is used to calculate the intractable part of the Q-function. The convergence analysis of the proposed algorithm is given. Two numerical examples and one water tank simulation are employed to indicate the effectiveness of the proposed method.

Estimation of treatment effect in presence of noncompliance with early or late switching: a simulation study

To assess the effect of treatment in survival studies, a randomized controlled trial (RCTs) is usually designed. Due to disease progression or other safety reasons, patients in the standard group may be allowed to switch to the experimental treatment. In such cases, estimation of the treatment effect is inevitably performed using naive methods such as intention-to-treat (ITT), per-protocol (PP), or more advanced methods such as the rank-preserving structural failure time (RPSFT) models or iterative parameter estimation (IPE). In some trials, people who progress early might be more likely to switch, and in other trials, people who progress late might be more likely to switch – depending on the characteristics of the disease and the drug being examined. So, this study simulated a new range of realistic scenarios with regard to patients’ prognosis and investigated the performance of the adjusted methods in the early and late switching with respect to the median of the survival time.

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

SummaryThis article mainly studies the iterative parameter estimation problems of a class of nonlinear systems. Based on the auxiliary model identification idea, this article utilizes the estimated parameters to construct an auxiliary model, and uses its outputs to replace the unknown noise‐free process outputs, and develops an auxiliary model least squares‐based iterative (AM‐LSI) identification algorithm. For further improving the parameter estimation accuracy, we use a particle filter to estimate the unknown noise‐free process outputs, and derive a particle filtering least squares‐based iterative (PF‐LSI) identification algorithm. During each iteration, the AM‐LSI and PF‐LSI algorithms can make full use of the measured input–output data. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems, and can generate more accurate parameter estimates than the auxiliary model‐based recursive least squares algorithm.

On parameter estimation for Amoroso family of distributions

The four-parameter generalized gamma (GΓ) distribution, also known as the Amoroso family of distributions, is a flexible and versatile statistical distribution that encapsulates many well-known lifetime distributions, including the exponential, Weibull, lognormal, and gamma distributions as special instances. The four-parameter GΓ distribution is shown to be appropriate for fitting skewed and heavy-tailed data sets. However, even though the GΓ distribution is very useful and flexible, it remains less studied than its counterparts, probably due to the difficulty in estimating the parameters of the distribution. In this paper, we explore several novel iterative parameter estimation approaches for the four-parameter GΓ distribution, which includes the maximum likelihood estimation and minimum distance estimation approaches.Standard error and confidence interval of a function of the parameter estimates based on bootstrap method are also discussed.An R package is developed based on the proposed estimation methods. Numerical examples and Monte Carlo simulations are used to illustrate the usefulness of the proposed approaches for fitting the four-parameter GΓ distribution.

Observer based switching ILC for consensus of nonlinear nonaffine multi-agent systems

This study considers the main challenges of presenting an iterative observer under a data-driven framework for nonlinear nonaffine multi-agent systems (MASs) that can estimate nonrepetitive uncertainties of initial states and disturbances by using the information from previous iterations. Consequently, an observer-based iterative learning control is proposed for the accurate consensus tracking. First, the dynamic effect of nonrepetitive initial states is transformed as a total disturbance of the linear data model which is developed to describe I/O iteration-dynamic relationship of nonlinear nonaffine MASs. Second, the measurement noises are considered as the main uncertainty of system output. Then, we present an iterative disturbance observer to estimate the total uncertainty caused by the nonrepetitive initial shifts and measurement noises together. Next, we further propose an observer-based switching iterative learning control (OBSILC) using the iterative disturbance observer to compensate the total uncertainty and an iterative parameter estimator to estimate unknown gradient parameters. The proposed OBSILC consists of two learning control algorithms and the only difference between the two is that an iteration-decrement factor is introduced in one of them to further reduce the effect of the total uncertainty. These two algorithms are switched to each other according to a preset error threshold. Theoretical results are demonstrated by the simulation study. The proposed OBSILC can reduce the influence of nonrepetitive initial values and measurement noises in the iterative learning control for MASs by only using I/O data.

Teasing Out the Overall Survival Benefit With Adjustment for Treatment Switching to Multiple Treatments

In oncology clinical trials, characterizing the long-term overall survival (OS) benefit for an experimental treatment is often unobservable if some patients in the control group switch to drugs in the experimental group and/or other cancer treatments after disease progression. A key question often raised by payers and reimbursement agencies is how to estimate the true benefit of the experimental treatment on OS that would have been estimated if there were no treatment switches. Several commonly used statistical methods are available to estimate OS benefit while adjusting for treatment switching, ranging from naive exclusion or censoring approaches to more advanced methods including inverse probability of censoring weighting (IPCW), two-stage model (2-stage), iterative parameter estimation (IPE) algorithm or rank-preserving structural failure time models (RPSFTM). However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments. While some existing methods including RPSFTM and IPE cannot handle complicated scenarios such as multilevel switching, other methods such as IPCW and two-stage methods are restricted with strong assumptions. To address this challenge, we propose two additional methods: stratified RPSFTM and random-forest-based prediction. A simulation study is conducted to assess the performance of the existing methods along with the two newly proposed approaches.

Iterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique

This paper considers the iterative parameter estimation for a dual-rate sampled-data bilinear system with autoregressive moving average noise. Through combining the auxiliary model identification idea with the data filtering technique, this paper derives two filtering auxiliary model gradient-based iterative algorithms by using two different filters. The key is to construct an auxiliary model for predicting the unavailable outputs, and to transform the dual-rate bilinear system identification model into two sub-identification models. Finally, an auxiliary model gradient-based iterative (AM-GI) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the dual-rate sampled-data bilinear systems, and can generate more accurate parameter estimates and have a higher computational efficiency than the AM-GI algorithm.

Analysis of the effect of crossover from placebo (PBO) to darolutamide (DARO) on overall survival (OS) benefit in the ARAMIS Trial.

240 Background: DARO is a structurally distinct androgen receptor inhibitor approved for the treatment of non-metastatic castration-resistant prostate cancer (nmCRPC) based on significantly prolonged metastasis-free survival compared with PBO (median 40.4 vs 18.4 months; hazard ratio [HR] 0.41; 95% confidence interval [CI] 0.34–0.50; P &lt; 0.0001) and a favorable safety profile in the phase III ARAMIS trial. Following unblinding at the primary analysis, crossover from PBO to DARO was permitted for the subsequent open-label treatment phase. Sensitivity analyses were performed to assess the effect of PBO–DARO crossover on OS benefit. Methods: Patients (pts) with nmCRPC receiving androgen deprivation therapy were randomized 2:1 to DARO (n = 955) or PBO (n = 554). In addition to OS, secondary endpoints included times to pain progression, first cytotoxic chemotherapy, first symptomatic skeletal event, and safety. The OS analysis was planned to occur after approximately 240 deaths, and secondary endpoints were evaluated in a hierarchical order. Iterative parameter estimation (IPE) and rank-preserving structural failure time (RPSFT) analyses were performed as pre-planned sensitivity analyses to adjust for the treatment effect of PBO–DARO crossover. The IPE method used a parametric model for the survival times and iteratively determined the model parameter describing the magnitude of the treatment effect, whereas a grid search and non-parametric log-rank test were used for the RPSFT analysis. The IPE and RPSFT analyses both generated a Kaplan–Meier curve for the PBO arm that predicts what would have been observed in the absence of PBO–DARO crossover. Results: After unblinding, 170 pts (30.7% of those randomized to PBO) crossed over from PBO to DARO; median treatment duration from unblinding to the final data cut-off was 11 months. Final analysis of the combined double-blind and open label periods was conducted after 254 deaths (15.5% of DARO and 19.1% of PBO pts) and showed a statistically significant OS benefit for DARO vs PBO (HR 0.69; 95% CI 0.53–0.88; P = 0.003). Results from the IPE (HR 0.66; 95% CI 0.51–0.84; P &lt; 0.001) and RPSFT (HR 0.68; 95% CI 0.51–0.90; P = 0.007) analyses were similar to those from the intention-to-treat population, showing that the impact of PBO–DARO crossover was small. Additional analyses accounting for the effect of PBO–DARO crossover will be presented. The safety profile of DARO continued to be favorable at the final analysis, and discontinuation rates at the end of the double-blind period remained unchanged from the primary analysis (8.9% with DARO and 8.7% with PBO). Conclusions: Early treatment with DARO in men with nmCRPC is associated with significant improvement in OS regardless of pts crossing over from PBO to DARO. The safety profile of DARO remained favorable at the final analysis. Clinical trial information: NCT02200614.

Sparse planar arrays for azimuth and elevation using experimental data.

Sparse arrays are special geometrical arrangements of sensors which overcome some of the drawbacks associated with dense uniform arrays and require fewer sensors. For direction finding applications, sparse arrays with the same number of sensors can resolve more sources while providing higher resolution than a dense uniform array. This has been verified numerically and with real data for one-dimensional microphone arrays. In this study the use of nested and co-prime arrays is examined with sparse Bayesian learning (SBL), which is a compressive sensing algorithm, for estimating sparse vectors and support. SBL is an iterative parameter estimation method and can process multiple snapshots as well as multiple frequency data within its Bayesian framework. A multi-frequency variant of SBL is proposed, which accounts for non-flat frequency spectra of the sources. Experimental validation of azimuth and elevation [two-dimensional (2D)] direction-of-arrival (DOA)estimation are provided using sparse arrays and real data acquired in an anechoic chamber with a rectangular array. Both co-prime and nested arrays are obtained by sampling this rectangular array. The SBL method is compared with conventional beamforming and multiple signal classification for 2D DOA estimation of experimental data.

Residual and local influence analyses for unit gamma regressions

AbstractWe obtain local influence measures and residuals for the unit gamma regression model. In particular, we introduce four residuals that are based on Fisher's iterative scoring parameter estimation algorithm and develop local influence analysis based on several different perturbation schemes: cases weighting, response additive perturbation, and covariate(s) additive perturbation. An empirical application in which variables related to education and investment in research and development are used to explain the proportion of nonpoor people in a set of countries is presented and discussed. Residual and local influence analyses show that the unit gamma regression model yields a good fit to the data, even outperforming the beta regression model. The diagnostic analysis singles out countries whose data are worthy of further investigation. Our results reveal that lower poverty levels are associated with higher shares of investment in high technology. The statistical significance of such a relationship is not sensitive to atypical data points.

A Trust-Region Framework for Real-Time Optimization with Structural Process-Model Mismatch

Advances in optimization theory, software, and modeling environments promote widespread application of optimization-based decision making. However, the gap between the optimization model and the real-world counterpart impedes optimality of the solution and leads to loss of performance in practice. To overcome this barrier, this paper proposes to reconcile the model and the real plant with iterative parameter estimation and real-time optimization based on a trust-region framework. Three algorithms are developed with different means of constructing/maintaining the local model, managing the trust-region radius, and assessing the trial point. Analysis shows that all these algorithms converge to the correct first-order KKT point despite structural model mismatch. Case studies of reduced model-based optimization demonstrate effectiveness and performance of the proposed methods.

Event-Triggered Nonlinear Iterative Learning Control.

An event-triggered nonlinear iterative learning control (ET-NILC) method is presented for repetitive nonaffine and nonlinear systems that have 2-D dynamic behavior along both time and iteration directions. Based on the virtual linear data model, the ET-NILC method is proposed by designing an event triggering condition based on the Lyapunov-like stability analysis conducted along the iteration direction. The learning gain function of ET-NILC is nonlinear and updated by designing an iterative learning parameter estimation law to enhance the robustness. From the perspective of the time dynamics, the proposed ET-NILC is a feedforward control and the event-triggering condition can be verified offline using tracking errors, event triggering errors, and the estimated parameters together. Moreover, the proposed ET-NILC is a data-driven scheme since it merely uses I/O data for the design. The results are also extended to repetitive multiple-input-multiple-output (MIMO) nonaffine nonlinear systems using the property of input-to-state stability as the basic mathematical tool. The convergence of the proposed ET-NILC methods is proved. Several simulations illustrate the effectiveness of the proposed methods.