Efficient Estimation Research Articles

State of Health Estimation of Lithium‐Ion Batteries Based on Differential Thermal Voltammetry and Improved Gray Wolf Optimizer Optimizing Gaussian Process Regression

Accurate estimation of the state of health (SOH) of lithium‐ion batteries (LIBs) is essential for their safe operation. Therefore, herein, a novel approach that combines Gaussian process regression (GPR) optimized using an improved gray wolf optimizer (IGWO) with differential thermal voltammetry (DTV) is introduced. In this approach, the peak and valley information of the DTV curves are used to reveal the battery‐aging mechanisms, with the slopes and durations between peaks and valleys used as health characteristics. The correlation between the characteristics and SOH of the battery is analyzed to build a health feature dataset. IGWO optimizes the GPR hyperparameters to address their dependence on the initial values and susceptibility to local optimization and employs a dimension‐learning strategy to enhance the population diversity and prevent premature convergence. DTV curves and an IGWO‐GPR model for SOH estimation using four cells from the NASA LIB public aging dataset are developed and validated. The results show root mean square errors below 0.007 and mean absolute errors under 0.006 for all cells. The coefficient of determination exceeds 0.92 for three cells, with one battery exhibiting a value of 0.866. This method provides accurate and efficient SOH estimation, essential for safe battery operation.

Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization

Forecasting urban traffic states is crucial to transportation network monitoring and management, playing an important role in the decision-making process. Despite the substantial progress that has been made in developing accurate, efficient, and reliable algorithms for traffic forecasting, most existing approaches fail to handle sparsity, high-dimensionality, and nonstationarity in traffic time series and seldom consider the temporal dependence between traffic states. To address these issues, this work presents a Hankel temporal matrix factorization (HTMF) model using the Hankel matrix in the lower dimensional spaces under a matrix factorization framework. In particular, we consider an alternating minimization scheme to optimize the factor matrices in matrix factorization and the Hankel matrix in the lower dimensional spaces simultaneously. To perform traffic state forecasting, we introduce two efficient estimation processes on real-time incremental data, including an online imputation (i.e., reconstruct missing values) and an online forecasting (i.e., estimate future data points). Through extensive experiments on the real-world Uber movement speed data set in Seattle, Washington, we empirically demonstrate the superior forecasting performance of HTMF over several baseline models and highlight the advantages of HTMF for addressing sparsity, nonstationarity, and short time series. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was supported by the Institute for Data Valorisation, the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, the National Natural Science Foundation of China [Grants 12371456, 72101049, 72232001], the Sichuan Science and Technology Program [Grant 2024NSFJQ0038], and the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045].

The MISTRAL Instrument and the Characterization of Its Detector Array

The MIllimeter Sardinia radio Telescope Receiver based on Array of Lumped elements KIDs, MISTRAL, is a cryogenic LEKID camera, operating in the W band (77-103GHz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${77}{-}{103\\,\ extrm{GHz}}$$\\end{document}) from the Gregorian focus of the 64-m aperture Sardinia Radio Telescope (SRT), in Italy. This instrument features a high angular resolution (∼12arcsec\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim {12\\,\ extrm{arcsec}}$$\\end{document}) and a wide instantaneous field of view (∼4arcmin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim {4\\,\ extrm{arcmin}}$$\\end{document}), allowing continuum surveys of the mm-wave sky with many scientific targets, including observations of galaxy clusters via the Sunyaev–Zel’dovich effect. In May 2023, MISTRAL has been installed at SRT for the technical commissioning. In this contribution, we will describe the MISTRAL instrument focusing on the laboratory characterization of its focal plane: a ∼400\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim {400}$$\\end{document}-pixel LEKID array. We will show the optical performance of the detectors highlighting the procedure for the identification of the pixels on the focal plane, the measurements of the optical responsivity and NEP, and the estimation of the optical efficiency.

A simple model for the estimation of turbofan engine performance in all airborne phases of flight

Abstract The overall efficiency of a turbofan engine may be expressed as a function of the Mach number, flight level and one other parameter. This may be either the net thrust, the turbine entry temperature or the fuel flow rate. Using basic aero-thermodynamic principles, dimensional analysis, normalisation and curve fitting, five approximate and “near universal” relations have been identified for engines having bypass ratios between 1 and 13. These relations contain five independent characteristic engine parameters. When these parameters are known, the relations form the basis of an estimation method for engine overall efficiency that is simple, fast, open source, completely transparent and, as new information appears, capable of further refinement. Since the empirical relations presented in this analysis are valid for Mach numbers greater than 0.2, the method is applicable to all airborne phases of flight. For a given aircraft, if the flight trajectory is specified in sufficient detail for the variation of net thrust with Mach number and flight level to be determined, only three of the five relations, together with the value of engine overall efficiency at a single reference condition, are needed to estimate the overall efficiency at every point on the trajectory. Comparisons with the data used in this analysis suggest that the accuracy is better than ±5% in most cases. In the completely general case, two additional engine characteristic parameters, one a total temperature ratio and the other a Mach number, are introduced. If these are known, both engine overall efficiency and net thrust can be expressed as functions of Mach number, flight level and turbine entry temperature. This allows the method to be used for the estimation of operating limits in the various phases of flight and in simplified optimisation studies, e.g. finding the environmentally optimum flight trajectory. In previous work, estimates of engine overall efficiency at the “design optimum” condition have been estimated for 53 aircraft and engine combinations. It is shown that the ‘design optimum’ condition is an appropriate choice for the engine reference condition. Updated and revised values for the relevant parameters for these 53 examples, together with estimates for the two additional engine characteristic parameters, are given in tabular form.

PSVI-6 Genomic determinants of the rumen microbiome and metabolome in Angus steers

Abstract The rumen microbiome provides approximately 70% of energy required for the ruminant host through fermentation byproducts. Due to microbial impacts on host health and productivity, several studies have associated microbiome and metabolome variation with important sustainability phenotypes, such as feed efficiency and methane emissions. Recent studies have indicated the microbial communities present in rumen fluid are under low-to-moderate host genetic control. However, microorganisms present on the rumen wall have been underrepresented in research, negating their unique role in host-microbiome interactions. Current studies investigating heritability of the rumen planktonic microorganisms are plagued by small sample sizes, high management and breed heterogeneity, and low resolution for classifying microbial communities. To determine the heritability of the rumen fluid and epithelium-associated microbiome and metabolome, Angus steers (n = 350) were enrolled in a 70-d feed efficiency trial, where real-time dry matter intake was measured. Body weights were collected throughout the trial to calculate average daily gain and residual feed intake. Mid-trial, a novel surgery method was used to swab the rumen wall to capture epithelium-associated microorganisms. On d 70, 50 mL of rumen fluid was obtained from steers using orogastric tubing for collection of planktonic microbial communities. Metagenomic DNA extracted from both rumen fluid and epimural samples was used for reduced representation sequencing to predict microbial features such as diversity, composition, and putative function. Genomic DNA isolated from whole blood was used to perform low-pass whole genome sequencing for use in downstream heritability estimates and variant association analyses. Many core microbial abundances are under low-to-moderate host genetic control (h2 > 0.15; P < 0.05); however, many microorganisms had low heritability estimates (h2 < 0.10). Preliminary genome-wide associations studies are vastly underpowered to detect host genetic variants associated with microbial features, but data generated using the total number of animals will better refine these polymorphisms. Several key heritable metabolites were identified in amino acid metabolism pathways in rumen fluid (P < 0.05). The rumen microbiome appears to act as a complex polygenic trait and incorporating microbial features into prediction models may improve feed efficiency estimations. Understanding host genetic control of the rumen microbiome and metabolome may empower downstream applications to enhance genomic and phenotypic predictions for feed efficiency and other sustainability traits in beef cattle.

Scaled envelope models for multivariate time series

Vector autoregressive (VAR) models have become a popular choice for modeling multivariate time series data due to their simplicity and ease of use. Efficient estimation of VAR coefficients is an important problem. The envelope technique for VAR models is demonstrated to have the potential to yield significant gains in efficiency and accuracy by incorporating linear combinations of the response vector that are essentially immaterial to the estimation of the VAR coefficients. However, inferences based on envelope VAR (EVAR) models are not invariant or equivariant upon the rescaling of the VAR responses, limiting their application to time series data that are measured in the same or similar units. In scenarios where VAR responses are measured on different scales, the efficiency improvements promised by envelopes are not always guaranteed. To address this limitation, we introduce the scaled envelope VAR (SEVAR) model, which preserves the efficiency-boosting capabilities of standard envelope techniques while remaining invariant to scale changes. The asymptotic characteristics of the proposed estimators are established based on different error assumptions. Simulation studies and real-data analysis are conducted to demonstrate the efficiency and effectiveness of the proposed model. The numerical results corroborate our theoretical findings.

Optimal decorrelated score subsampling for high-dimensional generalized linear models under measurement constraints

– When responses of massive data are hard to obtain due to some reasons such as privacy and security, high cost and administrative management, response-free subsampling is considered. In this paper, we propose a response-free decorrelated score subsampling approach to estimate and make statistical inference for a preconceived low-dimensional parameter in high-dimensional generalized linear models. The unconditional consistency and asymptotic normality of the resulting weighted subsample estimator are established using martingale techniques since the subsamples are no longer independent. The optimal response-free subsampling probabilities are derived based on A- and L-optimality criteria. Based on the optimal subsample, we further propose a more efficient and stable unweighted decorrelated score subsample estimator. The satisfactory performance of our proposed subsample estimators are demonstrated by simulation results and two real data applications.

Simultaneous identification of propeller fluctuating forces and unknown parameters based on a Bayesian inversion approach

Accurate quantification of propeller excitation is essential for effective vibro-acoustic analysis and mitigation in marine vessels. This study introduces a Bayesian statistical framework that indirectly reconstruct uncertain forces and system parameters by fusing available prior information, forward modeling and measured vibration responses through Bayesian theorem. The Markov Chain Monte Carlo (MCMC) algorithm, enhanced with Polynomial Chaos Kriging (PC-Kriging) surrogates, is employed to facilitate efficient estimation of likelihood functions and robust uncertainty propagation, providing posterior probabilistic estimates and credible intervals for the identified forces. Additionally, the framework incorporates model class selection based on model evidence to determine the most plausible empirical spectrum model for the propeller broadband thrust spectrum. Specifically, this study proposes and evaluates two candidate empirical spectrum models, the Ornstein–Uhlenbeck (OU)-Gaussian and OU-Weibull models, which effectively capture the frequency–amplitude characteristics of unsteady thrust. Numerical results demonstrate substantial parameter uncertainty reduction, accurate thrust spectrum reconstruction, and effective quantification of statistical properties. Moreover, the OU-Gaussian model is revealed to be more plausible for characterizing the propeller thrust spectrum and response prediction. This framework may offer a practical solution for propeller excitation identification, enabling rapid vibro-acoustic performance assessments of marine vessels.

Dual Transformation of Auxiliary Variables by Using Outliers in Stratified Random Sampling

To estimate the finite population variance of the study variable, this paper proposes an improved class of efficient estimators using different transformations. When both the minimum and maximum values of the auxiliary variable are known and the ranks of the auxiliary variable are associated with the study variable, these estimators are particularly useful. Therefore, the precision of the estimators can be effectively improved through the utilization of these rankings. We examine the properties of the proposed class of estimators, including bias and mean squared error (MSE), using a first-order approximation through a stratified random sampling method. To determine the performances and validate the findings mathematically, a simulation study is carried out. Based on the results, the proposed class of estimators performs better in terms of the mean squared error (MSE) and percent relative efficiency (PRE) as compared to other estimators in all scenarios. Furthermore, in order to prove that the performances of the improved class of estimators are better than those of the existing estimators, three data sets are examined in the application section.

Identifiability and Estimation for Potential-Outcome Means with Misclassified Outcomes

Potential outcomes play a fundamental and important role in many causal inference problems. If the potential-outcome means are identifiable, a series of causal effect measures, including the risk difference, the risk ratio, and the treatment benefit rate, among others, can also be identified. However, current identification and estimation methods for these means often implicitly assume that the collected data for analysis are measured precisely. In many fields such as medicine and economics, the collected variables may be subject to measurement errors, such as medical diagnostic results and individual wage data. Misclassification, as a non-classic measurement error, can lead to severely biased estimates in causal inference. In this paper, we leverage a combined sample to study the identifiability of potential-outcome means corresponding to different treatment levers under a plausible misclassification assumption for the outcome, allowing the misclassification probability to depend on not only the true outcome but also the covariates. Furthermore, we propose the multiply-robust and semiparametric efficient estimators for the means, consistent even under partial misspecification of the observed data law, based on the semiparametric theory framework. The simulation studies and real data analysis demonstrate the satisfactory performance of the proposed method.

A New Approach Using the Lindley Distribution in Stochastic Frontier Analysis

Abstract Stochastic Frontier Analysis plays a crucial role in assessing technical efficiency and modelling production processes across various disciplines. Traditionally, SFA assumes specific error distributions, such as the normal distribution for random effects and the half-normal distribution for technical efficiency. However, the choice of error distributions can significantly impact model estimation and interpretation. This study proposes a novel approach by incorporating the Lindley distribution as a flexible error distribution in SFA, termed Lindley Stochastic Frontier Analysis (L-SFA). This extension offers a more detailed representation of the error structure, potentially enhancing the accuracy of efficiency estimates. The derivation and solution of maximum likelihood estimators for the theoretical foundations of L-SFA are provided. Furthermore, a simulation study demonstrates the advantages of L-SFA over traditional SFA. The findings underscore the importance of flexible error distributions in capturing the complexities of production processes with this new SFA extension. Keywords: Efficiency, Error distribution, Lindley stochastic frontier analysis, Maximum likelihood estimation, Simulation

Adaptive and efficient isotonic estimation in Wicksell's problem

We consider nonparametric estimation in Wicksell's problem, which has applications in astronomy for estimating the distribution of star positions in a galaxy and in material sciences for determining a material's 3D microstructure from 2D cross sections. We focus on the isotonised version of the plug-in estimator (IIE) for the cdf F of the spheres' squared radii. This estimator is fully automatic, requiring no tuning parameters, and we show it is adaptive to local smoothness properties of the distribution function F to be estimated. We also prove a local asymptotic minimax lower bound in this non-standard setting, with log ⁡ n / n -asymptotics and where the functional F to be estimated is not regular. Combined, our results prove that the isotonic estimator (IIE) is an adaptive, easy-to-compute, and efficient estimator.

Efficient ARL estimation for general control charts using censored run lengths

The average run length (ARL), the average number of in-control signals before an out-of-control signal occurs, is a critical measure of the performance of a control chart. Various methods have been explored for estimating the ARL, including Markov chain-based approximation, integration method, and the Monte Carlo method, especially for time-dependent charting statistics. Although computationally expensive, the Monte Carlo method is generic and applicable to all control charts. Typically, it involves choosing the maximum run length in advance and discarding iterations that fail to produce out-of-control signals to accommodate some unusually lengthy runs. However, these discarded runs provide information for estimating the run length. This paper introduces a new Monte Carlo approximation that retains failed iterations as Type I censored observations for ARL estimation. Our approach relies on the memoryless expectation of the run length distribution to have a simple and efficient ARL estimator. This assumption is necessary for mean estimation in Type I censored data and aligns with existing ARL approximations used in popular control charts like Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts. Numerical simulations demonstrate our method’s computational and statistical efficiency across the mean, median, EWMA, and CUSUM charts.

Efficient semiparametric estimation in two‐sample comparison via semisupervised learning

AbstractWe develop a general semisupervised framework for statistical inference in the two‐sample comparison setting. Although the supervised Mann–Whitney statistic outperforms many estimators in the two‐sample problem for nonnormally distributed responses, it is excessively inefficient because it ignores large amounts of unlabelled information. To borrow strength from unlabelled data, we propose a class of efficient and adaptive estimators that use two‐step semiparametric imputation. The probabilistic index model is adopted primarily to achieve dimension reduction for multivariate covariates, and a follow‐up reweighting step balances the contributions of labelled and unlabelled data. The asymptotic properties of our estimator are derived with variance comparison through a phase diagram. Efficiency theory shows our estimators achieve the semiparametric variance lower bound if the probabilistic index model is correctly specified, and are more efficient than their supervised counterpart when the model is not degenerate. The asymptotic variance is estimated through a two‐step perturbation resampling procedure. To gauge the finite sample performance, we conducted extensive simulation studies which verify the adaptive nature of our methods with respect to model misspecification. To illustrate the merits of our proposed method, we analyze a dataset concerning homelessness in Los Angeles.

Distributed learning for kernel mode–based regression

AbstractWe propose a parametric kernel mode–based regression built on the mode value, which provides robust and efficient estimators for datasets containing outliers or heavy‐tailed distributions. To address the challenges posed by massive datasets, we integrate this regression method with distributed statistical learning techniques, which greatly reduces the required amount of primary memory and simultaneously accommodates heterogeneity in the estimation process. By approximating the local kernel objective function with a least squares format, we are able to preserve compact statistics for each worker machine, facilitating the reconstruction of estimates for the entire dataset with minimal asymptotic approximation error. Additionally, we explore shrinkage estimation through local quadratic approximation, showcasing that the resulting estimator possesses the oracle property through an adaptive LASSO approach. The finite‐sample performance of the developed method is illustrated using simulations and real data analysis.

Random Learning Leads to Faster Convergence in ‘Model‐Free’ ILC: With Application to MIMO Feedforward in Industrial Printing

ABSTRACTModel‐free iterative learning control (ILC) can lead to high performance by attenuating repeating disturbances completely, using dedicated experiments on the real system to replace the traditional model. The aim of this paper is to develop a fast data‐driven method for MIMO ILC that uses random learning in the form of efficient unbiased gradient estimates. This is achieved by developing a stochastic conjugate gradient algorithm, in which the search direction and optimal step size are generated using dedicated experiments. The approach is applied to MIMO automated feedforward tuning. Simulation and experimental results show that the method is superior to earlier stochastic and deterministic methods.

Optimum designs for clinical trials in personalized medicine when response variance depends on treatment

ABSTRACT We study optimal designs for clinical trials when the value of the response and its variance depend on treatment and covariates are included in the response model. Such designs are generalizations of Neyman allocation, commonly used in personalized medicine when external factors may have differing effects on the response depending on subgroups of patients. We develop theoretical results for D-, A-, E- and D A -optimal designs and construct semidefinite programming (SDP) formulations that support their numerical computation. D-, A-, and E-optimal designs are appropriate for efficient estimation of distinct properties of the parameters of the response models. Our formulation allows finding optimal allocation schemes for a general number of treatments and of covariates. Finally, we study frequentist sequential clinical trial allocation within contexts where response parameters and their respective variances remain unknown. We illustrate, with a simulated example and with a redesigned clinical trial on the treatment of neuro-degenerative disease, that both theoretical and SDP results, derived under the assumption of known variances, converge asymptotically to allocations obtained through the sequential scheme. Procedures to use static and sequential allocation are proposed.

Time-asymmetric fluctuation theorem and efficient free-energy estimation.

The free-energy difference ΔF between two high-dimensional systems is notoriously difficult to compute but very important for many applications such as drug discovery. We demonstrate that an unconventional definition of work introduced by Vaikuntanathan and Jarzynski (2008) satisfies a microscopic fluctuation theorem that relates path ensembles that are driven by protocols unequal under time reversal. It has been shown before that counterdiabatic protocols-those having additional forcing that enforces the system to remain in instantaneous equilibrium, also known as escorted dynamics or engineered swift equilibration-yield zero-variance work measurements for this definition. We show that this time-asymmetric microscopic fluctuation theorem can be exploited for efficient free-energy estimation by developing a simple (i.e., neural-network free) and efficient adaptive time-asymmetric protocol optimization algorithm that yields ΔF estimates that are orders of magnitude lower in mean squared error than the generic linear interpolation protocol with which it is initialized.

A road map to cosmological parameter analysis with third-order shear statistics

Context. Third-order lensing statistics contain a wealth of cosmological information that is not captured by second-order statistics. However, the computational effort it takes to estimate such statistics in forthcoming stage IV surveys is prohibitively expensive. Aims. We derive and validate an efficient estimation procedure for the three-point correlation function (3PCF) of polar fields such as weak lensing shear. We then use our approach to measure the shear 3PCF and the third-order aperture mass statistics on the KiDS-1000 survey. Methods We constructed an efficient estimator for third-order shear statistics that builds on the multipole decomposition of the 3PCF. We then validated our estimator on mock ellipticity catalogs obtained from N-body simulations. Finally, we applied our estimator to the KiDS-1000 data and presented a measurement of the third-order aperture statistics in a tomographic setup. Results. Our estimator provides a speedup of a factor of ∼100–1000 compared to the state-of-the-art estimation procedures. It is also able to provide accurate measurements for squeezed and folded triangle configurations without additional computational effort. We report a significant detection of tomographic third-order aperture mass statistics in the KiDS-1000 data (S/N = 6.69). Conclusions. Our estimator will make it computationally feasible to measure third-order shear statistics in forthcoming stage IV surveys. Furthermore, it can be used to construct empirical covariance matrices for such statistics.

Multispecies initial numerical validation of an efficient algorithm prototype for auditory brainstem response hearing threshold estimation.

The auditory brainstem response (ABR) can be used to evaluate hearing sensitivity of animals. However, typical measurement protocols are time-consuming. Here, an adaptive algorithm is proposed for efficient ABR threshold estimation. The algorithm relies on the update of the predicted hearing threshold from a Gaussian process model as ABR data are collected using iteratively optimized stimuli. To validate the algorithm, ABR threshold estimation is simulated by adaptively subsampling pre-collected ABR datasets. The simulated experiment is performed on 5 datasets of mouse, budgerigar, gerbil, and guinea pig ABRs (27 ears). The datasets contain 68-106 stimuli conditions, and the adaptive algorithm is configured to terminate after 20 stimuli conditions. The algorithm threshold estimate is compared against human rater estimates who visually inspected the full waveform stacks. The algorithm threshold matches the human estimates within 10 dB, averaged over frequency, for 15 of the 27 ears while reducing the number of stimuli conditions by a factor of 3-5 compared to standard practice. The intraclass correlation coefficient is 0.81 with 95% upper and lower bounds at 0.74 and 0.86, indicating moderate to good reliability between human and algorithm threshold estimates. The results demonstrate the feasibility of a Bayesian adaptive procedure for rapid ABR threshold estimation.