Function Estimation Method Research Articles

Finite‐Time Functional Interval Observer Design for Nonlinear Discrete‐Time Switched Descriptor Systems

ABSTRACTIn this article, the finite‐time functional interval estimation method for nonlinear discrete‐time switched descriptor systems with perturbations is investigated. To deal with the nonlinear terms in the switched system, the nonlinear error system is converted into a linear parameter varying form using the reformulated Lipschitz property. Firstly, a functional observer with finite‐time gain is constructed for the nonlinear switched descriptor system. Then, based on the proposed finite‐time functional observer, the reachability analysis is applied to design a finite‐time functional interval observer. Finally, the superiority and effectiveness of the interval estimation method presented in this paper are verified through a simulation of the circuit system.

Drive force allocation control based on pump displacement optimization for hydraulic hybrid motor-assisted system

To achieve rational coordination of the front and rear axle torque of commercial vehicles equipped with hydraulic hybrid motor-assisted systems (HHMAS), and further enhance the vehicle’s fuel efficiency and traction capability. A hydraulic pump displacement control strategy based on energy-efficiency-orientation (EEO) is proposed for driving force distribution. Firstly, the efficiency is analyzed from the perspective of system energy flow. Then, considering the time-varying efficiency characteristics of the hydraulic variable pump and hydraulic motor, the stochastic dynamic optimization problem is modeled as a markov decision process, and a value function estimation method is introduced to approximate the optimal energy efficiency of the entire HHMAS. Finally, the results indicate that the EEO strategy can enhance the fuel economy of vehicles, with a fuel-saving rate of 6.3% compared to the wheel-speed-following-control (WSFC) strategy. In addition, the proposed strategy exhibits superior traction capacity and climbing ability compared to the traditional WSFC strategy.

Kidney function assessment for eligibility in clinical cancer trials – Data from the European Organisation for Research and Treatment of Cancer

PurposeThere is no consensus on how to estimate kidney function for the assessment of eligibility in clinical cancer trials. Patients and methodsWe recalculated the creatinine clearance (CrCl)/glomerular filtration rate (GFR) at baseline in a total of 1768 patients enrolled in twelve clinical trials using the Cockcroft-Gault (CG), Modification of Diet in Renal Disease (MDRD), 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI 2021) and European Kidney Function Consortium (EKFC) formulas. Patients were classified as having renal impairment (RI; CrCl/GFR <60 mL/min) or no renal impairment (NRI; CrCl/GFR ≥60 mL/min) with each of the four formulas, respectively. Furthermore, we analyzed the number of adverse events (AE) per month under study treatment using measures of central tendency, variability and regression models. ResultsUsing CG, EKFC, MDRD and CKD-EPI 2021, 152 (8 %), 140 (8 %), 110 (6 %), and 61 (4 %) patients had RI respectively. Indeed, 47 (3 %) patients had RI using all 4 formulas, while 158 (9 %) had RI by at least one but not all four methods. CG showed the broadest variability and inconsistencies with other methods. All calculation methods performed similarly for excluding patients at risk of severe AE. EKFC demonstrated superior predictive ability for excluding patients at risk of renal and urinary tract AE. ConclusionThis post hoc analysis highlights the importance of choosing accurate and representative methods for kidney function estimation in clinical cancer trials. CG should be replaced by newer methods. While CKD-EPI 2021 may maximize trial accrual, EKFC should be considered for treatment affecting kidney function.

Stock market prices and Dividends in the US: Bubbles or Long-run equilibria relationships?

This paper presents a novel approach to identifying potential bubbles in the US stock market by employing alternative time series methods based on long memory, including fractional integration and cointegration, as well as duration dependence non-parametric models. To test for duration dependence, the paper employs a unique non-parametric hazard function estimation method, using monotonic P-splines, which enables the estimation of a coherent and sophisticated Bayesian confidence interval for the function. By analyzing historical data of real US stock index from January 1871 to November 2022, the results reveal strong evidence of bubbles in the US stock market. It is worth noting that this is the first time a fractional integration approach and a novel non-parametric hazard function have been utilized to test for rational speculative bubbles in the US stock market, making this paper a significant contribution to the field.

Spectral density estimation for random processes with stationary increments

AbstractSpectral density analysis plays an important role in studying a stationary random process on a real line. In this paper, we extend this discussion for the random process with stationary increments. We investigate the properties of the method of moments structure function estimation, and propose a nonparametric spectral density function estimator. Our numerical results show that the proposed spectral density estimator performs comparable with the parametric counterpart when the underlying process is assumed to be band‐limited. Additionally, this method is applied to analyze US Housing Starts Data, where the hidden periodicities are detected, providing consistent conclusions with previous economic studies.

Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia

Brain functional network analysis has become a popular method to explore the laws of brain organization and identify biomarkers of neurological diseases. However, it is still a challenging task to construct an ideal brain network due to the limited understanding of the human brain. Existing methods often ignore the impact of temporal-lag on the results of brain network modeling, which may lead to some unreliable conclusions. To overcome this issue, we propose a novel brain functional network estimation method, which can simultaneously infer the causal mechanisms and temporal-lag values among brain regions. Specifically, our method converts the lag learning into an instantaneous effect estimation problem, and further embeds the search objectives into a deep neural network model as parameters to be learned. To verify the effectiveness of the proposed estimation method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database by comparing the proposed model with several existing methods, including correlation-based and causality-based methods. The experimental results show that our brain networks constructed by the proposed estimation method can not only achieve promising classification performance, but also exhibit some characteristics of physiological mechanisms. Our approach provides a new perspective for understanding the pathogenesis of brain diseases. The source code is released at https://github.com/NJUSTxiazw/CTLN.

Influence of chest wall conformation on reproducibility of main echocardiographic indices of left ventricular systolic function.

The possible influence of chest wall conformation, as noninvasively assessed by Modified Haller Index (MHI, the ratio of chest transverse diameter over the distance between sternum and spine), on reproducibility of both left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) has never been previously investigated. Two equal groups of healthy individuals, matched by age, sex, and cardiovascular risk factors and categorized according to MHI in those with concave-shaped chest wall (MHI>2.5) and those with normal chest shape (MHI≤2.5), who underwent transthoracic echocardiography implemented with echocardiographic deformation imaging between June 2018 and May 2019, were retrospectively analyzed. LVEF and GLS were measured twice by the two echocardiographers in a double blinded manner. Intra-class correlation coefficients (ICCs), bias and limits of agreement determined with Bland-Altman analysis were calculated for repeated measurements of both LVEF and GLS. Thirty-four healthy individuals with MHI>2.5 (54.9±6.4 years, 58.8% females) and 34 matched controls with MHI≤2.5 (52.5±8.1 years, 50% females) were separately analyzed. In comparison to MHI≤2.5 group, the MHI>2.5 group was found with significantly smaller cardiac chambers and significantly lower GLS magnitude (-15.8±2.5 vs. -22.2±1.3%, P<0.001), despite similar LVEF (61.3±6.4 vs. 61.1±3.6%, P=0.87). In the MHI>2.5 group, intra-rater and inter-rater ICCs were ≤0.5 for both LVEF and LV-GLS, whereas in the MHI≤2.5 group intra-rater and inter-rater ICCs values indicated good reliability for LVEF and excellent reliability for GLS. The greatest bias and largest limits of agreement were detected for LVEF assessment (bias ranging from -1.09 to 2.94%, with the 95% limits of agreement ranging from -13.9 to 21.3%) in individuals with MHI>2.5. On the other hand, the smallest bias and narrowest limits of agreement were obtained for GLS measurement (bias ranging from -0.26 to 0.09%, with the 95% limits of agreement ranging from -1.4 to 1.4%) in participants with normal chest wall conformation (MHI≤2.5). The test reliability of LVEF and GLS is strongly influenced by the chest wall conformation. MHI might represent an innovative approach for selecting the best echocardiographic method for LV systolic function estimation in the individual case.

New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI.

Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In the first part of this work, we present a novel theoretical derivation of subspace-based sensitivity map estimation based on a linear-predictability/structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with distinct theory that may be more intuitive for some readers. In the second part of this work, we propose and evaluate a set of computational acceleration approaches (collectively known as PISCO) that can enable substantial improvements in computation time (up to ∼100× in the examples we show) and memory for subspace-based sensitivity map estimation.

A method for accurately extracting power lines and identifying potential intrusion risks from urban laser scanning data

Timely and accurate monitoring of power line safety is a key step in ensuring urban production and daily life. The rapid development of vehicle-borne mobile laser scanning (MLS) technology has provided an effective solution for intelligent maintenance of power grids. This paper proposes a comprehensive method for accurate extraction, missing completion, and intrusion risk detection of urban power lines. Firstly, local linear geometric features are described using principal component analysis (PCA) covariance matrix, and outliers are accurately separated using a joint Bezier curve distance threshold. Secondly, the statistical inference function estimation method is employed to accurately simulate the complete topology of power lines. Finally, a fast intrusion risk detection method based on the separating axis theorem (SAT) for boundary box neighboring objects is developed. Experimental results demonstrate that our method achieves a recall rate and precision rate of 98.28 % and 98.71 %, respectively, for power line extraction. The root mean square error (RMSE) accuracy and coordinate residual of catenary line fitting are both below 0.04 m, indicating strong noise resistance and robustness. Compared to mainstream geometric extraction methods, our approach exhibits superior performance.

Dynamic Risk Assessment of Voltage Violation in Distribution Networks with Distributed Generation.

In response to the growing demand for economic and social development, there has been a significant increase in the integration of distributed generation (DG) into distribution networks. This paper proposes a dynamic risk assessment method for voltage violations in distribution networks with DG. Firstly, considering the characteristics of random variables such as load and DG, a probability density function estimation method based on boundary kernel density estimation is proposed. This method accurately models the probability of random variables under different time and external environmental conditions, such as wind speed and global horizontal radiation. Secondly, to address the issue of correlated DG in the same region, an independent transformation method based on the Rosenblatt inverse transform is proposed, which enhances the accuracy of probabilistic load flow. Thirdly, a voltage violation severity index based on the utility function is proposed. This index, in combination with probabilistic load flow results, facilitates the quantitative assessment of voltage violation risks. Finally, the accuracy of the proposed method is verified on the IEEE-33 system.

Improved prescribed performance fuzzy control for nonlinear multiagent systems with actuator faults and dual input saturations

This paper conducts the distributed adaptive prescribed performance fuzzy control design for nonlinear multiagent systems (MASs) subjected to actuator faults and dual input saturations. A unified smooth characterizing function is constructed to approximate a practical input saturations model. Then, with the aid of the mean-value theorem, Nussbaum-type function and boundary estimation method, the distributed saturated fault-tolerant controllers are devised to deal with actuator faults and dual input saturations. In order to achieve excellent tracking performance of followers, an improved prescribed performance control method with a tight constraint region is proposed. Different from most existing control methods, the proposed method not only ensures that the tracking error rapidly converges to the specified area within a predetermined time, but also achieves transient performance without obvious overshoot. Stability analysis proves that the consensus tracking error converges to an arbitrary small specified area within a preset time and all signals of the overall controlled systems are semi-globally uniformly ultimately bounded (SUUB). Eventually, the comprehensive simulation results validate the feasibility and superiority of the control strategy.

Image defocus deblurring method based on gradient difference of boundary neighborhood

BackgroundFor static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge. MethodsTo this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior. ResultsExperimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods. ConclusionsExperimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

Extropy based inaccuracy measure in order statistics

In this paper, we provide a measure based on inaccuracy between distributions of the ith order statistic and the parent random variable. This measure characterizes the distribution function of parent random variable uniquely. We demonstrate that the extropy of the parent random variable is the average of the accuracy measure. It is also shown that the measure of inaccuracy defined is invariant under scale but not under location transformation. Nonparametric estimators for the proposed measures are also obtained. A Monte Carlo simulation study is performed to verify the performance of the suggested estimators. Simulation results show that the estimator based on the reflection boundary technique for probability density function estimation and the empirical method for cumulative distribution function estimation has the best performance among estimators. Also, a real dataset is considered to show an application of the proposed estimators on model selection.

Empirical Transfer Function Estimation With Differential Filtering and Its Application to Fine Positioning Control of Galvano Scanner

Fast and precise point-to-point (PTP) positioning control is a critical technology for improving the throughput and product quality of industrial machines. This study aims to investigate the frequency response function (FRF) estimation method with differential filtering (ETFE-Diff: empirical transfer function estimation with differential filtering) that uses the input and output signals of PTP motion during processing operation and to apply ETFE-Diff to a feedforward (FF) compensation adjustment. In this study, the principle of ETFE-Diff for FRF estimation was clarified, compared to the existing FRF estimation methods, i.e., empirical transfer function estimation and local rational modeling. Moreover, the effectiveness of ETFE-Diff for FF compensation adjustment in improving positioning accuracy of a galvano scanner as an industrial servo system was experimentally demonstrated.

Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging.

An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient's dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system.

Reliability of Ensemble Climatological Forecasts

AbstractEnsemble climatological forecasts play a critical part in benchmarking the predictive performance of hydroclimatic forecasts. Accounting for the skewness and censoring characteristics of hydroclimatic variables, ensemble climatological forecasts can be generated by the log, Box‐Cox and log‐sinh transformations, by the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and by the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. This paper is concentrated on the reliability of the 12 types of ensemble climatological forecasts. Specifically, mathematical formulations are presented and large‐sample tests are devised to verify the forecast reliability for the Multi‐Source Weighted‐Ensemble Precipitation version 2 across the globe. Climatological forecasts of monthly precipitation over 18,425 grid cells are generated for 30 years under leave‐one‐year‐out cross validation, leading to 6,633,000 (12 × 18425 × 30) sets of ensemble climatological forecasts. The results point out that the reliability of climatological forecasts considerably varies across the 12 methods, particularly in regions with high hydroclimatic variability. One observation is that climatological forecasts tend to deviate from the distributions of observations when there is inadequate flexibility to fit precipitation data. Another observation is that ensemble spreads can be overly wide when there exist overfits of sample‐specific noises in cross validation. Through the tests of global precipitation, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Overall, the investigations can serve as a guidance on the uses of transformations, distributions and non‐parametric methods in generating climatological forecasts.

Experimental modal analysis of a single-link flexible robotic manipulator with curved geometry using applied system identification methods

In this paper, the principal time-domain system identification methods are studied and used to carry out the experimental modal analysis of a single-link flexible manipulator with curved geometry in a comparative fashion. The identification process carried out in this study is aimed at deriving first-order state-space dynamical models and second-order configuration-space dynamical models of the single-link flexible manipulator by using an array of fundamental system identification procedures, which includes the ARX (AutoRegressive eXogenous method), SSEST (State-Space ESTimation method), N4SID (Numerical Algorithm for Subspace State-Space System Identification), ERA/OKID (Eigensystem Realization Algorithm combined with the Observer/Kalman Filter Identification method), and TFEST (Transfer Function ESTimation method) methods. The main goal of this paper is, therefore, to perform a comparative study between the most fundamental system identification procedures applied to the numerical and experimental analysis of the structural vibrations of a flexible manipulator having a curved geometric shape. First, this paper poses the theoretical foundations for the development of the numerical study and the experimental testing to be performed for the case study. Subsequently, the flexible manipulator analyzed in this work is modeled with various levels of complexity, which range from a simple lumped parameter model to a linear finite element model developed by integrating the interconnections between the SOLIDWORKS and ANSYS software. By doing so, the computational procedures applied to the time-domain experimental measurements of the dynamic behavior of the flexible manipulator allow for reconstructing first-order and second-order mechanical models of the structural system of interest in the MATLAB simulation environment. The numerical results and the experimental tests reported in this investigation demonstrate the effectiveness of all the time-domain system identification approaches considered in the paper.

Spatiotemporal divergence and convergence test of green total factor productivity of grain in China: based on the dual perspective of carbon emissions and surface source pollution.

The topic of grain production and quality has always been a fundamental issue for the country's livelihood. With the purpose of contributing to high-quality grain development and ensuring national food security, this paper explores the spatial and temporal evolution, regional differences and convergence of grain green total factor productivity (GTFP) in the main grain-producing regions of China from the dual perspectives of carbon emissions and surface pollution with the help of EBM-GML model, kernel density function estimation and convergence methods. The results show that (1) Grain GTFP has generally shown a positive growth trend, although there exists obvious spatial differences. (2) In terms of decomposition indices, technological progress is the source of the increase in grain GTFP. (3) There are σ convergence, absolute β convergence and conditional β convergence in the main producing region as well as in the Yellow River basin and the Yangtze River basin; there are only absolute β convergence and conditional β convergence in the Songhua River basin. Grain GTFP has a single high-efficiency convergence point, and the grain GTFP is enhancing year by year in each province, and the inter-provincial gap is shrinking.

Estimating function method for nonnegative autoregressive models

A stationary sequence of nonnegative random variables generated by autoregressive (AR) models may be used to describe the inter‐arrival times between events in counting processes. Even though, several such models are available in the literature, there is no unified approach to estimate their parameters. In this paper, we propose a class of combined estimating function method to estimate the model parameters of AR models with gamma marginals. The proposed method is compared with other estimation procedures and are illustrated by simulation and data analysis.

Counting animals in aerial images with a density map estimation model.

Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual postprocessing has been used extensively; however, volumes of such data are increasing, necessitating some level of automation, either for complete counting, or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as Pygoscelis penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster-RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations and demonstrably improve monitoring efforts from aerial imagery.