Robust Estimation Approach Research Articles

Post-Ventricular Premature Contraction Phase Correction Improves the Predictive Value of Average T-Wave Alternans in Ambulatory ECG Recordings.

We proposed and evaluated a method for correcting possible phase shifts provoked by the presence of ventricular premature contractions (VPCs) for a better assessment of T-wave alternans (TWA). Methods: First, we synthesized ECG signals with artificial TWA in the presence of different noise sources. Then, we assessed the prognostic value for sudden cardiac death (SCD) of the long-term average of TWA amplitude (the index of average alternans, ) in ambulatory ECG signals from congestive heart failure (CHF) and evaluated whether it is sensitive to the presence of VPCs. The inclusion of the phase correction after VPC in the processing always improved estimation accuracy of the under different noisy conditions and regardless of the number of the VPCs included in the sequence. It also presented a positive impact on the prognostic value of with increased hazard ratios (from 17% to 29%, depending of the scenario) in comparison to the noninclusion of this step. The proposed methodology for estimation, which corrects for the possible phase reversal on TWA after the presence of VPCs, represents a robust TWA estimation approach with a significant impact on the prognostic value of for SCD stratification in CHF patients. An accurate TWA estimation has a potential direct clinical impact on noninvasive SCD stratification, allowing better identification of patients at higher risk and helping clinicians in adopting the most appropriate therapeutic strategy.

Robust optimisation‐based state estimation considering parameter errors for systems observed by phasor measurement units

This study presents a new robust state estimation (SE) approach for power systems that are monitored by synchronised phasor measurements. The proposed robust SE utilises the least absolute value (LAV) of residuals to determine the system state. Although conventional deterministic LAV-based SE is robust against bad measurements, it is vulnerable with respect to the network parameters’ errors. In other words, the conventional LAV-SE method is not robust against the network parameters’ errors and these errors can make the SE results inaccurate. The proposed robust SE aims at solving this problem of the conventional LAV-SE. Robust optimisation, strong duality theorem, and Big-M linearisation technique have been used to construct the proposed SE approach, which is robust against the network parameters’ errors. Additionally, the proposed robust SE is finally formulated as a tractable mixed-integer linear programming optimisation problem. Comprehensive numerical experiments and a probabilistic evaluation approach are presented to evaluate the effectiveness of the proposed robust SE compared to the conventional deterministic SE in the presence of the uncertainty source of network parameters’ errors.

The Propagation of Local Undamped Motion (PLUM) Method: A Simple and Robust Seismic Wavefield Estimation Approach for Earthquake Early Warning

The Propagation of Local Undamped Motion (PLUM) Method: A Simple and Robust Seismic Wavefield Estimation Approach for Earthquake Early Warning

Robust Joint Synchronization and Channel Estimation Approach for Frequency-Selective Environments

Supporting spontaneous low-latency machine-type communications requires fast synchronization and channel estimation at the receiver. The problems of synchronizing the received frame and estimating the channel coefficients are often addressed separately with the later one relying on accurate timing acquisition. While these conventional approaches can be adequate in flat fading environments, time dispersive channels can have a negative impact on both tasks and severely degrade the performance of the receiver. To circumvent this large degradation, in this paper, we consider the use of a sparse-based reconstruction approach for joint timing synchronization and channel estimation by formulating the problem in a form that is closely related to compressive sensing framework. Using modified versions of well-known sparse reconstruction techniques, which can take into account the additional signal structure in addition to sparsity, it is shown through numerical simulations that, even with short training sequences, excellent timing synchronization and channel estimation performance can be achieved, both in single user and multiuser scenarios.

Linear Phasor Estimator Assisted Dynamic State Estimation

Measurements provided by the phasor measurement units (PMUs) in a power network can be highly erroneous. Furthermore, some generating units may not have a local PMU, therefore it may not be possible to obtain high accurate and reliable results based on the previously studied dynamic state estimation approaches, which rely on the raw measurements provided by the PMUs. In order to address these issues, this paper presents a robust distributed dynamic state estimation approach that not only is robust against bad data, but also makes it possible to obtain the dynamic state estimation results for the generators without a local PMU. The proposed approach also accounts for expected delays in receiving estimated measurements by using a multi-step ahead state predictor to correct for delayed inputs. This procedure can also be useful for the short-term transient stability predictions.

On the conditional conservatism measure: A robust estimation approach

AbstractRecent research, due to Patatoukas and Thomas (2011) and Ball, Kothari, and Nikolaev (2013), focuses on Basu's (1997) conditional conservatism measure and the existence of a denominator effect – whether the difference between the earnings‐return coefficients of bad and good news firms (‘the Basu coefficient’) is only due to the beginning‐of‐year price deflator. We address this issue head‐on by applying the Theil‐Sen (TS) estimation method, which obtains the same coefficient estimate regardless of the chosen deflator and is robust to outliers. Results show the following: (i) the Basu coefficient remains positive using TS; (ii) the Basu coefficients using TS are similar to those using OLS without scaling but much smaller than shown by scaled OLS; (iii) the scaled OLS estimates appear to be influenced by a few outliers; and (iv) OLS estimates are more volatile due to estimation error. In sum, the denominator effect does not overturn Basu's hypothesis but the magnitude and variation of the Basu coefficient is much smaller than traditional results show.

Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D model registration

Registering a 3D facial model onto a 2D image is important for constructing pixel-wise correspondences between different facial images. The registration is based on a 3 $$\times $$ 4 dimensional projection matrix, which is obtained from pose estimation. Conventional pose estimation approaches employ facial landmarks to determine the coefficients inside the projection matrix and are sensitive to missing or incorrect landmarks. In this paper, a landmark-free pose estimation method is presented. The method can be used to estimate the matrix when facial landmarks are not available. Experimental results show that the proposed method outperforms several landmark-free pose estimation methods and achieves competitive accuracy in terms of estimating pose parameters. The method is also demonstrated to be effective as part of a 3D-aided face recognition pipeline (UR2D), whose rank-1 identification rate is competitive to the methods that use landmarks to estimate head pose.

Semiparametric analysis of longitudinal data with informative observation times and censoring times

ABSTRACTWe focus on regression analysis of irregularly observed longitudinal data which often occur in medical follow-up studies and observational investigations. The model for such data involves two processes: a longitudinal response process of interest and an observation process controlling observation times. Restrictive models and questionable assumptions, such as Poisson assumption and independent censoring time assumption, were posed in previous works for analysing longitudinal data. In this paper, we propose a more general model together with a robust estimation approach for longitudinal data with informative observation times and censoring times, and the asymptotic normalities of the proposed estimators are established. Both simulation studies and real data application indicate that the proposed method is promising.

Robust Empirical Best Small Area Finite Population Mean Estimation Using a Mixture Model

We propose a new robust empirical best estimation approach to estimate small area finite population means that are relatively insensitive to a model misspecification or to the presence of outliers. This important robustness property is achieved by replacing the standard normality assumption of the sampling errors in a nested-error regression (NER) model by a scale mixture of two normal distributions with different variances. We present a formal statistical test to identify if a small area is an outlier and provide an efficient new computing algorithm to implement our procedure. We examine the finite sample robustness properties of our proposed method using a Monte Carlo simulation and compare the proposed method with alternative existing methods in a study using data from the Current Employment Statistics (CES) survey conducted by the US Bureau of Labor Statistics (BLS).

An efficient and hybrid timing offset estimation approach for universal-filtered multi-carrier based systems over multipath Rayleigh fading channel

For next generation (i.e., 5G) wireless communication, universal-filtered multi-carrier (UFMC) is a novel multi-carrier modulation technique, which is suitable for short packet and low latency transmissions. Although UFMC has lower sensitivity to synchronization errors compared with orthogonal frequency division multiplexing (OFDM), synchronization is still an important and open problem for UFMC-based systems. In this paper, in order to efficiently address the timing offset (TO) estimation issue of UFMC-based systems over multipath fading channels, a new preamble structure containing the pseudo-random noise (PN) sequences is devised in this paper. And that a robust and hybrid TO estimation approach is also proposed. This approach primarily consists of two steps. According to the devised preamble pattern, an improved timing metric is firstly developed for estimating the symbol TO coarsely. Moreover, a fine timing adjustable range is also offered. Secondly, the accurate estimation of the residual symbol TO is achieved within this adjustable range by utilizing both the threshold criterion and the cross-correlation operation between received signals and the local PN sequence. Finally, numerical simulations and comparisons are presented to confirm the practicability of the proposed algorithm under the condition of Rayleigh fading channels.

Robust switching signal estimation for a class of uncertain nonlinear switched systems

ABSTRACTIn this paper, a robust switching signal estimation approach is presented for a class of uncertain nonlinear switched systems using a sliding mode robust observer on the continuous state vector of system. In this approach, the estimation of switching signal is achieved at a bounded and arbitrary small time in spite of un-modelled uncertainties. The developed estimation technique can also be employed on mode-dependent control methods to reach a practical controller for nonlinear switched systems with the inaccessible switching signal. The simulation results illustrate the capability of proposed method.

Case Mix Planning using The Technique for Order of Preference by Similarity to Ideal Solution and Robust Estimation: a Case Study

Management of surgery units and operating room (OR) play key roles in optimizing the utilization of hospitals. On this line Case Mix Planning (CMP) is normally applied to long term planning of OR. This refers to allocating OR time to each patient’s group. In this paper a mathematical model is applied to optimize the allocation of OR time among surgical groups. In addition, another technique is applied to provide an Order of Preference by Similarity to Ideal Solution (TOPSIS) considering different hospital performance measures. Furthermore, robust estimation approach is used to estimate the models' parameters using real data. The proposed model is solved using GAMS software. The results of the study performed in this paper reveal that the proposed methods results in an increase in total hospital operated “value of patients” by 21.5%. This value is defined according to hospital priority and includes moral and ethical considerations. In addition, for each resource, a sensitivity analysis of the findings to the changes is conducted. The results of the sensitivity analysis indicated that the value of the objective function significantly increased via reallocating OR time to surgical groups and/or enhancing the OR facilities to support more surgical specialties.

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

The effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today’s typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence.

Output-Only Damage Identification Using Enhanced Structural Characteristic Deflection Shapes and Adaptive Gapped Smoothing Method

Structural characteristic deflection shapes (CDSs) such as mode shapes which contain spatial knowledge of structures are highly sensitive for damage detection and localization. Nevertheless, CDSs are vulnerable to measurement noise, which degrades the accuracy of damage identification. In order to enhance CDS-based damage identification, contributions are made in three aspects. First, a robust CDS estimation approach is proposed based on common principal component analysis, which estimates the CDSs as the common diagonalizer of a set of covariance matrices by joint approximation diagonalization (JAD). Second, an adaptive gapped smoothing method (GSM) is proposed and validated to be more accurate than the traditional GSM. Third, a new damage identification index capable of localizing damage and indicating relative damage severity is defined without requiring information of healthy structures. Finally, numerical and experimental examples of beams and a frame with cracks are studied to demonstrate the advantages of the proposed damage identification method in terms of noise robustness and accuracy.

Risk of colorectal cancer among immigrants to Ontario, Canada

BackgroundThe risk of colorectal cancer (CRC) varies around the world and between females and males. We aimed to compare the risk of CRC among immigrants to Ontario, Canada, to its general population.MethodsWe used an exposure-control matched design. We identified persons in the Immigration, Refugees and Citizenship Canada Permanent Resident Database with first eligibility for the Ontario Health Insurance Plan between July 1, 1991 and June 30, 2008 at age 40 years or older, and matched five controls by year of birth and sex on the immigrant’s first eligibility date. We identified CRC from the Ontario Cancer Registry between the index date and December 31, 2014. All analyses were stratified by sex. We calculated crude and relative rates of CRC. We estimated risk of CRC over time by the Kaplan-Meier method and compared immigrants to controls in age and sex stratified strata using log-rank tests. We modeled the hazard of CRC using Cox proportional hazards regression, accounting for within-cluster correlation by a robust sandwich variance estimation approach, and assessed an interaction with time since eligibility.ResultsAmong females, 1877 cases of CRC were observed among 209,843 immigrants, and 16,517 cases among 1,049,215 controls; the crude relative rate among female immigrants was 0.623. Among males, 1956 cases of CRC were observed among 191,792 immigrants and 18,329 cases among 958,960 controls; the crude relative rate among male immigrants was 0.582.. Comparing immigrants to controls in all age and sex stratified strata, the log rank test p < 0.0001 except for females aged > = 75 years at index, where p = 0.01. The age-adjusted hazard ratio (HR) for CRC among female immigrants was 0.63 (95% CI 0.59, 0.67) during the first 10 years, and 0.66 (95% CI 0.59, 0.74) thereafter. Among male immigrants the age-adjusted HR = 0.55 (95% CI 0.52, 0.59) during the first 10 years and increased to 0.63 (95% CI 0.57, 0.71) thereafter. The adjusted HR > = 1 only among immigrants born in Europe and Central Asia.ConclusionsThe risk of CRC among immigrants to Ontario relative to controls varies by origin and over time since immigration.

LTS-Based Robust Hybrid SE Integrating Correlation

The paper introduces a robust approach for hybrid power system static state estimation (SE) utilizing phasor measurement units (PMUs). The proposed PMU-based SE exploits the time and space correlation in the PMU measurements in order to provide rejection of bad measurements and improve the SE cyber security and accuracy. The SE robustness is introduced in two steps. The first step is to robustly fit multichannel or vector autoregressive models to adjacent PMUs. The second step replaces the classical weighted least squares algorithm with a robust regression estimator. The robustness is introduced by exploiting the least trimmed squares estimator. The effectiveness of the proposed robust PMU-based SE is assessed based on Monte Carlo simulations and compared with other existing robust SE methods. A theoretical analysis is provided to explain the improvement by higher PMU sampling rates and longer periods of SE.

Takagi–Sugeno Fuzzy Model Based Fault Estimation and Signal Compensation With Application to Wind Turbines

In response to the high demand of the operation reliability by implementing real-time monitoring and system health management, a robust fault estimation and fault-tolerant control approach is proposed for Takagi–Sugeno fuzzy systems in this study, by integrating the augmented system method, unknown input fuzzy observer design, linear matrix inequality optimization, and signal compensation techniques. Specifically, a fuzzy augmented system method is used to construct an augmented plant with the concerned faults and system states being the augmented states. An unknown input fuzzy observer technique is thus utilized to estimate the augmented states and decouple unknown inputs that can be decoupled. A linear matrix inequality approach is further addressed to ensure the global stability of the estimation error dynamics and attenuate the influences from the unknown inputs that cannot be decoupled. As a result, the robust estimates of the concerned faults and system states can be obtained simultaneously. Based on the fault estimates, a signal compensation scheme is developed to remove the effects of the faults on the system dynamics and outputs, leading to a stable dynamic satisfying the expected performance. Finally, the effectiveness of the proposed Takagi–Sugeno model based fault estimation and signal compensation algorithms is demonstrated by a case study on a 4.8-MW wind turbine benchmark system.

Thermal decomposition of rape straw: Pyrolysis modeling and kinetic study via particle swarm optimization

The aim of this work is to implement an efficient and robust optimization approach for parameter estimation of pyrolysis kinetic model to study the pyrolysis of a typical agricultural residue. Thermogravimetric profiles of rape straw were obtained at a wide heating range of 10, 20, 30 and 40°C/min in an inert atmosphere of nitrogen and the thermal decomposition process was studied in detail. First, three different kinetic methods (Friedman, KAS and OFW) were applied for activation energy determination. The activation energies ranged from 191.16kJ/mol to 264.31kJ/mol. Then, the reaction mechanism and pre-exponential factor were analyzed by using the generalized master-plots method. It is found that at conversion level higher than 0.4 the rape straw decomposition was governed by 3-D diffusion model and it tended to high order reaction model at lower conversion. Finally, a multi-component parallel reactions scheme incorporated into the Particle Swarm Optimization (PSO) technique was presented to determine kinetic parameters. The kinetic triplets and other stoichiometric parameters were optimized against profiles for heating rates of 20 and 30°C/min. The optimized activation energy value is 156.41kJ/mol, 211.26kJ/mol and 57.84kJ/mol for hemicellulose, cellulose and lignin, respectively. These results are in conformity with results reported in previous literatures. Meanwhile, the obtained pre-exponential factors values also lie in the reasonable range of lnAi=25.32–39.14ln/s according to intrinsic transition-state theory. Furthermore, cross-validated results show that the optimized parameters can be applied not only to conditions where they were obtained, but also to conditions beyond (10 and 40°C/min), indicating that the derived results are appropriate to simulate and predict rape straw pyrolysis under various heating rates, which will be useful for further design and sizing of biomass thermochemical process reactors.

State of Energy Estimation of Lithium Titanate Battery for Rail Transit Application

Abstract Using Li4Ti5O12 as its anode instead of graphite, the lithium titanate battery has the inherent advantages in rate characteristics, cycle life and chemical stability, which is more suitable for rail transit application. As an indicator of battery available energy, state of energy (SOE) is of great importance to estimate. However, most of the models utilized in the literature concerning state estimation of lithium battery become problematic for outlining the characteristics of lithium titanate battery. In this work, a robust approach for SOE estimation of lithium titanate battery is presented after considering the influence of temperature based on the proposed BV equation-based model, showing the capability of estimating the terminal voltage and SOE precisely at different temperatures and excitations.

Heritability of Heart Rate Response to Arousals in Twins.

To determine if the large and highly reproducible interindividual differences in arousal intensity and heart rate response to arousal (ΔHR) during non-REM sleep are heritable. Polysomnograms of 55 monozygotic (14 male and 41 female pairs) and 36 dizygotic (15 male and 21 female pairs) same-sex twin pairs were analyzed. Arousals were scored using the 2012 American Academy of Sleep Medicine criteria. Arousal intensity was scaled (between 0 and 9) using an automatic algorithm based on the change in electroencephalogram time and frequency characteristics. The ΔHR was determined at each arousal. We calculated average arousal duration, average arousal intensity, average overall ΔHR, average ΔHR at a given arousal intensity, slope of ΔHR per arousal intensity, and arousal intensity threshold of ΔHR. The intraclass correlations among monozygotic and dizygotic twin pairs were 0.663 and 0.146, respectively, for average arousal intensity, and 0.449 and 0, respectively, for arousal intensity threshold of ΔHR controlling for age, sex, and race. These values imply large broad sense heritability (H2) for these traits. This evidence was confirmed by a robust maximum likelihood-based variance components estimation approach, with an additive genetic heritability of 0.64 (95% confidence interval: 0.48 to 0.80) for average arousal intensity and a combined additive and dominance genetic heritability and of 0.46 (0.25 to 0.68) for arousal intensity threshold of ΔHR. Results also suggested significant additive genetic effects for average arousal duration, ΔHR at arousal intensity scale 4 and the overall average ΔHR. Genetic factors explain a significant fraction of the phenotypic variability for average arousal intensity and arousal intensity threshold of ΔHR. Results suggest that the duration of arousals and specific average ΔHR values may also be heritable traits. NCT02827461.