Proposed Estimation Method Research Articles

Design and implementation of a state estimator in a real distribution network

AbstractControlling and operating modern distribution networks are challenging tasks. The implementation of monitoring systems and the development of tools are helping to address this challenge. State estimators are one of the most promising tools for this purpose. These are used to reconstruct the current state of a system based on measurement outputs, which can then be used to design controllers that can adjust the inputs to achieve the desired output. This article deals with the problem of estimating the state of a real‐world distribution network. A combination of conventional supervisory control and data acquisition is used to estimate the state, together with a synchrophasor monitoring system. The proposed method uses the Fortescue transformation to separate the three‐phase coupled equations into three independent modal measurement equations. Single‐phase estimators based on the extended Kalman filter are used to solve three sub‐problems associated with the sequence reference frame, which correctly capture the transient and steady‐state behaviour of the distribution network. The proposed estimation method is easy to implement and robust to measure model noise and uncertainties. Furthermore, an already‐established distribution network tests the estimation scheme under various conditions.

Framework to Estimate Operating Intention for a Leader–Follower Robot

The teleoperation feature of a leader–follower robot is used to operate the robot in a working environment that is dangerous to the operator. This paper proposes a method for estimating the intended operation of the operator of the leader–follower robot and generating behaviors for the follower robot. By generating partially autonomous robot behavior, our proposed method decreases the burden on the operator and realizes a safe controller that is robust against misoperation or disturbances. Owing to the impact of misoperation on estimation and the potential for unintended movement of the follower robot, our proposed estimation method relies on historical values of intended operation. The proposed method was verified through simulation using real operation inputs to the leader system based on practical scenarios, including misoperation.

Hybrid optimization based on the analytical approach and the particle swarm optimization algorithm (Ana-PSO) for the extraction of single and double diode models parameters

Mathematical Modelling is one of the approved strategies for simulating the performances of photovoltaic devices. Each proposed model contains several parameters; and the accurate determination of these parameters remains a significant challenge for photovoltaic researchers. To this end, in this article, we propose a hybrid analytical/metaheuristic technique for extracting the parameters of single and double diode models. The proposed method estimates some parameters analytically. The other parameters are determined using the particle swarm optimization (PSO) algorithm. This proposed approach is validated by applying it to different types of PV cells and modules. The quality of the obtained solutions is evaluated by comparison with the results proposed by other optimization algorithms. The minimum RMSE values found (7.8093×10−4 for R.T.C France, 3.1491×10−4 for PVM 752, 2.1547×10−3 for Photo-watt-PWP 201, 1.7920×10−3 for STM6-40/36 and 1.5330×10−2 for STP6-120/36) demonstrate the high reliability of the approach used compared to other approaches and validate the performance of our approach. Furthermore, the analysis of convergence, speed, and robustness reveals the superiority of our proposed algorithm over the standard PSO algorithm.

An improved wide-range, low-complexity, fully-parallel digital background timing mismatch calibration method for dual-channel TIADC based on perfect reconstruction filter bank

This paper presents a fully-digital background calibration method for calibrating timing mismatch in dual-channel time-interleaved analog-to-digital converter (TIADC). The proposed method estimates timing mismatch by performing correlation calculations on the outputs of sub-channels and corrects this mismatch using the improved perfect reconstruction filter bank (PRFB). The PRFB, which is improved by employing a polynomial fitting method, offers superior performance with fewer filter taps and extends the timing mismatch correction capability to almost the entire sampling clock cycle. The improved PRFB uses only one twentieth of the taps used in the original PRFB, and its dual-channel FIR filters have anti-symmetric taps. To enhance the real-time calibration capability of the system, this paper gives a fully-parallel hardware structure for calibrating offset, gain, bandwidth, and timing mismatches. The effectiveness of the proposed hardware structure is verified through a 10-bit 3GS/s dual-channel TIADC test platform. Simulation and measurement results show that the proposed calibration method significantly improves the signal-to-noise-and-distortion (SNDR) and spurious-free dynamic range (SFDR) of both single-tone and multi-tone input signals, which makes the proposed method a viable solution for practical TIADC applications.

Marshall–Olkin Weibull–Burr XII distribution with application to physics data

The focus of this study is a new lifetime distribution with five parameters created by combining the Weibull–Burr XII model and the Marshall–Olkin-G family. The newly suggested model is known as the Marshall–Olkin Weibull–Burr XII (MOWBXII) distribution. The new distribution has the benefit of being able to model different types of data, and it is useful in reliability and lifespan statistics. Several current distributions as well as new distributions are included in the MOWBXII distribution. The MOWBXII density function is represented as a linear combination of Burr XII densities. Some statistical properties of the MOWBXII distribution are discussed. Various techniques for estimating the model’s parameters are used. The proposed estimation methods are weighted least squares, maximum likelihood, least squares, and maximum product of spacing methods. The effectiveness of different estimates is evaluated in terms of relative bias and mean squared error of the simulation study. Practical illustrations of the MOWBXII distribution are demonstrated using two real datasets. Furthermore, it is shown that the proposed distribution fits well, and this is claimed by comparing with Burr XII-based distributions and some other distributions by means of some measures of goodness-of-fit.

Rotating-Coordinate-Based Mutual Inductance Estimation for Drone In-Flight Wireless Charging Systems

This article proposes a fast and real-time mutual inductance estimation method for drone in-flight wireless charging systems against the continuous varying of relative position between coupling coils. In previous studies, many efforts have been made to achieve accurate mutual inductance estimation when fixed misalignment occurs in coupling coils. However, the critical challenge for in-flight wireless charging systems is to track variations of mutual inductance caused by continuously changed misalignments between coupling coils, which is rarely explored in previous studies. Accordingly, this article proposes a rotating-coordinate-based mutual inductance estimation method to track the variation of the mutual inductance timely. By applying the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> dynamic model, the proposed estimation method is effectively enhanced in terms of rapidity and implemented simply as well as eliminating wireless communication links. With the salient features of accuracy, rapidity, and real time, it can be an ideal solution against the continuous disturbance of mutual inductance, especially for drone in-flight wireless charging systems. In this article, both simulated and experimental results are given to verify the validity of the proposed rotating-coordinate-based estimation method, wherein the relative error of the mutual inductance estimation is within 1%, and the estimation rapidity is less than 4 ms.

Disturbance rejection control of shaking table based on adaptive-model-based state estimator

Real-time hybrid simulation (RTHS) is an important method for the seismic testing of building structures. This method requires high control accuracy for the waveform reproduction of a shaking table. An active disturbance rejection control (ADRC) method is introduced to control the shaking table in this research. In view of the difficulty of parameters setting caused by the large deviation of the extended state observer (ESO) to the state estimation of the shaking table, a model-based adaptive state estimator (AMSE) is designed that uses the model information to estimate the state, and introduces an adaptive strategy to dynamically compensate for the amplitude and phase deviation of the model. The total disturbance is observed by the ESO. Additionally, an AMSE-based ADRC is proposed to achieve high-precision seismic waveform reproduction. The separation principle of the designed system is proved by the Routh–Hurwitz criterion, and the pole assignment bandwidth method is proposed to improve the control performance of the system by flexibly assigning the controller poles. Finally, the shaking table experiments are designed for different specimens. These experiments verify the advantages of the proposed method for state estimation and parameter setting and can effectively improve the stability and control accuracy of shaking table control.

Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example.

The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.

FedPos: A Federated Transfer Learning Framework for CSI-Based Wi-Fi Indoor Positioning

This article proposes FedPos, a federated transfer learning framework together with a novel position estimation method for Wi-Fi indoor positioning. Compared with traditional machine learning with privacy leakage problems and the cloud model trained through federated learning (FL) fails in personalization, the FedPos framework aggregates nonclassification layer parameters of models trained from different environments to build a robust and versatile encoder on the cloud server while preserving user privacy. The global cloud encoder can aggregate different classifiers and then construct personalized models for new users through fine-tuning. The proposed framework can be updated incrementally and is highly extensible. Specifically, we exploit channel state information (CSI) as the positioning feature and assess the transferability of a lightweight convolutional neural network (CNN) in unfamiliar environments. We evaluate the performance of our proposed framework and position estimation method in different indoor environments. Our experimental results indicate that the proposed framework can achieve a mean localization error of 42.18 cm in a 64-position living room. They also confirm that FedPos can achieve a 5.22% average localization performance boost and reduce the average model training time by about 34.78% when compared with normal training. By reusing part of the feature extractor layers that are trained from other environments, at least 65% of training data can be saved to achieve a localization performance that is similar to the base model. Overall, the proposed position estimation method can effectively improve localization accuracy as compared with seven other existing CSI-based methods.

Automated estimation of mitral annular plane systolic excursion by artificial intelligence from 3D ultrasound recordings

Perioperative monitoring of cardiac function is beneficial for early detection of cardiovascular complications. The standard of care for cardiac monitoring performed by trained cardiologists and anesthesiologists involves a manual and qualitative evaluation of ultrasound imaging, which is a time-demanding and resource-intensive process with intraobserver- and interobserver variability. In practice, such measures can only be performed a limited number of times during the intervention. To overcome these difficulties, this study presents a robust method for automatic and quantitative monitoring of cardiac function based on 3D transesophageal echocardiography (TEE) B-mode ultrasound recordings of the left ventricle (LV). Such an assessment obtains consistent measurements and can produce a near real-time evaluation of ultrasound imagery. Hence, the presented method is time-saving and results in increased accessibility. The mitral annular plane systolic excursion (MAPSE), characterizing global LV function, is estimated by landmark detection and cardiac view classification of two-dimensional images extracted along the long-axis of the ultrasound volume. MAPSE estimation directly from 3D TEE recordings is beneficial since it removes the need for manual acquisition of cardiac views, hence decreasing the need for interference by physicians. Two convolutional neural networks (CNNs) were trained and tested on acquired ultrasound data of 107 patients, and MAPSE estimates were compared to clinically obtained references in a blinded study including 31 patients. The proposed method for automatic MAPSE estimation had low bias and low variability in comparison to clinical reference measures. The method accomplished a mean difference for MAPSE estimates of (−0.16±1.06) mm. Thus, the results did not show significant systematic errors. The obtained bias and variance of the method were comparable to inter-observer variability of clinically obtained MAPSE measures on 2D TTE echocardiography. The novel pipeline proposed in this study has the potential to enhance cardiac monitoring in perioperative- and intensive care settings.

Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

Distributed drive electric vehicles (DDEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size, and inertial parameter has seldom been tackled and systematically estimated. This paper presents a dual central difference Kalman filter (DCDKF) where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters, such as vehicle sideslip angle, vehicle mass, vehicle yaw moment of inertia, the distance from the front axle to centre of gravity. The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs. The four-wheel nonlinear vehicle dynamics estimation model considering payload variations, Pacejka tire model, wheel and motor dynamics model is developed, the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory. To address system nonlinearities in vehicle dynamics estimation, the DCDKF and dual extended Kalman filter (DEKF) are also investigated and compared. Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-Carsim®. The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions. This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

Spatially correlated channel estimation for RIS‐assisted MIMO systems with correlated gaussian perturbation

AbstractThis paper studies the problem of spatially correlated channel estimation for reconfigurable intelligent surface (RIS)‐assisted multiple‐input multiple‐output (MIMO) systems with arbitrarily correlated Gaussian perturbation. In particular, composite RIS channel estimation is first explored based on minimum mean square error (MMSE) method, and optimal training sequence design of pilots with MSE minimization. Applying the Kronecker‐structured model, correlated composite RIS‐related channels and correlated Gaussian perturbation are then characterized. The optimal training sequence structure and spatial training power allocation conditions are derived, and the choice of the optimal training sequence length is also analyzed. Finally, numerical results are provided to assess the performance of the proposed estimate method under different training sequence structures, the optimal length of the training sequence, and information statistics. Extensive numerical results show that the proposed estimate method has supreme performance compared to state of‐the‐art baseline methods (e.g. maximum likelihood and two‐sided linear channel estimation methods).

Patient Positioning Assistive Technology Applicable to the Existing Computed Tomography System: Estimation by Pixel Value of Scout Image.

This study aimed to propose a patient positioning assistive technique using computed tomography (CT) scout images. A total of 210 patients who underwent CT scans in a single center, including on the upper abdomen, were divided into a study set of 127 patients for regression and 83 patients for verification. Linear regression analysis was performed to determine the R2 coefficient and the linear equation related to the mean pixel value of the scout image and ideal table height (TH ideal ). The average pixel values of the scout image were substituted into the regression equation to estimate the TH ideal . To verify the accuracy of this method, the distance between the estimated table height (TH est ) and TH ideal was measured. The medians of age (in years), gender (male/female), height (in centimeters), and body weight (in kilograms) for the regression and verification groups were 68 versus 70, 85/42 versus 55/28, 163.8 versus 163.0, and 59.9 versus 61.9, respectively. Linear regression analysis indicated a high coefficient of determination ( R2 = 0.91) between the mean pixel value of the scout image and TH ideal . The correlation coefficient between TH ideal and TH est was 0.95 (95% confidence interval, 0.92-0.97; P < 0.0001), systematic bias was 0.2 mm, and the limits of agreement were -5.4 to 5.9 ( P = 0.78). The offset of the table height with TH est was 2.8 ± 2.1 mm. The proposed estimation method using scout images could improve the automatic optimization of table height in CT, and it can be used as a general-purpose automatic positioning technique.

A non-parametric panel model for climate data with seasonal and spatial variation

Abstract We consider a panel data model that allows for heterogeneous time trends at different locations. The model is well suited to identifying trends in climate data recorded at multiple stations. We propose a new estimation method for the model and derive an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the finite-sample properties of the proposed model and estimation method through extensive simulated studies. Finally, we use the newly proposed model and method to investigate monthly rainfall, temperature, and sunshine data of the UK, respectively. Overall, we find spring and winter have changed significantly over the past 50 years. Changes vary with respect to locations for the other seasons.

The angle of attack and side-slip angle estimation for civil aircraft

The angle of attack and side-slip angle are two important flight parameters that directly affect the flight safety of civil aircraft. This paper studies the estimation method for the angle of attack and side-slip angle according to air-data system, inertial navigation system, and aircraft aerodynamic data to improve flight safety under the condition of icing, failures, and improper maintenance. Flight tests show that the proposed estimation method is feasible, reliable, and has better accuracy. Furthermore, flight test data analysis shows that the angle of attack estimation error is within 1 degree, and the side-slip angle estimation error is within 1.5 degrees which can meet the requirements of engineering application.

Semi-profiled distributed estimation for high-dimensional partially linear model

As a leading example in the semiparametric model, the partially linear model is prominent in modeling complex data. The challenges associated with designing an efficient estimation algorithm in the distributed environment are not yet well addressed. The existing works require a constraint on the number of local machines to guarantee the optimality of the global estimator. In addition, a multi-round profiled estimator will lead to huge communication complexity. To further reduce communication costs, a novel semi-profiled estimation procedure is proposed, which provides an iterative skeleton to reduce estimation error. A new multi-round distributed algorithm based on the centralized semi-profiled estimator is developed, which can estimate the parametric and nonparametric simultaneously. The theoretical results indicate that the corresponding convergence rates can achieve the optimal orders within a constant number of communication rounds. The advantages of the proposed estimation method are demonstrated via numerical experiments on synthetic and real data.

A novel estimation method for weight and center-of-gravity via the aircraft trim data

Aircraft weight and center-of-gravity are significant parameters that affect flight stability and controllability, and unreasonable CG position may cause serious flight accidents. However, there is no airborne sensor can directly measure aircraft weight and center-of-gravity. In view of this, a novel estimation method for weight and center-of-gravity via the aircraft trim data is introduced. Specifically, the trim data calculation process for the aircraft with variable center of mass is given. Then, an inverse interpolation approach based on the binary search algorithm is presented to estimate the aircraft weight and center-of-gravity, and an improved moving average filter is introduced to eliminate the adverse effect on estimation results caused by the high-frequency noise of airborne sensors. Some simulations are given to demonstrate the validity of the proposed estimation method, and it can be drawn from the result that the aircraft longitudinal CG position estimation error is within 0.05 %MAC.

Modelling optimal control of air pollution to reduce respiratory diseases

Respiratory diseases caused by inhalation of air pollutants are affected by seasonal changes and mitigated by air pollution control, resulting in complex dynamics. In order to investigate the effects of various factors such as random noise and air pollution control on respiratory diseases, we developed deterministic and stochastic two-dimensional coupled SIS models with multiple control measures. The proposed models and parameter estimation methods, including determinations of unknown parameter values, were used to fit the Air Quality Index (AQI) data for Xi’an city in recent 10 years. The existence of the optimal solutions for the deterministic and stochastic models were analyzed theoretically and provided to compare the parameter fitting solutions with the optimal solutions, and give theoretical support for seeking a more reasonable air pollution optimization prevention and control scheme. To show this, we conducted numerical simulations of the optimal control solution and state evolution trajectories under different weight coefficient ratios and control objectives. The results show that the stochastic optimal control problem is more consistent with the practical scenario. We also formulate the optimal control problem assuming that the control variable depends on the concentration of air pollutants. The optimal control solution reflected the periodic variation of the air pollution control strategy well. A comparison of cost values for different combinations of the three control measures illustrated that air pollution reduction is the most effective control measure.

Robust inference for destructive one-shot device test data under Weibull lifetimes and competing risks

Data obtained from destructive one-shot devices are usually studied through a binary response variable indicating failure or success of the device. But, in many practical situations, it may be more realistic to consider that the failure of the device may be due to several competing risks. In this paper, we develop divergence-based inference for one-shot device testing under Weibull lifetimes. This inference is shown to be more robust than the classical MLE-based inference, without a significant loss in efficiency when the data arise from the true model. A detailed simulation study is done to show the behaviour of the proposed estimation method and the associated inference. Finally, the developed methods are applied to a motor insulation dataset for illustrative purposes.

Soil moisture estimation from Sentinel-1 interferometric observations over arid regions

We present an experimental methodology based on interferometric synthetic aperture radar (InSAR) time series analysis that can provide surface (top 5 cm) soil moisture (SSM) estimations over sandy arid soils. A co-registered Single Look Complex (SLC) SAR stack as well as meteorological information are required as input of the proposed methodological workflow. The proposed methodology consists of five steps. In the first step, meteorological data are exploited to identify the snow/ice free SAR acquisition related to lowest SSM level. In the second step, interferometric (coherence and phase closure) observables over a spatial grid are calculated. In the third step, for each grid cell the ordering of SAR acquisitions according to the SSM is performed. The sorting is performed by starting from the SAR acquisition related to lowest SSM level and by exploiting coherence and backscattering information. In the fourth step, for each grid cell the coherence values due to SSM variations are calculated by exploiting the ordering of SAR acquisitions according to the SSM. In the fifth step, SSM is estimated by inverting an analytical interferometric model using coherence values due to SSM variations and phase closure information. The proposed method estimates SSM over arid regions by ordering SAR acquisitions based on SSM, by calculating coherence values due to SSM variations and by inverting an interferometric model.A case study over an arid region in California/Arizona is presented. The proposed workflow was applied in Sentinel-1 (C-band) VV-polarized InSAR observations. The estimated SSM results were assessed with independent SSM observations from a station of the International Soil Moisture Network (ISMN) (RMSE: 0.029 m3/m3 R: 0.78) and with ERA5-Land reanalysis model data (RMSE: 0.049 m3/m3 R: 0.62). In summary, the proposed methodology was able to provide accurate SSM estimations at high spatial resolution (∼250 m). A discussion of the benefits and the limitations of the proposed methodology highlighted the potential of interferometric observables for SSM estimation over arid regions.