A New Method of Insulation Detection on Electric Vehicles Based on a Variable Forgetting Factor Recursive Least Squares Algorithm

Ocular Artifacts (OAs) have a significant impact on the performance of Electroencephalogram (EEG) activities in the frontal region because of its higher amplitude. In this paper, Robust Variable Forgetting Factor (RVFF) and Recursive Least Square (RLS) based RVFF-RLS algorithm is implemented for removal of OAs from the raw EEG signal. Reference signals such as horizontal electro-oculogram and vertical electro-oculogram are recorded and then processed through the finite impulse response filter, whose coefficients are adaptively updated using the RVFF-RLS algorithm. Thereafter, obtained signals are subsequently subtracted from the raw EEG signal to obtain an EEG signal, which is free from OAs. The performance of proposed technique is compared with conventional techniques such as numerical variable forgetting factor RLS, fixed step size normalized least mean squares, fixed forgetting factor- RLS. The proposed technique shows least mean square error under a dynamic environment.

State of charge estimation of power lithium-ion battery based on an adaptive time scale dual extend Kalman filtering

Force and vibration generated in apical direction by three endodontic files of different kinematics during simulated canal preparation: An in vitro analytical study.

This article proposes a square-root (SR) extended instrumental variable (EIV) projection approximation subspace tracking (PAST) algorithm with variable forgetting factor (VFF) and variable regularization (VR). A new local polynomial modeling (LPM) based VFF is proposed by minimizing the mean squares deviation of the EIV linear model and the IV-PAST algorithm. A new variable $\ell 2$ regularization term is also derived to reduce the variance of the estimator resulting from possibly ill conditioned covariance matrix at low input signal level. An SR version of the algorithm is developed to improve the numerical stability of the algorithm and avoid the problem of loss of positive definiteness of the inverse covariance matrix. The proposed LOFF-VR-SREIV-PAST algorithm can be implemented by both the conventional EIV-PAST algorithm and numerically more stable hyperbolic rotations. Furthermore, the convergence of the proposed VFF-EIV-PAST algorithm using the ordinary differential equation method is analyzed. Its application to the estimation and tracking of direction of arrival under spatial color sensor noise in both stationary and nonstationary scenarios are presented. Simulations demonstrate that the proposed algorithm yields improved performance over the conventional PAST and EIV-PAST algorithms, especially at medium to low signal-to-noise ratio, which is more frequently encountered in practical situations.

Three-Dimensional Quantitative Comparative Analysis of Trapezial-Metacarpal Joint Surface Curvatures in Human Populations

The effects of compensating and restraining the power system harmonics using active power filter are determined by the detection precision and its dynamic response characters. To improve both of them, a harmonic current detection algorithm based on variable forgetting factor Recursive Least-Squares algorithm is presented. The occurrence of the dynamic process is identified firstly by the judgment condition which is given by the algorithm, and then the forgetting factor is assigned dynamically, so that the convergent speed is significantly improved. The algorithm overcomes the impact of low-pass filter of traditional p-q or ip-iq algorithm, and releases the contradiction cased by the conflicting requirements of forgetting factor value between steady process and dynamic process. So it has better dynamic performance. Simulation and experiments prove the validity and feasibility of the approaches.

In order to improve the convergence speed of iterative learning control and reduce the fluctuation of the system error, a class of linear steady-state systems is considered. The convergence of the algorithm and error fluctuations are studied by introducing the variable-gain idea into the D-type iterative learning control algorithm with variable forgetting factor. According to the related properties of the λ norm theory, the convergence of the improved iterative learning algorithm is proved. Compared with iterative learning control with forgetting factor and iterative learning control with variable gain, MATLAB simulation analysis is performed. The simulation results show that the algorithm is effective. The improved iterative learning law not only makes the iterative error smoother, but also improves the convergence speed.

Abstract. Hydrological models are usually systems of nonlinear differential equations for which no analytical solutions exist and thus rely on numerical solutions. While some studies have investigated the relationship between numerical method choice and model error, the extent to which extreme precipitation such as that observed during hurricanes Harvey and Katrina impacts numerical error of hydrological models is still unknown. This knowledge is relevant in light of climate change, where many regions will likely experience more intense precipitation. In this experiment, a large number of hydrographs are generated with the modular modeling framework FUSE (Framework for Understanding Structural Errors), using eight numerical techniques across a variety of forcing data sets. All constructed models are conceptual and lumped. Multiple model structures, parameter sets, and initial conditions are incorporated for generality. The computational cost and numerical error associated with each hydrograph were recorded. Numerical error is assessed via root mean square error and normalized root mean square error. It was found that the root mean square error usually increases with precipitation intensity and decreases with event duration. Some numerical methods constrain errors much more effectively than others, sometimes by many orders of magnitude. Of the tested numerical methods, a second-order adaptive explicit method is found to be the most efficient because it has both a small numerical error and a low computational cost. A small literature review indicates that many popular modeling codes use numerical techniques that were suggested by this experiment to be suboptimal. We conclude that relatively large numerical errors may be common in current models, highlighting the need for robust numerical techniques, in particular in the face of increasing precipitation extremes.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Radar antennas in the open air are affected by dynamic wind loading during azimuthal scanning. In this state, the acquisition and prediction of the aerodynamic load is the focus of the structure design for the antenna. We performed wind tunnel force tests on flat plate antennas with different aspect ratios. The changes in mean, maximum, and root mean square values of the aerodynamic coefficients relative to the antenna aspect ratio and reduced frequency are given for the first time. The effects of the antenna aspect ratio and reduced frequency on aerodynamic characteristics are analyzed. The experimental results indicate that the magnitude of the azimuthal moment is related to the antenna aspect ratio and the reduced frequency. It is worth noting that the antenna aspect ratio has a significant impact on the mean, maximum, and root mean square values of dynamic aerodynamic coefficients. The mean, maximum, and root mean square values of the rolling moment coefficient, azimuth moment coefficient, and pitching moment coefficient monotonously decrease with the increase in aspect ratio. In addition, the influence of reduced frequency on the dynamic aerodynamic coefficient is closely related to the antenna aspect ratio. Under the same antenna aspect ratio, increasing the reduced frequency will increase the mean, maximum, and root mean square values of the aerodynamic coefficient. For flat plate antennas with different aspect ratios, at the condition of increasing the aspect ratios, the mean, maximum, and root mean square values of aerodynamic coefficients do not change significantly with the change of reduced frequency.

Soil Roughness Measuring System Combined With Image Processing

Comments on: ‘Implementation of self-tuning regulators with variable forgetting factors’

Adulteration detection of corn oil, rapeseed oil and sunflower oil in camellia oil by in situ diffuse reflectance near-infrared spectroscopy and chemometrics

Refractive and Aberrometric Outcomes of Intracorneal Ring Segments for Keratoconus: Mechanical versus Femtosecond-assisted Procedures

Benchmarking Functional Status in Older Adults