scholarly journals Estimation of Scintillation Indices: A Novel Approach Based on Local Kernel Regression Methods

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammed Ouassou ◽  
Oddgeir Kristiansen ◽  
Jon G. O. Gjevestad ◽  
Knut Stanley Jacobsen ◽  
Yngvild L. Andalsvik
Keyword(s):  
Information Criteria ◽  
Ionospheric Scintillation ◽  
Local Regression ◽  
Regression Methods ◽  
Novel Approach ◽  
Low Pass ◽  
Discontinuous Phase ◽  
Novel Method ◽  
Highly Correlated ◽  
Scintillation Indices

We present a comparative study of computational methods for estimation of ionospheric scintillation indices. First, we review the conventional approaches based on Fourier transformation and low-pass/high-pass frequency filtration. Next, we introduce a novel method based on nonparametric local regression with bias Corrected Akaike Information Criteria (AICC). All methods are then applied to data from the Norwegian Regional Ionospheric Scintillation Network (NRISN), which is shown to be dominated by phase scintillation and not amplitude scintillation. We find that all methods provide highly correlated results, demonstrating the validity of the new approach to this problem. All methods are shown to be very sensitive to filter characteristics and the averaging interval. Finally, we find that the new method is more robust to discontinuous phase observations than conventional methods.

scholarly journals A novel approach to the computation of one-loop three- and four-point functions: I. The real mass case

2019 ◽  
Vol 2019 (11) ◽  
Author(s):  
J Ph Guillet ◽  
E Pilon ◽  
Y Shimizu ◽  
M S Zidi
Keyword(s):  
Building Blocks ◽  
The Real ◽  
Alternative Method ◽  
Novel Approach ◽  
Reduction Methods ◽  
Real Mass ◽  
Novel Method ◽  
Algebraic Reduction ◽  
The One ◽  
Mass Case

Abstract This article is the first of a series of three presenting an alternative method of computing the one-loop scalar integrals. This novel method enjoys a couple of interesting features as compared with the method closely following ’t Hooft and Veltman adopted previously. It directly proceeds in terms of the quantities driving algebraic reduction methods. It applies to the three-point functions and, in a similar way, to the four-point functions. It also extends to complex masses without much complication. Lastly, it extends to kinematics more general than that of the physical, e.g., collider processes relevant at one loop. This last feature may be useful when considering the application of this method beyond one loop using generalized one-loop integrals as building blocks.

scholarly journals Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate
Keyword(s):  
Parameter Estimation ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mean Squared Error ◽  
Time Dependent ◽  
Regression Methods ◽  
Squared Error ◽  
Highly Correlated ◽  
Time Dependent Covariates ◽  
Independence Structure

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

scholarly journals New Capabilities for Prediction of High-Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning

Space Weather ◽  
2018 ◽  
Vol 16 (11) ◽  
pp. 1817-1846 ◽  
Author(s):  
Ryan M. McGranaghan ◽  
Anthony J. Mannucci ◽  
Brian Wilson ◽  
Chris A Mattmann ◽  
Richard Chadwick
Keyword(s):  
Machine Learning ◽  
High Latitude ◽  
Ionospheric Scintillation ◽  
Novel Approach

GPR-based novel approach for non-linear aerodynamic modelling from flight data

2018 ◽  
Vol 123 (1259) ◽  
pp. 79-92
Author(s):  
A. Kumar ◽  
A. K. Ghosh
Keyword(s):  
Aerodynamic Force ◽  
Equations Of Motion ◽  
Gaussian Process Regression ◽  
Likelihood Estimation ◽  
Flight Data ◽  
Novel Approach ◽  
Non Linear ◽  
Research Aircraft ◽  
Novel Method ◽  
Aerodynamic Modelling

ABSTRACTIn this paper, a Gaussian process regression (GPR)-based novel method is proposed for non-linear aerodynamic modelling of the aircraft using flight data. This data-driven regression approach uses the kernel-based probabilistic model to predict the non-linearity. The efficacy of this method is examined and validated by estimating force and moment coefficients using research aircraft flight data. Estimated coefficients of aerodynamic force and moment using GPR method are compared with the estimated coefficients using maximum-likelihood estimation (MLE) method. Estimated coefficients from the GPR method are statistically analysed and found to be at par with estimated coefficients from MLE, which is popularly used as a conventional method. GPR approach does not require to solve the complex equations of motion. GPR further can be directed for the generalised applications in the area of aeroelasticity, load estimation, and optimisation.

scholarly journals A novel approach toward the synthesis of some new tridentate Schiff bases from anil-like compounds

2016 ◽  
Vol 81 (10) ◽  
pp. 1111-1119 ◽  
Author(s):  
Fatemeh Bagheri ◽  
Abolfazl Olyaei
Keyword(s):  
Schiff Base ◽  
Formic Acid ◽  
Schiff Bases ◽  
Condensation Product ◽  
Reaction Times ◽  
Acid Catalyst ◽  
Schiff Base Ligands ◽  
Novel Approach ◽  
Solvent Free Conditions ◽  
Novel Method

A novel method was developed for synthesizing a series of new three dentate Schiff base ligands starting from hydroxynaphthalidene pyrimidinyl amines with o-phenylenediamines or o-aminophenol or 2-amino-3-hydroxy-pyri-dine in the presence of formic acid catalyst under solvent-free conditions. In these reactions [1+1] condensation product as half-unit ligand was obtained. Moreover, the reaction of hydroxynaphthalidene pyrimidinyl amines with 3,4-diamino-pyridine and 1,8-naphthalenediamine lead to the formation of C2-naphthylated imidazopyridine and dihydropyrimidine, respectively. The attractive features of this protocol are: use of inexpensive catalyst, operationally simple, short reaction times, easy handling, and good yields.

scholarly journals A Novel Method to Detect Inner Emotion States of Human using Artificial Neural Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 5820-5825
Keyword(s):  
Physiological Signals ◽  
Classification Rate ◽  
Discrete Emotion ◽  
Novel Approach ◽  
Proposed Model ◽  
Depression Detection ◽  
Novel Method ◽  
Artificial Neural ◽  
Ann Classifier ◽  
Performance Rate

Human computer interaction is a fast growing area of research where in the physiological signals are used to identify human emotion states. Identifying emotion states can be done using various approaches. One such approach which gained interest of research is through physiological signals using EEG. In the present work, a novel approach is proposed to elicit emotion states using 3-D Video-audio stimuli. Around 66 subjects were involved during data acquisition using 32 channel Enobio device. FIR filter is used to preprocess the acquired raw EEG signals. The desired frequency bands like alpha, delta, beta and theta are extracted using 8-level DWT. The statistical features, Hurst exponential, entropy, power, energy, differential entropy of each bands are computed. Artificial Neural network is implemented using Sequential Keras model and applied on the extracted features to classify in to four classes (HVLA, HVHA, LVHA and LVLA) and eight discrete emotion states like clam, relax, happy, joy, sad, fear, tensed and bored. The performance of ANN classifier found to perform better for 4- classes than 8-classes with a classification rate of 90.835% and 74.0446% respectively. The proposed model achieved better performance rate in detecting discrete emotion states. This model can be used to build applications on health like stress / depression detection and on entertainment to build emotional DJ.

scholarly journals Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data – ValCalHyp airborne hyperspectral campaign under the EUFAR framework

2014 ◽  
Vol XL-7 ◽  
pp. 51-53
Author(s):  
A. Brook ◽  
E. Ben Dor
Keyword(s):  
Atmospheric Correction ◽  
Ground Truth ◽  
Critical Issue ◽  
Radiometric Calibration ◽  
Vicarious Calibration ◽  
Flight Direction ◽  
Novel Approach ◽  
Novel Method ◽  
General Goal ◽  
Cross Calibration

A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.

scholarly journals Two Stepped Majority Voting for Efficient EEG based Emotion Classification

2020 ◽  
Author(s):  
aras Masood Ismael ◽  
Ömer F Alçin ◽  
Karmand H Abdalla ◽  
Abdulkadir k sengur
Keyword(s):  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Noise Removal ◽  
Majority Voting ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
Discrimination Accuracy ◽  
Novel Approach ◽  
Knn Classifier ◽  
Low Pass

Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG based emotion classification. Emotion recognition is important for human-machine interactions. Facial-features and body-gestures based approaches have been generally proposed for emotion recognition. Recently, EEG based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG based emotion classification.

A Robust Procedure to Implement Dynamic Stall Models Into Actuator Line Methods for the Simulation of Vertical-Axis Wind Turbines

2021 ◽  
Author(s):  
Pier Francesco Melani ◽  
Francesco Balduzzi ◽  
Alessandro Bianchini
Keyword(s):  
Flow Field ◽  
Wind Turbines ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Vertical Axis ◽  
Dynamic Stall ◽  
Lumped Parameter ◽  
Low Pass ◽  
Unsteady Rans ◽  
Novel Method

Abstract The Actuator Line Method (ALM), combining a lumped-parameter representation of the rotating blades with the CFD resolution of the turbine flow field, stands out among the modern simulation methods for wind turbines as probably the most interesting compromise between accuracy and computational cost. Being however a method relying on tabulated coefficients for modeling the blade-flow interaction, the correct implementation of the sub-models to account for higher order aerodynamic effects is pivotal. Inter alia, the introduction of a dynamic stall model is extremely challenging: first, it is important to extrapolate a correct value of the angle of attack (AoA) from the solved flow field; second, the AoA history needed to calculate the rate of dynamic variation of the angle itself is characterized by a low signal-to-noise ratio, leading to severe numerical oscillations of the solution. The study introduces a robust procedure to improve the quality of the AoA signal extracted from an ALM simulation. It combines a novel method for sampling the inflow velocity from the numerical flow field with a low-pass filtering of the corresponding AoA signal based on Cubic Spline Smoothing. Such procedure has been implemented in the Actuator Line module developed by the authors for the commercial ANSYS® FLUENT® solver. To verify the reliability of the methodology, two-dimensional unsteady RANS simulations of a test 2-blade Darrieus H-rotor, for which high-fidelity experimental and numerical blade loading data were available, have been performed for a selected unstable operation point.

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