scholarly journals Comparison of Linear and Nonlinear Behavior of Track Elements in Contact-Impact Models

2018 ◽  
Author(s):  
Jabbar Ali Zakeri ◽  
Mosab Reza Tajalli
Keyword(s):  
Linear Model ◽  
3D Model ◽  
Linear Models ◽  
Wave Length ◽  
Short Wave ◽  
Contact Forces ◽  
Reliable Estimate ◽  
Impact Forces ◽  
Linear Behavior ◽  
Non Linear

Existence of short wave length irregularities and discontinuities in the rail, such as corrugation, isolated rail joints, crossings and rail breakage, result in impact forces and an increase in wheel-rail contact force. Extreme forces in such could result in non-linear behavior of ballast and pads, and as a result, employing common linear models mihgt over/under estimate contact forces. A 3D model of wheel and rail is developed in this paper, and by considering rail breakage, validity of linear models and considering non-linear behavior of materials are studied. Wheel-rail interactions are studied for two common pads with high stiffness (HDPE) and low stiffness (Studded) for speeds of 20 to 160 km/h. Three behavioral patterns are considered for the developed 3D model: linear pad and ballast (LP-LB), nonlinear pad and linear ballast (NLP, LB), and nonlinear pad and ballast (NLP, NLB), and results are compared. According to the results, for HDPE pads and impact forces of up to 30 tons, linear model for material could estimate acceptable results. Yet for studded pads, linear model estimates forces that are comparably less than those estimated by non-linear model. Moreover employing NLP-LB model overestimates pad and wheel-rail contact forces by a rather small margin, compared to those estimated by NLP-NLB model, and hence, could be a suitable replacement for it. It is also observed that in order to have a reliable estimate of ballast forces, using non-linear ballast models are mandatory, and neither LP-LB nor NLP-LB could be acceptable replacements.

scholarly journals Early Detection of Defects through the Identification of Distortion Characteristics in Ultrasonic Responses

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 850
Author(s):  
Pietro Burrascano ◽  
Matteo Ciuffetti
Keyword(s):  
Early Detection ◽  
Additive Noise ◽  
Linear Models ◽  
High Stress ◽  
Damage Index ◽  
Hammerstein Model ◽  
Linear Behavior ◽  
Non Linear ◽  
Ultrasonic Techniques ◽  
Detection Of Defects

Ultrasonic techniques are widely used for the detection of defects in solid structures. They are mainly based on estimating the impulse response of the system and most often refer to linear models. High-stress conditions of the structures may reveal non-linear aspects of their behavior caused by even small defects due to ageing or previous severe loading: consequently, models suitable to identify the existence of a non-linear input-output characteristic of the system allow to improve the sensitivity of the detection procedure, making it possible to observe the onset of fatigue-induced cracks and/or defects by highlighting the early stages of their formation. This paper starts from an analysis of the characteristics of a damage index that has proved effective for the early detection of defects based on their non-linear behavior: it is based on the Hammerstein model of the non-linear physical system. The availability of this mathematical model makes it possible to derive from it a number of different global parameters, all of which are suitable for highlighting the onset of defects in the structure under examination, but whose characteristics can be very different from each other. In this work, an original damage index based on the same Hammerstein model is proposed. We report the results of several experiments showing that our proposed damage index has a much higher sensitivity even for small defects. Moreover, extensive tests conducted in the presence of different levels of additive noise show that the new proposed estimator adds to this sensitivity feature a better estimation stability in the presence of additive noise.

scholarly journals Experimental validation of wind energy estimation

2020 ◽  
Vol 24 (6 Part A) ◽  
pp. 3795-3806
Author(s):  
Predrag Zivkovic ◽  
Mladen Tomic ◽  
Vukman Bakic
Keyword(s):  
Wind Speed ◽  
Linear Model ◽  
Linear Models ◽  
Stokes Equations ◽  
Momentum Conservation ◽  
Electricity Production ◽  
Navier Stokes ◽  
Energy Estimation ◽  
Navier Stokes Equations ◽  
Non Linear

Wind power assessment in complex terrain is a very demanding task. Modeling wind conditions with standard linear models does not sufficiently reproduce wind conditions in complex terrains, especially on leeward sides of terrain slopes, primarily due to the vorticity. A more complex non-linear model, based on Reynolds averaged Navier-Stokes equations has been used. Turbulence was modeled by modified two-equations k-? model for neutral atmospheric boundary-layer conditions, written in general curvelinear non-orthogonal co-ordinate system. The full set of mass and momentum conservation equations as well as turbulence model equations are numerically solved, using the as CFD technique. A comparison of the application of linear model and non-linear model is presented. Considerable discrepancies of estimated wind speed have been obtained using linear and non-linear models. Statistics of annual electricity production vary up to 30% of the model site. Even anemometer measurements directly at a wind turbine?s site do not necessarily deliver the results needed for prediction calculations, as extrapolations of wind speed to hub height is tricky. The results of the simulation are compared by means of the turbine type, quality and quantity of the wind data and capacity factor. Finally, the comparison of the estimated results with the measured data at 10, 30, and 50 m is shown.

scholarly journals Hybrid MARMA-NARX model for flow forecasting based on the large-scale climate signals, sea-surface temperatures, and rainfall

2018 ◽  
Vol 49 (6) ◽  
pp. 1788-1803 ◽  
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Arezoo Ahmadian ◽  
Mohammad Valipour
Keyword(s):  
Linear Model ◽  
Hybrid Model ◽  
Large Scale ◽  
Linear Models ◽  
Moving Average ◽  
Effective Parameters ◽  
Forecasting Accuracy ◽  
Local Factor ◽  
Non Linear ◽  
Climate Signals

Abstract In this study, to reflect the effect of large-scale climate signals on runoff, these indices are accompanied with rainfall (the most effective local factor in runoff) as the inputs of the hybrid model. Where one-year in advance forecasting of reservoir inflows can provide data to have an optimal reservoir operation, reports show we still need more accurate models which include all effective parameters to have more forecasting accuracy than traditional linear models (ARMA and ARIMA). Thus, hybridization of models was employed for improving the accuracy of flow forecasting. Moreover, various forecasters including large-scale climate signals were tested to promote forecasting. This paper focuses on testing MARMA-NARX hybrid model to enhance the accuracy of monthly inflow forecasts. Since the inflow in different periods of the year has in linear and non-linear trends, the hybrid model is proposed as a means of combining linear model, monthly autoregressive moving average (MARMA), and non-linear model, nonlinear autoregressive model with exogenous (NARX) inputs to upgrade the accuracy of flow forecasting. The results of the study showed enhanced forecasting accuracy through using the hybrid model.

Machine Learning Techniques on Multidimensional Curve Fitting Data Based on R- Square and Chi-Square Methods

2016 ◽  
Vol 6 (3) ◽  
pp. 974 ◽  
Author(s):  
Vidyullatha P ◽  
D. Rajeswara Rao
Keyword(s):  
Machine Learning ◽  
Linear Model ◽  
Curve Fitting ◽  
Linear Models ◽  
Machine Learning Techniques ◽  
Chi Square ◽  
Curve Fit ◽  
Learning Techniques ◽  
Non Linear ◽  
Data Points

<p>Curve fitting is one of the procedures in data analysis and is helpful for prediction analysis showing graphically how the data points are related to one another whether it is in linear or non-linear model. Usually, the curve fit will find the concentrates along the curve or it will just use to smooth the data and upgrade the presence of the plot. Curve fitting checks the relationship between independent variables and dependent variables with the objective of characterizing a good fit model. Curve fitting finds mathematical equation that best fits given information. In this paper, 150 unorganized data points of environmental variables are used to develop Linear and non-linear data modelling which are evaluated by utilizing 3 dimensional ‘Sftool’ and ‘Labfit’ machine learning techniques. In Linear model, the best estimations of the coefficients are realized by the estimation of R- square turns in to one and in Non-Linear models with least Chi-square are the criteria. </p>

scholarly journals Describing fluctuating indoor aerosol dust measurements with application to house dust mite allergens

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
F. E. van Boven ◽  
N. W. de Jong ◽  
M. G. L. C. Loomans ◽  
G. J. Braunstahl ◽  
R. Gerth van Wijk ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Linear Model ◽  
House Dust ◽  
House Dust Mite ◽  
Linear Models ◽  
Airborne Dust ◽  
Dust Mite ◽  
Non Linear ◽  
The Mean ◽  
Indoor Particulate Matter

Abstract Measuring house dust mite aeroallergen concentrations is essential in understanding mite allergen exposure. Physically, the aerolized house dust mite faeces are part of indoor particulate matter. We studied the statistical ways of summarizing measurements of fluctuating mite aeroallergen exposure inside homes through indoor particulate matter. To study emissions from beddings, we measured the time-related airborne dust concentration after shaking a duvet. Analysis was performed both by a method based on the estimated mean and by a non-linear model. Twenty-eight studies reported a sum of concentrations; only one also reported the peak. In our four experiments on shaking a duvet (245 to 275 measurements each), the peak value was two to four times higher than the mean. The mean-based and non-linear models both predicted the sum of concentrations exactly. A 1% upper prediction bound and the non-linear model predicted the peak emission rate moderately well (64 to 92%, and 63 to 93%, respectively). Mean levels of indoor particulate matter measurements differ substantially from peak concentrations. The use of the mean is only sufficient to predict the sum of concentrations. We suggest that, mite aeroallergen measurements should include information on the peak as well as the mean.

scholarly journals Linear and Non-Linear Models for Remotely-Sensed Hyperspectral Image Visualization

2020 ◽  
Vol 12 (15) ◽  
pp. 2479
Author(s):  
Radu-Mihai Coliban ◽  
Maria Marincaş ◽  
Cosmin Hatfaludi ◽  
Mihai Ivanovici
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Hyperspectral Image ◽  
Point Of View ◽  
Hyperspectral Images ◽  
Image Visualization ◽  
Color Formation ◽  
Amount Of Information ◽  
Non Linear ◽  
The Impact

The visualization of hyperspectral images still constitutes an open question and may have an important impact on the consequent analysis tasks. The existing techniques fall mainly in the following categories: band selection, PCA-based approaches, linear approaches, approaches based on digital image processing techniques and machine/deep learning methods. In this article, we propose the usage of a linear model for color formation, to emulate the image acquisition process by a digital color camera. We show how the choice of spectral sensitivity curves has an impact on the visualization of hyperspectral images as RGB color images. In addition, we propose a non-linear model based on an artificial neural network. We objectively assess the impact and the intrinsic quality of the hyperspectral image visualization from the point of view of the amount of information and complexity: (i) in order to objectively quantify the amount of information present in the image, we use the color entropy as a metric; (ii) for the evaluation of the complexity of the scene we employ the color fractal dimension, as an indication of detail and texture characteristics of the image. For comparison, we use several state-of-the-art visualization techniques. We present experimental results on visualization using both the linear and non-linear color formation models, in comparison with four other methods and report on the superiority of the proposed non-linear model.

Forecasting the macro determinants of bank credit quality: a non-linear perspective

2020 ◽  
Vol 21 (4) ◽  
pp. 423-443
Author(s):  
Maria Grazia Fallanca ◽  
Antonio Fabio Forgione ◽  
Edoardo Otranto
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Strong Dependence ◽  
Bank Loans ◽  
Bank Loan ◽  
Linear Perspective ◽  
Loan Default ◽  
Content Type ◽  
Non Linear ◽  
Bad Loans

Purpose This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability. Design/methodology/approach Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach. Findings Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017. Research limitations/implications The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique. Practical implications The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans. Originality/value The authors show a clear outperformance of the MS model concerning the linear one.

Effect of Non-Linear Components on the Performance of a Hydro -Pneumatic Slow-Active Suspension System

1996 ◽  
Vol 210 (1) ◽  
pp. 23-34 ◽  
Author(s):  
S M El-Demerdash ◽  
D A Crolla
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Ride Comfort ◽  
Active Suspension ◽  
Front Wheel ◽  
Suspension System ◽  
Approximation Technique ◽  
Active System ◽  
Working Space ◽  
Non Linear

In this work, the effects of component non-linearities on the ride performance of a hydro-pneumatic slow-active suspension system are studied theoretically. Based on the quarter car linear model, linear optimal control theory is used to calculate the feedback and feedforward gains. These gains are used in both linear and non-linear models with and without preview control. The Pade approximation technique is used to represent the preview time resulting from a preview sensor mounted on the vehicle front bumper to measure the road irregularities ahead of the front wheel. The results on a typical major road showed that at similar r.m.s. values of suspension working space, the non-linear slow-active system with preview provided a 28 per cent improvement in ride comfort and a 17 per cent reduction in dynamic tyre load compared with a passive system. However, the inclusion of non-linear effects of the components increases the ride comfort acceleration by 10 per cent and suspension working space by 12 per cent compared to the equivalent linear model at approximately equal values of r.m.s. dynamic tyre load.

scholarly journals Estimation of annual compound growth rates of guava (Psidium guajava L.) fruit in Haryana using Non linear model

2019 ◽  
Vol 11 (4) ◽  
pp. 778-784
Author(s):  
Pardeep Panghal ◽  
Manoj Kumar ◽  
Sarita Rani
Keyword(s):  
Growth Rate ◽  
Linear Model ◽  
Growth Rates ◽  
Logistic Model ◽  
Linear Models ◽  
Psidium Guajava ◽  
Annual Growth Rate ◽  
Guava Fruit ◽  
Non Linear ◽  
Best Fit

Computation of growth rates plays an important role in agricultural and economic research to study growth pattern of a various commodities. Many of the research workers used the parametric approach for computation of annual growth rate but not use the concept of non-linear model.  In this paper, an attempt has been made to study growth rates of guava for three districts (Hisar, and Kurukshetra) and Haryana state as a whole using different non-linear models. The time series data on annual area and production of guava (Psidium guajava L.) in different districts of Haryana from 1990-91 to 2015-16 were collected to fit non linear models. Growth rates were computed through best fitted non-linear models. It was found that Logistic model could be best fit for computation of growth rates of area for guava fruit in Hisar and Kurukshetra district and Haryana state as a whole whereas Gompertz model was best fit for Yamunanagar district based on high R2 and least MSE and RMSE values. It was also observed that monomolecular model was best fit for production of guava fruits in Hisar and Yamunanagar district whereas Logistic model was best fit for production of guava fruit in Kurukshetra and Haryana state as a whole because of high R2 and least MSE and RMSE values. R and excel software have been used for fitting the non linear model and computation of growth rates for area and production of guava fruit for the year 1990-91 to 2015-16. None has been used the non linear model growth model for computation of annual growth rate of guava fruit for area and production of Haryana state. But in this work non linear growth model has been used for computation of growth rate instead of parametric approaches.

scholarly journals Time Series Analysis and Forecasting of Rainfall for Agricultural Crops in India: An Application of Artificial Neural Network

2020 ◽  
Vol 12 (4) ◽  
pp. 1
Author(s):  
Debasis Mithiya ◽  
Kumarjit Mandal ◽  
Simanti Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Analysis ◽  
Linear Model ◽  
Agricultural Production ◽  
Linear Models ◽  
Monthly Rainfall ◽  
Series Analysis ◽  
Non Linear

Indian agriculture depends heavily on rainfall. It not only influences agricultural production but also affects the prices of all agricultural commodities. Rainfall is an exogenous variable which is beyond farmers’ control. The outcome of rainfall fluctuation is quite natural. It has been observed that fluctuation in rainfall brings about fluctuation in output leading to price changes. Considering the importance of rainfall in determining agricultural production and prices, the study has attempted to forecast monthly rainfall in India with the help of time series analysis using monthly rainfall data. Both linear and non-linear models have been used. The value of diagnostic checking parameters (MAE, MSE, RMSE) is lower in a non-linear model compared to a linear one. The non-linear model - Artificial Neural Network (ANN) has been chosen instead of linear models, namely, simple seasonal exponential smoothing and Seasonal Auto-Regressive Integrated Moving Average to forecast rainfall. This will help to identify the proper cropping pattern.

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