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.

Selection of models to describe the temperature-dependent development of Neoleucinodes elegantalis (Lepidoptera: Crambidae) and its application to predict the species voltinism under future climate conditions

2021 ◽  
pp. 1-9
Author(s):  
Hevellyn Talissa dos Santos ◽  
Cesar Augusto Marchioro
Keyword(s):  
Climate Change ◽  
Linear Model ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Temperature Dependent ◽  
Dependent Development ◽  
Degree Day ◽  
Non Linear ◽  
The Impact

Abstract The small tomato borer, Neoleucinodes elegantalis (Guenée, 1854) is a multivoltine pest of tomato and other cultivated solanaceous plants. The knowledge on how N. elegantalis respond to temperature may help in the development of pest management strategies, and in the understanding of the effects of climate change on its voltinism. In this context, this study aimed to select models to describe the temperature-dependent development rate of N. elegantalis and apply the best models to evaluate the impacts of climate change on pest voltinism. Voltinism was estimated with the best fit non-linear model and the degree-day approach using future climate change scenarios representing intermediary and high greenhouse gas emission rates. Two out of the six models assessed showed a good fit to the observed data and accurately estimated the thermal thresholds of N. elegantalis. The degree-day and the non-linear model estimated more generations in the warmer regions and fewer generations in the colder areas, but differences of up to 41% between models were recorded mainly in the warmer regions. In general, both models predicted an increase in the voltinism of N. elegantalis in most of the study area, and this increase was more pronounced in the scenarios with high emission of greenhouse gases. The mathematical model (74.8%) and the location (9.8%) were the factors that mostly contributed to the observed variation in pest voltinism. Our findings highlight the impact of climate change on the voltinism of N. elegantalis and indicate that an increase in its population growth is expected in most regions of the study area.

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.

Heat and Cold-related Hospitalization Risk in North-East of Iran: A Time-stratified Case Crossover Design

2021 ◽  
Author(s):  
Omid Aboubakri ◽  
Hamid Reza Shoraka ◽  
Joan Ballester ◽  
Rahim Sharafkhani
Keyword(s):  
Linear Model ◽  
Hospital Admissions ◽  
Extreme Values ◽  
Crossover Design ◽  
Preventive Program ◽  
North East ◽  
Distributed Lag ◽  
Case Crossover ◽  
Non Linear ◽  
The Impact

Abstract Background: This study aimed to estimate hospitalization risk/number attributed to air extreme temperatures using time-stratified case crossover study and distributed lag non-linear model in a region of Iran during 2015-2019.Methods: A time-stratified case crossover design based on aggregated exposure data was used in this study. In order to have no overlap bias in the estimations, a fixed and disjointed window by using one-month strata was used in the design. A conditional Poisson regression model allowing for over dispersion (Quasi-Poisson) was applied into Distributed Lag Non-linear Model (DLNM). Different approaches were applied to estimate Optimum Temperature (OT). In the model, the interaction effect between temperature and humidity was assessed to see if the impact of heat or cold on Hospital Admissions (HAs) are different between different levels of humidity.Results: The cumulative effect of heat during 21 days was not significant and it was the cold that had significant cumulative adverse effect on all groups. While the number of HAs attributed to any ranges of heat, including medium, high, extreme and even all values were negligible, but a large number was attributable to cold values; about 10000 HAs were attributable to all values of cold temperature, of which about 9000 were attributed to medium range and about 1000 and less than 500 were attributed to high and extreme values of cold, respectively.Conclusion: This study highlights the need for interventions in cold seasons by policymakers. The results inform researchers as well as policy makers to address both men and women and elderly when any plan or preventive program is developed in the area under study.

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.

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>

A simulation study of impacts of error structure on modeling stock–recruitment data using generalized linear models

2004 ◽  
Vol 61 (1) ◽  
pp. 122-133 ◽  
Author(s):  
Yan Jiao ◽  
Yong Chen ◽  
David Schneider ◽  
Joe Wroblewski
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Linear Models ◽  
Least Squares Method ◽  
Estimation Of Parameters ◽  
Estimation Errors ◽  
Error Structure ◽  
Stock Recruitment ◽  
Data Points ◽  
The Impact

Stock–recruitment (S–R) models are commonly fitted to S–R data with a least-squares method. Errors in modeling are usually assumed to be normal or lognormal, regardless of whether such an assumption is realistic. A Monte Carlo simulation approach was used to evaluate the impact of the assumption of error structure on S–R modeling. The generalized linear model, which can readily deal with different error structures, was used in estimating parameters. This study suggests that the quality of S–R parameter estimation, measured by estimation errors, can be influenced by the realism of error structure assumed in an estimation, the number of S–R data points, and the number of outliers in modeling. A small number of S–R data points and the presence of outliers in S–R data could increase the difficulty in identifying an appropriate error structure in modeling, which might lead to large biases in the S–R param eter estimation. This study shows that generalized linear model methods can help identify an appropriate error distribution in S–R modeling, leading to an improved estimation of parameters even when there are outliers and the number of S–R data points is small. We recommend the generalized linear model be used for quantifying stock–recruitment relationships.

scholarly journals A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 263
Author(s):  
Amal Altamimi ◽  
Belgacem Ben Ben Youssef
Keyword(s):  
Remote Sensing ◽  
Systematic Review ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Sensor Technology ◽  
Future Research ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
The Impact

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

scholarly journals The impact of bankruptcy regimes on entrepreneurship and innovation. Is there any relationship?

2021 ◽  
Author(s):  
Błażej Prusak ◽  
Sylwia Morawska ◽  
Michał Łukowski ◽  
Przemysław Banasik
Keyword(s):  
Legal System ◽  
Linear Models ◽  
Least Squares Method ◽  
Point Of View ◽  
Bankruptcy Law ◽  
Cross Sectional ◽  
Research Hypothesis ◽  
Risk Acceptance ◽  
The Impact ◽  
Level Of Risk

AbstractThe literature review indicates that bankruptcy law may play an important role in and be one of the factors influencing the development of entrepreneurship, innovation, and thus economic growth, among other things. In previous studies, the analysis of the impact of bankruptcy law on individual variables has been conducted independently. Our aim was to conduct a holistic analysis, taking several factors into account simultaneously. Therefore, a descriptive model was proposed, based on which the following research hypothesis was formulated: In countries characterised by an effective legal system and at the same time debtor-friendly bankruptcy law, the level of risk acceptance among entrepreneurs is higher, which is reflected in higher levels of entrepreneurship and innovation. Based on the selected variables, a cross-sectional analysis was conducted using linear models estimated on the basis of the least-squares method. Additionally, to strengthen the conclusions drawn, the models were assessed in such a way enabling the analysis of causality as defined by Granger based on the two-step process. The results obtained allowed us to confirm the research hypothesis: in countries characterised by an efficient legal system and at the same time debtor-friendly bankruptcy law, the level of risk acceptance among entrepreneurs is higher, which is reflected in higher levels of entrepreneurship and innovation. The research results are particularly important from the point of view of legislators who are responsible for drafting amendments to bankruptcy law. Including certain debtor-friendly provisions may, in the long run, lead to increased entrepreneurship and innovation, and thus economic development.

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