scholarly journals EVALUATION OF NON-LINEAR TAPER EQUATIONS FOR PREDICTING THE DIAMETER OF EUCALYPTUS TREES

2018 ◽  
Vol 42 (1) ◽  
Author(s):  
Guilherme Silverio Aquino de Souza ◽  
Diogo Nepomuceno Cosenza ◽  
Ana Carolina da Silva Cardoso Araújo ◽  
Lucas Veiga Ayres Pimenta ◽  
Ramon Barreto Souza ◽  
...  
Keyword(s):  
Variable Exponent ◽  
Linear Models ◽  
Mean Squared Error ◽  
Graphical Analysis ◽  
Statistical Validation ◽  
Absolute Deviation ◽  
Stem Taper ◽  
Non Linear ◽  
Identity Test ◽  
Taper Equations

ABSTRACT This study aims to evaluate non-linear stem taper models for predicting the pre-commercial diameter of eucalyptus trees and to analyze the effect of genotype on stem taper. The treatments comprise three different genotypes of Eucalyptus sp. in a 3 × 3 m plantation spacing. Seventy sample trees aged 10 years were felled for each treatment. The outside bark diameter measurements were taken at 0.5 m; 1.0 m; 1.5 m; 2.0 m, and then at intervals of 2.0 m till the top of the stem. Four non-linear models were evaluated, namely, the sigmoid model of Garay (1979), the variable exponent model of Kozak (1988), the segmented model of Max and Burkhart (1976), and the compatible model of Demaerschalk (1972). The performance of the models was assessed using the following statistical validation methods: correlation coefficient, standard error of estimate, mean bias, bias variance, root mean squared error, and mean absolute deviation. Graphical analysis of residues was used to evaluate the accuracy and precision of the estimates. Compared with other models, the variable exponent model of Kozak (1988) best described the stem profile, and predicted the total volume of the trees. The identity test showed that the stem profile is affected by the genotype.

scholarly journals Non-linear growth models for bullfrog tadpoles

2012 ◽  
Vol 36 (4) ◽  
pp. 454-462 ◽  
Author(s):  
Cleber Fernando Menegasso Mansano ◽  
Marta Verardino De Stéfani ◽  
Marcelo Maia Pereira ◽  
Beatrice Ingrid Macente
Keyword(s):  
Growth Rate ◽  
Weight Gain ◽  
Linear Models ◽  
Logistic Models ◽  
Biological Data ◽  
Von Bertalanffy ◽  
Initial Weight ◽  
Absolute Deviation ◽  
Total Length ◽  
Non Linear

Describing animal growth rate using non-linear models allows a detailed evaluation of growth behavior. Four non-linear models were used to fit weight gain and total length data of bullfrog (Lithobates catesbeianus) tadpoles, as follows: Gompertz, Y = A exp (-exp (-b (t-T))); Von Bertalanffy, Y = A (1 - K exp (-B t))³; Logistic, Y = A (1+ K exp (-B t))-1 and Brody, Y=A (1 - K exp (-B t)). We used 3,240 tadpoles, with average initial weight 0.044 g and average total length 12.79 mm, stage 25 Gosner. The measurements were conducted every ten days on 10% of the animals in every tank. The criteria used to select the model that best described the growth curve were: Residual Mean Square (RMS); determination coefficient (R²); residual graphical analysis; residual mean absolute deviation (MAD). Brody mathematical model was not a good fit for weight gain and total length, while Von Bertalanffy model underestimated tadpole initial weight, thus showing the difficulty of mathematical models to describe biological data at this growth stage. However, the Gompertz and Logistic models were considered to be an adequate fitting to describe growth rate and total length of bullfrog tadpoles in captivity.

G Optimal Design in Non linear Models to Increase Silicon Oxide Purity Levels and Electrical Conductivity

2018 ◽  
pp. 150-155
Author(s):  
Muklas Rivai
Keyword(s):  
Electrical Conductivity ◽  
Optimal Design ◽  
Linear Models ◽  
Silicon Oxide ◽  
Information Matrix ◽  
Relevant Information ◽  
Non Linear

Optimal design is a design which required in determining the points of variable factors that would be attempted to optimize the relevant information so that fulfilled the desired criteria. The optimal fulfillment criteria based on the information matrix of the selected model.

scholarly journals Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

2021 ◽  
Vol 13 (3) ◽  
pp. 455
Author(s):  
Md Nazrul Islam ◽  
Murat Tahtali ◽  
Mark Pickering
Keyword(s):  
Light Field ◽  
Mean Squared Error ◽  
Specular Reflection ◽  
Similarity Index ◽  
Detection Algorithm ◽  
Color Channel ◽  
Absolute Deviation ◽  
Significant Information ◽  
Transparent Objects ◽  
Transparent Object

Multispectral polarimetric light field imagery (MSPLFI) contains significant information about a transparent object’s distribution over spectra, the inherent properties of its surface and its directional movement, as well as intensity, which all together can distinguish its specular reflection. Due to multispectral polarimetric signatures being limited to an object’s properties, specular pixel detection of a transparent object is a difficult task because the object lacks its own texture. In this work, we propose a two-fold approach for determining the specular reflection detection (SRD) and the specular reflection inpainting (SRI) in a transparent object. Firstly, we capture and decode 18 different transparent objects with specularity signatures obtained using a light field (LF) camera. In addition to our image acquisition system, we place different multispectral filters from visible bands and polarimetric filters at different orientations to capture images from multisensory cues containing MSPLFI features. Then, we propose a change detection algorithm for detecting specular reflected pixels from different spectra. A Mahalanobis distance is calculated based on the mean and the covariance of both polarized and unpolarized images of an object in this connection. Secondly, an inpainting algorithm that captures pixel movements among sub-aperture images of the LF is proposed. In this regard, a distance matrix for all the four connected neighboring pixels is computed from the common pixel intensities of each color channel of both the polarized and the unpolarized images. The most correlated pixel pattern is selected for the task of inpainting for each sub-aperture image. This process is repeated for all the sub-aperture images to calculate the final SRI task. The experimental results demonstrate that the proposed two-fold approach significantly improves the accuracy of detection and the quality of inpainting. Furthermore, the proposed approach also improves the SRD metrics (with mean F1-score, G-mean, and accuracy as 0.643, 0.656, and 0.981, respectively) and SRI metrics (with mean structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (IMMSE), and mean absolute deviation (MAD) as 0.966, 0.735, 0.073, and 0.226, respectively) for all the sub-apertures of the 18 transparent objects in MSPLFI dataset as compared with those obtained from the methods in the literature considered in this paper. Future work will exploit the integration of machine learning for better SRD accuracy and SRI quality.

Modelling development ofCallosobruchus maculatusandAnisopteromalus calandraeat various constant temperatures using linear and non-linear models

2014 ◽  
Vol 24 (11) ◽  
pp. 1308-1320 ◽  
Author(s):  
M. Mobarakian ◽  
A.A. Zamani ◽  
J. Karmizadeh ◽  
N. Moeeny Naghadeh ◽  
M.S. Emami
Keyword(s):  
Linear Models ◽  
Non Linear

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.

Testing linear restrictions on non-linear models

1984 ◽  
Vol 15 (1-2) ◽  
pp. 91-96
Author(s):  
K.R. Sawyer ◽  
M.C. Rosalsky
Keyword(s):  
Linear Models ◽  
Non Linear ◽  
Linear Restrictions ◽  
Testing Linear
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