Foundations of Measurement Fractal Theory for the Fracture Mechanics

Alves, Lucas Máximo

doi:10.5772/51813

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…This behavior has been confirmed for a large variety of experimental situations [23,49]. However, other studies have also shown that a different value for f l ¼ 0:5 may be applicable [50][51][52]. These two values of the roughness exponent are connected to the length scale at which the crack is examined.…”

Section: Resultssupporting

confidence: 52%

“…(34) and (35) are rewritten as, The local and the global roughness dimensions determined in this work, for the mortar and the heavy clay specimens, are compatible with results in the literature [24,[26][27][28]30,31,52,53]. However, for some specimens, the values seem to exceed the expected number, as in the case of mortar C1 (13.699 for the sides 1 and 2) and D2 (12.1456 for the sides 1 and 2).…”

Section: Specimenmentioning

confidence: 60%

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Analytical fractal model for rugged fracture surface of brittle materials

Alves

Chinelatto

Grzebielucka

et al. 2016

Engineering Fracture Mechanics

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Section: Resultssupporting

confidence: 52%

Section: Specimenmentioning

confidence: 60%

Analytical fractal model for rugged fracture surface of brittle materials

Alves

Chinelatto

Grzebielucka

et al. 2016

Engineering Fracture Mechanics

View full text Add to dashboard Cite

Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

Ghaderi

2023

Journal of Nanomaterials

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One of the most fundamental subjects in nanoscience and nanotechnology is structural analysis. We employed a scanning electron microscope (SEM) image of the manufactured Gd3+/13X/DOX/FA nanocomposite in this study. The size, dimensions, and morphology of nanocomposite materials were studied to ensure the uniformity and homogeneity of SEM images. This is the first study to look at segmented SEM images for fractal dimension (FD) and other statistical criteria, including average, maximum, minimum, skewness, and range for magnetic resonance imaging (MRI) nanocomposite. The average of FD (FDavg), the standard deviation of FD (FDsd), and the lacunarity of FD (FDlac) fractal data analysis criteria were also employed. The findings show that particle sizes and shapes vary because the minimum-to-maximum range is not zero, and our data provide a reasonable range. This interpretation is further supported by an analysis of the nanocomposite’s SEM image. At first glance, the image seemed to be uniform. However, when the calculations were performed, it was discovered that the generated particles were not particularly uniform. The particles were uniformly dispersed throughout all surfaces, although their sizes, dimensions, and morphologies varied. In conclusion, the study was supported by fractal data analysis, emphasizing the importance of structural analysis for future research, particularly for medical applications like MRI.

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A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

Ghahfarrokhi

Khodadadi

2021

Preprint

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Skin cancer affects people of all skin tones, including those with darker complexions. Melanomas are known as the malignant tumors of skin cancer, resulting in an adverse prognosis, responsible for most deaths relating to skin cancer. Early diagnosis and treatment of skin cancer from dermoscopic images can significantly reduce mortality and save lives. Although several Computer-Aided Diagnosis (CAD) systems with satisfactory performance have been introduced in the literature for skin cancer detection, the high false detection rate has made it inevitable to have an expert physician for more examination. In this paper, a CAD system based on machine learning algorithms is provided to classify various skin cancer types. The proposed method uses the Online Region-based Active Contour Model (ORACM) to extract the Region Of Interest (ROI) of skin lesions. This model uses a new binary level set equation and regularization operation such as morphological opening and closing.Additionally, various combinations of different textures and nonlinear features are extracted for the ROI to show the multiple aspects of skin lesions. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are applied to the combination of the extracted features in which, Non-dominated Sorting Genetic Algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fitting neural network (Fit net), Feed-Forward neural network (FF net), and Pattern recognition network (Pat net) are employed for the classification. Accordingly, the best-obtained precision of 99.24% based on five-fold cross-validation is attained by the selected features of texture and nonlinear indices through NSGA II, applying the pattern net classifier. Also, the comparison between this paper's experimental results and other similar works with the same dataset demonstrates the proposed method's efficiency.

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Foundations of Measurement Fractal Theory for the Fracture Mechanics

Cited by 4 publications

References 57 publications

Analytical fractal model for rugged fracture surface of brittle materials

Analytical fractal model for rugged fracture surface of brittle materials

Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

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