Strain-rate-sensitive mechanical response, twinning, and texture features of NiCoCrFe high-entropy alloy: Experiments, multi-level crystal plasticity and artificial neural networks modeling

High entropy alloys (HEAs) are still a largely unexplored class of materials with high potential for applications in various fields. Motivated by the huge number of compounds in a given HEA class, we develop machine learning techniques, in particular artificial neural networks, coupled to ab initio calculations, in order to accurately predict some basic HEA properties: equilibrium phase, cohesive energies, density of states at the Fermi level and the stress-strain relation, under conditions of isotropic deformations. Known for its high tensile ductility and fracture toughness, the Co-Cr-Fe-Ni-Al alloy has been considered as a test candidate material, particularly by adjusting the Al content. However, further enhancement of the microstructure, mechanical and thermal properties is possible by modifying also the fractions of the base alloy. Using deep neural networks, we map structural and chemical neighborhood information onto the quantities of interest. This approach offers the possibility for an efficient screening over a huge number of potential candidates, which is essential in the exploration of multi-dimensional compositional spaces.

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Texture Features and Artificial Neural Networks: A Way to Improve the Specificity of a CAD System for Multiparametric MR Prostate Cancer

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 - IFMBE Proceedings ◽

10.1007/978-3-319-32703-7_59 ◽

2016 ◽

pp. 296-301 ◽

Cited By ~ 1

Author(s):

Valentina Giannini ◽

Samanta Rosati ◽

Daniele Regge ◽

Gabriella Balestra

Keyword(s):

Prostate Cancer ◽

Neural Networks ◽

Artificial Neural Networks ◽

Texture Features ◽

Cad System ◽

Artificial Neural

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Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) ◽

10.1109/itaic.2019.8785563 ◽

2019 ◽

Cited By ~ 2

Author(s):

Fuqian Shi ◽

Gaoxiang Chen ◽

Yu Wang ◽

Ningning Yang ◽

Yating Chen ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Image Classification ◽

Texture Features ◽

Microscopic Image ◽

Artificial Neural

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Specificity improvement of a CAD system for multiparametric MR prostate cancer using texture features and artificial neural networks

Health and Technology ◽

10.1007/s12553-016-0150-6 ◽

2016 ◽

Vol 7 (1) ◽

pp. 71-80 ◽

Cited By ~ 6

Author(s):

V. Giannini ◽

S. Rosati ◽

D. Regge ◽

G. Balestra

Keyword(s):

Prostate Cancer ◽

Neural Networks ◽

Artificial Neural Networks ◽

Texture Features ◽

Cad System ◽

Artificial Neural

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Pruning Artificial Neural Networks: A Way to Find Well-Generalizing, High-Entropy Sharp Minima

Artificial Neural Networks and Machine Learning – ICANN 2020 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-61616-8_6 ◽

2020 ◽

pp. 67-78

Author(s):

Enzo Tartaglione ◽

Andrea Bragagnolo ◽

Marco Grangetto

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

High Entropy ◽

Artificial Neural ◽

Sharp Minima

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AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w5-395-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 395-399 ◽

Cited By ~ 8

Author(s):

E. Khesali ◽

H. Enayati ◽

M. Modiri ◽

M. Mohseni Aref

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Texture Features ◽

Speckle Noise ◽

Sar Images ◽

Time Saving ◽

Ship Detection ◽

Operational Solution ◽

Novel Method ◽

Artificial Neural

This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR) images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1) feature extraction; 2) feature selection; and 3) ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

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