About: University of Southern Brittany is a education organization based out in Lorient, France. It is known for research contribution in the topics: Ultimate tensile strength & Population. The organization has 1175 authors who have published 2486 publications receiving 47488 citations.
Topics: Ultimate tensile strength, Population, Low-density parity-check code, Finite element method, Context (language use)
Papers published on a yearly basis
TL;DR: In this paper, the Young's modulus of a flax fiber is estimated by taking into account the composition of the fibre and the evolution of the orientation of the fibrils during a tensile test.
Abstract: The knowledge of the behaviour of flax fibres is of crucial importance for their use as a reinforcement for composites materials. Flax fibres were tested under tensile loading and in repeated loading–unloading experiments. We have shown that fibre stiffness increases with the strain. This phenomenon is attributed to the orientation of the fibrils with the axis of the fibre when a strain occurs. By using micro-mechanical equations, the Young's modulus of a flax fibre is estimated by taking into account the composition of the fibre and the evolution of the orientation of the fibrils during a tensile test. A good agreement is found between experimental and calculated results. The origin of the large spread observed in the mechanical characteristics is analysed here.
TL;DR: Baley et al. as discussed by the authors studied the tensile properties of natural fiber-biopolymer composites in order to determine whether or not, biocomposites may replace glass fibre reinforced unsaturated polyester resins.
TL;DR: An important step towards finding the AlexNet network for TSC is taken by presenting InceptionTime---an ensemble of deep Convolutional Neural Network models, inspired by the Inception-v4 architecture, which outperforms HIVE-COTE's accuracy together with scalability.
Abstract: This paper brings deep learning at the forefront of research into time series classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE cannot be applied to many real-world datasets because of its high training time complexity in $$O(N^2\cdot T^4)$$ for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 8 days to learn from a small dataset with $$N=1500$$ time series of short length $$T=46$$ . Meanwhile deep learning has received enormous attention because of its high accuracy and scalability. Recent approaches to deep learning for TSC have been scalable, but less accurate than HIVE-COTE. We introduce InceptionTime—an ensemble of deep Convolutional Neural Network models, inspired by the Inception-v4 architecture. Our experiments show that InceptionTime is on par with HIVE-COTE in terms of accuracy while being much more scalable: not only can it learn from 1500 time series in one hour but it can also learn from 8M time series in 13 h, a quantity of data that is fully out of reach of HIVE-COTE.
TL;DR: This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels.
Abstract: This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels Using the characteristic function of difference histogram (DHCF), we prove that the center of mass of DHCF (DHCF COM) decreases after messages are embedded Accordingly, the DHCF COMs are calculated as distinguishing features from the pixel pairs with different distances The features are calibrated with an image generated by average operation, and then used to train a support vector machine (SVM) classifier The experimental results prove that the features extracted from the differences between nonadjacent pixels can help to tackle LSB matching as well
TL;DR: In this paper, the tensile mechanical properties of flax fibres from the Hermes variety are estimated according to their diameter and their location in the stems, and the large scattering of these properties is ascribed to the variation of the fibre size along its longitudinal axis, as revealed by SEM observations.
Abstract: The tensile mechanical properties of flax fibres from the Hermes variety are estimated according to their diameter and their location in the stems. The large scattering of these properties is ascribed to the variation of the fibre size along its longitudinal axis, as revealed by SEM observations. The higher values of the mechanical properties for the fibres issued from the middle of the stems are associated with the chemical composition of their cell walls. The mechanical properties of unidirectional flax fibre/epoxy matrix composites are studied as a function of their fibre content. The properties of the composites are lower than those expected from single fibre characteristics.
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|Geoffrey I. Webb
|Adriaan S. Luyt
|Kishor Kumar Sadasivuni
|Christophe M. Thomas
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