scholarly journals Modelling of Acoustic Emission Signals Due to Fiber Break in a Model Composite Carbon/Epoxy: Experimental Validation and Parametric Study

2019 ◽  
Vol 9 (23) ◽  
pp. 5124 ◽  
Author(s):  
Hamam ◽  
Godin ◽  
Fusco ◽  
Monnier
Keyword(s):  
Acoustic Emission ◽  
Parametric Study ◽  
Supervised Classification ◽  
Model Composite ◽  
Fiber Break ◽  
Wave Effects ◽  
Propagation Medium ◽  
Composite Carbon ◽  
Ae Signals

The present paper focuses on experiments and numerical simulation of the acoustic emission (AE) signals due to fiber break in a model composite. AE signals are related to wave effects due to the source, the propagation medium and the sensor. For quantitative AE analysis, it is very important to understand the effect of the piezoelectric sensors and propagation on the “primitive” AE signals. In this study, we investigate the influence of sensors, thickness, and position of the fiber by finite element simulations. This parametric study can allow an enlargement of the library for supervised classification of AE signals.

scholarly journals Acoustic Emission Signal Due to Fiber Break and Fiber Matrix Debonding in Model Composite: A Computational Study

2021 ◽  
Vol 11 (18) ◽  
pp. 8406
Author(s):  
Zeina Hamam ◽  
Nathalie Godin ◽  
Claudio Fusco ◽  
Aurélien Doitrand ◽  
Thomas Monnier
Keyword(s):  
Acoustic Emission ◽  
Computational Study ◽  
Fiber Composite ◽  
Carbon Fiber Composite ◽  
Model Composite ◽  
Fiber Break ◽  
Emission Signal ◽  
Detection And Identification ◽  
Fiber Matrix ◽  
Ae Signals

Acoustic emission monitoring is a useful technique to deal with detection and identification of damage in composite materials. Over the last few years, identification of damage through intelligent signal processing was particularly emphasized. Data-driven models are developed to predict the remaining useful lifetime. Finite elements modeling (FEM) was used to simulate AE signals due to fiber break and fiber/matrix debonding in a model carbon fiber composite and thereby better understand the AE signals and physical phenomena. This paper presents a computational analysis of AE waveforms resulting from fiber break and fiber/matrix debonding. The objective of this research was to compare the AE signals from a validated fiber break simulation to the AE signals obtained from fiber/matrix debonding and fiber break obtained in several media and to discuss the capability to detect and identify each source.

Pre-failure indicators detected by Acoustic Emission: Alfas stone, cement-mortar and cement-paste specimens under 3-point bending

2018 ◽  
Vol 11 (40) ◽  
pp. 74-84
Author(s):  
Stavros K. Kourkoulis ◽  
Ioanna Dakanali
Keyword(s):  
Acoustic Emission ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Cement Mortar ◽  
Clear Understanding ◽  
Stone Monuments ◽  
Good Accordance ◽  
Structural Tests ◽  
Ae Signals

Acoustic Emission (AE) is the technique most widely used nowadays for Structural Health Monitoring (SHM). Application of this technique for continuous SHM of restored elements of stone monuments is a challenging task. The co-existence of different materials creates interfaces rendering “identification” of the signals recorded very complicated. To overcome this difficulty one should have a clear overview of the nature of AE signals recorded when each one of the constituent materials is loaded mechanically. In this direction, an attempt is here described to enlighten the signals recorded, in case a series of structural materials (natural and artificial), extensively used for restoration projects of classic monuments in Greece, are subjected to 3-point bending. It is hoped that obtaining a clear understanding of the nature of AE signals recorded during these elementary tests will provide a valuable tool permitting “identification” and “classification” of signals emitted in case of structural tests. The results appear encouraging. In addition, it is concluded that for all materials tested (in spite their differences in microstructure and composition) clear prefailure indicators are detected, in good accordance to similar indicators provided by other techniques like the Pressure Stimulated Currents (PSC) one.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

scholarly journals A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

2021 ◽  
Vol 9 (5) ◽  
pp. 1034
Author(s):  
Carlos Sabater ◽  
Lorena Ruiz ◽  
Abelardo Margolles
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Human Milk Oligosaccharides ◽  
Future Studies ◽  
High Fiber ◽  
Machine Learning Approach ◽  
Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

scholarly journals Deep learning for supervised classification of temporal data in ecology

2021 ◽  
Vol 61 ◽  
pp. 101252
Author(s):  
César Capinha ◽  
Ana Ceia-Hasse ◽  
Andrew M. Kramer ◽  
Christiaan Meijer
Keyword(s):  
Deep Learning ◽  
Supervised Classification ◽  
Temporal Data
Sign in / Sign up

Export Citation Format

Share Document