PERFORM Publications

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Title		Authors	PubMed ID
1	Exploiting fluctuations in gene expression to detect causal interactions between genes	Joly-Smith E; Talpur MM; Allard P; Papazotos F; Potvin-Trottier L; Hilfinger A;	41401079 BIOLOGY
2	Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings	Guo J; Fan W; Amayri M; Bouguila N;	39662201 ENCS
3	A Survey on Error Exponents in Distributed Hypothesis Testing: Connections with Information Theory, Interpretations, and Applications	Espinosa S; Silva JF; Céspedes S;	39056958 ENCS
4	Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration	Hou D; Zhan D; Wang L; Hassan IG; Sezer N;	37936825 ENCS
5	The Convergence Model of Brain Reward Circuitry: Implications for Relief of Treatment-Resistant Depression by Deep-Brain Stimulation of the Medial Forebrain Bundle	Pallikaras V; Shizgal P;	35431828 PSYCHOLOGY
6	Anterior cingulate neurons signal neutral cue pairings during sensory preconditioning	Hart EE; Gardner MPH; Schoenbaum G;	34936884 PSYCHOLOGY
7	Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images	Bourouis S; Alharbi A; Bouguila N;	34460578 ENCS
8	Exploring the decentralized treatment of sulfamethoxazole-contained poultry wastewater through vertical-flow multi-soil-layering systems in rural communities.	Song P, Huang G, An C, Xin X, Zhang P, Chen X, Ren S, Xu Z, Yang X	33065414 ENCS
9	BENIN: Biologically enhanced network inference.	Wonkap SK, Butler G	32698722 ENCS
10	Adaptive Neuro-fuzzy Inference System Trained for Sizing Semi-elliptical Notches Scanned by Eddy Currents.	Mohseni E, Viens M, Xie WF	31929668 ENCS

Title:	Adaptive Neuro-fuzzy Inference System Trained for Sizing Semi-elliptical Notches Scanned by Eddy Currents.
Authors:	Mohseni E, Viens M, Xie WF
Link:	https://www.ncbi.nlm.nih.gov/pubmed/31929668?dopt=Abstract
DOI:	10.1007/s10921-019-0648-8
Publication:	Journal of nondestructive evaluation
Keywords:	Adaptive neuro-fuzzy inference system; Eddy current testing; Finite element modeling; Split-D reflection differential probe; Tilt and lift-off study;
PMID:	31929668	Category:	J Nondestr Eval	Date Added:	2020-01-14
Dept Affiliation:	ENCS 1 1Department of Electronics and Electrical Engineering, Center for Ultrasonic Engineering (CUE), Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD UK. 2 Département de génie mécanique, L'École de technologie supérieure, 1100 Rue Notre-Dame O, Montréal, QC H3C 1 K3 Canada. 3 3Department of Mechanical & Industrial Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montréal, QC H3G 1M8 Canada.

Description:

Adaptive Neuro-fuzzy Inference System Trained for Sizing Semi-elliptical Notches Scanned by Eddy Currents.

J Nondestr Eval. 2020;39(1):5

Authors: Mohseni E, Viens M, Xie WF

Abstract

The present study explores the capability of COMSOL Multiphysics, as a finite element modelling (FEM) tool, to model the interaction between a split-D differential surface eddy current (ECT) probe and semi-elliptical surface electrical discharge machined (EDM) notches. The effect of the small probe's lift-off and tilt on its signal is investigated through modelling and subsequently, the simulation outcomes are validated using the probe's impedance measurements. In the next stage, an adaptive neuro-fuzzy inference system (ANFIS) is designed to take the signal features as inputs and consequently, provide the length of the scanned notch as the system's output. The system is trained by extracted features of thirty model-generated signals obtained from scanning of the same number of semi-elliptical notches by means of the split-D probe. The trained ANFIS is tested afterwards using the measured signals of 3 calibration EDM notches together with 5 model-based ones. A very low average estimation error is observed with regard to the length estimation of the test notches and the accuracy of the length estimation is found to be quite reasonable.

PMID: 31929668 [PubMed]

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