Wavelet Coherence and Fuzzy Subtractive Clustering for Defect Classification in Aeronautic CFRP

Cacciola, Matteo; Calcagno, Salvatore; Megali, Giuseppe; Pellicanó, Diego; Versaci, Mario; Morabito, Francesco Carlo

doi:10.1109/cisis.2010.234

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…These works, are concerned primarily with the generation of fuzzy ontologies, usually by extending the traditional formal concept analysis and by employing fuzzy similarity measures to establish object-membership and hierarchical relations between concepts. Also, recent works in the engineering domain [30,31] provide further insight on fuzzy divergence and similarity among objects, which has not been addressed before [30].…”

Section: Fuzzy Domain Relevant Workmentioning

confidence: 99%

An Ontological Analysis and Natural Language Processing of Figures of Speech

Panayiotou¹

2020

IJAIA

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The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional figures of speech via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper shows that a set of rules based on WordNet classes and an ontology representing human behaviour and properties, can be used to identify figures of speech due to the discrepancies in the semantic relations of the concepts involved. Based on this realization, the paper describes a method for determining poetic vs. non-poetic prepositional figures of speech, using WordNet class hierarchies. The paper also addresses the problem of inconsistency resulting from the assertion of figures of speech in ontological knowledge bases, identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem.

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Section: Fuzzy Domain Relevant Workmentioning

confidence: 99%

An Ontological Analysis and Natural Language Processing of Figures of Speech

Panayiotou¹

2020

IJAIA

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“…Table 2 shows the comparison of our current work with other previous studies available in the current scientific state-of-the-art. [18] 90.9 support vector machines [19] 90.8 wavelet coherence and fuzzy clustering [20] 98.85 statistical classifier [21] 99.36…”

Section: Implementation Of Classification Softwarementioning

confidence: 99%

Incremental Bayesian learning for in‐service analysis of aeronautic composites

Cacciola

Megali

Lay-Ekuakille

2013

IET Science, Measurement & Technology

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Incremental learning could be really useful for fault detection and anticipation in non-destructive testing and evaluation. The real-time monitoring could be proficiently exploited when an early warning system is required for the human safety. This is the case of aeronautic transportation of persons and goods. Here, an automated neural-based system for defect detection in aeronautic composites is proposed. The entire system consists of a stand-alone defect classifier based on a Bayesian neural network (BNN) combined with advantages of Very Large Scale Integration (VLSI )implementation. Exploiting a parallel implementation is worthwhile when high computational speed, special operating conditions, portability, limited physical size, low-power dissipation and reliability are required. This study shows how hardware-based neural network can increase processing speed and defect identification rate. Secondary random access memory-based field programmable gate arrays represent a suitable platform to realise these models, since their re-programmability can rapidly change the parameters of the network if a new training is needed. With the hardware-based BNN, 100% of delamination bottom/top, inclusion bottom/middle/top, porosity and 99.6% of delamination middle were correctly identified. The achieved results highlight the efficient design of the hardware network, obtained also using a new circuit to compute the activation function of neurons.

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“…Cacciola et al [8] [9] proposed an heuristic approach for classifying the ultrasonic echoes measured on defective CFRP specimen. The proposed method was based on the use of DWT and PCA for feature extraction and selection.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Transform Based Feature Extraction for Ultrasonic Flaw Signal Classification

Yu¹

2014

JCP

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In this paper, an automatic system is designed to classify the ultrasonic flaw signals from carbon fiber reinforced polymer (CFRP) specimens with void, delamination and debonding. In such system, different methods based on discrete wavelet transform (DWT) and wavelet packet transform (WPT) are first utilized for feature extraction. After that, the linear mapping is applied for dimensionality reduction. Artificial neural networks (ANNs) and support vector machines (SVMs) are trained to validate the effectiveness of different wavelet transform based features for flaw signal classification. Experimental results show that the normalized energy of WPT coefficients coupled with the statistical parameters of WPT representation of original signals can be taken as the reliable features to effectively classify different ultrasonic flaw signals with lower training elapsed time.

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Wavelet Coherence and Fuzzy Subtractive Clustering for Defect Classification in Aeronautic CFRP

Cited by 8 publications

References 10 publications

An Ontological Analysis and Natural Language Processing of Figures of Speech

An Ontological Analysis and Natural Language Processing of Figures of Speech

Incremental Bayesian learning for in‐service analysis of aeronautic composites

Wavelet Transform Based Feature Extraction for Ultrasonic Flaw Signal Classification

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