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Artificial neural networks applied for simultaneous analysis of mixtures of nitrophenols by conductometric acid — base titration / Gholamhossein Rounaghi in Industrial & engineering chemistry research, Vol. 50 N° 19 (Octobre 2011)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11375-11381 Titre : Artificial neural networks applied for simultaneous analysis of mixtures of nitrophenols by conductometric acid — base titration Type de document : texte imprimé Auteurs : Gholamhossein Rounaghi, Auteur ; Roya Mohammad Zadeh Kakhki, Auteur ; Tahereh Heidari, Auteur Année de publication : 2011 Article en page(s) : pp. 11375-11381 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Neural network Résumé : In this study, the simultaneous conductometric titration method for determination of mixtures of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6- trinitrophenol based on principal component artificial neural network (ANN) calibration model was proposed. The three-layered feed-forward ANN trained by back-propagation learning was used to model the complex nonlinear relationship between the concentration of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol in their ternary mixtures and the conductance of the solutions at different volumes of titrant. The principal components of the conductance matrix were used as the input of the network. The network architecture and parameters were optimized to give low prediction error. The optimized networks predicted the concentrations of nitrophenols in synthetic mixtures. The results showed that the usedANN can proceed the titration data with low relative prediction errors (5.53%, 4.03%, and 4.71% for 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol, respectively) and satisfactory recoveries. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573334 [article] Artificial neural networks applied for simultaneous analysis of mixtures of nitrophenols by conductometric acid — base titration [texte imprimé] / Gholamhossein Rounaghi, Auteur ; Roya Mohammad Zadeh Kakhki, Auteur ; Tahereh Heidari, Auteur . - 2011 . - pp. 11375-11381. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11375-11381 Mots-clés : Neural network Résumé : In this study, the simultaneous conductometric titration method for determination of mixtures of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6- trinitrophenol based on principal component artificial neural network (ANN) calibration model was proposed. The three-layered feed-forward ANN trained by back-propagation learning was used to model the complex nonlinear relationship between the concentration of 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol in their ternary mixtures and the conductance of the solutions at different volumes of titrant. The principal components of the conductance matrix were used as the input of the network. The network architecture and parameters were optimized to give low prediction error. The optimized networks predicted the concentrations of nitrophenols in synthetic mixtures. The results showed that the usedANN can proceed the titration data with low relative prediction errors (5.53%, 4.03%, and 4.71% for 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol, respectively) and satisfactory recoveries. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573334

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Enhancing deep learning based classifiers using out of distribution data detection / Abdelghani Halimi (2022) 

Public ISBD Titre : Enhancing deep learning based classifiers using out of distribution data detection Type de document : document électronique Auteurs : Abdelghani Halimi, Auteur ; Ahmed Hadjadj, Auteur ; Sid-Ahmed Berrani, Directeur de thèse Editeur : [S.l.] : [s.n.] Année de publication : 2022 Importance : 1 fichier PDF (10.3 Mo) Présentation : ill. Note générale : Mode d'accès : accès au texte intégral par intranet. Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2022 Bibliogr. f. 80 - 84 . - Annexe f. 85 - 94 Langues : Anglais (eng) Mots-clés : Neural network  Out-of-distribution detection Image data Comparative evaluation Classification Proposed method Web interface Index. décimale : PN00322 Résumé : In this work, three out-of distribution detection methods are implemented, evaluated and compared on several common benchmarks (different natural image datasets), as well as on the ImageNet-O dataset, a novel dataset that has been created to aid research in OOD detection for ImageNet models. In this thesis, we also investigate the effect of label space size on the OOD detection performance, for that we used three different in-distribution datasets (CIFAR-10, CIFAR-100 and ImageNet-1K), and we showed that the performance degrades rapidly as the number of in-distribution classes increases. We concluded by proposing a method that surpasses the three previous methods in detection performances and by creating a web user interface to test out our OOD detection method. Enhancing deep learning based classifiers using out of distribution data detection [document électronique] / Abdelghani Halimi, Auteur ; Ahmed Hadjadj, Auteur ; Sid-Ahmed Berrani, Directeur de thèse . - [S.l.] : [s.n.], 2022 . - 1 fichier PDF (10.3 Mo) : ill. Mode d'accès : accès au texte intégral par intranet. Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2022 Bibliogr. f. 80 - 84 . - Annexe f. 85 - 94 Langues : Anglais (eng) Mots-clés : Neural network  Out-of-distribution detection Image data Comparative evaluation Classification Proposed method Web interface Index. décimale : PN00322 Résumé : In this work, three out-of distribution detection methods are implemented, evaluated and compared on several common benchmarks (different natural image datasets), as well as on the ImageNet-O dataset, a novel dataset that has been created to aid research in OOD detection for ImageNet models. In this thesis, we also investigate the effect of label space size on the OOD detection performance, for that we used three different in-distribution datasets (CIFAR-10, CIFAR-100 and ImageNet-1K), and we showed that the performance degrades rapidly as the number of in-distribution classes increases. We concluded by proposing a method that surpasses the three previous methods in detection performances and by creating a web user interface to test out our OOD detection method.

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Intelligent image-based gas-liquid two-phase flow regime recognition / Soheil Ghanbarzadeh in Transactions of the ASME . Journal of fluids engineering, Vol. 134 N° 6 (Juin 2012)  

Public ISBD [article]  inTransactions of the ASME . Journal of fluids engineering > Vol. 134 N° 6 (Juin 2012) . - 10 p. Titre : Intelligent image-based gas-liquid two-phase flow regime recognition Type de document : texte imprimé Auteurs : Soheil Ghanbarzadeh, Auteur ; Pedram Hanafizadeh, Auteur ; Mohammad Hassan Saidi, Auteur Année de publication : 2012 Article en page(s) : 10 p. Note générale : fluids engineering Langues : Anglais (eng) Mots-clés : two-phase flow  flow regime  image processing  fuzzy logic  neural network Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : Identification of different flow regimes in industrial systems operating under two-phase flow conditions is necessary in order to safely design and optimize their performance. In the present work, experiments on two-phase flow have been performed in a large scale test facility with the length of 6 m and diameter of 5 cm. Four main flow regimes have been observed in vertical air-water two-phase flow at moderate superficial velocities of gas and water namely: Bubbly, Slug, Churn, and Annular. An image processing technique was used to extract information from each picture. This information includes the number of bubbles or objects, area, perimeter, as well as the height and width of objects (second phase). In addition, a texture feature extraction procedure was applied to images of different regimes. Some features which were adequate for regime identification were extracted such as contrast, energy, entropy, etc. To identify flow regimes, a fuzzy interface was introduced using characteristic of second phase in picture. Furthermore, an Adaptive Neuro Fuzzy (ANFIS) was used to identify flow patterns using textural features of images. The experimental results show that these methods can accurately identify the flow patterns in a vertical pipe. DEWEY : 620.1 ISSN : 0098-2202 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JFEGA4000134000006 [...] [article] Intelligent image-based gas-liquid two-phase flow regime recognition [texte imprimé] / Soheil Ghanbarzadeh, Auteur ; Pedram Hanafizadeh, Auteur ; Mohammad Hassan Saidi, Auteur . - 2012 . - 10 p. fluids engineering Langues : Anglais (eng) in Transactions of the ASME . Journal of fluids engineering > Vol. 134 N° 6 (Juin 2012) . - 10 p. Mots-clés : two-phase flow  flow regime  image processing  fuzzy logic  neural network Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : Identification of different flow regimes in industrial systems operating under two-phase flow conditions is necessary in order to safely design and optimize their performance. In the present work, experiments on two-phase flow have been performed in a large scale test facility with the length of 6 m and diameter of 5 cm. Four main flow regimes have been observed in vertical air-water two-phase flow at moderate superficial velocities of gas and water namely: Bubbly, Slug, Churn, and Annular. An image processing technique was used to extract information from each picture. This information includes the number of bubbles or objects, area, perimeter, as well as the height and width of objects (second phase). In addition, a texture feature extraction procedure was applied to images of different regimes. Some features which were adequate for regime identification were extracted such as contrast, energy, entropy, etc. To identify flow regimes, a fuzzy interface was introduced using characteristic of second phase in picture. Furthermore, an Adaptive Neuro Fuzzy (ANFIS) was used to identify flow patterns using textural features of images. The experimental results show that these methods can accurately identify the flow patterns in a vertical pipe. DEWEY : 620.1 ISSN : 0098-2202 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JFEGA4000134000006 [...]

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