Application of artificial intelligence techniques in textile wastewater decolorisation fields : A systematic and citation network analysis review / Senbiao Liu in COLORATION TECHNOLOGY, Vol. 138, N° 2 (04/2022)  

[article]  inCOLORATION TECHNOLOGY > Vol. 138, N° 2 (04/2022) . - p. 117-136 Titre : Application of artificial intelligence techniques in textile wastewater decolorisation fields : A systematic and citation network analysis review Type de document : texte imprimé Auteurs : Senbiao Liu, Auteur ; Chris K. Y. Lo, Auteur ; Chi-Wai Kan, Auteur Année de publication : 2022 Article en page(s) : p. 117-136 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Comptage Décoloration Eaux usées -- Décontamination Eaux usées -- Epuration Intelligence artificielle Publications scientifiques Réseaux neuronaux (informatique) Index. décimale : 667.3 Teinture et impression des tissus Résumé : This study reviewed 155 journal articles to examine how artificial intelligence techniques are being applied in textile coloration and related fields. Distribution of the reviewed articles was assessed in terms of the type of journals, year of publication, methods, and research background. Based on the citation network analysis method, an objective approach, CitNetExplorer and VOSviewer are used to identify the clusters. It is found that artificial intelligence techniques are mainly three-layer artificial neural networks with different designs, which are mainly used in textile wastewater decolorisation fields, including wastewater treatment and colour fading, such as colour prediction and real-time monitoring of textile wastewater, dye removal efficiency, and dye degradation. Finally, the future research direction and limitations of this article are put forward. Note de contenu : - METHODOLOGY - DESCRIPTIVE STATISTICS : Distribution of articles by journal - Distribution of articles by year of publication - Distribution of articles by region - Distribution of methodologies used in textile coloration-related fields - Research fields while using artificial intelligence techniques - CLASSIFICATION OF RESEARCH DOMAINS - MAIN PATH ANALYSIS OF THE MAJOR RESEARCH DOMAINS - FUTURE DEVELOPMENT - LIMITATIONS DOI : https://doi.org/10.1111/cote.12589 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12589 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=37843 [article]

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Artificial intelligence for electrical percolation of AOT-based microemulsions prediction / Antonio Cid in TENSIDE, SURFACTANTS, DETERGENTS, Vol. 48, N° 6 (11-12/2011)

[article]  inTENSIDE, SURFACTANTS, DETERGENTS > Vol. 48, N° 6 (11-12/2011) . - p. 477-483 Titre : Artificial intelligence for electrical percolation of AOT-based microemulsions prediction Type de document : texte imprimé Auteurs : Antonio Cid, Auteur ; Gonzalo Astray, Auteur ; J. A. Manso, Auteur ; Juan Carlos Mejuto, Auteur ; Oscar Adrian Moldes, Auteur Année de publication : 2011 Article en page(s) : p. 477-483 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Microémulsions Percolation Prévision, Théorie de la Réseaux neuronaux (informatique) Surfactants Index. décimale : 668.1 Agents tensioactifs : savons, détergents Résumé : Different Artificial Neural Network architectures have been assayed to predict percolation temperature of AOT/i-C8/H2O microemulsions. A Perceptron Multilayer Artificial Neural Network with five entrance variables (W value of the microemulsions, additive concentration, molecular weight of the additive, atomic radii and ionic radii of the salt components) was used. Best ANN architecture was formed by five input neurons, two middle layers (with eleven and seven neurons respectively) and one output neuron. Root Mean Square Errors (RMSEs) are 0.188C (R = 0.9994) for the training set and 0.64 °C (R = 0.9789) for the prediction set. Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=12616 [article]

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Artificial neural network modeling of tablet coating in a pan coater / Assia Benayache in JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH, Vol. 20, N° 2 (03/2023)  

[article]  inJOURNAL OF COATINGS TECHNOLOGY AND RESEARCH > Vol. 20, N° 2 (03/2023) . - p. 485-499 Titre : Artificial neural network modeling of tablet coating in a pan coater Type de document : texte imprimé Auteurs : Assia Benayache, Auteur ; Lynda Lamoudi, Auteur ; Kamel Daoud, Auteur Année de publication : 2023 Article en page(s) : p. 485-499 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Caractérisation Comprimés Enrobage pharmaceutique Ethylcellulose Matériaux -- Epaisseur Réseaux neuronaux (informatique) Revêtements organiques Index. décimale : 667.9 Revêtements et enduits Résumé : Our study decided to use the new and revolutionary approach in the field of pharmaceutical coating processes called the artificial neural network (ANN) by using the neural networks toolbox derived from the Matlab® software. The experiments were performed using tablets of Alfuzosin Chlorhydrate as a model filler, and an aqueous solution of Surelease as a polymer in different contents. The various parameters that can affect coating thickness, weight gain, and the coefficient of variation CV, such as spray rate, air pressure, solid content, speed of the drum, pan loading, and time of coating, were studied. The properties of the coated tablets were evaluated using the ANN, and both the parameters of the coating process and the properties of the coated tablets were used as a basis for optimization, as well as the choice of the optimal structure of the ANN model. It was found that the best neural network architecture had 7 neurons in the hidden layer, with a mean square error of 3.515 and a determination coefficient of nearly 1. The relative importance of each independent variable was quantified using the Garson equation. In this study, spray rate was found to have the highest impact on the properties of tablets. Note de contenu : - MATERIALS AND METHODS : Characterization of core tablets - Coating dispersion - Coating process - Characterization of the coating solution - Characterization of coating tablets - RESULTS AND DISCUSSION : Model architecture and prediction - Relative importance of input variables - The influence of parameters on the properties of coating tablets - Optimization of coating tablets - Table 1 : Effect of solids concentration on the viscosity, density, and surface tension of coating fluids measured coating - Table 2 : Variable parameters - Table 3 : ANN model's weight and bias matrix - Table 4 : Effects of coating parameters on the relative standard deviation at the final process stage - Table 5 : Ideal values of inputs, predicted and experimental values of outputs DOI : https://doi.org/10.1007/s11998-022-00683-1 En ligne : https://link.springer.com/content/pdf/10.1007/s11998-022-00683-1.pdf?pdf=button Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=39293 [article]

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Artificial neural networks for colour prediction in leather dyeing on the basis of a tristimulus system / Malathy Jawahar in COLORATION TECHNOLOGY, Vol. 131, N° 1 (02/2015)  

[article]  inCOLORATION TECHNOLOGY > Vol. 131, N° 1 (02/2015) . - p. 48-57 Titre : Artificial neural networks for colour prediction in leather dyeing on the basis of a tristimulus system Type de document : texte imprimé Auteurs : Malathy Jawahar, Auteur ; Chandra Babu Narasimhan Kannan, Auteur ; Mehta Kondamudi Manobhai, Auteur Année de publication : 2015 Article en page(s) : p. 48-57 Langues : Anglais (eng) Catégories : Couleur Couleur -- Analyse Echantillonnage Essais (technologie) Prévision, Théorie de la Qualité -- Contrôle Réseaux neuronaux (informatique) Teinture -- Fibres textiles Index. décimale : 667.3 Teinture et impression des tissus Résumé : Computer-assisted colour prediction and quality control have become increasingly important to the dyeing process in many consumer goods manufacturing industries, including textile and leather. The most challenging aspect concerns dye recipe prediction for the production of the required shade on a given substrate. Computer recipe prediction based on the conventional and widely used Kubelka–Munk model often fails under a variety of conditions. In the present investigation, an attempt has been made to develop an artificial neural network model to predict colour in terms of tristimulus values (X, Y, Z) given the concentration of dyes. An artificial neural network model was trained with 300 pairs of known input vectors, i.e. dye concentrations, and output vectors, i.e. colour parameters, using a backpropagation algorithm. The artificial neural network topology consists of three neurons in the input layer to represent the concentration of dyes, three neurons in the output layer to represent the tristimulus values X, Y, and Z, and five neurons in the hidden layer with a log-sigmoid transfer function. The artificial neural network results showed a good level of colour prediction during the training and testing phase. The results also indicate that the artificial neural network has the potential to give better predictive performance than the conventional Kubelka–Munk model. Note de contenu : - INTRODUCTION : Conventional colour prediction model (the Kubelka-Munk model) - Neural network and colour prediction - EXPERIMENTAL : Materials - Leather sample preparation - Colour analysis - Database generation for the Kubelka-Munk model - Artificial neural network design - RESULTS AND DISCUSSION : Conventional colour prediction using the Kubelka-Munk model - ANN design optimisation - Artificial neural network training - Validation of the Kubelka-Munk and artificial neural network models DOI : 10.1111/cote.12123 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12123 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=22983 [article]

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Artificial neural networks for optimising camera-based colour measurements of prints enhanced with pearlescent pigments / Ivana Tomic in COLORATION TECHNOLOGY, Vol. 134, N° 5 (10/2018)  

[article]  inCOLORATION TECHNOLOGY > Vol. 134, N° 5 (10/2018) . - p. 364-372 Titre : Artificial neural networks for optimising camera-based colour measurements of prints enhanced with pearlescent pigments Type de document : texte imprimé Auteurs : Ivana Tomic, Auteur ; Sandra Dedijer, Auteur ; Dragoljub Novakovic ; Ivana Juric Année de publication : 2018 Article en page(s) : p. 364-372 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Colorimétrie Photographie numérique Pigments nacrés Réseaux neuronaux (informatique) Index. décimale : 667.3 Teinture et impression des tissus Résumé : Pearlescent pigments are widely used in printing due to their optical, chemical and physical properties. To analyse the effects of goniochromism they produce, the colorimetric characterisation of materials printed with pearlescent pigments requires multi-angular measurements. In this study, the colours of prints enhanced with pearlescent pigments were measured by means of a digital camera, relying on the empirical camera characterisation method. Since this method is time-consuming, it was altered to enable estimates of colorimetric values for different geometries to be measured on the basis of images captured at one viewing angle. This approach was based on the use of artificial neural networks which were shown to provide sufficient flexibility for the given task. The results indicate that the images obtained at the viewing angle of 45° aspecular (measuring geometry 45°/asp 45°) accurately estimate CIELab values for all of the tested measuring geometries. The proposed method is therefore not only time-efficient but also reduces the associated errors due to the camera's movement, and enables the estimation of colorimetric values for those viewing angles inaccessible by camera. DOI : 10.1111/cote.12346 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12346 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=31161 [article]

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Automatic fabric defect detection using a deep convolutional neural network / Jun-Feng Jing in COLORATION TECHNOLOGY, Vol. 135, N° 3 (06/2019)  

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Comparative study of neural networks and least mean square algorithm applied to the optimization of cosmetic formulations / A. C. Balfagón in INTERNATIONAL JOURNAL OF COSMETIC SCIENCE, Vol. 32, N° 5 (10/2010)  

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Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation / Praveen Kumar Moganam in JOURNAL OF LEATHER SCIENCE AND ENGINEERING, Vol. 4 (Année 2022)  

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Developing formulations with an artificial brain in KUNSTSTOFFE INTERNATIONAL, Vol. 102, N° 8 (08/2012)

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Diving deeper into VOCs / Jessica Lum in COATINGS TECH, Vol. 19, N° 4 (04/2022)  

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Dyeing recipe prediction of cotton fabric based on hyperspectral colour measurement and an improved recurrent neural network / Jianxin Zhang in COLORATION TECHNOLOGY, Vol. 137, N° 2 (04/2021)  

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Ecological evaluation of leather industry by neural network / Cheng Shuqiang in JOURNAL OF THE SOCIETY OF LEATHER TECHNOLOGISTS & CHEMISTS (JSLTC), Vol. 104, N° 1 (01-02/2020)  

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Estimation of friction and wear properties of additively manufactured recycled-ABS parts using artificial neural network approach : effects of layer thickness, infill rate, and building direction / Cagin Bolat in INTERNATIONAL POLYMER PROCESSING, Vol. 39, N° 3 (2024)  

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Evaluation of leather ecosystem based on BP neural network / Juan Luo in JOURNAL OF THE SOCIETY OF LEATHER TECHNOLOGISTS & CHEMISTS (JSLTC), Vol. 104, N° 2 (03-04/2020)  

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Influence prediction of small organic molecules (ureas and thioureas) upon electrical percolation of AOT-based microemulsions using artificial neural networks / Iago Antonio Montoya in TENSIDE, SURFACTANTS, DETERGENTS, Vol. 49, N° 4 (07-08/2012)

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Investigation and feed-forward neural network-based estimation of dyeing properties of air plasma treated wool fabric dyed with natural dye obtained from Hibiscus sabdariffa / Zeynep Omerogullari Basyigit in COLORATION TECHNOLOGY, Vol. 139, N° 4 (08/2023)  

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Machine vision inspection system for detection of leather surface defects / Malathy Jawahar in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA), Vol. CXIV, N° 1 (01/2019)  

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Mesure à l'aide d'un nez électronique / Laurent Moy in PARFUMS COSMETIQUES AROMES, N° 115 (02-03/94)

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Modeling, prediction, and analysis of alkyd enamel coating properties via neural computing / Javier E. Vitela in JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH, Vol. 3, N° 2 (04/2006)  

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Neural network approach to a colorimetric value transform based on a large-scale spectral dataset / Qiang Liu in COLORATION TECHNOLOGY, Vol. 133, N° 1 (02/2017)  

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