Autor: Amer, F.A. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: New arylhydrazonothiazolidin-5-one disperse dyes for dyeing polyester fibers
Autorzy:: Metwally, M.A.
Khalifa, M.E.
Attia, E.A.
Amer, F.A.
Powiązania:: https://bibliotekanauki.pl/articles/778416.pdf
Data publikacji:: 2010
Wydawca:: Zachodniopomorski Uniwersytet Technologiczny w Szczecinie. Wydawnictwo Uczelniane ZUT w Szczecinie
Tematy:: tiazolidyn-5-on
sole diazoniowe
barwniki zawiesinowe
włókna poliestrowe
właściwości barwne
thiazolidin-5-one
diazonium salts
azo coupling
disperse dyes
polyester fibers
fastness properties
color properties
Opis:: A series of new thiazolidin-5-one disperse dyes was synthesized from the reactions of 2-substituted 3-phenylthiazolidinones with various aryldiazonium chlorides. The synthesized dyes were characterized by UV-visible absorption, IR, NMR and MS spectroscopy. The dyes gave orange to reddish-violet shades with very good depth on polyester fibers. The dyed fabrics show moderate to good fastness to light and very good to excellent fastness to washing and perspiration. Also, the assessment of color coordinates was discussed.
Źródło:: Polish Journal of Chemical Technology; 2010, 12, 1; 1-6
1509-8117
1899-4741
Pojawia się w:: Polish Journal of Chemical Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
Autorzy:: Amer, F. Z.
El-Garhy, A. M.
Awadalla, M. H.
Rashad, S. M.
Abdien, A. K.
Powiązania:: https://bibliotekanauki.pl/articles/147652.pdf
Data publikacji:: 2011
Wydawca:: Instytut Chemii i Techniki Jądrowej
Tematy:: support vector machine (SVM)
fault classification
multi fault classification
genetic algorithm (GA)
machine learning
Opis:: Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.
Źródło:: Nukleonika; 2011, 56, 4; 323-332
0029-5922
1508-5791
Pojawia się w:: Nukleonika
Dostawca treści:: Biblioteka Nauki

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