scholarly journals Search Algorithm for Image Recognition Based on Learning Algorithm for Multivariate Data Analysis

10.5772/52179 ◽  
2013 ◽  
Author(s):  
Juan G. ◽  
E. Guzman-Ramirez ◽  
Oleksiy Pogrebnyak
Keyword(s):  
Data Analysis ◽  
Image Recognition ◽  
Multivariate Data Analysis ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Multivariate Data

scholarly journals Gestion supervisée d’une unité de coagulation pour la potabilisation des eaux à partir d’une méthodologie d’apprentissage et d’expertise

2008 ◽  
Vol 20 (4) ◽  
pp. 325-338
Author(s):  
Bouchra Lamrini ◽  
Marie-Véronique Le Lann ◽  
El Khadir Lakhal ◽  
Ahmed Benhammou
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Learning Algorithm ◽  
Multivariate Data ◽  
Suspension Ph

Résumé Le travail présenté propose une méthodologie de classification par apprentissage qui permet l’identification des états fonctionnels sur une unité de coagulation impliquée dans le traitement des eaux de surface. La supervision et le diagnostic de ce procédé ont été réalisés en utilisant la méthode de classification LAMDA (Learning Algorithm for Multivariate Data Analysis). Cette méthodologie d’apprentissage et d’expertise permet d’exploiter et d’agréger toutes les informations provenant du procédé et de son environnement ainsi que les connaissances de l’expert. L’étude montre qu’il est possible d’ajouter aux informations issues des capteurs classiques (température, matières en suspension, pH, conductivité, oxygène dissous), la valeur de la dose de coagulant calculée par un capteur logiciel développé dans une étude antérieure afin d’affiner le diagnostic. Le site d’application choisi pour l’identification des états fonctionnels est la station de production d’eau potable Rocade de la ville de Marrakech, Maroc.

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

scholarly journals Mathematical prediction of ileal energy and protein digestibility in broilers using multivariate data analysis

2021 ◽  
pp. 101106
Author(s):  
Naja Bloch Pedersen ◽  
Faegheh Zaefarian ◽  
Adam Christian Storm ◽  
Velmurugu Ravindran ◽  
Aaron J. Cowieson
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Protein Digestibility
Sign in / Sign up

Export Citation Format

Share Document