<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kulkarni, A.</style></author><author><style face="normal" font="default" size="100%">Jayaraman, Valadi K.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, B. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process</style></title><secondary-title><style face="normal" font="default" size="100%">Computers &amp; Chemical Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">fault detection</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge</style></keyword><keyword><style  face="normal" font="default" size="100%">support vector machines</style></keyword><keyword><style  face="normal" font="default" size="100%">Tennessee Eastman Process</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">10</style></number><publisher><style face="normal" font="default" size="100%">PERGAMON-ELSEVIER SCIENCE LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND</style></pub-location><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">2128-2133</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A support vector machine with knowledge incorporation is applied to detect the faults in Tennessee Eastman Process, a benchmark problem in chemical engineering. The knowledge incorporated algorithm takes advantage of the information on horizontal translation invariance in tangent direction of the instances in dataset. This essentially changes the representation of the input data while training the algorithm. These local translations do not alter the class membership of the instances in the dataset. The results on binary as well as multiple fault detection justify the use of knowledge incorporation. (c) 2005 Elsevier Ltd. All rights reserved.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">2.581</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Firdous, R.</style></author><author><style face="normal" font="default" size="100%">Aherrao, S.</style></author><author><style face="normal" font="default" size="100%">Narute, B.</style></author><author><style face="normal" font="default" size="100%">Borkar, V.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault detection and analysis of Kapton film in fuel cells using image processing</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Computing, Analytics and Security Trends, CAST 2016</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Dimensional accuracy</style></keyword><keyword><style  face="normal" font="default" size="100%">Fault analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">fault detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Fossil energy</style></keyword><keyword><style  face="normal" font="default" size="100%">fuel cell</style></keyword><keyword><style  face="normal" font="default" size="100%">Gas fuel purification</style></keyword><keyword><style  face="normal" font="default" size="100%">image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Image processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Image processing tools</style></keyword><keyword><style  face="normal" font="default" size="100%">Kapton films</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane electrode assemblies</style></keyword><keyword><style  face="normal" font="default" size="100%">Offline</style></keyword><keyword><style  face="normal" font="default" size="100%">polyimides</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">28 April 2017</style></date></pub-dates></dates><isbn><style face="normal" font="default" size="100%">978-150901338-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Fuel cell is one of the best alternatives of fossil energy in the modern scenario. But still there are some shortcomings such as durability and reliability, which block the wide application of fuel cells. To overcome these barriers, fault diagnosis is an efficient solution. This paper proposes a method for finding dimensional accuracies in Kapton films in membrane electrode assemblies (MEA) of fuel cells using image processing tools for offline fault analysis. © 2016 IEEE.&lt;/p&gt;</style></abstract><custom3><style face="normal" font="default" size="100%">Indian </style></custom3></record></records></xml>