1000 resultados para Peel, Joseph Alexander.


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The peel test is commonly used to determine the strength of adhesive joints. In its simplest form, a thin flexible strip which has been bonded to a rigid surface is peeled from the substrate at a constant rate and the peeling force which is applied to the debonding surfaces by the tension in the tape is measured. Peeling can be carried out with the peel angle, i.e. the angle made by the peel force with the substrate surface, from any value above about 10° although peeling tests at 90 and 180° are most common. If the tape is sufficiently thin for its bending resistance to be negligibly small then as well as the debonding or decohesion energy associated with the adhesive in and around the point of separation, the relation between the peeling force and the peeling angle is influenced both by the mechanical properties of the tape and any pre-strain locked into the tape during its application to the substrate. The analytic solution for a tape material which can be idealised as elastic perfectly-plastic is well established. Here, we present a more general form of analysis, applicable in principle to any constitutive relation between tape load and tape extension. Non-linearity between load and extension is of increasing significance as the peel angle is decreased: the model presented is consistent with existing equations describing the failure of a lap joint between non-linear materials. The analysis also allows for energy losses within the adhesive layer which themselves may be influenced by both peel rate and peel angle. We have experimentally examined the application of this new analysis to several specific peeling cases including tapes of cellophane, poly-vinyl chloride and PTFE. © 2005 Elsevier Ltd. All rights reserved.

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http://www.archive.org/details/alexandermackay00unknuoft/

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http://www.archive.org/details/experiencesofab00hiltuoft

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As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.