By Max Planck

ISBN-10: 0486678679

ISBN-13: 9780486678672

Planck M., Jones R., Williams D.H. A Survey of actual concept (Dover, 1994)(ISBN 0486678679)(600dpi)(T)(126s)

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Mie theoretical matches to one-dimensional (row) slices of measured two-dimensional diffraction patterns from polyethylene glycol particles produced from microdroplets of solution with different PEG weight fractions A key issue in forming homogeneous composites from co-dissolved bulk-immiscible polymers from solution is that the droplet evaporation rate must be faster than the polymer self-organization time scale. Since the time scale for solvent evaporation is proportional to 1/(r3/2), (r is the droplet radius), the most straightforward way to satisfy this condition is to make droplets smaller.

D. Barnes, K. G. W. 5 Successful Training Techniques One of the dangers in backpropagation training of CNNs is the tendency to overﬁt the training set. Over-ﬁtting is due to the combination of the nonlinear modeling properties of the network over long training times. It can also result from a training set that does not totally represent the relevant population or an oversized network Cross-validation can minimize over-ﬁtting. A cross-validation data set is drawn from the same population as the training set, but it is not used for training.

To methods were used to determine predictive models for the weight fractions of the powders having micrometer ranges speciﬁed by Y1 through Y5 (forward prediction mode) and for prediction of process variables such as X1 (temperature), X2 (melt stream size), and X3 (material type) based on weight fractions of powders having the micrometer ranges already speciﬁed above (reverse prediction). In the forward prediction mode, two methods were used to develop a model, partial least squares regression (PLS) and CNNs (three inputs, ﬁve hidden, and ﬁve outputs trained using k-fold cross validation and a Levenberg-Marquardt optimization method) as already described.

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