Design parameters for continuously variable power-split transmissions using planetaries with 3 active shafts

Since 1996, when the first agricultural tractor with CVT transmission was shown, the presence of this type of transmissions has been increasing. All companies offer them in their products range. Nevertheless, there is little technical documentation that explains the basics of its operation. This report shows all types of CVT transmissions: non-power-split type and power-split ones, as well as the three types used in agricultural tractors, hydro-mechanical power-split transmissions (3 active shafts, input coupled planetary; 3 active shafts, output coupled planetary and 4 active shafts). The report also describes the design parameters of a type of CVT transmission, which use a power-split system with 3 active shafts as well as the fundamental relations among them. Crown Copyright 2010 Published by Elsevier Ltd. on behalf of ISTVS. All rights reserved.

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.