The work reported here presents a methodology based on a two-fluid model to assess the degree of influence of various geometric and control features of an urban network on the quality of traffic service. The two-fluid model gives a curvilinear relation between the trip time and stop time per unit distance and its parameters characterize the quality of traffic service in urban networks. Any Chan...

The computer industry has defined the IEEE 802.16 family of standards that will enable mobile devices to access a broadband network as an alternative to digital subscriber line technology. As the mobile devices join and leave a network, security measures must be taken to ensure the safety of the network against unauthorized usage by encryption and group key management. IEEE 802.16e uses Multica...

This paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) by using a large wide of experimental investigations. The input parameters were selected based on past studies such as outer diameter of column, compressive strength...

The paper introduces Voting EM, an adaptive online learning algorithm of Bayesian network parameters. Voting EM is an extension of the EM( ) algorithm suggested by [1]. We show convergence properties of the Voting EM that uses a constant learning rate. We use the convergence properties to formulate an error driven scheme for adapting the learning rate. The resultant algorithm converges with the...

Bayesian networks typically require thousands of probability parameters for their specification, many of which are bound to be inaccurate. Knowledge of the direction of change in an output probability of a network occasioned by changes in one or more of its parameters, i.e. the qualitative effect of parameter changes, has been shown to be useful both for parameter tuning and in pre-processing f...

The first order design problem in geodesy is generalized here, to seek the network configuration that optimizes the precision of geophysical parameters. An optimal network design that satisfies intuitively appropriate criteria corresponds to minimizing the sum of logarithmic variances of eigenparameters. This is equivalent to maximizing the determinant of the design matrix, allowing for closed-...

The problem of learning Bayesian networks with hidden variables is known to be a hard problem. Even the simpler task of learning just the conditional probabilities on a Bayesian network with hidden variables is hard. In this paper, we present an approach that learns the conditional probabilities on a Bayesian network with hidden variables by transforming it into a multi-layer feedforward neural...

We propose a principled approach for learning parameters in Bayesian networks from incomplete datasets, where the examples of a dataset are subject to equivalence constraints. These equivalence constraints arise from datasets where examples are tied together, in that we may not know the value of a particular variable, but whatever that value is, we know it must be the same across different exam...