A generic fusion problem is studied for multiple sensors whose outputs are probabilistically related to their inputs according to unknown distributions. Sensor measurements are provided as iid input-output samples, and an empirical risk minimization method is described for designing fusers with distribution-free performance bounds. The special cases of isolation and projective fusers for classi...

optimization of structures for minimum weight has become important in the recent designs. in this study, a genetic optimization algorithm for weight minimization of steel frames has been used. the genetic algorithm is an optimization and search technique based on the principles of genetics and natural selection. constraints regarding material strength and serviceability are taken from “aisc cod...

This article deals with the optimal transfer of a satellite between Keplerian orbits using low propulsion. It is based on preliminary results of Geffroy [Généralisation des techniques de moyennation en contrôle optimal, application aux problèmes de rendez-vous orbitaux à poussée faible, Ph.D. Thesis, Institut National Polytechnique de Toulouse, France, Octobre 1997] where the optimal trajectori...

In this paper, we study a two-category classification problem. We indicate the categories by labels Y = 1 and Y = −1. We observe a covariate, or feature, X ∈ X ⊂ R. Consider a collection {ha} of classifiers indexed by a finite-dimensional parameter a, and the classifier ha∗ that minimizes the prediction error over this class. The parameter a∗ is estimated by the empirical risk minimizer ân over...

We address the problem of sequential prediction in the heteroscedastic setting, when both the signal and its variance are assumed to depend on explanatory variables. By applying regret minimization techniques, we devise an efficient online learning algorithm for the problem, without assuming that the error terms comply with a specific distribution. We show that our algorithm can be adjusted to ...

In the paper we discuss a new approach for learning classification rules from examples. We sketch out the algorithm JoJo and its extension to a four step procedure which can be used to incrementally refine a set of classification rules. Incorrect rules are refined, the entire rule set is completed, redundant rules are deleted and the rule set can be minimized. The first two steps are done by ap...

Automatic and semi-automatic magnetic resonance angiography (MRA) segmentation techniques can potentially save radiologists large amounts of time required for manual segmentation and can facilitate further data analysis. Our proposed MRA segmentation method uses a mathematical modeling technique which is well-suited to the complicated curve-like structure of blood vessels. Specifically, we defi...

the forced convection heat transfer of turbulent al2o3-water nanofluid flow inside the grooved tubeswith the different aspect ratio of the rectangular grooves is numerically investigated. the governingequations have been solved using finite volume method (fvm) coupled with simple algorithm. it isassumed the heat flux is constant on the grooved walls. the single-phase approach is applied for the...

We present a fast incremental algorithm for constructing minimal DFCA for a given language. Since it was shown that the minimal DFCA for a language L has less states than the minimal DFA for the same language L, this technique seems to be the best choice for incrementally building the automaton for a large language, especially when the number of states in the DFCA is significantly less than the...

| A generalization of the classical state minimization problem for Finite State Machine (FSM) is proposed and discussed. In contrast to classical state minimization algorithms that minimize in one dimension (states), our algorithmminimizes the FSM in two dimensions: the numbers of both input symbols and internal state symbols are minimized in an iterative sequence of input minimization and stat...