Synthesis and thermal stability of ball-milled and melt-quenched amorphous and nanostructured Al-Ni-Nd-Co alloys

In this work the Al₈₅Ni₉Nd₄Co₂ alloy was used as a starting point for examining the possibility of forming bulk glassy Al-based materials by combining rapid quenching and ball milling techniques. Fine glassy powders were obtained by ball milling melt-spun amorphous ribbons using a severe cryogenic processing regime. The thermal stability data of the powders as obtained by constant-rate heating and isothermal DSC experiments together with viscosity measurements are discussed with respect to feasible consolidation conditions. The powder compaction was done by two methods (uni-axial hot pressing and extrusion) at 513 K for up to 15 min. Only the uni-axial hot pressing led to bulk Al₈₅Ni₉Nd₄Co₂ samples with similar glassy structure and Vickers microhardness values comparable to those of the initial melt-spun ribbons.     

Mining evolving data streams for frequent patterns

A data stream is a potentially uninterrupted flow of data. Mining this flow makes it necessary to cope with uncertainty, as only a part of the stream can be stored. In this paper, we evaluate a statistical technique which biases the estimation of the support of patterns, so as to maximize either the precision or the recall, as chosen by the user, and limit the degradation of the other criterion. Theoretical results show that the technique is not far from the optimum, from the statistical standpoint. Experiments performed tend to demonstrate its potential, as it remains robust even under significant distribution drifts.     

On the number of broadcast schemes in networks

Broadcasting is the process of spreading one piece of information among a group of individuals connected by an interconnection network. In this paper we give exact lower and upper bounds for the number of broadcast schemes in arbitrary networks. Also, we give the exact value for complete bipartite graphs and an upper bound for regular networks. Based on the counting method we describe a new random algorithm for broadcasting in networks.     

The Effect of Small Additions of Fe and Heavy Deformation on the Precipitation in an Al–1.1Mg–0.5Cu–0.3Si At. Pct Alloy

Metallurgical and Materials Transactions A - The effect of 0.03 and 0.08 at. pct Fe additions on the formation of secondary phases in an Al–1.1Mg–0.5Cu–0.3Si at. pct alloy was...     

Data-driven latent-variable model-based predictive control for continuous processes

A model-based predictive control methodology in the space of the latent variables for continuous processes is presented. Implementing identification and control in the latent variable space eases identification in the case of correlation in the data set, acts as a prefilter reducing the effect of noisy data, and reduces computational complexity. The proposed data-driven LV-MPC approach deals with setting the control horizon different to the prediction horizon, improves Hessian conditioning, and attains offset-free tracking. Additionally, a weighting matrix is introduced in the identification stage so that the performance of the predictor in the near horizon can be enhanced. A MIMO example shows how the proposed methodology can outperform conventional data-driven MPC in terms of computational complexity and reference tracking.     

Air management in a diesel engine using fuzzy control techniques

Air management for diesel engines is a major challenge from the control point of view because of the highly nonlinear behavior of this system. For this reason, linear control techniques are unable to provide the required performance, and nonlinear controllers are used instead. This article discusses two fundamental steps when designing a control system. Firstly, a methodology to identify Takagi–Sugeno (T–S) structures using experimental data is proposed. Secondly, the design of a fuzzy controller in PDC structure (Parallel Distributed Compensation) is presented. The parameters of this controller are obtained from a LMI (Linear Matrix Inequalities) minimization problem.     

Color image segmentation in HSI space for automotive applications

This article presents a method for classifying color points for automotive applications in the Hue Saturation Intensity (HSI) Space based on the distances between their projections onto the SI plane. Firstly the HSI Space is analyzed in detail. Secondly the projection of image points from a typical automotive scene onto the SI plane is shown. The minimal classes relevant for driver assistance applications are derived. The requirements for the classification of the points into those classes are obtained. Several weighting functions are proposed and a fast form of an euclidean metric is investigated in detail. In order to improve the sensitivity of the weighting function, dynamic coefficients are introduced. It is shown how to compute them automatically in order to get optimal results for the classification. Finally some results of applying the metric to the sample images are shown and the conclusions are drawn.