The work described in this paper is concerned with controlling the strain of the constraining layer of a composite structure in such a way as to enhance the shear generated in the viscoelastic material and hence improve the overall damping of the composite structure. & ( x ) ith mode shape function CZ displacement function ith damping ratio of the composite beam added damping due to velocity feedback objective function.

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Analysing data to predict market trends of products and services and to improve performances of enterprise business systems has always been part of running a competitive business. But it is becoming essential nowadays that not only is the analysis done on real-time data, but also actions in response to analysis results can be performed in real time and instantaneously change parameters of business processes. This paper discusses issues and problems of current business intelligence systems, and then outlines our vision of future real-time business intelligence. We present a list of emerging technologies which could contribute to the realisation of real-time business intelligence and some examples of applying them to improve BT's systems and services.

In many applications, it is useful to extract structured data from sections of unstructured text. A common approach is to use pattern matching (e.g., regular expressions) or more general grammar-based techniques. In cases where exact templates or grammar fragments are not known, it is possible to use machine learning approaches, based on words or n-grams, to identify the structured data. This is generally a two-stage (train/use) process that cannot easily cope with incremental extensions of the training set. In this paper, we combine a fuzzy grammar-based approach with incremental learning. This enables a set of grammar fragments to evolve incrementally, each time a new example is given, while guaranteeing that it can parse previously seen examples. We propose a novel measure of overlap between fuzzy grammar fragments that can also be used to determine the degree to which a string is parsed by a grammar fragment. This measure of overlap allows us to compare the range of two fuzzy grammar fragments (i.e., to estimate and compare the sets of strings that fuzzily conform to each grammar) without explicitly parsing any strings. A simple application shows the method's validity.