Abstract

Abstract

Abstract
Purpose - The purpose of this paper is to show how workflow management can be applied in the context of business networking. Design/methodology/approach - The paper looks at workflow management systems (WfMSs) as an enterprise integration tool and, as such, it shows that WfMS require an appropriate integration infrastructure. The paper then describes how a WfMS that supports business networking can be built. Findings - It is found that business networking requires a decentralized, peer-to-peer (P2P) integration infrastructure. On the other hand, it is found that it is possible to develop a generic and reusable workflow engine. Both components are then integrated in order to come up with a solution that supports business networking. Research limitations/implications - Issues concerning information and document management are not addressed. Neither mechanisms of storage and retrieval of exchanged documents, nor the format/structure of those documents are specified. Practical implications - The paper assumes that enterprises will be willing to build up a P2P network where they will conduct their business-to-business (B2B) exchanges. Then, the proposed solution would allow their business processes to be linked without requiring tight coupling between their information systems. Originality/value - The paper is useful to IT strategists and researchers dealing with WfMS, B2B integration architectures and applications of P2P technology. © Emerald Group Publishing Limited.

Abstract
The study of a single short length Bragg grating for sensing applications is presented. The dependences of the central wavelength, reflected optical power and spectral width of a 250 mu m length grating on strain and temperature are analyzed. The obtained results indicate the feasibility of this novel sensing head for simultaneous measurement of strain and temperature.

Abstract

Abstract
In this paper we introduce a new identification algorithm for MIMO bilinear systems driven by white noise inputs. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state space approximations, thus considered a Picard based method. The key to the algorithm is the fact that the bilinear terms behave like white noise processes. Using a linear Kalman filter, the bilinear terms can be estimated and combined with the system inputs at each iteration, leading to a linear system which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a bilinear model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality.