Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

Developing NGO competencies in post-disaster reconstruction: a theoretical framework

In the aftermath of recent natural disasters, NGOs have become increasingly involved in the permanent reconstruction of affected communities. These organizations, often operating well outside their expertise, encounter significant barriers as they implement reconstruction programmes. This paper presents the theoretical bedrock of a current research project, the overall goal of which is to design a competency-based framework model that can be used by NGOs in post-disaster reconstruction projects. Drawing on established theories of management, a unique perspective has been developed from which a competency-based reconstruction theory emerges. This theoretical framework brings together three distinct fields; Disaster Management, Strategic Management and Project Management, each vital to the success of the model. This theoretical study will incorporate a critical review of literature within each field. It is imperative that NGOs involved in post-disaster reconstruction familiarize themselves with concepts and strategies. It is hoped that the competence-based frame-work model that is produced on the basis of this theory will help define the standard of best practice to which future NGO projects might align themselves.

Equalisation of time variant multipath channels using amplitude banded LMS algorithms

For the purpose of equalisation of rapidly time variant multipath channels, we derive a novel adaptive algorithm, the amplitude banded LMS (ABLMS); which implements a nonlinear adaptation based on a coefficient matrix. Then we develop the: ABLMS algorithm as the adaptation procedure for a linear transversal equaliser (LTE) and a decision feedback equaliser (DFE) where a parallel adaptation scheme is deployed. Computer simulations demonstrate that with a small increase of computational complexity, the ABLMS based parallel equalisers provide a significant improvement related to the conventional LMS DFE and the LMS LTE in the case of a second order Markov communication channel model.

Noise resilience and entanglement evolution in two nonequivalent classes of quantum algorithms

The speedup provided by quantum algorithms with respect to their classical counterparts is at the origin of scientific interest in quantum computation. However, the fundamental reasons for such a speedup are not yet completely understood and deserve further attention. In this context, the classical simulation of quantum algorithms is a useful tool that can help us in gaining insight. Starting from the study of general conditions for classical simulation, we highlight several important differences between two nonequivalent classes of quantum algorithms. We investigate their performance under realistic conditions by quantitatively studying their resilience with respect to static noise. This latter refers to errors affecting the initial preparation of the register used to run an algorithm. We also compare the evolution of the entanglement involved in the different computational processes.