This paper studies the problem of optimal sensor placement for impact detection and location in composite materials. The study involves a simple impact experiment on a composite box panel. The time-varying strain data are measured using piezoceramic sensors. An effective impact detection procedure is established using a neural network approach. The procedure determines the location and amplitude of impacts. A genetic algorithm is used to determine the optimum sensor positions for a diagnostic system. The main object of the paper is to study fail-safe distributions, i.e. those whose sub-distributions also have a low probability of detection error. The results are validated against an exhaustive search. The study shows that genetic algorithms combined with neural networks can be effectively used to find near-optimal sensor distributions for damage detection. The methods presented are generic and can be used in similar sensor position problems.
(2016) 'Fostering active network management through SMEs'practises.', Energy eciency., 9 (3). pp. 591-604.Further information on publisher's website:http://dx.doi.org/10.1007/s12053-015-9382-y Publisher's copyright statement:The nal publication is available at Springer via http://dx.doi.org/10.1007/s12053-015-9382-y Additional information:
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AbstractManaging the electricity network through 'smart grid' systems is a key strategy to address challenges of energy security, low carbon transitions and the replacement of aging infrastructure networks in the UK.Small and Medium Enterprises (SMEs) have a significant role in shaping patterns of energy consumption.Understanding how their activities interrelate with changes in electricity systems is critical for active network management. A significant challenge for the transformation of electricity systems involves comprehending the complexity that stems from the variety of commercial activities and diversity of social and organisational practices amongst SMEs that interact with material infrastructures. We engage with SMEs to consider how smart grid interventions 'fit' into everyday operational activities. Drawing on analysis of empirical data on electricity use, smart meter data, surveys, interviews and 'energy tours' with SMEs to understand lighting, space heating and cooling, refrigeration and IT use, this paper argues for experimenting with the use of practice theory as a framework for bringing together technical and social aspects of energy use in SMEs. This approach reveals that material circumstances and temporal factors shape current energy demand amongst SMEs, with 'connectedness' an emergent factor.
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