Smart Multifunctional Composite Materials for Improvement of Structural and Non-Structural Properties

The principal aim of this thesis is to analyse the effectiveness of multifunctional smart materials as intelligent structures to improve mechanical properties and activate additional non-structural features. In order to investigate these multiple aspects, a comprehensive literature review has been presented focusing on the stale of the art in multifunctional and smart materials. From this analysis, jive different systems based on different designing solutions and manufacturing techniques were developed and experimentally validated Multiscaled composites are a typical example of multifunctional materials and are based on the addition of engineered nanoscaled reinforcements to traditional mesoscopic systems. To test the effectiveness of nanomodijication, an experimental campaign has been carried out, aimed to the characterisation of a nanocomposite obtained embedding Graphene Nanoplatelets (GNPs) in the polymeric structure of Low Density Polyethylene films at difference concentrations. Nanoscaled fillers were subsequently used to manufacture a threephasic multi-scaled composite based on the inclusion of nanometric Si02 particles in a traditional carbon fabric/epoxy system. Following a different approach, hybrid structures with embedded Non-Newtonian fluids have been manufactured and tested and the results showed that nonlinear viscosity can be exploited to dynamically enhance material properties during an impact event. The possibility to intervene both on structural and non-structural properties has been investigated with another hybrid system, based on the embodiment of Shape memory Alloys (SMA) within a traditional unidirectional CFRP. The study of the impact properties pointed out that the superelasticity effect and the hysteretic stress/strain behaviour of the embedded wires reduce the extent of the internal delamination for samples subjected to low velocity impacts. Moreover, by exploiting the SMAs thermoelectrical properties it is possible to use the embedded metallic network as a strain sensor by measuring the electrical resistance variation and as an embedded heat source to be used for rapid thermographic damage location and evaluationEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Learning and mining from personal digital archives

Given the explosion of new sensing technologies, data storage has become significantly cheaper and consequently, people increasingly rely on wearable devices to create personal digital archives. Lifelogging is the act of recording aspects of life in digital format for a variety of purposes such as aiding human memory, analysing human lifestyle and diet monitoring. In this dissertation we are concerned with Visual Lifelogging, a form of lifelogging based on the passive capture of photographs by a wearable camera. Cameras, such as Microsoft's SenseCam can record up to 4,000 images per day as well as logging data from several incorporated sensors. Considering the volume, complexity and heterogeneous nature of such data collections, it is a signifcant challenge to interpret and extract knowledge for the practical use of lifeloggers and others. In this dissertation, time series analysis methods have been used to identify and extract useful information from temporal lifelogging images data, without benefit of prior knowledge. We focus, in particular, on three fundamental topics: noise reduction, structure and characterization of the raw data; the detection of multi-scale patterns; and the mining of important, previously unknown repeated patterns in the time series of lifelog image data. Firstly, we show that Detrended Fluctuation Analysis (DFA) highlights the feature of very high correlation in lifelogging image collections. Secondly, we show that study of equal-time Cross-Correlation Matrix demonstrates atypical or non-stationary characteristics in these images. Next, noise reduction in the Cross-Correlation Matrix is addressed by Random Matrix Theory (RMT) before Wavelet multiscaling is used to characterize the `most important' or `unusual' events through analysis of the associated dynamics of the eigenspectrum. A motif discovery technique is explored for detection of recurring and recognizable episodes of an individual's image data. Finally, we apply these motif discovery techniques to two known lifelog data collections, All I Have Seen (AIHS) and NTCIR-12 Lifelog, in order to examine multivariate recurrent patterns of multiple-lifelogging users

Influences on the nonlinear dynamics of human running stride time series

Includes absstract.Includes bibliographical references

Surveillance of Complex Auction Markets: a Market Policy Analytics Approach

The dissertation consists of four essays that investigates the merits of big data-driven decision-making in the surveillance of complex auction markets. In the first essay, Avci and her co-researchers examine the aggregate-level bidding strategies and market efficiency in a multi-time tariff setting by using parametric and semi parametric methods. In the second essay, they address three key forecasting challenges; risk of selection of an inadequate forecasting method and transparency level of the market and market-specific multi-seasonality factors in a semi-transparent auction market. In the third essay, they demonstrate the effect of information feedback mechanisms on bidders’ price expectations in complex auction markets with the existence of forward contracts. They develop a research model that empirically tests the impact of bidders’ attitudes on their price expectation through their trading behavior and tested their hypotheses on real ex-ante forecasts, evaluated ex-post. In the fourth essay, they investigate characterization of bidding strategies in an oligopolistic multi-unit auction and then examine the interactions between different strategies and auction design parameters. This dissertation offers important implications to theory and practice of surveillance of complex auction markets. From the theoretical perspective, this is, to our best knowledge, the first research that systematically examines the interplay of different informational and strategic factors in oligopolistic multi-unit auction markets. From the policy perspective, Avci’s research shows that integration of big data analytics and domain-specific knowledge improves decision-making in surveillance of complex auction markets