Towards Bayesian System Identification: With Application to SHM of Offshore Structures

Within the offshore industry Structural Health Monitoring remains a growing area of interest. The oil and gas sectors are faced with ageing infrastructure and are driven by the desire for reliable lifetime extension, whereas the wind energy sector is investing heavily in a large number of structures. This leads to a number of distinct challenges for Structural Health Monitoring which are brought together by one unifying theme --- uncertainty. The offshore environment is highly uncertain, existing structures have not been monitored from construction and the loading and operational conditions they have experienced (among other factors) are not known. For the wind energy sector, high numbers of structures make traditional inspection methods costly and in some cases dangerous due to the inaccessibility of many wind farms. Structural Health Monitoring attempts to address these issues by providing tools to allow automated online assessment of the condition of structures to aid decision making. The work of this thesis presents a number of Bayesian methods which allow system identification, for Structural Health Monitoring, under uncertainty. The Bayesian approach explicitly incorporates prior knowledge that is available and combines this with evidence from observed data to allow the formation of updated beliefs. This is a natural way to approach Structural Health Monitoring, or indeed, many engineering problems. It is reasonable to assume that there is some knowledge available to the engineer before attempting to detect, locate, classify, or model damage on a structure. Having a framework where this knowledge can be exploited, and the uncertainty in that knowledge can be handled rigorously, is a powerful methodology. The problem being that the actual computation of Bayesian results can pose a significant challenge both computationally and in terms of specifying appropriate models. This thesis aims to present a number of Bayesian tools, each of which leverages the power of the Bayesian paradigm to address a different Structural Health Monitoring challenge. Within this work the use of Gaussian Process models is presented as a flexible nonparametric Bayesian approach to regression, which is extended to handle dynamic models within the Gaussian Process NARX framework. The challenge in training Gaussian Process models is seldom discussed and the work shown here aims to offer a quantitative assessment of different learning techniques including discussions on the choice of cost function for optimisation of hyperparameters and the choice of the optimisation algorithm itself. Although rarely considered, the effects of these choices are demonstrated to be important and to inform the use of a Gaussian Process NARX model for wave load identification on offshore structures. The work is not restricted to only Gaussian Process models, but Bayesian state-space models are also used. The novel use of Particle Gibbs for identification of nonlinear oscillators is shown and modifications to this algorithm are applied to handle its specific use in Structural Health Monitoring. Alongside this, the Bayesian state-space model is used to perform joint input-state-parameter inference for Operational Modal Analysis where the use of priors over the parameters and the forcing function (in the form of a Gaussian Process transformed into a state-space representation) provides a methodology for this output-only identification under parameter uncertainty. Interestingly, this method is shown to recover the parameter distributions of the model without compromising the recovery of the loading time-series signal when compared to the case where the parameters are known. Finally, a novel use of an online Bayesian clustering method is presented for performing Structural Health Monitoring in the absence of any available training data. This online method does not require a pre-collected training dataset, nor a model of the structure, and is capable of detecting and classifying a range of operational and damage conditions while in service. This leaves the reader with a toolbox of methods which can be applied, where appropriate, to identification of dynamic systems with a view to Structural Health Monitoring problems within the offshore industry and across engineering

PAO: A general particle swarm algorithm with exact dynamics and closed-form transition densities

A great deal of research has been conducted in the consideration of meta-heuristic optimisation methods that are able to find global optima in settings that gradient based optimisers have traditionally struggled. Of these, so-called particle swarm optimisation (PSO) approaches have proven to be highly effective in a number of application areas. Given the maturity of the PSO field, it is likely that novel variants of the PSO algorithm stand to offer only marginal gains in terms of performance -- there is, after all, no free lunch. Instead of only chasing performance on suites of benchmark optimisation functions, it is argued herein that research effort is better placed in the pursuit of algorithms that also have other useful properties. In this work, a highly-general, interpretable variant of the PSO algorithm -- particle attractor algorithm (PAO) -- is proposed. Furthermore, the algorithm is designed such that the transition densities (describing the motions of the particles from one generation to the next) can be computed exactly in closed form for each step. Access to closed-form transition densities has important ramifications for the closely-related field of Sequential Monte Carlo (SMC). In order to demonstrate that the useful properties do not come at the cost of performance, PAO is compared to several other state-of-the art heuristic optimisation algorithms in a benchmark comparison study.Comment: Preprin

B851: A Comparative Analysis of Organic Dairy Farms in Maine and Vermont: Farm Financial Information from 2004 to 2006

The purpose of this bulletin is to provide an insight into the relative financial performance of organic dairy farming through the examination of three years of detailed farm financial records. Farm financial records were collected in person by trained enumerators from organic dairy operations in Maine and Vermont for the 2004–2006 production years. These farm records are complemented by surveys on farm and farmer characteristics along with farmers’ motivational interests for organic dairy production and performance satisfaction. This report, therefore, provides a rich financial perspective on the evolution of organic dairy farming performance unlike single-season surveys.https://digitalcommons.library.umaine.edu/aes_bulletin/1003/thumbnail.jp

The emergence of view-symmetric neural responses to familiar and unfamiliar faces

Successful recognition of familiar faces is thought to depend on the ability to integrate view-dependent representations of a face into a view-invariant representation. It has been proposed that a key intermediate step in achieving view invariance is the representation of symmetrical views. However, key unresolved questions remain, such as whether these representations are specific for naturally occurring changes in viewpoint and whether view-symmetric representations exist for familiar faces. To address these issues, we compared behavioural and neural responses to natural (canonical) and unnatural (noncanonical) rotations of the face. Similarity judgements revealed that symmetrical viewpoints were perceived to be more similar than non-symmetrical viewpoints for both canonical and non-canonical rotations. Next, we measured patterns of neural response from early to higher level regions of visual cortex. Early visual areas showed a view-dependent representation for natural or canonical rotations of the face, such that the similarity between patterns of response were related to the difference in rotation. View symmetric patterns of neural response to canonically rotated faces emerged in higher visual areas, particularly in face-selective regions. The emergence of a view-symmetric representation from a view-dependent representation for canonical rotations of the face was also evident for familiar faces, suggesting that view-symmetry is an important intermediate step in generating view-invariant representations. Finally, we measured neural responses to unnatural or non-canonical rotations of the face. View-symmetric patterns of response were also found in face-selective regions. However, in contrast to natural or canonical rotations of the face, these view-symmetric responses did not arise from an initial view-dependent representation in early visual areas. This suggests differences in the way that view-symmetrical representations emerge with canonical or non-canonical rotations. The similarity in the neural response to canonical views of familiar and unfamiliar faces in the core face network suggests that the neural correlates of familiarity emerge at later stages of processing

A spectrum of physics-informed Gaussian processes for regression in engineering

Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as ``big,'' they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a spectrum of possible Gaussian process models are introduced that enable the incorporation of different levels of expert knowledge of a system. Examples illustrate how these approaches can significantly reduce reliance on data collection whilst also increasing the interpretability of the model, another important consideration in this context

Summary Report of Mission Acceleration Measurements for STS-79. Launched 16 Sep. 1996

The Space Acceleration Measurement System (SAMS) collected acceleration data in support of the Mechanics of Granular Materials experiment during the STS-79 Mir docking mission, September 1996. STS-79 was the first opportunity to record SAMS data on an Orbiter while it was docked to Mir. Crew exercise activities in the Atlantis middeck and the Mir base module are apparent in the data. The acceleration signals related to the Enhanced Orbiter Refrigerator Freezer had different characteristics when comparing the data recorded on Atlantis on STS-79 with the data recorded on Mir during STS-74. This is probably due, at least in part, to different transmission paths and SAMS sensor head mounting mechanisms. Data collected on Atlantis during the STS-79 docking indicate that accelerations due to vehicle and solar array structural modes from Mir transfer to Atlantis and that the structural modes of the Atlantis-Mir complex are different from those of either vehicle independently. A 0.18 Hz component of the SAMS data, present while the two vehicles were docked, was probably caused by the Mir solar arrays. Compared to Atlantis structural modes of about 3.9 and 4.9 Hz, the Atlantis-Mir complex has structural components of about 4.5 and 5.1 Hz. After docking, apparent structural modes appeared in the data at about 0.8 and 1.8 Hz. The appearance, disappearance, and change in the structural modes during the docking and undocking phases of the joint Atlantis-Mir operations indicates that the structural modes of the two spacecraft have an effect on the microgravity environment of each other. The transfer of structural and equipment related accelerations between vehicles is something that should be considered in the International Space Station era

Introduction: Historical thinking, historical consciousness

In September, 2014, the University of Ottawa Education Research Unit, Making History / Faire l’histoire, hosted Canadian History at the Crossroads, a SSHRC-funded symposium in collaboration with the Canadian Museum of History in Gatineau, Québec. The symposium brought together multiple stakeholders, historians, history and museum educators, classroom teachers—including Governor General’s award winners as well as teacher education and graduate students—to stimulate further public dialogue on pedagogies of history and the politics of remembrance. Building on some of the symposium’s original contributions as well as other submissions, this Canadian Journal of Education Special Capsule advances current debates in history education, historical thinking, and historical consciousness, and forges new directions for collective understandings of the past, by connecting with everyday lived experiences in the present. The contributions range from discussions of how young people themselves understand their past to the link- ages between forms of remembering and conceptions of the nation itself.

MicroRNAs Can Regulate Human APP Levels

A number of studies have shown that increased APP levels, resulting from either a genomic locus duplication or alteration in APP regulatory sequences, can lead to development of early-onset dementias, including Alzheimer's disease (AD). Therefore, understanding how APP levels are regulated could provide valuable insight into the genetic basis of AD and illuminate novel therapeutic avenues for AD. Here we test the hypothesis that APP protein levels can be regulated by miRNAs, evolutionarily conserved small noncoding RNA molecules that play an important role in regulating gene expression. Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs