On Nonlinear Cointegration Methods for Structural Health Monitoring

Structural health monitoring (SHM) is emerging as a crucial technology for the assessment and management of important assets in various industries. Thanks to the rapid developments of sensing technology and computing machines, large amounts of sensor data are now becoming much easier and cheaper to obtain from monitored structures, which consequently has enabled data-driven methods to become the main work forces for real world SHM systems. However, SHM practitioners soon discover a major problem for in-service SHM systems; that is the effect of environmental and operational variations (EOVs). Most assets (bridges, aircraft engines, wind turbines) are so important that they are too costly to be isolated for testing and examination purposes. Often, their structural properties are heavily in uenced by ambient environmental and operational conditions, or EOVs. So, the most important question raised for an effective SHM system is, how one could tell whether an alarm signal comes from structural damage or from EOVs? Cointegration, a method originating from econometric time series analysis, has proven to be one of the most promising approaches to address the above question. Cointegration is a property of nonstationary time series, it models the long-run relationship among multiple nonstationary time series. The idea of employing the cointegration method in the SHM context relies on the fact that this long-run relationship is immune to the changes caused by EOVs, but when damage occurs, this relationship no longer stands. The work in this thesis aims to further strengthen and extend conventional linear cointegration methods to a nonlinear context, by hybridising cointegration with machine learning and time series models. There are three contributions presented in this thesis: The first part is about a nonlinear cointegration method based on Gaussian process (GP) regression. Instead of using a linear regression, this part attempts to establish a nonlinear cointegrating regression with a GP. GP regression is a powerful Bayesian machine learning approach that can produce probabilistic predictions and avoid overfitting. The proposed method is tested with one simulated case study and with the Z24 Bridge SHM data. The second part concerns developing a regime-switching cointegration approach. Instead of modelling nonlinear cointegration as a smooth function, this part sees cointegration as a piecewise-linear function, which is triggered by some external variable. The model is trained with the aid of the augmented Dickey-Fuller (ADF) test statistics. Two case studies are presented in this part, one simulated mulitidegree-of-freedom system, and also the Z24 Bridge data. The third part of this work introduces a cointegration method for heteroscedastic data. Heteroscedasticity, or time-dependent noise is often observed in SHM data, normally caused by seasonal variations. In order to address this issue, the TBATS (an acronym for key features of the model: Trigonometric, Box-Cox transformation, ARMA error, Trend, Seasonal components) model is employed to decompose the seasonal-corrupted time series, followed by conventional cointegration analysis. A simulated cantilever beam and real measurement data from the NPL Bridge are used to validate the proposed method

The Environmental Plasticity of Diverse Body Color Caused by Extremely Long Photoperiods and High Temperature in Saccharosydne procerus (Homoptera: Delphacidae)

Melanization reflects not only body color variation but also environmental plasticity. It is a strategy that helps insects adapt to environmental change. Different color morphs may have distinct life history traits, e.g., development time, growth rate, and body weight. The green slender planthopper Saccharosydne procerus (Matsumura) is the main pest of water bamboo (Zizania latifolia). This insect has two color morphs. The present study explored the influence of photoperiod and its interaction with temperature in nymph stage on adult melanism. Additionally, the longevity, fecundity, mating rate, and hatching rate of S. procerus were examined to determine whether the fitness of the insect was influenced by melanism under different temperature and photoperiod. The results showed that a greater number of melanic morphs occurred if the photoperiod was extremely long. A two-factor ANOVA showed that temperature and photoperiod both have a significant influence on melanism. The percentages of variation explained by these factors were 45.53% and 48.71%, respectively. Moreover, melanic morphs had greater advantages than non-melanic morphs under an environmental regime of high temperatures and a long photoperiod, whereas non-melanic morphs were better adapted to cold temperatures and a short photoperiod. These results cannot be explained by the thermal melanism hypothesis. Thus, it may be unavailable to seek to explain melanism in terms of only one hypothesis

A regime-switching cointegration approach for removing environmental and operational variations in structural health monitoring

Cointegration is now extensively used to model the long term common trends among economic variables in the field of econometrics. Recently, cointegration has been successfully implemented in the context of structural health monitoring (SHM), where it has been used to remove the confounding influences of environmental and operational variations (EOVs) that can often mask the signature of structural damage. However, restrained by its linear nature, the conventional cointegration approach has limited power in modelling systems where measurands are nonlinearly related; this occurs, for example, in the benchmark study of the Z24 Bridge, where nonlinear relationships between natural frequencies were induced during a period of very cold temperatures. To allow the removal of EOVs from SHM data with nonlinear relationships like this, this paper extends the well-established cointegration method to a nonlinear context, which is to allow a breakpoint in the cointegrating vector. In a novel approach, the augmented Dickey-Fuller (ADF) statistic is used to find which position is most appropriate for inserting a breakpoint, the Johansen procedure is then utilised for the estimation of cointegrating vectors. The proposed approach is examined with a simulated case and real SHM data from the Z24 Bridge, demonstrating that the EOVs can be neatly eliminated

Automated vulnerability discovery and exploitation in the internet of things

Recently, automated software vulnerability detection and exploitation in Internet of Things (IoT) has attracted more and more attention, due to IoT’s fast adoption and high social impact. However, the task is challenging and the solutions are non-trivial: the existing methods have limited effectiveness at discovering vulnerabilities capable of compromising IoT systems. To address this, we propose an Automated Vulnerability Discovery and Exploitation framework with a Scheduling strategy, AutoDES that aims to improve the efficiency and effectiveness of vulnerability discovery and exploitation. In the vulnerability discovery stage, we use our Anti-Driller technique to mitigate the “path explosion” problem. This approach first generates a specific input proceeding from symbolic execution based on a Control Flow Graph (CFG). It then leverages a mutation-based fuzzer to find vulnerabilities while avoiding invalid mutations. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits, via the use of several proposed attack techniques that can produce a shell based on the detected vulnerabilities. We also propose a genetic algorithm (GA)-based scheduling strategy (AutoS) that helps with assigning the computing resources dynamically and efficiently. The extensive experimental results on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset clearly demonstrate the effectiveness and efficiency of the proposed framework

Replication and Transmission of H9N2 Influenza Viruses in Ferrets: Evaluation of Pandemic Potential

H9N2 avian influenza A viruses are endemic in poultry of many Eurasian countries and have caused repeated human infections in Asia since 1998. To evaluate the potential threat of H9N2 viruses to humans, we investigated the replication and transmission efficiency of H9N2 viruses in the ferret model. Five wild-type (WT) H9N2 viruses, isolated from different avian species from 1988 through 2003, were tested in vivo and found to replicate in ferrets. However these viruses achieved mild peak viral titers in nasal washes when compared to those observed with a human H3N2 virus. Two of these H9N2 viruses transmitted to direct contact ferrets, however no aerosol transmission was detected in the virus displaying the most efficient direct contact transmission. A leucine (Leu) residue at amino acid position 226 in the hemagglutinin (HA) receptor-binding site (RBS), responsible for human virus-like receptor specificity, was found to be important for the transmission of the H9N2 viruses in ferrets. In addition, an H9N2 avian-human reassortant virus, which contains the surface glycoprotein genes from an H9N2 virus and the six internal genes of a human H3N2 virus, showed enhanced replication and efficient transmission to direct contacts. Although no aerosol transmission was observed, the virus replicated in multiple respiratory tissues and induced clinical signs similar to those observed with the parental human H3N2 virus. Our results suggest that the establishment and prevalence of H9N2 viruses in poultry pose a significant threat for humans

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements