Master of Science

thesisNondestructive evaluation (NDE) is a means of assessing the reliability and integrity of a structural component and provides such information as the presence, location, extent, and type of damage in the component. Structural health monitoring (SHM) is a subfield of NDE, and focuses on a continuous monitoring of a structure while in use. SHM has been applied to structures such as bridges, buildings, pipelines, and airplanes with the goal of detecting the presence of damage as a means of determining whether a structure is in need of maintenance. SHM can be posed as a modeling problem, where an accurate model allows for a more reliable prediction of structural behavior. More reliable predictions make it easier to determine if something is out of the ordinary with the structure. Structural models can be designed using analytical or empirical approaches. Most SHM applications use purely analytical models based on finite element analysis and fundamental wave propagation equations to construct behavioral predictions. Purely empirical models exist, but are less common. These often utilize pattern recognition algorithms to recognize features that indicate damage. This thesis uses a method related to the k-means algorithm known as dictionary learning to train a wave propagation model from full wavefield data. These data are gathered from thin metal plates that exhibit complex wavefields dominated by multipath interference. We evaluate our model for its ability to detect damage in structures on which the model was not trained. These structures are similar to the training structure, but variable in material type and thickness. This evaluation will demonstrate how well learned dictionaries can both detect damage in a complex wavefield with multipath interference, and how well the learned model generalizes to structures with slight variations in properties. The damage detection and generalization results achieved using this empirical model are compared to similar results using both an analytical model and a support vector machine model

Correspondence - J. Melville Broughton and Edwin Gill

Correspondence from Joseph Melville Broughton to Honorable Edwin Gill notifying him that he has accepted the invitation to speak at Gardner-Webb College on Easter.https://digitalcommons.gardner-webb.edu/gardner-webb-buildings-and-grounds-o-max-gardner-building/1020/thumbnail.jp

Correspondence - J. Melville Broughton and P. L. Elliott

Correspondence from Joseph Melville Broughton to Phillip Loven Elliot, president of Gardner-Webb College, accepting the invitation to the dedication of the Gardner Memorial Student Union Buildinghttps://digitalcommons.gardner-webb.edu/gardner-webb-buildings-and-grounds-o-max-gardner-building/1008/thumbnail.jp

Methods for Data-centric Small Satellite Anomaly Detection and Fault Prediction

Autonomy can increase reaction speed, flexibility, and accuracy of satellite operations, especially in uncertain environments caused by delayed communication and/or adversarial conditions. An increased focus on small satellites makes the development of satellite autonomy even more salient, given fewer operators per satellite. Anomaly detection automates satellite health monitoring, ensuring it functions as designed. This is typically achieved using various forms of recurrent neural networks (RNN). While many of these model-based works show promise, a majority use simulated data or assume lossless communication. In contrast, raw satellite telemetry often has dropped packets, sampling frequency mismatches, noise from electrical systems and radiation, and a lack of clear labels for training. This work demonstrates how data-centric artificial intelligence (AI) can be utilized in satellite autonomy, using telemetry from the Very Low Frequency Propagation Mapper (VPM) small satellite flown by the Air Force Research Lab Space Vehicle Directorate in 2020. We introduce simple, but effective, tools for extracting fault labels from system parameters, resampling outliers to a common, uniform timeline, and evaluating outlier fault predictability. Results find that detected outliers were able to predict faults 1-10 minutes before they occurred with high accuracy

Impact of ionizing radiation on superconducting qubit coherence

The practical viability of any qubit technology stands on long coherence times and high-fidelity operations, with the superconducting qubit modality being a leading example. However, superconducting qubit coherence is impacted by broken Cooper pairs, referred to as quasiparticles, with a density that is empirically observed to be orders of magnitude greater than the value predicted for thermal equilibrium by the Bardeen-Cooper-Schrieffer (BCS) theory of superconductivity. Previous work has shown that infrared photons significantly increase the quasiparticle density, yet even in the best isolated systems, it still remains higher than expected, suggesting that another generation mechanism exists. In this Letter, we provide evidence that ionizing radiation from environmental radioactive materials and cosmic rays contributes to this observed difference, leading to an elevated quasiparticle density that would ultimately limit superconducting qubits of the type measured here to coherence times in the millisecond regime. We further demonstrate that introducing radiation shielding reduces the flux of ionizing radiation and positively correlates with increased coherence time. Albeit a small effect for today's qubits, reducing or otherwise mitigating the impact of ionizing radiation will be critical for realizing fault-tolerant superconducting quantum computers.Comment: 16 pages, 12 figure

A qualitative study of stakeholders' perspectives on the social network service environment

Over two billion people are using the Internet at present, assisted by the mediating activities of software agents which deal with the diversity and complexity of information. There are, however, ethical issues due to the monitoring-and-surveillance, data mining and autonomous nature of software agents. Considering the context, this study aims to comprehend stakeholders' perspectives on the social network service environment in order to identify the main considerations for the design of software agents in social network services in the near future. Twenty-one stakeholders, belonging to three key stakeholder groups, were recruited using a purposive sampling strategy for unstandardised semi-structured e-mail interviews. The interview data were analysed using a qualitative content analysis method. It was possible to identify three main considerations for the design of software agents in social network services, which were classified into the following categories: comprehensive understanding of users' perception of privacy, user type recognition algorithms for software agent development and existing software agents enhancement

Rates, causes and predictors of all-cause and avoidable mortality in 163 686 children and young people with and without intellectual disabilities:A record linkage national cohort study

Objectives: To investigate mortality rates and associated factors, and avoidable mortality in children/young people with intellectual disabilities. Design: Retrospective cohort; individual record-linked data between Scotland’s 2011 Census and 9.5 years of National Records for Scotland death certification data. Setting: General community. Participants: Children and young people with intellectual disabilities living in Scotland aged 5–24 years, and an age-matched comparison group. Main outcome measures: Deaths up to 2020: age of death, age-standardised mortality ratios (age-SMRs); causes of death including cause-specific age-SMRs/sex-SMRs; and avoidable deaths. Results: Death occurred in 260/7247 (3.6%) children/young people with intellectual disabilities (crude mortality rate=388/100 000 person-years) and 528/156 439 (0.3%) children/young people without intellectual disabilities (crude mortality rate=36/100 000 person-years). SMRs for children/young people with versus those without intellectual disabilities were 10.7 for all causes (95% CI 9.47 to 12.1), 5.17 for avoidable death (95% CI 4.19 to 6.37), 2.3 for preventable death (95% CI 1.6 to 3.2) and 16.1 for treatable death (95% CI 12.5 to 20.8). SMRs were highest for children (27.4, 95% CI 20.6 to 36.3) aged 5–9 years, and lowest for young people (6.6, 95% CI 5.1 to 8.6) aged 20–24 years. SMRs were higher in more affluent neighbourhoods. Crude mortality incidences were higher for the children/young people with intellectual disabilities for most International Statistical Classification of Diseases and Related Health Problems, 10th Revision chapters. The most common underlying avoidable causes of mortality for children/young people with intellectual disabilities were epilepsy, aspiration/reflux/choking and respiratory infection, and for children/young people without intellectual disabilities were suicide, accidental drug-related deaths and car accidents. Conclusion: Children with intellectual disabilities had significantly higher rates of all-cause, avoidable, treatable and preventable mortality than their peers. The largest differences were for treatable mortality, particularly at ages 5–9 years. Interventions to improve healthcare to reduce treatable mortality should be a priority for children/young people with intellectual disabilities. Examples include improved epilepsy management and risk assessments, and coordinated multidisciplinary actions to reduce aspiration/reflux/choking and respiratory infection. This is necessary across all neighbourhoods

A Bright Short Period M-M Eclipsing Binary from the KELT Survey: Magnetic Activity and the Mass–Radius Relationship for M Dwarfs

We report the discovery of KELT J041621-620046, a moderately bright (J ~ 10.2) M-dwarf eclipsing binary system at a distance of 39 ± 3 pc. KELT J041621-620046 was first identified as an eclipsing binary using observations from the Kilodegree Extremely Little Telescope (KELT) survey. The system has a short orbital period of ~1.11 days and consists of components with and in nearly circular orbits. The radii of the two stars are and . Full system and orbital properties were determined (to ∼10% error) by conducting an EBOP (Eclipsing Binary Orbit Program) global modeling of the high precision photometric and spectroscopic observations obtained by the KELT Follow-up Network. Each star is larger by 17%–28% and cooler by 4%–10% than predicted by standard (non-magnetic) stellar models. Strong H α emission indicates chromospheric activity in both stars. The observed radii and temperature discrepancies for both components are more consistent with those predicted by empirical relations that account for convective suppression due to magnetic activity