An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, employing ensemble modeling reduces bias and variation, leading to more consistent and accurate predictions. When inferencing on wildfire propagation at 30-minute intervals, the architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1%.

<p>In this study, we concentrate on spin-1/2 systems. A series of tools using the Liouville space method have been developed for simulating of NMR of arbitrary pulse sequences.</p> <p>We have calculated one- and two-spin symbolically, and larger systems numerically of steady states. The one-spin calculations show how SSFP converges to continuous wave NMR. A general formula for two-spin systems has been derived for the creation of double-quantum signals as a function of irradiation strength, coupling constant, and chemical shift difference. The formalism is general and can be extended to more complex spin systems.</p> <p>Estimates of transverse relaxation, R₂, are affected by frequency offset and field inhomogeneity. We find that in the presence of expected B₀ inhomogeneity, off-resonance effects can be removed from R₂ measurements, when ||omega||<= 0.5 gamma\,B₁ in Hahn echo experiments, when ||omega||<=gamma\,B₁ in CPMG experiments with specific phase variations, by fitting exact solutions of the Bloch equations given in the Lagrange form.</p> <p>Approximate solutions of CPMG experiments show the specific phase variations can significantly smooth the dependence of measured intensities on frequency offset in the range of +/- 1/2 gamma\,B₁. The effective R₂ of CPMG experiments when using a phase variation scheme can be expressed as a second-order formula with respect to the ratio of offset to pi-pulse amplitude.</p> <p>Optimization problems using the exact or approximate solution of the Bloch equations are established for designing optimal broadband universal rotation (OBUR) pulses. OBUR pulses are independent of initial magnetization and can be applied to replace any pulse of the same flip angles in a pulse sequence. We demonstrate the process to exactly and efficiently calculate the first- and second-order derivatives with respect to pulses. Using these exact derivatives, a second-order optimization method is employed to design pulses. Experiments and simulations show that OBUR pulses can provide more uniform spectra in the designed offset range and come up with advantages in CPMG experiments.</p> / Doctor of Philosophy (PhD)

<p>This thesis is concerned with a theoretical and experimental investigation of a duckbill valve (DBV). Duckbill valves are non-return valves made of a composite material, which deforms to open the valve as the upstream pressure increases. The head-discharge behavior is a fluid-structure interaction (FSI) problem since the discharge depends on the valve opening that in turn depends on the pressure distribution along the valve produced by the discharge. To design a duckbill valve, a theoretical model is required, which will predict the head-discharge characteristics as a function of the fluid flow through the valve and the valve material and geometry.</p> <p>The particular valves of concern in this study, which can be very large, are made from laminated, fiber-reinforced rubber. Thus, the structural problem has strong material as well as geometric nonlinearities due to large deflections. Clearly, a fully coupled FSI analysis using three-dimensional viscous flow would be very challenging and therefore, a simplified approach was sought that treats the essential aspects of the problem in a tractable way. For this purpose, the DBV was modeled using thick shell finite elements, which included the laminates of hyperelastic rubber and orthotropic fabric reinforcement. The finite element method (FEM) was simplified by assuming that the arch side edges of the valve were clamped. The unsteady 1D flow equation was used to model the ideal fluid dynamics that enabled a full FSI analysis. Moreover, verification for the ideal flow was carried out using a transient, Reynolds-averaged Navier-Stokes finite volume solver for the viscous flow corresponding to the deformed valve predicted by the simplified FSI model.</p> <p>In order to validate the predictions of the FSI simulations, an experimental study was performed at several mass flow rates. Pressure drops along the water tunnel, valve inlet and outlet velocity profiles were measured, as well as valve opening deformations as functions of upstream pressures.</p> <p>Additionally, the valve deformations under various back pressures were analyzed when the downstream pressure exceeded the upstream pressure using the layered shell model without coupling and with simplified boundary constraints to avoid solving the contact problem for the inward-deformed duckbill valve. Flow-induced vibration (FIV) of the valve at small openings was also examined to improve our understanding of the valve stability behaviour. Some interesting valve oscillation phenomena were observed.</p> <p>Conclusions are drawn regarding the FSI model on the predictions and comparisons with the experimental results. The transient 1D flow equation has been demonstrated to adequately model the fluid dynamics of a duckbill valve, largely due to the fact that viscous effects are negligible except when the valve is operating at very small openings. Fiber reinforcement of the layered composite rubber was found to play an important role in controlling duckbill valve material stretch, especially at large openings. The model predicts oscillations at small openings but more research is required to better understand this behaviour.</p> / Doctor of Philosophy (PhD)

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of categorical attributes. Visualizing preferences is essential to provide users with insights into the process of decision making, while learning preferences from data is practically important, as it is ineffective to elicit preference models directly from users. The results obtained from this thesis are two parts: 1) for preference visualization, aweb- basedsystem is created that visualizes various types of lexicographic preference tree models learned by a greedy learning algorithm; 2) for preference learning, a genetic algorithm is designed and implemented, called GA, that learns a restricted type of lexicographic preference tree, called unconditional importance and unconditional preference tree, or UIUP trees for short. Experiments show that GA achieves higher accuracy compared to the greedy algorithm at the cost of more computational time. Moreover, a Dynamic Programming Algorithm (DPA) was devised and implemented that computes an optimal UIUP tree model in the sense that it satisfies as many examples as possible in the dataset. This novel exact algorithm (DPA), was used to evaluate the quality of models computed by GA, and it was found to reduce the factorial time complexity of the brute force algorithm to exponential. The major contribution to the field of machine learning and data mining in this thesis would be the novel learning algorithm (DPA) which is an exact algorithm. DPA learns and finds the best UIUP tree model in the huge search space which classifies accurately the most number of examples in the training dataset; such model is referred to as the optimal model in this thesis. Finally, using datasets produced from randomly generated UIUP trees, this thesis presents experimental results on the performances (e.g., accuracy and computational time) of GA compared to the existent greedy algorithm and DPA.

Dans cette thèse, nous proposons et analysons des méthodes numériques partitionnées pour la simulation de phénomènes d'interaction fluide-structure (IFS) dans le système cardiovasculaire. Nous considérons en particulier l'interaction mécanique du sang avec la paroi des grosses artères, avec des valves cardiaques et avec le myocarde. Dans les algorithmes IFS partitionnés, le couplage entre le fluide et la structure peut être imposé de manière implicite, semi-implicite ou explicite. Dans la première partie de cette thèse, nous faisons l'analyse de convergence d'un algorithme de projection semi-implicite. Puis, nous proposons une nouvelle version de ce schéma qui possède de meilleures propriétés de stabilité. La modification repose sur un couplage Robin-Robin résultant d'une ré-interprétation de la formulation de Nitsche. Dans la seconde partie, nous nous intéressons à la simulation de valves cardiaques. Nous proposons une stratégie partionnée permettant la prise en compte du contact entre plusieurs structures immergées dans un fluide. Nous explorons également l'utilisation d'une technique de post-traitement récente, basée sur la notion de structures Lagrangiennes cohérentes, pour analyser qualitativement l'hémodynamique complexe en aval des valves aortiques. Dans la dernière partie, nous proposons un modèle original de valves cardiaques. Ce modèle simplifié offre un compromis entre les approches 0D classiques et les simulations complexes d'interaction fluide-structure 3D. Diverses simulations numériques sont présentées pour illustrer l'efficacité et la robustesse de ce modèle, qui permet d'envisager des simulations réalistes de l'hémodynamique cardiaque, à un coût de calcul modéré.

Die vollständige Ausnutzung des Leichtbaupotentials bei der Dimensionierung von mehrschichtigen endlosfaserverstärkten Strukturbauteilen erfordert die Bereitstellung von geeigneten Optimierungswerkzeugen, da bei der Auslegung eine große Anzahl von Entwurfsvariablen zu berücksichtigen sind. In dieser Arbeit werden Optimierungsalgorithmen und -strategien zur Lösung wissenschaftlicher Fragestellungen für industrielle Anwendungen bei der Konstruktion von entsprechenden Faserkunststoffverbunden entwickelt und bewertet. Um das breite Anwendungsspektrum aufzuzeigen, werden drei unterschiedliche repräsentative Problemstellungen bearbeitet. Dabei wird für Mehrschichtverbunde die Festigkeitsoptimierung hinsichtlich eines bruchtypbezogenen Versagenskriteriums vorgenommen, ein Dämpfungsmodell zur Materialcharakterisierung entworfen sowie eine bivalente Optimierungsstrategie zur Auslegung von gewickelten Hochdruckbehältern erstellt. Die Grundlage der entwickelten Methoden bilden dabei jeweils stochastische naturanaloge Optimierungsheuristiken, da die betrachteten Aufgabenstellungen nicht konvex sind und derartige Verfahren flexibel eingesetzt werden können. / The full utilization of the light weight potential in the dimensioning of multilayer fiber reinforced composites requires suitable optimization tools, since a large number of design variables has to be taken into account. In this work, optimization algorithms and strategies for the solution of scientific questions for industrial applications are developed and evaluated in the design of corresponding fiber-plastic composites. In order to show the wide range of applications, three different representative topics have been chosen. It will carry out a strength optimization for multilayer composites with regard to a type-related failure criterion, devolop a damping model for material characterization and established a bivalent optimization strategy for the design of wound high-pressure vessels. The developed methods are based on stochastic natural-analog optimization heuristics, since the considered tasks are not convex and such methods can be used in a very flexible manner.

Scanning Tunneling Microscopy (STM) uses quantum tunneling effect to study the surfaces of materials on an atomic scale. Since the probe of the microscope is on the order of nanometers away from the surface, the device is prone to noises due to vibrations from the surroundings. To minimize the random noises and floor vibrations, passive vibration isolation is a commonly used technique due to its low cost and simpler design compared to active vibration isolation, especially when the entire vibration isolation system (VIS) stays inside an Ultra High Vacuum (UHV) environment. This research aims to analyze and build a single-stage passive VIS for an STM. The VIS consists of a mass-spring system staying inside an aluminum hollow tube. The mass-spring system is comprised of a circular copper stage suspended by a combination of six extension springs, and the STM stays on top of the copper stage. Magnetic damping with neodymium magnets, which induces eddy currents in the copper conductor, is the primary damping method to reduce the vibrations transferred to the mass-spring system. FEMM and MATLAB® are used to model magnetic flux density and damping coefficients from eddy current effect, which will help determine the necessary damping ratios for the VIS. Viton, which demonstrates a high compatibility with vacuum environments, will also serve as a great damping material between joints and contacts for the housing tube. Viton will be modeled as a Mooney-Rivlin hyperelastic material whose material parameters are previous studied, and Abaqus will be used as a Finite Element Analysis software to study the Viton gaskets’ natural frequencies. The natural frequencies of the aluminum hollow tube will also be investigated through Abaqus.

Available security standards for RFID networks (e.g. ISO/IEC 29167) are designed to secure individual tag-reader sessions and do not protect against active attacks that could also compromise the system as a whole (e.g. tag cloning or replay attacks). Proper traffic characterization models of the communication within an RFID network can lead to better understanding of operation under “normal” system state conditions and can consequently help identify security breaches not addressed by current standards. This study of RFID traffic characterization considers two piecewise-constant data smoothing techniques, namely Bayesian blocks and Knuth’s algorithms, over time-tagged events and compares them in the context of rate-based anomaly detection. This was accomplished using data from experimental RFID readings and comparing (1) the event counts versus time if using the smoothed curves versus empirical histograms of the raw data and (2) the threshold-dependent alert-rates based on inter-arrival times obtained if using the smoothed curves versus that of the raw data itself. Results indicate that both algorithms adequately model RFID traffic in which inter-event time statistics are stationary but that Bayesian blocks become superior for traffic in which such statistics experience abrupt changes.