Matching Items (3)
Filtering by
- All Subjects: Computer Science
- All Subjects: Life Testing
Description
Polar ice masses can be valuable indicators of trends in global climate. In an effort to better understand the dynamics of Arctic ice, this project analyzes sea ice concentration anomaly data collected over gridded regions (cells) and builds graphs based upon high correlations between cells. These graphs offer the opportunity to use metrics such as clustering coefficients and connected components to isolate representative trends in ice masses. Based upon this analysis, the structure of sea ice graphs differs at a statistically significant level from random graphs, and several regions show erratically decreasing trends in sea ice concentration.
ContributorsWallace-Patterson, Chloe Rae (Author) / Syrotiuk, Violet (Thesis director) / Colbourn, Charles (Committee member) / Montgomery, Douglas (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Reliability growth is not a new topic in either engineering or statistics and has been a major focus for the past few decades. The increasing level of high-tech complex systems and interconnected components and systems implies that reliability problems will continue to exist and may require more complex solutions. The most heavily used experimental designs in assessing and predicting a systems reliability are the "classical designs", such as full factorial designs, fractional factorial designs, and Latin square designs. They are so heavily used because they are optimal in their own right and have served superbly well in providing efficient insight into the underlying structure of industrial processes. However, cases do arise when the classical designs do not cover a particular practical situation. Repairable systems are such a case in that they usually have limitations on the maximum number of runs or too many varying levels for factors. This research explores the D-optimal design criteria as it applies to the Poisson Regression model on repairable systems, with a number of independent variables and under varying assumptions, to include the total time tested at a specific design point with fixed parameters, the use of a Bayesian approach with unknown parameters, and how the design region affects the optimal design. In applying experimental design to these complex repairable systems, one may discover interactions between stressors and provide better failure data. Our novel approach of accounting for time and the design space in the early stages of testing of repairable systems should, theoretically, in the final engineering design improve the system's reliability, maintainability and availability.
ContributorsTAYLOR, DUSTIN (Author) / Montgomery, Douglas (Thesis advisor) / Pan, Rong (Thesis advisor) / Rigdon, Steve (Committee member) / Freeman, Laura (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2023
Description
Complex systems appear when interaction among system components creates emergent behavior that is difficult to be predicted from component properties. The growth of Internet of Things (IoT) and embedded technology has increased complexity across several sectors (e.g., automotive, aerospace, agriculture, city infrastructures, home technologies, healthcare) where the paradigm of cyber-physical systems (CPSs) has become a standard. While CPS enables unprecedented capabilities, it raises new challenges in system design, certification, control, and verification. When optimizing system performance computationally expensive simulation tools are often required, and search algorithms that sequentially interrogate a simulator to learn promising solutions are in great demand. This class of algorithms are black-box optimization techniques. However, the generality that makes black-box optimization desirable also causes computational efficiency difficulties when applied real problems. This thesis focuses on Bayesian optimization, a prominent black-box optimization family, and proposes new principles, translated in implementable algorithms, to scale Bayesian optimization to highly expensive, large scale problems. Four problem contexts are studied and approaches are proposed for practically applying Bayesian optimization concepts, namely: (1) increasing sample efficiency of a highly expensive simulator in the presence of other sources of information, where multi-fidelity optimization is used to leverage complementary information sources; (2) accelerating global optimization in the presence of local searches by avoiding over-exploitation with adaptive restart behavior; (3) scaling optimization to high dimensional input spaces by integrating Game theoretic mechanisms with traditional techniques; (4) accelerating optimization by embedding function structure when the reward function is a minimum of several functions. In the first context this thesis produces two multi-fidelity algorithms, a sample driven and model driven approach, and is implemented to optimize a serial production line; in the second context the Stochastic Optimization with Adaptive Restart (SOAR) framework is produced and analyzed with multiple applications to CPS falsification problems; in the third context the Bayesian optimization with sample fictitious play (BOFiP) algorithm is developed with an implementation in high-dimensional neural network training; in the last problem context the minimum surrogate optimization (MSO) framework is produced and combined with both Bayesian optimization and the SOAR framework with applications in simultaneous falsification of multiple CPS requirements.
ContributorsMathesen, Logan (Author) / Pedrielli, Giulia (Thesis advisor) / Candan, Kasim (Committee member) / Fainekos, Georgios (Committee member) / Gel, Esma (Committee member) / Montgomery, Douglas (Committee member) / Zabinsky, Zelda (Committee member) / Arizona State University (Publisher)
Created2021