Matching Items (6)
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- All Subjects: Dynamical Systems
Description
Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme.
ContributorsIlkturk, Utku (Author) / Gelb, Anne (Thesis advisor) / Platte, Rodrigo (Thesis advisor) / Cochran, Douglas (Committee member) / Renaut, Rosemary (Committee member) / Armbruster, Dieter (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Kuramoto model is an archetypal model for studying synchronization in groups
of nonidentical oscillators where oscillators are imbued with their own frequency and
coupled with other oscillators though a network of interactions. As the coupling
strength increases, there is a bifurcation to complete synchronization where all oscillators
move with the same frequency and show a collective rhythm. Kuramoto-like
dynamics are considered a relevant model for instabilities of the AC-power grid which
operates in synchrony under standard conditions but exhibits, in a state of failure,
segmentation of the grid into desynchronized clusters.
In this dissertation the minimum coupling strength required to ensure total frequency
synchronization in a Kuramoto system, called the critical coupling, is investigated.
For coupling strength below the critical coupling, clusters of oscillators form
where oscillators within a cluster are on average oscillating with the same long-term
frequency. A unified order parameter based approach is developed to create approximations
of the critical coupling. Some of the new approximations provide strict lower
bounds for the critical coupling. In addition, these approximations allow for predictions
of the partially synchronized clusters that emerge in the bifurcation from the
synchronized state.
Merging the order parameter approach with graph theoretical concepts leads to a
characterization of this bifurcation as a weighted graph partitioning problem on an
arbitrary networks which then leads to an optimization problem that can efficiently
estimate the partially synchronized clusters. Numerical experiments on random Kuramoto
systems show the high accuracy of these methods. An interpretation of the
methods in the context of power systems is provided.
of nonidentical oscillators where oscillators are imbued with their own frequency and
coupled with other oscillators though a network of interactions. As the coupling
strength increases, there is a bifurcation to complete synchronization where all oscillators
move with the same frequency and show a collective rhythm. Kuramoto-like
dynamics are considered a relevant model for instabilities of the AC-power grid which
operates in synchrony under standard conditions but exhibits, in a state of failure,
segmentation of the grid into desynchronized clusters.
In this dissertation the minimum coupling strength required to ensure total frequency
synchronization in a Kuramoto system, called the critical coupling, is investigated.
For coupling strength below the critical coupling, clusters of oscillators form
where oscillators within a cluster are on average oscillating with the same long-term
frequency. A unified order parameter based approach is developed to create approximations
of the critical coupling. Some of the new approximations provide strict lower
bounds for the critical coupling. In addition, these approximations allow for predictions
of the partially synchronized clusters that emerge in the bifurcation from the
synchronized state.
Merging the order parameter approach with graph theoretical concepts leads to a
characterization of this bifurcation as a weighted graph partitioning problem on an
arbitrary networks which then leads to an optimization problem that can efficiently
estimate the partially synchronized clusters. Numerical experiments on random Kuramoto
systems show the high accuracy of these methods. An interpretation of the
methods in the context of power systems is provided.
ContributorsGilg, Brady (Author) / Armbruster, Dieter (Thesis advisor) / Mittelmann, Hans (Committee member) / Scaglione, Anna (Committee member) / Strogatz, Steven (Committee member) / Welfert, Bruno (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms.
ContributorsBuscaglia, Robert, Ph.D (Author) / Kamarianakis, Yiannis (Thesis advisor) / Armbruster, Dieter (Committee member) / Lanchier, Nicholas (Committee member) / McCulloch, Robert (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2018
Description
This paper intends to analyze the Phoenix Suns' shooting patterns in real NBA games, and compare them to the "NBA 2k16" Suns' shooting patterns. Data was collected from the first five Suns' games of the 2015-2016 season and the same games played in "NBA 2k16". The findings of this paper indicate that "NBA 2k16" utilizes statistical findings to model their gameplay. It was also determined that "NBA 2k16" modeled the shooting patterns of the Suns in the first five games of the 2015-2016 season very closely. Both, the real Suns' games and the "NBA 2k16" Suns' games, showed a higher probability of success for shots taken in the first eight seconds of the shot clock than the last eight seconds of the shot clock. Similarly, both game types illustrated a trend that the probability of success for a shot increases as a player holds onto a ball longer. This result was not expected for either game type, however, "NBA 2k16" modeled the findings consistent with real Suns' games. The video game modeled the Suns with significantly more passes per possession than the real Suns' games, while they also showed a trend that more passes per possession has a significant effect on the outcome of the shot. This trend was not present in the real Suns' games, however literature supports this finding. Also, "NBA 2k16" did not correctly model the allocation of team shots for each player, however, the differences were found only in bench players. Lastly, "NBA 2k16" did not correctly allocate shots across the seven regions for Eric Bledsoe, however, there was no evidence indicating that the game did not correctly model the allocation of shots for the other starters, as well as the probability of success across the regions.
ContributorsHarrington, John P. (Author) / Armbruster, Dieter (Thesis director) / Kamarianakis, Ioannis (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Analytic research on basketball games is growing quickly, specifically in the National Basketball Association. This paper explored the development of this analytic research and discovered that there has been a focus on individual player metrics and a dearth of quantitative team characterizations and evaluations. Consequently, this paper continued the exploratory research of Fewell and Armbruster's "Basketball teams as strategic networks" (2012), which modeled basketball teams as networks and used metrics to characterize team strategy in the NBA's 2010 playoffs. Individual players and outcomes were nodes and passes and actions were the links. This paper used data that was recorded from playoff games of the two 2012 NBA finalists: the Miami Heat and the Oklahoma City Thunder. The same metrics that Fewell and Armbruster used were explained, then calculated using this data. The offensive networks of these two teams during the playoffs were analyzed and interpreted by using other data and qualitative characterization of the teams' strategies; the paper found that the calculated metrics largely matched with our qualitative characterizations of the teams. The validity of the metrics in this paper and Fewell and Armbruster's paper was then discussed, and modeling basketball teams as multiple-order Markov chains rather than as networks was explored.
ContributorsMohanraj, Hariharan (Co-author) / Choi, David (Co-author) / Armbruster, Dieter (Thesis director) / Fewell, Jennifer (Committee member) / Brooks, Daniel (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2013-05
DescriptionUnderstanding the evolution of opinions is a delicate task as the dynamics of how one changes their opinion based on their interactions with others are unclear.
ContributorsWeber, Dylan (Author) / Motsch, Sebastien (Thesis advisor) / Lanchier, Nicolas (Committee member) / Platte, Rodrigo (Committee member) / Armbruster, Dieter (Committee member) / Fricks, John (Committee member) / Arizona State University (Publisher)
Created2021