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Observability methods in sensor scheduling

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…

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

Adding Observability to the Girard Training Stables Scheduling Tool

Description
Girard Training Stables is a horse-based nonprofit organization that offers riding lessons, mental health support, and physical therapy. A scheduling tool was recently built for them to assist in managing as many as 90 volunteers across many different events. Our goal was to add observability to this scheduling tool, as…

Girard Training Stables is a horse-based nonprofit organization that offers riding lessons, mental health support, and physical therapy. A scheduling tool was recently built for them to assist in managing as many as 90 volunteers across many different events. Our goal was to add observability to this scheduling tool, as being able to better observe the tool’s internal state would make fixing any problems easier. To add this observability we added both frontend and backend monitoring to track metrics such as how many users sign up for new accounts, when users start and finish creating an event, how much the server running the website is using its resources, and how many errors are caught while the server is running. Using these metrics, we were able to gain much insight into the internal state of the website and its users. We found that the frontend metrics were useful to non-technical users, with 70% of the users surveyed being able to correctly understand the data generated and theorize about parts of the website UI that could be improved based on said data. We were also able to correctly catch and log 100% of the test errors that were generated, and send alerts to administrators if these errors led to system failure. Overall, we were able to significantly improve the observability of the Girard Training Stables scheduling tool by adding monitoring, making it more robust, scalable, and easy to improve for the future.
ContributorsMoore, Peter (Author) / Ross, Michael (Co-author) / Chavez, Helen (Thesis director) / Vannoni, Greg (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12

Adding Observability to the Girard Training Stables Scheduling Tool

Description
Girard Training Stables is a horse-based nonprofit organization that offers riding lessons, mental health support, and physical therapy. A scheduling tool was recently built for them to assist in managing as many as 90 volunteers across many different events. Our goal was to add observability to this scheduling tool, as…

Girard Training Stables is a horse-based nonprofit organization that offers riding lessons, mental health support, and physical therapy. A scheduling tool was recently built for them to assist in managing as many as 90 volunteers across many different events. Our goal was to add observability to this scheduling tool, as being able to better observe the tool’s internal state would make fixing any problems easier. To add this observability we added both frontend and backend monitoring to track metrics such as how many users sign up for new accounts, when users start and finish creating an event, how much the server running the website is using its resources, and how many errors are caught while the server is running. Using these metrics, we were able to gain much insight into the internal state of the website and its users. We found that the frontend metrics were useful to non-technical users, with 70% of the users surveyed being able to correctly understand the data generated and theorize about parts of the website UI that could be improved based on said data. We were also able to correctly catch and log 100% of the test errors that were generated, and send alerts to administrators if these errors led to system failure. Overall, we were able to significantly improve the observability of the Girard Training Stables scheduling tool by adding monitoring, making it more robust, scalable, and easy to improve for the future.
ContributorsRoss, Michael (Author) / Moore, Peter (Co-author) / Chavez, Helen (Thesis director) / Vannoni , Greg (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12