Theses and Dissertations at Montana State University (MSU)
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Item Developing bio-inspired methodologies for encoding angular position from strain(Montana State University - Bozeman, College of Engineering, 2020) Lange, Christopher William; Chairperson, Graduate Committee: Mark JankauskiAs mechanical systems rely more on closed-loop control, the sensors which supply feedback information are essential. Additionally, in systems where sensor function is critical, sensor redundancy is important to retain functionality if one or more sensors fail. Redundancy can be achieved through multiple high-fidelity sensors which measure the same type of information, such as gyroscopes or accelerometers. However, multiple high-fidelity sensors can increase cost significantly. This thesis explores the potential to replace or augment the functionality of angular position sensors using strain measurements. Strain gauges are already used in system health monitoring systems. By utilizing these already implemented sensors to measure angular position, we can remove the additional cost of redundant angular position sensors. However, for complex systems, the mapping between strain and angular position is unclear. By incorporating reduced order, physics-based models into machine learning techniques, we can efficiently transform high-order strain data into angular position. To demonstrate the potential of using alternative sensing methods, we developed a reduced order model of a parametrically excited flexible pendulum. Inspiration for this simplified system comes from insect halteres, which are small sensory organs evolved from insect hind wings which provide rapid information about body rotation. The parametrically excited flexible pendulum allows a single axis of rotation and single direction of flexibility to be paired, and their relationship studied. By varying parameters within the model such as pendulum length and modulus as well as parametric excitation amplitude and frequency, the Gaussian process regression learning can be optimized to reduce training time and increase untrained prediction accuracy. Inputs of strain and parametric excitation position along with their respective first and second derivatives are then analyzed to determine which inputs are interrelated and therefore un-necessary, thus reducing the input required. This provides the essential first steps towards using machine learning to implement multiple sensor, deformation based, multi axial angular position sensing in complex systems.Item Predictive models for 30-day patient readmissions in a small community hospital(Montana State University - Bozeman, College of Engineering, 2013) Lovejoy, Matthew Walter; Chairperson, Graduate Committee: David ClaudioPresently, national healthcare initiatives have a strong emphasis on improving patient quality of care through a reduction in patient readmissions. Current federal regulations created through the Patient Protection and Affordable Care Act (PPACA); focus on the reduction in readmissions to improve patient quality of care (Stone & Hoffman, 2010). This legislation mandates decreased reimbursement for services if a facility has high 30-day patient readmissions related to the core measures Congestive Heart Failure (CHF), Acute Myocardial Infarction (AMI) and Pneumonia (PNM). This research focuses on building predictive models to aid Bozeman Deaconess Health Services (BDHS), a small community hospital, reduce their readmission rates. Assistance was performed through identification of patient characteristics influencing patient readmission risk, along with advanced statistical regression techniques used to develop readmission risk prediction models. Potential predictor variables and prediction models were obtained through retrospective analysis of patient readmission data from BDHS during January 2009 through December 2010. For increased prediction accuracy seven separate readmission dataset types were developed: General population, and ICD-9 code related populations for AMI, CHF, PNM, Alcoholic Psychoses (291), Cardiac Dysrhythmias (427) and Disorders of the Function of the Stomach (536). For the greatest benefit from readmission reduction, analysis focused on readmissions categorized as Potentially Preventable Readmissions (PPR); defined as unplanned, medically related readmissions within 30-days of a patient's previous inpatient visit. General exploratory analysis was performed on the PPR patient data to discover patterns which may indicate certain variables as good predictors of patient readmission risk. The prediction model methods compared were binary logistic regression, and multivariate adaptive regression splines (MARS). Usable binary logistic regression models for 536 (Nagelkerke R 2=0.676) and CHF (Nagelkerke R 2=0.974) were achieved. MARS developed usable models for 427 (Naïve Adj. R 2=0.63288), 536 (Naïve Adj. R 2=0.77395), AMI (Naïve Adj. R 2=0.76705), CHF (Naïve Adj. R 2=0.99385) and PNM (Naïve Adj. R 2=0.82615). Comparison of the modeling methods suggest MARS is more accurate at developing usable prediction models, however a tradeoff between model complexity and predictability is present. The usable readmission risk prediction models developed for BDHS will aid BDHS in reducing their readmissions rates, consequently improving patient quality of care.