Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item MACHINERY ANOMALY DETECTION UNDER INDETERMINATE OPERATING CONDITIONS(2018) Tian, Jing; Pecht, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Anomaly detection is a critical task in system health monitoring. Current practice of anomaly detection in machinery systems is still unsatisfactory. One issue is with the use of features. Some features are insensitive to the change of health, and some are redundant with each other. These insensitive and redundant features in the data mislead the detection. Another issue is from the influence of operating conditions, where a change in operating conditions can be mistakenly detected as an anomalous state of the system. Operating conditions are usually changing, and they may not be readily identified. They contribute to false positive detection either from non-predictive features driven by operating conditions, or from influencing predictive features. This dissertation contributes to the reduction of false detection by developing methods to select predictive features and use them to span a space for anomaly detection under indeterminate operating conditions. Available feature selection methods fail to provide consistent results when some features are correlated. A method was developed in this dissertation to explore the correlation structure of features and group correlated features into the same clusters. A representative feature from each cluster is selected to form a non-correlated set of features, where an optimized subset of predictive features is selected. After feature selection, the influence of operating conditions through non-predictive variables are removed. To remove the influence on predictive features, a clustering-based anomaly detection method is developed. Observations are collected when the system is healthy, and these observations are grouped into clusters corresponding to the states of operating conditions with automatic estimation of clustering parameters. Anomalies are detected if the test data are not members of the clusters. Correct partitioning of clusters is an open challenge due to the lack of research on the clustering of the machinery health monitoring data. This dissertation uses unimodality of the data as a criterion for clustering validation, and a unimodality-based clustering method is developed. Methods of this dissertation were evaluated by simulated data, benchmark data, experimental study and field data. These methods provide consistent results and outperform representatives of available methods. Although the focus of this dissertation is on the application of machinery systems, the methods developed in this dissertation can be adapted for other application scenarios for anomaly detection, feature selection, and clustering.Item Instrument Development For Continuing Medical Education Evaluation(2007-08-28) Tian, Jing; Atkinson, Nancy L; Portnoy, Barry; Public and Community Health; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The purpose of this study was to develop a valid, reliable and adaptable CME evaluation instrument to facilitate the future CME evaluation effort as well as contribute to the literature of CME evaluation studies. A generic instrument template was first developed addressing variables in the second evaluation level based on the TPB, i.e. attitude, behavioral belief, subjective norm, perceived behavioral control and behavioral intention. The instrument was then adapted to a CME-related conference, Preoperative Therapy in Invasive Breast Cancer: Reviewing the State of the Science and Exploring New Research Directions. Data were collected at the conference. A total of 134 physicians returned their questionnaires. Principle axis factoring with oblique rotation was used to examine the underlying structure of the data and reduced the items in the instrument to six subscales: positive beliefs, negative beliefs, subjective norms, perceived behavioral control and behavioral intention. Factor loadings supported the existence of six valid scales. The consistency between the a priori subscales and the factors emerged served as evidence for content validity of the instrument. Overall, all the subscales had sufficient reliability (alpha>= 0.70) for early stage instrument development showing the unidimensionality of the subscales. Scale modifications based on item analyses were conducted. The problematic items were eliminated, and the analyses were rerun. A 22-item instrument and a revised generic instrument template were finally developed. This study determined the adaptability of the theory based instrument template to the NCI CME conference and the feasibility of developing a content specific, valid and reliable CME evaluation instrument from the template assessing the changes in the concepts listed in the second evaluation level. The established and validated instrument could further be used to evaluate the effectiveness of other CME activities having the template adapted to different clinical domains addressed by each individual CME activity.