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Cooperative Target Tracking Enhanced With The Sequence Memoizer, Everett A. Bryan
Cooperative Target Tracking Enhanced With The Sequence Memoizer, Everett A. Bryan
Theses and Dissertations
Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target behavior are …
Application Of Machine Learning And Parametric Nurbs Geometry To Mode Shape Identification, Robert Mceuen Porter
Application Of Machine Learning And Parametric Nurbs Geometry To Mode Shape Identification, Robert Mceuen Porter
Theses and Dissertations
In any design, the dynamic characteristics of a part are dependent on its geometric and material properties. Identifying vibrational mode shapes within an iterative design process becomes difficult and time consuming due to frequently changing part definition. Although research has been done to improve the process, visual inspection of analysis results is still the current means of identifying each vibrational mode determined by a modal analysis. This research investigates the automation of the mode shape identification process through the use of parametric geometry and machine learning.In the developed method, displacement results from finite element modal analysis are used to create …
Practical Cost-Conscious Active Learning For Data Annotation In Annotator-Initiated Environments, Robbie A. Haertel
Practical Cost-Conscious Active Learning For Data Annotation In Annotator-Initiated Environments, Robbie A. Haertel
Theses and Dissertations
Many projects exist whose purpose is to augment raw data with annotations that increase the usefulness of the data. The number of these projects is rapidly growing and in the age of “big data” the amount of data to be annotated is likewise growing within each project. One common use of such data is in supervised machine learning, which requires labeled data to train a predictive model. Annotation is often a very expensive proposition, particularly for structured data. The purpose of this dissertation is to explore methods of reducing the cost of creating such data sets, including annotated text corpora.We …
Probabilistic Explicit Topic Modeling, Joshua Aaron Hansen
Probabilistic Explicit Topic Modeling, Joshua Aaron Hansen
Theses and Dissertations
Latent Dirichlet Allocation (LDA) is widely used for automatic discovery of latent topics in document corpora. However, output from analysis using an LDA topic model suffers from a lack of identifiability between topics not only across corpora, but across runs of the algorithm. The output is also isolated from enriching information from knowledge sources such as Wikipedia and is difficult for humans to interpret due to a lack of meaningful topic labels. This thesis introduces two methods for probabilistic explicit topic modeling that address these issues: Latent Dirichlet Allocation with Static Topic-Word Distributions (LDA-STWD), and Explicit Dirichlet Allocation (EDA). LDA-STWD …