A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item AN INTEGER PROGRAMMING MODEL FOR DYNAMIC TAXI-SHARING CONSIDERING PROVIDER PROFIT(2018) Hao, Yeming; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis proposes an integer programming model for Dynamic Taxi-Sharing (DTS), which allows two groups of taxi users to ride on the same taxi together. The model matches taxi drivers and user pairs in certain sequences with the goal of maximizing taxi providers’ profit. We also develop a DTS fare calculation scheme which can automatically calculate the fare for each DTS user and self-adjust to balance the taxi occupancy rate in real time. A customized spectral clustering approach for preselection on DTS trips is also designed to narrow down the search space for the model. Real-world taxi trip data is used to demonstrate the DTS system is beneficial to providers, taxi users, and taxi drivers.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 Spatial and temporal modeling of large-scale brain networks(2017) Najafi, Mahshid; Pessoa, Luiz; Simon, Jonathan Z.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The human brain is the most fascinating and complex organ. It directs all our actions and thoughts. Despite the large body of brain studies, little is known about the neural basis of its large-scale structure. In this dissertation, I take advantage of several network-based and statistical techniques to investigate the spatial and temporal aspects of large-scale functional networks of the human brain during "rest" and "task" conditions using functional MRI data. Large-scale analysis of human brain function has revealed that brain regions can be grouped into networks or communities. Most studies adopt a framework in which brain regions belong to only one community. Yet studies in general fields of knowledge suggest that in most cases complex networks consist of interwoven sets of overlapping communities. A mixed-membership framework can better characterize the complex networks. In this dissertation, I employed a mixed-membership Bayesian model to characterize overlapping community structure of the brain at both "rest" and "task" conditions. The approach allowed us to quantify how task performance reconfigures brain communities at rest, and determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities). Furthermore, I could study the distribution of key regions, named "bridges", in transferring information across the brain communities. Our findings revealed that the overlapping framework described the brain in ways that were not captured by disjoint clustering, and thus provided a richer landscape of large-scale brain networks. Overall, I suggest that overlapping networks are better suited to capture the flexible and task-dependent mapping between brain regions and their functions. Finally, I developed a dynamic intersubject network analysis technique to study the temporal changes of the emotional brain at the level of large-scale brain networks by formulating a manipulation in which threat levels varied continuously during the experiment. Our results illustrate that cohesion within and between networks changed dynamically with threat level. Together, our findings reveal that characterizing emotional processing should be done at the level of distributed networks, and not simply at the level of evoked responses in specific brain regions.