THESIS
2018
Abstract
Recent advances in measurement and sensing technology have generated data-rich environments in many industrial and service applications with complex systems, which has
led to an increasing need to handling datasets generated from multiple sources. These
data contain detailed information of the engineering process, and provide great opportunity for getting better understanding of the system and thus improving quality. However,
difficulties and challenges remain in modeling and monitoring data from such systems
due to great variety and variability of such datasets with various sources. This thesis
contains three projects which conducts data fusion methods to address the issues in different applications. In the first project, a hierarchical Bayesian method is proposed to
model and mo...[
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Recent advances in measurement and sensing technology have generated data-rich environments in many industrial and service applications with complex systems, which has
led to an increasing need to handling datasets generated from multiple sources. These
data contain detailed information of the engineering process, and provide great opportunity for getting better understanding of the system and thus improving quality. However,
difficulties and challenges remain in modeling and monitoring data from such systems
due to great variety and variability of such datasets with various sources. This thesis
contains three projects which conducts data fusion methods to address the issues in different applications. In the first project, a hierarchical Bayesian method is proposed to
model and monitor the customer reviews with both textual content and numerical ratings
in E-commerce feedback systems. The second project is concerned with modeling and
improving estimation of covariance structure of data from multiple sensors with combining associated data sources including geographical information and group information.
The method is applied in a sensor system designed to detect and predict landslides and
hill-slopes. The third project propose a statistical transfer learning based method to integrate information from multiple sites and sensors with auto-correlated sensor readings
for newly set-up sensors. These essays provide effective solutions of integrating multiple data sources to help modeling and monitoring complex systems appeared in modern
engineering applications.
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