Masters Theses


“Cyber-Physical Systems (CPSs) are complex systems that integrate physical systems with their counterpart cyber components to form a close loop solution. Due to the ability of deep learning in providing sensor data-based models for analyzing physical systems, it has received increased interest in the CPS community in recent years. However, developing vision data-based deep learning models for CPSs remains critical since the models heavily rely on intensive, tedious efforts of humans to annotate training data. Besides, most of the models have a high tradeoff between quality and computational cost. This research studies deep learning algorithms to achieve affordable and upgradable network architecture which will provide better performance. Two important applications of CPS are studied in this work. In the first study, a Mask Region-based Convolutional Neural Network (Mask R-CNN) was adopted to segment regions of interest from surveillance videos of manufacturing plants. Then, the Mask R-CNN model was modified to have consistent detection results from videos using temporal coherence information of detected objects. This method was extended to the second study, a task of bridge inspection to detect and segment critical structural components. A cellular automata-based pattern recognition algorithm was integrated with the Mask R-CNN model to find the crack propagation rate in the structural components. Decision-makers can make a maintenance decision based on the rate. A discrete event simulation model was also developed to validate the proposed methodology. The work of this research demonstrates approaches to developing and implementing vision data-based deep neural networks to make the CPS more affordable, scalable, and efficient”--Abstract, page iv.


Qin, Ruwen

Committee Member(s)

Corns, Steven
Dagli, Cihan H., 1949-


Engineering Management and Systems Engineering

Degree Name

M.S. in Systems Engineering


Research work of this MS thesis is partially supported by the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) under Grant No. 69A3551747126 through INSPIRE University Transportation Center and Grant No. 69A3551747107 through Mid-America Transportation Center (MATC). This work is also partially supported by Intelligent Systems Center (ISC) and the Department of Engineering Management and Systems Engineering at Missouri University of Science and Technology.

Research Center/Lab(s)

Intelligent Systems Center


Missouri University of Science and Technology

Publication Date

Summer 2020

Journal article titles appearing in thesis/dissertation

  • A region-based deep learning algorithm for detecting and tracking objects in manufacturing plants
  • Modeling and simulation of a robotic bridge inspection system


xi, 49 pages

Note about bibliography

Includes bibliographic references.


© 2020 Muhammad Monjurul Karim, All rights reserved.

Document Type

Thesis - Open Access

File Type




Thesis Number

T 11750

Electronic OCLC #