عنوان

Computer Vision Based Deep Learning Models for Cyber Physical Systems

شماره

TL53493

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Computer Vision Based Deep Learning Models for Cyber Physical Systems

نام عام مواد

[Thesis]

نام نخستين پديدآور

Karim, Muhammad Monjurul

نام ساير پديدآوران

Qin, Ruwen

نام ناشر، پخش کننده و غيره

Missouri University of Science and Technology

تاریخ نشرو بخش و غیره

2020

متن يادداشت

64 p.

جزئيات پايان نامه و نوع درجه آن

M.S.

کسي که مدرک را اعطا کرده

Missouri University of Science and Technology

امتياز متن

2020

متن يادداشت

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.

اصطلاح موضوعی

Artificial intelligence

اصطلاح موضوعی

Computer science

اصطلاح موضوعی

Systems science

مستند نام اشخاص تاييد نشده

Karim, Muhammad Monjurul

مستند نام اشخاص تاييد نشده

Qin, Ruwen

مستند نام تنالگان تاييد نشده

Missouri University of Science and Technology

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y