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عنوان
Computer Vision Based Deep Learning Models for Cyber Physical Systems

پدید آورنده
Karim, Muhammad Monjurul

موضوع
Artificial intelligence,Computer science,Systems science

رده

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

NATIONAL BIBLIOGRAPHY NUMBER

Number
TLpq2448475401

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Computer Vision Based Deep Learning Models for Cyber Physical Systems
General Material Designation
[Thesis]
First Statement of Responsibility
Karim, Muhammad Monjurul
Subsequent Statement of Responsibility
Qin, Ruwen

.PUBLICATION, DISTRIBUTION, ETC

Name of Publisher, Distributor, etc.
Missouri University of Science and Technology
Date of Publication, Distribution, etc.
2020

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
64

DISSERTATION (THESIS) NOTE

Dissertation or thesis details and type of degree
M.S.
Body granting the degree
Missouri University of Science and Technology
Text preceding or following the note
2020

SUMMARY OR ABSTRACT

Text of Note
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.

TOPICAL NAME USED AS SUBJECT

Artificial intelligence
Computer science
Systems science

PERSONAL NAME - PRIMARY RESPONSIBILITY

Karim, Muhammad Monjurul
Qin, Ruwen

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

p

[Thesis]
276903

a
Y

Proposal/Bug Report

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