Thursday, February 24, 2022 - 10:30 to 11:30
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present…
MSc Thesis Proposal by: Joseph El-Ghaname
Date: Thursday, February 24th, 2022
Time: 10:30 AM – 11:30 AM
Meeting URL: https://us06web.zoom.us/j/83555931000?from=addon
Passcode: If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca with sufficient notice before the event to obtain the passcode.
Abstract:
Automotive industry has witnessed expensive, dangerous, and potentially fatal recalls over the years. With the rollout of new electric vehicles (EV), additional complexity is brought into the manufacturing process. Companies must use expensive fault detection systems to identify defects in the assembly process. Knowledge-based approaches for fault detection in manufacturing and assembly systems reflect modern practices in Industry 4.0. In this thesis, we will examine the feasibility of using knowledge graphs (KG) for their effectiveness in detecting faults in a custom dataset. We will implement a KG Completion (KGC) algorithm and compare different KG Embedding (KGE) models. In our experiments we plan to measure the Mean Reciprocal Rank (MRR) and Hits@K to evaluate the algorithm.
Keywords: Fault Detection, Knowledge Graphs, Knowledge Graph Completion, Knowledge Graph Embedding
MSc Thesis Proposal Announcement 
5113 Lambton Tower 401 Sunset Ave. Windsor ON, N9B 3P4 (519) 253-3000 Ext. 3716 csgradinfo@uwindsor.ca (working remotely)