Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- American Popular Culture (1)
- American Studies (1)
- Art Practice (1)
- Arts and Humanities (1)
- Business (1)
-
- Computer Engineering (1)
- Computer and Systems Architecture (1)
- Digital Circuits (1)
- Digital Communications and Networking (1)
- Digital Humanities (1)
- Film and Media Studies (1)
- Fine Arts (1)
- Food Studies (1)
- Operational Research (1)
- Operations Research, Systems Engineering and Industrial Engineering (1)
- Operations and Supply Chain Management (1)
- Other Languages, Societies, and Cultures (1)
- Other Operations Research, Systems Engineering and Industrial Engineering (1)
- Photography (1)
- Social and Behavioral Sciences (1)
- Sociology (1)
- Sociology of Culture (1)
- Visual Studies (1)
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Computational Engineering
Data-Driven Predictive Maintenance Scheduling Policies For Railways, Pedro Cesar Lopes Gerum, Ayca Altay, Melike Baykal-Gürsoy
Data-Driven Predictive Maintenance Scheduling Policies For Railways, Pedro Cesar Lopes Gerum, Ayca Altay, Melike Baykal-Gürsoy
Supply Chain Management
Inspection and maintenance activities are essential to preserving safety and cost-effectiveness in railways. However, the stochastic nature of railway defect occurrence is usually ignored in literature; instead, defect stochasticity is considered independently of maintenance scheduling. This study presents a new approach to predict rail and geometry defects that relies on easy-to-obtain data and integrates prediction with inspection and maintenance scheduling activities. In the proposed approach, a novel use of risk-averse and hybrid prediction methodology controls the underestimation of defects. Then, a discounted Markov decision process model utilizes these predictions to determine optimal inspection and maintenance scheduling policies. Furthermore, in the …
Recipe For Disaster, Zac Travis
Recipe For Disaster, Zac Travis
MFA Thesis Exhibit Catalogs
Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.
The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a …