Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Forecasting of Product Quality Through Anomaly Detection
Date
2019-10-23
Author
Dinc, Mehmet
Ertekin Bolelli, Şeyda
Ozkan, Hadi
Meydanli, Can
Atalay, Mehmet Volkan
Metadata
Show full item record
Item Usage Stats
46
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/70884
Collections
Unclassified, Article
Suggestions
OpenMETU
Core
Forecasting of Product Quality Through Anomaly Detection
Dinç, Mehmet; Ertekin Bolelli, Şeyda; Özkan, Hadi; Meydanlı, Can; Atalay, Mehmet Volkan (2020)
Forecasting of product quality by means of anomaly detection is crucial in real-world applications such as manufacturing systems. In manufacturing systems, the quality is assured through tests performed on sample units randomly chosen from a batch of manufactured units. One of the major issues is to detect defective units among the sample test units as early as possible in terms of test time and of course as accurate as possible. Traditional way of detecting defective units is to make use of human experts d...
Forecasting of ionospheric critical frequency using neural networks
Altinay, O; Tulunay, E; Tulunay, Yurdanur (1997-06-15)
Multilayer perceptron type neural networks (NN) are employed for forecasting ionospheric critical frequency (foF2) one hour in advance. The nonlinear black-box modeling approach in system identification is used. The main contributions: 1. A flexible and easily accessible training database capable of handling extensive physical data is prepared, 2. Novel NN design and experimentation software is developed, 3. A training strategy is adopted in order to significantly enhance the generalization or extrapolation...
Forecasting magnetopause crossing locations by using Neural Networks
Tulunay, Yurdanur; Sibeck, DG; Senalp, ET; Tulunay, E (Elsevier BV, 2005-01-01)
Given the highly complex and nonlinear nature of Near Earth Space processes, mathematical modeling of these processes is usually difficult or impossible. In such cases, modeling methods involving Artificial Intelligence may be employed. We demonstrate that data driven models, such as the Neural Network based approach, shows promise in its ability to forecast or predict the behavior of these processes. In this paper, modeling studies for forecasting magnetopause crossing locations are summarized and a Neural...
Forecasting the Hydro Inflow and Optimization of Virtual Power Plant Pricing
Çabuk, Sezer; Mert, Özenç Murat; Kestel, Ayşe Sevtap; Kalaycı, Erkan (Springer, London/Berlin , 2021-07-01)
Forecasting the Hydro Inflow and Optimization of Virtual Power Plant Pricing
Çabuk, Sezer; Mert, Özenç Murat; Kestel, Sevtap Ayşe; Kalaycı, Erkan (Springer, 2021-01-01)
Hydro inflow forecasting is crucial for effective hydro optimization, virtual power plant pricing, volume risk management, and weather derivatives pricing in the electricity markets. Predicting hydro inflow allows the decision-makers to economically use water for optimal periods, quantify volume risk and determine effective portfolio management strategies. This study aims pricing a hydroelectricity power plant as a Virtual Power Plant based on Turkish energy markets. For pricing of such a non-standard optio...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Dinc, Ş. Ertekin Bolelli, H. Ozkan, C. Meydanli, and M. V. Atalay, “Forecasting of Product Quality Through Anomaly Detection,” 2019, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/70884.