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Ebru Turanoglu

Ebru Turanoglu Bekar – Ebru Turanoğlu

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unicode name: Ebru Turanoğlu

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2020 – today

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2024
[c7]
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  - conf/ifip5-7/RoselliDSBS24
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Sabino Francesco Roselli, Martin Dahl, Mukund Subramaniyan, Ebru Turanoglu Bekar, Anders Skoogh:
Integrating Machine and Quality Data for Predictive Maintenance in Manufacturing System. APMS (5) 2024: 95-107
2023
[c6]
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Mélanie Despeisse, Qi Fang, Ebru Turanoglu Bekar, Nils Ólafur Egilsson, Karolina Kazmierczak, Lena Moestam, Helena Söderberg, Dennis Andersson, Jenny Hörnlund, Björn Molin:
Developing Data Models for Smart Environmental Performance Management in Production. APMS (4) 2023: 3-15
[c5]
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Mélanie Despeisse, Björn Johansson, Jon Bokrantz, Greta Braun, Arpita Chari, Xiaoxia Chen, Qi Fang, Clarissa A. González Chávez, Anders Skoogh, Johan Stahre, Ninan Theradapuzha Mathew, Ebru Turanoglu Bekar, Hao Wang, Roland Örtengren:
Battery Production Systems: State of the Art and Future Developments. APMS (4) 2023: 521-535
[c4]
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Paulo Victor Lopes, Siyuan Chen, Juan Pablo González Sánchez, Ebru Turanoglu Bekar, Jon Bokrantz, Anders Skoogh:
Data-Driven Smart Maintenance Decision Analysis: A Drone Factory Demonstrator Combining Digital Twins and Adapted AHP. WSC 2023: 1996-2007
[c3]
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Siyuan Chen, Juan Pablo González Sánchez, Ebru Turanoglu Bekar, Jon Bokrantz, Anders Skoogh, Paulo Victor Lopes:
Understanding Stakeholder Requirements for Digital Twins In Manufacturing Maintenance. WSC 2023: 2008-2019
2022
[j8]
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Camilla Lundgren, Ebru Turanoglu Bekar, Maja Bärring, Johan Stahre, Anders Skoogh, Björn Johansson, Richard Hedman:
Determining the impact of 5G-technology on manufacturing performance using a modified TOPSIS method. Int. J. Comput. Integr. Manuf. 35(1): 69-90 (2022)
2021
[j7]
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Alexander Karlsson, Ebru Turanoglu Bekar, Anders Skoogh:
Multi-Machine Gaussian Topic Modeling for Predictive Maintenance. IEEE Access 9: 100063-100080 (2021)
[c2]
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Harshad Kurrewar, Ebru Turanoglu Bekar, Anders Skoogh, Per Nyqvist:
A Machine Learning Based Health Indicator Construction in Implementing Predictive Maintenance: A Real World Industrial Application from Manufacturing. APMS (3) 2021: 599-608
2020
[c1]
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Mélanie Despeisse, Ebru Turanoglu Bekar:
Challenges in Data Life Cycle Management for Sustainable Cyber-Physical Production Systems. APMS (2) 2020: 57-65

2010 – 2019

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2017
[j6]
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Ebru Turanoglu Bekar, Mehmet Çakmakci, Cengiz Kahraman:
Using Adaptive Neuro-Fuzzy Inference System, Artificial Neural Network and Response Surface Method to Optimize Overall Equipment Effectiveness for An Automotive Supplier Company. J. Multiple Valued Log. Soft Comput. 28(4-5): 375-407 (2017)
2016
[j5]
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Özlem Senvar, Gözde Ulutagay, Ebru Turanoglu Bekar:
ANFIS Modeling for Forecasting Oil Consumption of Turkey. J. Multiple Valued Log. Soft Comput. 26(6): 609-624 (2016)
[p1]
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Cengiz Kahraman, Ebru Turanoglu Bekar, Özlem Senvar:
A Fuzzy Design of Single and Double Acceptance Sampling Plans. Intelligent Decision Making in Quality Management 2016: 179-211
2015
[j4]
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Ebru Turanoglu Bekar, Mehmet Çakmakci, Cengiz Kahraman:
An Anfis Algorithm for Forecasting Overall Equipment Effectiveness Parameter in Total Productive Maintenance. J. Multiple Valued Log. Soft Comput. 25(6): 535-554 (2015)
2014
[j3]
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  - journals/kbs/KahramanOST14
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Cengiz Kahraman, Basar Öztaysi, Irem Uçal Sari, Ebru Turanoglu:
Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowl. Based Syst. 59: 48-57 (2014)
2012
[j2]
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  - journals/ijcisys/TuranogluKK12
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Ebru Turanoglu, Ihsan Kaya, Cengiz Kahraman:
Fuzzy Acceptance Sampling and Characteristic Curves. Int. J. Comput. Intell. Syst. 5(1): 13-29 (2012)
[j1]
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Ebru Turanoglu, Özlem Senvar, Cengiz Kahraman:
Oil Consumption Forecasting in Turkey using Artificial Neural Network. Int. J. Energy Optim. Eng. 1(4): 89-105 (2012)

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