Akademik Yayınlarımız
TEKNOPAR
AKADEMİK YAYIN LİSTESİ
2025
1. Kaleli, I. S., Unal, P., Deveci, B. U., & Albayrak, O. (2024, August). A Tool Condition Monitoring Study to Support Circular Economy. In 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE. Link
2024
1. Kaleli, I. S., Unal, P., Deveci, B. U., & Albayrak, O. (2024, August). A Tool Condition Monitoring Study to Support Circular Economy. In 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE. Link
2. Kaleli, I. S., Unal, P., Deveci, B. U., Albayrak, O., & Ozbayoglu, A. M. (2024, August). A Domain-Aware Federated Learning Study for CNC Tool Wear Estimation. In 2024 20th International Conference on Mobile Web and Intelligent Information Systems (MobiWIS). Cham: Springer International Publishing. Link
3. Unal, P., Hatipoglu, O. I., Turker, A., Unal, A.F., Deveci, B.U., Kirci, P., Albayrak, O., & Ozbayoglu, M. (2024). Comparative Analysis and Performance Evaluation of Underwater Cable Detection and Tracking Techniques: A Comprehensive Survey. International Transactions on Electrical Energy Systems, 2024(1). Link
2023
1. Ünal, A. F., Albayrak, Ö., Ünal, P. (2023). Impact of Digital Twin Technology Utilization in Manufacturing on Sustainability:An Industrial Case Study. Link
2. Deveci, B.U., Çeltikoğlu, M., Alp, T., Albayrak, Ö., Ünal, P., Kırcı, P., "Transfer Learning Enabled Bearing Fault Detection Methods Based on Image Representations of Single Dimensional Signals" (Information Systems Frontiers). Link
3. Johansen, S. T., Unal, P., Albayrak, Ö., Ikonen, E., Linnestad, K. J., Jawahery, S., ... & Løvfall, B. T. (2023). Hybrid and cognitive digital twins for the process industry. Open Engineering, 13(1), 20220418. Link
4. Durmaz, A., Albayrak, Ö., Ünal, P., Ankaralı, M.M., “Modeling, Simulation, and Control of a “Sensorless” Cable-Driven Robot”, 2023. Link
5. Keremer O., Malay F. C. , Deveci B., Kirci P., Unal P. (2023). Optimisation of a Chemical Process by Using Machine Learning Algorithms with Surrogate Modeling. Conference paper, Part of the Lecture Notes in Computer Science book series (LNCS,volume 13977). Link
6. Koç, A., Kurt Y., Ünal P. (2023, December) A Study to Compare YOLO Family Models for Metal Surface Anomaly Detection. Link
7. Ünal, P., Temel, S., Ummak, E., Özbayoğlu, A. M. (2023, August). Tool Condition Monitoring and Remaining Useful Life Prediction for CNC Milling Machines. In The 10th International Conference on Future Internet of Things and Cloud (FiCloud 2023). IEEE. Link
2022
1. Yallıç, F., Albayrak, Ö. and Ünal, P. (2022). Asset Administration Shell Generation and Usage for Digital Twins: A Case Study for Non-destructive Testing. In Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics, ISBN 978-989-758-612-5, ISSN 2184-9285, pages 299-306. Link
2. Ünal, P., Albayrak, Ö., Kubatova, M., Deveci, B.U., Cirakman, E., Kocal, Ç.İ., Özbayoğlu, A. M., “A Big Data Application in Manufacturing Industry-Computer Vision to Detect Defects on Bearings”, 2022. Link
3. Deveci, B.U., Ummak E., Albayrak, Ö., Ünal, P., “A Thorough Analysis and Comparison of Data Communication Protocols Used in Industry 4.0: The Case of Smart-CNC”, 2022. Link
4. Temel, S., Ummak, E., Tokgöz, A., Işık, F., Albayrak, Ö., Ünal, P.,Özbayoğlu, M., “Control System Design and Implementation Based on Big Data and Ontology", IEEE International Conference on Big Data Special session, 8th Special Session on Intelligent Data Mining, 2022. Link
5. Kunduz, İ., Durmaz, A., Albayrak, Ö., Alp, T., Ünal, P., Ankaralı, M.M., "Sensörsüz Seri Elastik Tekniği ile Bir Kablo Sürümlü Düzlemsel Mekanizmanın Kontrolü”, Avrupa Bilim ve Teknoloji Dergisi,2022. Link
6. Ünal, P., Deveci B.U., Özbayoğlu, A.M., “A Review: Sensors Used in Tool Wear Monitoring and Prediction”, 2022. Link
7. Erdogan, A., Unal, P., "Dijital İkiz Teknolojilerinin Savunma Sistemlerinde Kullanımı", 10.Savunma Teknolojileri Kongresi, 2022. Link
2021
1. Albayrak, Ö. and Ünal, P., “Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery” in Advances in Intelligent Systems and Computing Volume 1338: “Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry: Ongoing Applications, Perspectives and Future Trends”, V. Colla, C. Pietrosanti Eds., ISBN 978-3-030-69366-4, pp. 132-143. Link
2. Albayrak, Ö, Ünal, P., "Digitalization of a Steel Pipe Production Factory: STEEL4.0- A Family of Products Developed on Routes from Industry 3.0 to Industry 4.0", Fifth International Iron & Steel Symposium, Data Science in Process Engineering 2021, ISBN 978-605-9554-57-2, pp.271-274. Link
3. Ünal, P., Albayrak, O.,"A data Processing Platform for Predictive Maintenance in Industrial Context", European Network for Business and Industrial Statistics, ENBIS,17-18 May 2021. Link
4. Özdilli, B., Arslan, B., Bozkurt, Ö., Ünal, P., Alp, T., Albayrak, Ö., Özbayoğlu, A.M., “Sualtı Video Görüntülerinde Nesne Tanıma Amaçlı Yapay Öğrenme Modellerinin Performans Analizi”, 29th Signal Processing and Communications Applications Conference (SIU) , 2021. Link
5. Ünal, A.F., Kaleli, A.Y., Ummak, E., Albayrak, Ö. "A Comprehensive Comparison of State-of-Art Machine Learning Algorithms on Fault Indication and Remaining Useful Life Determination by Telemetry Data", The 8th International Conference on Future Internet of Things and Cloud (FiCloud 2021). Link
6. Deveci, B.U., Çeltikoğlu, M., Alp, T., Albayrak, Ö., Ünal, P., Kırcı, P., "A Comparison of Deep Transfer Learning Methods on Bearing Fault Detection", 8th International Conference on Future Internet of Things and Cloud (FiCloud 2021). Link
7. Özdilli, B. G., Arslan, M. B., Alp, T., Albayrak, Ö., Ünal, P., Bozkurt, Ö., & Özbayoğlu, A. M. (2021, June).PerformanceAnalysis of MachineLearning Models for Object Recognition in Underwater Video Images. In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. Link
8. Selcuk, ŞY, Ünal, P., Albayrak, Ö, Jomâa, Moez., " A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data", MDPI21. Link
9. Ünal, P., Albayrak, Ö, Jomâa, Moez., Berre, A.J., "Data-driven Artificial Intelligence and Predictive Analytics for the Maintenance of Industrial Machinery with Hybrid and Cognitive Digital Twins", TBDVA, 2021. Link
10. Kaleli, A. Y., Ünal, A. F., & Ozer, S. (2021, July). Simultaneous Prediction of Remaining-Useful- Life and Failure-Likelihood with GRU-based Deep Networks for Predictive Maintenance Analysis. In 2021 44th International Conference on Telecommunications and Signal Processing (TSP) (pp.301-304). IEEE. Link
2020
1. Albayrak, Ö., & Ünal, P. (2020, October). Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery. In Cybersecurity workshop by European Steel Technology Platform (pp. 132-143). Springer, Cham. Link
2. Ünal, P. (Contributor) Strategic Research, Innovation and Deployment Agenda (2020) AI, Data and Robotics Partnership, BDVA. Link
3. Ünal, P. Big Data challenges in Smart Manufacturing Industry (2020) A Whitepaper on Digital Europe Big Data Challenges for Smart Manufacturing Industry. Link
2019
1. Awan, I., Younas, M., Ünal, P., & Aleksy, M. Mobile Web and Intelligent Information Systems. Link
2. Ünal, P. (2019, August). Reference Architectures and Standards for the Internet of Things and Big Data in Smart Manufacturing. In 2019 7th International Conference on Future Internet of Things and Cloud (FiCloud) (pp. 243-250). IEEE. Link
2018
1. Unal, P., Kocak, Y., & Donmez, Y. (2018). A Smart Winter Service Platform and Route Planning Algorithm. In 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud) (pp. 192-196). Link
2. Kocak, Y. Unal, P., (2018). Progress gained within the scope of digitization of the manufacturing industry with INDUSTRY 4.0- ENDÜSTRİ 4.0 ile İmalat Sektörünün Dijitalleştirilmesi Kapsamında Yapılan Çalışmalar. SAVTEK 9. Savunma Teknolojileri Kongresi, ODT. Link
3. Unal, P. (2018) Big Data challenges in Smart Manufacturing Industry. A Discussion Paper on Digital Europe Big Data challenges for Smart Manufacturing Industry. Link
