Browsing by Author "Ozbayoglu, A. Murat"

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Citation - WoS: 29
Citation - Scopus: 50
An Artificial Neural Network-Based Stock Trading System Using Technical Analysis And Big Data Framework
(Assoc Computing Machinery, 2017) Sezer, Omer Berat; Doğdu, Erdoğan; Ozbayoglu, A. Murat; Dogdu, Erdogan; Bilgisayar Mühendisliği
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.
Citation - WoS: 1
Citation - Scopus: 1
Autonomous Structural Health Monitoring and Remaining Useful Life Estimation of Floating Offshore Wind Turbine Cables: Part I
(Johnson Matthey Publ Ltd Co, 2025) Berker, Metehan; Unal, Perin; Deveci, Bilgin U.; Unal, Aras Firat; Avenoglu, Bilgin; Ozbayoglu, A. Murat
Floating offshore wind (FOW) farms are key in meeting Europe's renewable energy targets, harnessing wind energy from waters 60 m or deeper, where bottom-fixed farms are unfeasible. Additionally, floating structures allow for the installation of larger turbines than stationary farms, which in turn leads to a greater energy output. However, cable failures dramatically impact the energy transmission from the farms and cause most of the financial losses. Monitoring and maintenance tasks are challenging due to the harsh ocean conditions. The FLoating Offshore Wind turbine CAble Monitoring (FLOW-CAM) project, supported by European Union's HORIZON 2020 programme, studies the structural health monitoring (SHM) of defects in the power cables of the FOW farms which encompass inspection and detection applications. An SHM system integrated with a remotely operated vehicle (ROV) was developed for underwater inspection and maintenance, supporting collection and presentation of essential data through an advanced interface. Part I details the technologies and methods used in this research.
Citation - WoS: 1
Citation - Scopus: 1
Autonomous Structural Health Monitoring and Remaining Useful Life Estimation of Floating Offshore Wind Turbine Cables: Part Ii
(Johnson Matthey Publ Ltd Co, 2025) Berker, Metehan; Unal, Perin; Deveci, Bilgin U.; Unal, Aras Firat; Avenoglu, Bilgin; Ozbayoglu, A. Murat
Part II reports on a new structural health monitoring (SHM) system integrated with a remotely operated vehicle (ROV) developed for underwater inspection and maintenance, part of the FLoating Offshore Wind turbine CAble Monitoring (FLOW-CAM) project, supported by European Union's HORIZON 2020 programme. Image data from underwater systems are analysed using computer vision techniques. Investigations into cable defect detection and the estimation of corrosion and remaining useful life (RUL) have been held to monitor cable health, achieving results close to reality. FLOW-CAM's collective works establish a basis for advancing underwater inspection and maintenance, concentrating on the development of practical and effective tools and strategies to optimise the functionality and reliability of floating offshore wind (FOW) farms.