Browsing by Author "Demir, Ebubekir"

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Citation Count: 0
An Evaluation of AI Models' Performance for Three Geothermal Sites
(Mdpi, 2024) Demir, Ebubekir; Cavur, Mahmut; Yu, Yu-Ting; Duzgun, H. Sebnem
Current artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites-Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals.
Citation Count: 0
Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms
(Mdpi, 2024) Cavur, Mahmut; Yu, Yu-Ting; Demir, Ebubekir; Duzgun, Sebnem
Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.
Rssi-Based Hybrid Algorithm for Real-Time Pedestrian Tracking in Indoor Environments by Using Rfid Technology
(Kadir Has Üniversitesi, 2019) Demir, Ebubekir; Çavur, Mehmet
The usage and importance of Location Based Services for indoor environments are increasing recently. The knowledge of the exact and real-time location is required by many of these services. Since Global Positioning System (GPS) is not designed for indoor environment, new positioning systems based on new technologies and methods are needed for these type of environments. In this thesis, RFID-based real-time indoor positioning systems and algorithm are developed. Received Signal Strength (RSS) based positioning techniques, are studied in detail. A hybrid algorithm is developed which depends on the mainly fingerprinting. The advantages of each method are emphasized. An original and unique hybrid algorithm is developed in this study in order to overcome available algorithm's' drawbacks. The algorithm and methodology is tested in two different indoor environments. As a result, the accuracy of this original and unique methodology and algorithm is 2,5 m.
Citation Count: 4
Rssi-Based Hybrid Algorithm for Real-Time Tracking in Underground Mining by Using Rfid Technology
(Elsevier, 2022) Cavur, Mahmut; Demir, Ebubekir
Knowing the precise and real-time location of underground mining workers is essential for their health and safety in any emergency. However, the standard Global Positioning System (GPS) is insufficient for such indoor environments as it requires new infrastructure based on different technologies and algorithms. Instead, Radio Frequency Identification (RFID)-based real-time indoor localization systems and a hybrid algorithm are developed. The received-signal-strength (RSS) based positioning techniques are investigated and applied in indoor environments. A unique hybrid approach based on fingerprinting is proposed and developed to solve the disadvantages of the existing techniques. Consequently, the accuracy of this one-of-a-kind algorithm is found to be 2.52 m in an office and 3.13 m in an underground mine. We also compared the proposed hybrid algorithm to the Weighted K-Nearest Neighbor (WKNN). WKNN, on the other hand, has an accuracy of 4.01 m in the office and 4.33 m in underground mining environments. (C) 2022 Elsevier B.V. All rights reserved.