Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Ozer, Can"'
The exploration of underwater environments is essential for applications such as biological research, archaeology, and infrastructure maintenanceHowever, underwater imaging is challenging due to the waters unique properties, including scattering, abs
Externí odkaz:
http://arxiv.org/abs/2412.03995
Autor:
Devecioglu, Ozer Can, Kiranyaz, Serkan, Yilmaz, Zafer, Avci, Onur, Gabbouj, Moncef, Taciroglu, Ertugrul
Vibration sensors are essential in acquiring seismic activity for an accurate earthquake assessment. The state-of-the-art sensors can provide the best signal quality and the highest bandwidth; however, their high cost usually hinders a wide range of
Externí odkaz:
http://arxiv.org/abs/2407.11040
Autor:
Kiranyaz, Serkan, Devecioglu, Ozer Can, Alhams, Amir, Sassi, Sadok, Ince, Turker, Avci, Onur, Gabbouj, Moncef
Robust and real-time detection of faults on rotating machinery has become an ultimate objective for predictive maintenance in various industries. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault det
Externí odkaz:
http://arxiv.org/abs/2312.10742
Autor:
Devecioglu, Ozer Can, Kiranyaz, Serkan, Elhmes, Amer, Sassi, Sadok, Ince, Turker, Avci, Onur, Soleimani-Babakamali, Mohammad Hesam, Taciroglu, Ertugrul, Gabbouj, Moncef
Automatic sensor-based detection of motor failures such as bearing faults is crucial for predictive maintenance in various industries. Numerous methodologies have been developed over the years to detect bearing faults. Despite the appearance of numer
Externí odkaz:
http://arxiv.org/abs/2305.07960
Publikováno v:
2023 Photonics & Electromagnetics Research Symposium (PIERS)
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can cause quickly spreading fires and make them harder to control; therefor
Externí odkaz:
http://arxiv.org/abs/2304.09721
Autor:
Ince, Turker, Kiranyaz, Serkan, Devecioglu, Ozer Can, Khan, Muhammad Salman, Chowdhury, Muhammad, Gabbouj, Moncef
Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant envi
Externí odkaz:
http://arxiv.org/abs/2212.14618
Autor:
Kiranyaz, Serkan, Devecioglu, Ozer Can, Alhams, Amir, Sassi, Sadok, Ince, Turker, Abdeljaber, Osama, Avci, Onur, Gabbouj, Moncef
Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detec
Externí odkaz:
http://arxiv.org/abs/2212.06154
Autor:
Kiranyaz, Serkan, Devecioglu, Ozer Can, Ince, Turker, Malik, Junaid, Chowdhury, Muhammad, Hamid, Tahir, Mazhar, Rashid, Khandakar, Amith, Tahir, Anas, Rahman, Tawsifur, Gabbouj, Moncef
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe arti
Externí odkaz:
http://arxiv.org/abs/2202.00589
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularl
Externí odkaz:
http://arxiv.org/abs/2110.02215
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks
Autor:
Ince, Turker, Malik, Junaid, Devecioglu, Ozer Can, Kiranyaz, Serkan, Avci, Onur, Eren, Levent, Gabbouj, Moncef
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods espec
Externí odkaz:
http://arxiv.org/abs/2109.14873