Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 1Citation - Scopus: 1A Novel Ml-Dem Algorithm for Predicting Particle Motion in Rotary Drums(Elsevier Sci Ltd, 2025) Kazemi, Saman; Zarghami, Reza; Mostoufi, Navid; Sotudeh-Gharebagh, Rahmat; Al-Raoush, Riyadh I.The discrete element method (DEM) is a widely used approach for studying the behavior of particles in industrial equipment, including rotary drums. Although DEM is highly accurate and efficient, it suffers from the computational cost in simulations. The primary objective of this research is to reduce the computational costs of DEM by introducing a novel machine learning (ML) approach based on a deep neural network for predicting particle behavior in rotary drums. The proposed approach utilizes a continuous convolution operator in a neural network. To evaluate its effectiveness, the results of the proposed ML-DEM approach were compared quantitatively and qualitatively with the experimental data and the conventional DEM results. It was shown that in addition to its high accuracy, the proposed approach reduces the computational costs by approximately 35 % and 65 % compared to the conventional DEM simulations on GPU and CPU (with 8 processors), respectively. Furthermore, to ensure the comprehensive and independent validation of the proposed algorithm, the study investigated the effects of various parameters such as drum rotational speed and fill ratio on lateral entropy-based mixing, circulation time, and velocity profile in the active layer. The results were then compared with those obtained using the conventional DEM and found to be in good agreement. This new algorithm can serve as a starting point for reducing computational costs in simulating particle motion in granular systems.Article Citation - WoS: 6Citation - Scopus: 10Using Chemosensory-Induced Eeg Signals To Identify Patients With <i>de Novo</I> Parkinson's Disease(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, CagdasObjective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects' behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100-700 and 200-800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects' olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.