Sharif Digital Repository

In the present work, the numerical solution of 2D inviscid compressible Magneto-hydrodynamic flow is performed by using the spectral difference (SD) method on quadrilateral grids. In this numerical method, similar to the discontinuous Galerkin (DG) and spectral volume (SV) methods, the concept of the discontinuous and high-order local representations is used to achieve conservation property and high-order accuracy. In the SD method, the test function or the surface integral is not involved and thus it has a simpler formulation than the DG and SV methods. In this numerical method, two sets of structured points, namely unknown points and flux points, are defined in each cell to support the... 

Quality of Experience (QoE) indicates the overall quality of one service such as Voice over IP (VoIP) from users' point of view by considering several systems, human, and contextual factors. QoE measurement and prediction are more challenging than Quality of Service (QoS) which is only related to network parameters. There exist various objective and subjective methods for QoE prediction. This research investigates various features affecting QoE by proposing a comprehensive subjective evaluation by employing a large number of users. We show that many unconsidered factors including speaker specifications and signal properties, such as signal-to-noise ratio (SNR), can affect QoE so that the SNR... 

Condition monitoring of machinery is of significant economic importance to mitigate production losses resulting from downtimes. Unforeseen failure of roller element bearings is the most common issue observed in industrial units. However, detecting and tracking the progression of these failures through machine vibration monitoring and predicting the deterioration of these rotating components are viable solutions. Numerous studies have focused on using laboratory accelerated life test data for fault detection and remaining useful life prediction of these components. While online monitoring of all equipment in the industry may not be feasible, and conditions in the field differ from laboratory... 

Intelligent predictive maintenance is recognized as a cornerstone of Industry 4.0, where intelligent software is employed for the early detection of faults and the prevention of unexpected failures. Recent research indicates that the integration of multi-sensor data for fault diagnosis of gearboxes and bearings, using artificial intelligence models, has been successful. However, conventional methods face several challenges. These include an over-reliance on the signal characteristics of a single sensor and the impracticality of applying intelligent learning methods, particularly deep learning, despite their high potential, due to the unavailability of sufficiently large and diverse...