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The model used is a combined approach of discrete element method (DEM) and computational fluid dynamics (CFD).
A two-stage simulation approach of discrete event simulation and continuous path simulation, improves development and testing of SAVS.
Jaipuria and Mahapatra (2014) proposed an integrated approach of discrete wavelet transforms (DWT) analysis and artificial neural network (ANN) denoted as DWT-ANN for demand forecasting.
In this work, a mathematical analysis of particle fluidization has been carried out by employing a combined approach of discrete element method (DEM) and computational fluid dynamics (CFD).
Flow behavior of small and big particles with the same particle density in a bubbling fluidized bed is modeled by a combined approach of discrete particle method and computational fluid dynamics (CFD DPM).
In this work, the combined approach of discrete element method (DEM) and computational fluid dynamics (CFD) is extended to study the heat transfer in packed and fluidized beds of ellipsoids.
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For large network topologies, the baseline routing protocol model is shown to require an order of magnitude less run-time and memory to calculate the same path sets as the de facto approach of discrete-event network simulation.
A hybrid incremental learning approach composed of Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this work.
In this paper, a new approach to simulation of discrete linear time-invariant dynamic systems with parameters and initial conditions described by fuzzy numbers is proposed.
This paper presents an effective method, for extracting features, so-called "integrated approach", using integration of Discrete Wavelet Transform and Hyperbolic S Transform.
Our approach allows identification of discrete regions associated with individual language functions while controlling for general severity-based neural correlates.
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