The recent emergence of the discrete fractional Fourier transform has spurred research activity aiming at generating Hermite-Gaussian-like (HGL) orthonormal eigenvectors of the discrete Fourier transform (DFT) matrix F. By exploiting the unitarity of matrix F – resulting in the orthogonality of its eigenspaces pertaining to the distinct eigenvalues – the problem decouples into finding orthonorm...

Simulating fluid flow at micro level is an ongoing problem. Simplified macroscopic flow models like Darcy’s law is unable to estimate fluid dynamic properties of porous media. The digital sample reconstruction by high resolution X-ray computed tomography scanning and fluid-dynamics simulation, together with the increasing power of super-computers, allow to carry out pore-scale simulations throu...

In this paper, the particular solution technique for inverse simulation applied to the quadrotor maneuvering flight is investigated. The ‎trust-region dogleg (DL) technique which is proposed alleviates the weakness of Newton’s method used for numerical differentiation of system states in the solution process. The proposed technique emphasizes global convergence solution to the inverse simulatio...

The Markov chain random field (MCRF) theory and the transiogram spatial measure were proposed several years ago. Basic sequential simulation algorithms based on simple MCRF models such as the Markov chain sequential simulation algorithm and the Markov chain sequential co-simulation algorithm have been developed and used in a series of application studies. However, misunderstanding of these two ...

Sequential Monte Carlo (SMC) methods are a powerful set of simulation-based techniques for sampling sequentially from a sequence of complex probability distributions. These methods rely on a combination of importance sampling and resampling techniques. In a Markov chain Monte Carlo (MCMC) framework, block sampling strategies often perform much better than algorithms based on one-at-a-time sampl...

Sequential Quadratic Programming (SQP) for trajectory optimization of entry vehicle was presented. Firstly, equations of motion were normalized and an independent variable is introduced to reduce the difficulty of iterative computation. And then, optimal control problem was transformed into a nonlinear programming problem using direct collocation method. Finally, sequential quadratic programmin...

Many simulation problems can take advantage of distributed simulation approach when dealing with the huge timeor spacecomplexity and the conventional sequential solution is not satisfying. We have proposed and implemented an object-oriented toolset for developing both sequential and conservative distributed simulation architectures, based on a sound formal background and our previous experience...

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. The...

Machine health condition (MHC) prediction is useful for preventing unexpected failures and minimizing overall maintenance costs since it provides decision-making information for condition-based maintenance (CBM). This paper presents a novel bearing health condition prediction approach based on enhanced online sequential learning fuzzy neural networks (EOSL-FNNs). Based on extreme learning machi...

LP.EventList if it has already been executed by the sequential portion (so that e.execTime 6 = ?1:0.) So if there is any event not yet simulated, it must be in either SuperStepList or LP.EventList. When the analyzer nds that: SuperStepList = fg and LP i .EventList = fg 8LP i the sequential event list must be empty. And since the set of events in the sequential simulation is the sum of every LP'...