The Experts below are selected from a list of 8760 Experts worldwide ranked by ideXlab platform
Anton Schick - One of the best experts on this subject based on the ideXlab platform.
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an adaptive estimator of the Autocorrelation Coefficient in regression models with autoregressive errors
Journal of Time Series Analysis, 1998Co-Authors: Anton SchickAbstract:In this paper an adaptive estimator of the Autocorrelation Coefficient is constructed in regression models whose error variables follow a stationary autoregressive process of order 1. Examples of nonparametric, additive and semiparametric regression models are discussed.
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estimation of the Autocorrelation Coefficient in the presence of a regression trend
Statistics & Probability Letters, 1994Co-Authors: Anton SchickAbstract:Abstract In this paper estimation of the Autocorrelation parameter σ is studied in nonparametric and semiparametric regression models with autoregressive errors. It is shown that under mild assumptions one can construct an estimator that is asymptotically equivalent to the least squares estimator based on the autoregressive error process.
S Elghobashi - One of the best experts on this subject based on the ideXlab platform.
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on the two way interaction between homogeneous turbulence and dispersed solid particles ii particle dispersion
Physics of Fluids, 1994Co-Authors: G C Truesdell, S ElghobashiAbstract:Part I of this paper [Elghobashi and Truesdell, Phys. Fluids A 5, 1790 (1993)] examined the modulation of turbulence by the particles. Here the effects of the two‐way interaction on particle dispersion are discussed. In zero gravity, the two‐way coupling enhances the alignment of the surrounding fluid velocity vector with the direction of the solid particle trajectory. This alignment reduces the mean‐square relative velocity and increases the Lagrangian velocity Autocorrelation Coefficient of the solid particle, the fluid point and the surrounding fluid, and the mean‐square displacement of the solid particles. However, the fluid point mean‐square displacement decreases because the larger inertia of the solid particles increases the decay rate of turbulence energy. In gravity environment, the particles augment the component of turbulence energy in the gravity direction, and thus increase the mean‐square displacement of the solid particles and fluid points in that direction. However, their dispersion in the lateral directions is reduced due to the crossing trajectories effect [Yudine, Adv. Geophys. 6, 185 (1959)].
Han Yan - One of the best experts on this subject based on the ideXlab platform.
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long short term memory neural network for network traffic prediction
IEEE International Conference on Intelligent Systems and Knowledge Engineering, 2017Co-Authors: Qinzheng Zhuo, Han YanAbstract:This paper proposes a model of neural network which can be used to combine Long Short Term Memory networks (LSTM) with Deep Neural Networks (DNN). Autocorrelation Coefficient is added to model to improve the accuracy of prediction model. It can provide better than the other traditional precision of the model. And after considering the Autocorrelation features, the neural network of LSTM and DNN has certain advantages in the accuracy of the large granularity data sets. Several experiments were held using real-world data to show effectivity of LSTM model and accuracy were improve with Autocorrelation considered.
Rajesh K. Upadhyay - One of the best experts on this subject based on the ideXlab platform.
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time series analysis of a binary gas solid conical fluidized bed using radioactive particle tracking rpt technique data
Chemical Engineering Journal, 2019Co-Authors: Lipika Kalo, H J Pant, Miryan Cassanello, Rajesh K. UpadhyayAbstract:Abstract In current work, the radioactive particle tracking (RPT) technique has been used to investigate the behavior of gas-solid conical mono and binary fluidized bed. The dynamics of the bed has been analyzed using both time-averaged and fluctuation quantities at different gas inlet velocities and bed compositions. The binary bed was composed of glass beads of two different diameters 1 mm and 0.6 mm. The bed of 0:100, 50:50 and 100:0 by wt % of both the particles were investigated. Time-averaged quantities like mean axial velocities, RMS velocities, and granular temperature indicate that behavior of conical bed at the top and bottom sections are significantly different. Gas-solid interactions mainly dominate the bottom section while particle-particle interaction plays a critical role at the top section. Further, time series and chaos analysis of RPT data were performed. Hurst exponent, Autocorrelation Coefficient, and mixing index were calculated through time series analysis. The results indicate that better mixing is observed in conical bed even at low velocity compared to cylindrical fluidized-bed. It also reveals a regime transition around 5.7 m/s gas inlet velocity. Finally, Kolmogorov entropy and correlation dimension calculated through chaos analysis of RPT data confirm flow regime transition at gas inlet velocity around 5.7 m/s, for all the examined bed compositions.
S Degerine - One of the best experts on this subject based on the ideXlab platform.
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sample partial Autocorrelation function
IEEE Transactions on Signal Processing, 1993Co-Authors: S DegerineAbstract:A new estimation procedure for a partial Autocorrelation Coefficient of a stationary time series based on a natural geometrical analysis of the sample data is proposed. A method for autoregressive parameter estimation, which is midway between the constrained least squares procedure of Burg and the unconstrained one given by the for- ward-backward least squares method, is then obtained. The method provides a stable filter and operates in a recursive model-order fash- ion. Simulation results indicate that the method eliminates some short- comings of classical least squares procedures and stays close to the ex- act maximum likelihood estimation method.
