A novel time series analysis is presented to locate damage sources in a mechanical system, which is running in various operational environments. The source of damage is located by solely analyzing the acceleration time histories recorded from a structure of interest. First, a data normalization procedure is proposed. This procedure selects a reference signal that is ‘closest’ to a newly obtaine...

In this work, a powerful parametric spectral estimation technique, 2D-auto regressive moving average modeling (ARMA), has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as auto regressive (AR) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier transform of the complete imag...

A novel time series analysis procedure is presented to localize damage sources in a mechanical system. An attempt is made to pinpoint the sources of nonlinear damage by solely analysing the vibration signatures recorded from a structure of interests. First, a linear prediction model, combining Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) techniques, is estimated using a ...

HIV-1 genome is highly heterogeneous. Due to this variation, features of HIV-I genome is in a wide range. For this reason, the ability to infection of the virus changes depending on different chemokine receptors. From this point of view, R5 HIV viruses use CCR5 coreceptor while X4 viruses use CXCR5 and R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have bee...

The field of Operational Modal Analysis (OMA) has recently become an emerging research interest. OMA, also known as Response-Only Modal Analysis, extracts the modal parameters by processing only the system response data. In many experimental measurement situations, the system’s output data series is short in length and buried under potentially correlated noise. In such cases, the separation of ...

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term flow needs to consider many factors, the results of existing models for subway forecasting are often unsatisfactory. Along this line, we propose parallel architecture, called seasonal nonli...

This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, t...

Count-valued time series data are routinely collected in many application areas. We particularly motivated to study the count of daily new cases, arising from COVID-19 spread. First, we propose a Bayesian framework time-varying semiparametric AR(p) model for and then extend it more sophisticated INGARCH model. calculate posterior contraction rates proposed methods with respect average Hellinger...