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Huang Transform (huang + transform)

Distribution by Scientific Domains
Engineering100%

Selected Abstracts

Near-Term Travel Speed Prediction Utilizing Hilbert,Huang Transform

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 8 2009
Khaled Hamad
In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert,Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters. [source]

A Comparative Study of Modal Parameter Identification Based on Wavelet and Hilbert,Huang Transforms

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 1 2006
Banfu Yan
Special attention is given to some implementation issues, such as the modal separation and end effect in the WT, the optimal parameter selection of the wavelet function, the new stopping criterion for the empirical mode decomposition (EMD) and the end effect in the HHT. The capabilities of these two techniques are compared and assessed by using three examples, namely a numerical simulation for a damped system with two very close modes, an impact test on an experimental model with three well-separated modes, and an ambient vibration test on the Z24-bridge benchmark problem. The results demonstrate that for the system with well-separated modes both methods are applicable when the time,frequency resolutions are sufficiently taken into account, whereas for the system with very close modes, the WT method seems to be more theoretical and effective than HHT from the viewpoint of parameter design. [source]

Near-Term Travel Speed Prediction Utilizing Hilbert,Huang Transform

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 8 2009
Khaled Hamad
In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert,Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters. [source]

The use of intrinsic mode functions to characterize shock and vibration in the distribution environment

PACKAGING TECHNOLOGY AND SCIENCE, Issue 1 2005
Vincent Rouillard
Abstract This paper describes an innovative approach, based on the instrinsic mode functions (IMFs), to characterize the nature of mechanical vibration encountered in transport vehicles. The paper highlights the importance of understanding the nature of transport vibration and shows that their accurate characterization is essential for the optimization of protective packaging. Although there have been numerous studies aimed at characterizing random vibration during transport, the majority of those have been limited to applying relatively conventional signal analysis techniques, such as the average power spectral density (PSD). This paper investigates the benefits offered by the recently introduced Hilbert,Huang transform when characterizing non-stationary random vibration in comparison with more traditional Fourier analysis-based techniques. The paper describes the operation of the Hilbert,Huang transform, which was developed to assist in the analysis of non-Gaussian and non-stationary random data. The Hilbert,Huang transform is based on the empirical mode decomposition (EMD) technique used to produce a finite number of IMFs, which, as a set, provide a complete description of the process. It is shown how these IMFs are well suited to the application of the Hilbert,Huang transform to determine the magnitude and instantaneous frequency of each IMF. The technique is applied to various records of random vibration data collected from transport vehicles in order to illustrate the benefits of the method in characterizing the nature of non-stationarities present in transport vibration. Copyright © 2004 John Wiley & Sons, Ltd. [source]