Dr. John B. Ferris Profile

Dr. John B. Ferris

Associate Professor at Virginia Polytechnic Institute and State Univ

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Author

Publications (6)

Proceedings Article | 30 April 2009 Paper

Review of current developments in terrain characterization and modeling

Heather Chemistruck, Zachary Detweiler, John Ferris, Alexander Reid, David Gorsich

Proceedings Volume 7348, 73480N (2009) https://doi.org/10.1117/12.818128

KEYWORDS: Roads, Autoregressive models, IRIS Consortium, Data modeling, Stochastic processes, Computer simulations, Mathematical modeling, Radon, Reliability, Statistical modeling

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Proceedings Article | 30 April 2009 Paper

Excitation event design and accuracy verification procedure for high-fidelity terrain measurement systems

Hurtford Smith, John Ferris

Proceedings Volume 7348, 73480M (2009) https://doi.org/10.1117/12.817934

KEYWORDS: Motion measurement, Roads, Calibration, Distance measurement, Sensors, Profiling, Motion models, Oscillators, Spindles, Systems modeling

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Proceedings Article | 10 May 2007 Paper

Developing Markov chain models for road surface simulation

Wescott Israel, John Ferris

Proceedings Volume 6564, 65640J (2007) https://doi.org/10.1117/12.720066

KEYWORDS: Roads, Data modeling, 3D modeling, Computer simulations, Stochastic processes, Statistical modeling, Process modeling, Analytical research, Mechanical engineering, Prototyping

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Proceedings Article | 10 May 2007 Paper

Characterizing 2D road profiles using ARIMA modeling techniques

Joshua Kern, John Ferris, David Gorsich, Alexander Reid

Proceedings Volume 6564, 65640L (2007) https://doi.org/10.1117/12.720088

KEYWORDS: Roads, Data modeling, Autoregressive models, Tin, Stochastic processes, Statistical modeling, Process modeling, Statistical analysis, Protactinium, Integrated modeling

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Proceedings Article | 10 May 2007 Paper

A polynomial chaos approach to ARIMA modeling and terrain characterization

Shannon Wagner, John Ferris

Proceedings Volume 6564, 65640M (2007) https://doi.org/10.1117/12.720081

KEYWORDS: Chaos, Roads, Autoregressive models, Data modeling, Stochastic processes, Statistical modeling, Systems modeling, Dynamical systems, Statistical analysis, Mathematical modeling

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Proceedings Article | 20 May 2006 Paper

Preliminary results for model identification in characterizing 2D topographic road profiles

Joshua Kern, John Ferris

Proceedings Volume 6228, 622806 (2006) https://doi.org/10.1117/12.665647

KEYWORDS: Roads, Autoregressive models, Data modeling, Stochastic processes, Statistical analysis, Signal processing, Distance measurement, Databases, Mechanical engineering, Associative arrays

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Load data representing severe customer usage is needed throughout a chassis development program; the majority of these chassis loads originate with the excitation from the road. These chassis loads are increasingly derived from vehicle simulations. Simulating a vehicle traversing long roads is simply impractical, however, and a greatly reduced set of characteristic roads must be found. In order to characterize a road, certain modeling assumptions must be made. Several models have been proposed making various assumptions about the properties that road profiles possess. The literature in this field is reviewed before focusing on two modeling assumptions of particular interest: the stationarity of the signal (homogeneity of the road) and the corresponding interval over which previous data points are correlated to the current data point. In this work, 2-D topographic road profiles are considered to be signals that are realizations of a stochastic process. The objective of this work is to investigate the stationarity assumption and the interval of influence for several carefully controlled sections of highway pavement in the United States. Two statistical techniques are used in analyzing these data: the autocorrelation and the partial autocorrelation. It is shown that the road profile signals in their original form are not stationary and have an extremely long interval of influence on the order of 25m. By differencing the data, however, it is often possible to generate stationary residuals and a very short interval of influence on the order of 250mm. By examining the autocorrelation and the partial autocorrelation, various versions of ARIMA models appear to be appropriate for further modeling. Implications to modeling the signals as Markov Chains are also discussed. In this way, roads can be characterized by the model architecture and the particular parameterization of the model. Any synthetic road realized from a particular model represents all profiles in this set. Realizations of any length can be generated, allowing efficient simulation and timely information about the chassis loads that can be used for design decisions. This work provides insights for future development in the modeling and characterization of 2-D topographic road profiles.

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