Browsing by Subject "Autoregressive"
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Item Open Access Bayesian Techniques for Adaptive Acoustic Surveillance(2010) Morton, Kenneth DAutomated acoustic sensing systems are required to detect, classify and localize acoustic signals in real-time. Despite the fact that humans are capable of performing acoustic sensing tasks with ease in a variety of situations, the performance of current automated acoustic sensing algorithms is limited by seemingly benign changes in environmental or operating conditions. In this work, a framework for acoustic surveillance that is capable of accounting for changing environmental and operational conditions, is developed and analyzed. The algorithms employed in this work utilize non-stationary and nonparametric Bayesian inference techniques to allow the resulting framework to adapt to varying background signals and allow the system to characterize new signals of interest when additional information is available. The performance of each of the two stages of the framework is compared to existing techniques and superior performance of the proposed methodology is demonstrated. The algorithms developed operate on the time-domain acoustic signals in a nonparametric manner, thus enabling them to operate on other types of time-series data without the need to perform application specific tuning. This is demonstrated in this work as the developed models are successfully applied, without alteration, to landmine signatures resulting from ground penetrating radar data. The nonparametric statistical models developed in this work for the characterization of acoustic signals may ultimately be useful not only in acoustic surveillance but also other topics within acoustic sensing.
Item Open Access Comparison of Different Wind Time Series Simulation Methods(2015-04-23) Wu, ShiyaoThe assessment of power system reliability under increasing penetration of wind power requires long-term wind data that is not available or does not exist and hence must be simulated. In this research, autoregressive models (AR) ranging from 1st order to 12th order and Markov-switching autoregressive models (MS-AR) ranging from MS(2)-AR(2) to MS(5)-AR(5) are used for wind simulation using 10-minutes wind speed data from NREL for years 2004 and 2005. Simulation results are compared between models, across different seasons, and different data lengths. Consistent with the literature, we find that AR models can efficiently replicate the autocorrelation function (ACF) but not the probability distribution function (PDF) observed in the original data. MS-AR models perform better than AR models in terms of both ACF and PDF and their performance improves with the increasing number of states in the Markov Chain.