Due to civilian noise complaints and damage claims, there is a need to establish an accurate record of impulse noise generated at military installations. Current noise monitoring systems are susceptible to false positive detection of impulse events due to wind noise. In order to analyze the characteristics of noise events, multiple channel data methods were investigated. A microphone array was used to collect four channel data of military impulse noise and wind noise. These data were then analyzed using cross-correlation functions to characterize the input waveforms. Four different analyses of microphone array data are presented. A new value, the min peak correlation coefficient, is defined as a measure of the likelihood that a given waveform originated from a correlated noise source. Using a sound source localization technique, the angle of incidence of the noise source can be calculated. A method was also developed to combine the four individual microphone channels into one. This method aimed to preserve the correlated part of the overall signal, while minimizing the effects of uncorrelated noise, such as wind. Lastly, a statistical method called the acoustic likelihood test is presented as a method of determining if a signal is correlated or not.
Noise monitoring stations are in place around some military installations to provide records that assist in processing noise complaints and damage claims. However, they are known to produce false positives (by incorrectly attributing naturally occurring noise to military operations) and also fail to detect many impulse events. In this project, classifiers based on artificial neural networks were developed to improve the accuracy of military impulse noise identification. Two time-domain metrics--kurtosis and crest factor--and two custom frequency-domain metrics--spectral slope and weighted square error-were inputs to the artificial neural networks. The classification algorithm was able to achieve up to 100% accuracy on the training data and the validation data, while improving detection threshold by at least 40 dB.
A nonlinear control algorithm is proposed that greatly reduces settling time in precision instruments with rolling element bearings. Reductions of 80.5-87.4% in settling time were achieved when settling to within 3-100 nm of the commanded position. Final settling of such systems is typically impacted by the nonlinearity in the pre-rolling friction regime, which manifests as a hysteretic stiffness. Consequently, the integral term in the controller can take a long time to respond. In this work, the Nonlinear Integral Action Settling Algorithm (NIASA) is presented. The nonlinear integral gain takes the form of a Dahl friction model. Since the integral gain mimics hysteretic stiffness, the output of the integral control term is instantaneously set to a large value after each direction change, greatly improving settling response. A nearly first order error dynamic results, which has a user-definable time constant. Before the algorithm can be implemented, the Coulomb friction and initial contact stiffness in the Dahl model must be experimentally determined for the stage. A sensitivity study was performed on the initial contact stiffness, which was found in other works to dictate stability of the algorithm [1, 2]. I. INTRODUCTION Rapid point-to-point motion of a servo mechanism has obvious industrial applications. In these systems, the objective is to move as quickly as possible from one location to the next with no concern for the particular motion profile used. Many such mechanisms contain rolling element bearings, given their low cost and generally good performance. As industrial processes Manuscript
Achieving uniform flow among the cells of a fuel cell stack plays a significant role in being able to operate at maximum capability and efficiency. This paper presents experimental data showing the importance of cell-to-cell fuel flow balancing on fuel cell performance, and a Fuel Cell Energy Management (FCEM) technique that has demonstrated the ability to improve stack performance. In a specially instrumented four-cell polymer electrolyte fuel cell that allows external control of the air, fuel, and water cooling flows to each cell, fuel to a single cell was reduced. V-I curves collected under these unbalanced conditions are compared to curves collected when the fuel flow to each cell was balanced. Reducing the fuel flow to a single cell by 11% decreased the V-I curve cutoff load by 10%-demonstrating the degree of negative effect that unbalanced fuel flows can have on stack performance. Typical fuel cell stacks have no dynamic means to keep flows in the stack balanced between the cells, but through the use of custom-built, piezoelectric micro-valves, a simple flow control strategy, and this custom 4-cell laboratory stack, the performance benefits of FCEM fuel flow balancing with respect to overall V-I performance was demonstrated. With the external FCEM technique, the stack's maximum fuel utilization was increased by as much as 5 percent.
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