Six Noise Type Military Sound Classifier

Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This negatively impacts the military's testing \& training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh previously developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Recent work will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and thunder. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives

Bayesian military impulse noise classifier

ABSTRACT Civilian noise complaints and damage claims have created the need for stations to monitor military impulse noise. However, the stations currently in service suffer from numerous false positive detections (due to wind noise) of impulse events and often miss many events of interest. To improve the accuracy of military impulse noise monitoring, an algorithm based upon a Bayesian classifier with inputs of conventional and custom acoustic metrics is proposed. To train and evaluate the noise classifier approximately 1,000 waveforms were field collected. The final Bayesian noise classifier used kurtosis and crest factor and, the frequency domain metrics, spectral slope and weighted square error as inputs. The EM algorithm is utilized to fit multi-Gaussian distributions to the different classes of data. In testing the classifier performed to accuracies of up to 99.6%

Experimental active control of a typical section using a trailing-edge flap

This paper presents an experimental implementation of an active control system used to suppress flutter in a typical section airfoil. The H2 optimal control system design is based on experimental system identifications of the transfer functions between three measured system variables - pitch, plunge, and flap position - and a single control signal that commands the flap of the airfoil. Closed-loop response of the airfoil demonstrated gust alleviation below the open-loop flutter boundary. In addition, the flutter boundary was extended by 12.4% through the application of active control. Cursory robustness tests demonstrate stable control for variations in flow speed of ± 10%

Comparison of μ- and H2-Synthesis Controllers on an Experimental Typical Section

Reprinted with permission of the American Institute of Aeronautics and Astronautics, Inc. See http://www.aiaa.org/content.cfm?pageid=2 for more information.An experimental comparison of H2 - and μ-synthesized flutter suppression control systems was performed. A simple parametric uncertainty can be used to track changes in system dynamics as a function of dynamic pressure. The control system was implemented experimentally on a NACA 0012 test model of a typical section mounted in a low-speed wind tunnel. The pitching angle, flap angle, and plunge deflection of the airfoil were measured with sensors and fed back through the control compensator to generate a single control signal commanding the trailing-edge flap of the airfoil. The model of the aeroelastic system, including the dynamics of the sensors and actuators in the bandwidth of interest, was obtained using system identification techniques. For comparison purposes, an H2 control system with standard linear quadratic Gaussian weightings also was designed and implemented. When compared to the H2 control system, the μ-synthesis controller provided better disturbance rejection in the bandwidth of the unsteady aeroelastic dynamics. In addition, the μ controller required less control energy than the H2 control system. The final advantage of μ-synthesis is the ability to design an aggressive μ control system that is stabilizing across the range of operating dynamic pressures

Computational study of human head response to primary blast waves of five levels from three directions.

Human exposure to blast waves without any fragment impacts can still result in primary blast-induced traumatic brain injury (bTBI). To investigate the mechanical response of human brain to primary blast waves and to identify the injury mechanisms of bTBI, a three-dimensional finite element head model consisting of the scalp, skull, cerebrospinal fluid, nasal cavity, and brain was developed from the imaging data set of a human female. The finite element head model was partially validated and was subjected to the blast waves of five blast intensities from the anterior, right lateral, and posterior directions at a stand-off distance of one meter from the detonation center. Simulation results show that the blast wave directly transmits into the head and causes a pressure wave propagating through the brain tissue. Intracranial pressure (ICP) is predicted to have the highest magnitude from a posterior blast wave in comparison with a blast wave from any of the other two directions with same blast intensity. The brain model predicts higher positive pressure at the site proximal to blast wave than that at the distal site. The intracranial pressure wave invariably travels into the posterior fossa and vertebral column, causing high pressures in these regions. The severities of cerebral contusions at different cerebral locations are estimated using an ICP based injury criterion. Von Mises stress prevails in the cortex with a much higher magnitude than in the internal parenchyma. According to an axonal injury criterion based on von Mises stress, axonal injury is not predicted to be a cause of primary brain injury from blasts