Abstract: An exemplary scheduling optimization tool and method are disclosed that can determine optimally scheduled predictive maintenance actions during regularly scheduled inspection or operation periods, e.g., for tire replacement or maintenance or for fuel purchases, to improve logistical operations at a military base while maintaining mission readiness.

Abstract: An exemplary virtual sensing method and system are disclosed for predictive reliability (VIPR) procedure and/or controls that employ artificial intelligence and machine learning (AI/ML), particularly deep neural networks and multi-modal deep learning, with vehicle sensor data to create virtual sensors. The virtual sensors can be used to estimate measurements and operating conditions in a hostile environment in rockets and vehicle systems.

Abstract: An aerial acoustic acquisition system including: an unmanned aerial vehicle (UAV); an acoustic sensing payload attached to the UAV including: at least one SOI microphone configured to detect a first audio signal including a signal of interest; and at least one noise detection microphone configured to detect a second audio signal including sound generated by the UAV, and a processing suite including a processor configured to receive first audio data corresponding to the first audio signal and second audio data corresponding to the second audio signal from the acoustic sensing suite, and process the first audio data using the second audio data to extract the signal of interest from the first audio data.

Abstract: An aerial acoustic acquisition system including: an unmanned aerial vehicle (UAV); an acoustic sensing payload attached to the UAV including: at least one SOI microphone configured to detect a first audio signal including a signal of interest; and at least one noise detection microphone configured to detect a second audio signal including sound generated by the UAV, and a processing suite including a processor configured to receive first audio data corresponding to the first audio signal and second audio data corresponding to the second audio signal from the acoustic sensing suite, and process the first audio data using the second audio data to extract the signal of interest from the first audio data.