Browse Topic: Commercial aircraft
As per Committee/Henry E. Harschburger recommendations
This standard is intended to apply to portable compressed gaseous oxygen equipment. When properly configured, this equipment is used either for the administration of supplemental oxygen, first aid oxygen or smoke protection to one or more occupants of either private or commercial transport aircraft.
The Primary Author has been involved in Army Aviation Development and Acquisition since the Utility Tactical Transport Aircraft System (UTTAS), Advanced Attack Helicopter (AAH), Army Helicopter Improvement Program (AHIP), and Light Helicopter Experimental (LHX) Programs in the mid-1970s to the mid-1980s. The first three of these programs successfully made it to production aircraft, while the LHX became the RAH-66 Comanche and was canceled primarily due to technical problems and cost overruns. The initiation of the next phase by the Army Aviation Development (ADD) Directorate for Future Vertical Lift (FVL) did not occur until the beginning of the 2015-2000 timeframe. This was 35 years since the last Army Aviation Development in 1980. To help sustain this FVL development, the Primary Author led, oversaw, and helped conduct a program through the National Rotorcraft Technology Center (NRTC) in the 2015-2016 timeframe. It was called the Development Assurance Value-Based Acquisition (DAVBA
The rotorcraft community faces significantly higher accident rates compared to fixed-wing commercial aircraft, underscoring the critical need for enhanced safety measures. While Helicopter Flight Data Monitoring programs hold promise in improving safety, their widespread adoption remains limited, partly due to challenges associated with the acquisition and analysis of flight data. This paper proposes a Deep Learning (DL) solution to address safety concerns within the rotorcraft community by efficiently acquiring and analyzing flight data for a more automated and comprehensive safety assessment. Specifically, we leverage data obtained with cost-effective off-the-shelf cameras, and process it through Convolutional Neural Networks for automated detection and classification of gauges from several helicopters' cockpits. Our DL pipeline integrates a classifier for helicopter identification, an object detector for cockpit gauges detection and classification, and a network to infer the reading
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