An HFACS Analysis of German F-104 Starfighter Accidents

From 1961 onwards, Germany acquired 916 Lockheed F-104 Starfighters, of which 292 aircraft crashed and 116 pilots lost their lives. The purpose of this research project was to find out why these aircraft crashed and whether the Starfighters crashed for reasons different from those for other military aircraft in Germany. Seventy-one German F-104 accidents between 1978 and 1986 were analyzed by reviewing the original accident files. A Human Factors Analysis and Classification System (HFACS) Level-1 analysis was used as methodology. It was found that more than 50% of the reviewed German F-104 accidents occurred due to technology and/or physical environment. More than half of the sample’s accidents were engine related. It was concluded that the F-104 was indeed more accident-prone than other co-era types. Moreover, the J-79 engine was found to be a weak link in the F-104’s safety record, and the Starfighter’s unforgiving handling characteristics induced an elevated level of skill-based errors

Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference

For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with the simplest networks requiring the least computation, memory, and power. Quantizing networks to lower precision is a powerful technique for simplifying networks. As each layer of a network may have different sensitivity to quantization, mixed precision quantization methods selectively tune the precision of individual layers to achieve a minimum drop in task performance (e.g., accuracy). To estimate the impact of layer precision choice on task performance, two methods are introduced: i) Entropy Approximation Guided Layer selection (EAGL) is fast and uses the entropy of the weight distribution, and ii) Accuracy-aware Layer Precision Selection (ALPS) is straightforward and relies on single epoch fine-tuning after layer precision reduction. Using EAGL and ALPS for layer precision selection, full-precision accuracy is recovered with a mix of 4-bit and 2-bit layers for ResNet-50, ResNet-101 and BERT-base transformer networks, demonstrating enhanced performance across the entire accuracy-throughput frontier. The techniques demonstrate better performance than existing techniques in several commensurate comparisons. Notably, this is accomplished with significantly lesser computational time required to reach a solution

Not-So-Risky Business? Assessing the Risk of Integrating Large RPVs into the Current Air Traffic System

In spite of considerable efforts to commercialize large remotely piloted vehicles (RPV), an integration of these RPVs into the existing Air Transportation System (ATS) and Airspace Structure is pending. The purpose of this paper was to assess the risks of an exemplary integration of a Heron 1 type RPV into the existing European air space structure within the current regulatory framework and without the availability of sense and avoid technology. Six incident occurrence scenarios were investigated, based on a modified Fault Tree Analysis and Eurocontrol’s risk matrix. It was found that without the implementation of technological or procedural changes, an integration of a Heron 1 RPV presents a low to medium risk to air traffic. Recommended risk mitigation measures include equipping the RPV-operator with a recognized air picture, equipping the RPV with TCAS, installing a conventional telephone land line between the RPV-operator and air traffic control, and commencing the integration of large RPVs into air traffic at night. Further, simulation and flight based research is necessary to demonstrate whether these control measures would reduce the risk to an overall low level