A study of noise metric and tone correction accuracy

Methods currently used to measure human response to aircraft flyover noise were investigated. Response to high level aircraft noise usually experienced outdoors was obtained. Response to aircraft flyover noise typical of indoor exposure was also investigated. It was concluded that current methods for evaluating response to aircraft flyover are more accurate for outdoor noise

On Rendering Synthetic Images for Training an Object Detector

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available

Airborne forward pointing UV Rayleigh lidar for remote clear air turbulence (CAT) detection: system design and performance

A high-performance airborne UV Rayleigh lidar system was developed within the European project DELICAT. With its forward-pointing architecture it aims at demonstrating a novel detection scheme for clear air turbulence (CAT) for an aeronautics safety application. Due to its occurrence in clear and clean air at high altitudes (aviation cruise flight level), this type of turbulence evades microwave radar techniques and in most cases coherent Doppler lidar techniques. The present lidar detection technique relies on air density fluctuations measurement and is thus independent of backscatter from hydrometeors and aerosol particles. The subtle air density fluctuations caused by the turbulent air flow demand exceptionally high stability of the setup and in particular of the detection system. This paper describes an airborne test system for the purpose of demonstrating this technology and turbulence detection method: a high-power UV Rayleigh lidar system is installed on a research aircraft in a forward-looking configuration for use in cruise flight altitudes. Flight test measurements demonstrate this unique lidar system being able to resolve air density fluctuations occurring in light-to-moderate CAT at 5 km or moderate CAT at 10 km distance. A scaling of the determined stability and noise characteristics shows that such performance is adequate for an application in commercial air transport.Comment: 17 pages, 19 figures. Pre-publish to Applied Optics (OSA

Feasibility of an onboard wake vortex avoidance system

It was determined that an onboard vortex wake detection system using existing, proven instrumentation is technically feasible. This system might be incorporated into existing onboard systems such as a wind shear detection system, and might provide the pilot with the location of a vortex wake, as well as an evasive maneuver so that the landing separations may be reduced. It is suggested that this system might be introduced into our nation's commuter aircraft fleet and major air carrier fleet and permit a reduction of current landing separation standards, thereby reducing takeoff and departure delays

Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation

Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a field in which anomaly detection is crucial. Manufacturers collect large amount of engine related data during flights which are used, among other applications, to detect anomalies. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that builds upon human expertise and that remains understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: arXiv admin note: substantial text overlap with arXiv:1408.6214, arXiv:1409.4747, arXiv:1407.088

A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation

Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.Comment: Proceedings of the 14th Industrial Conference, ICDM 2014, St. Petersburg : Russian Federation (2014