In the context of aircrew training on flight simulators, the simulation of the radar system requires a large amount of calculation which needs to be performed in real-time. Mapping the virtual environment (landmass, vegetation, buildings, roads, etc.), modeling energy propagation and performing the post-processing are among the most intensive tasks. Most of these are currently done on a computers Central Processing Unit (CPU) in a sequential fashion.
The project aims to improve recently developed algorithms by our research team for the automatic definition of pushbacks in open pit mining that meet complex geometric constraints. Three specific objectives are pursued: a) include an approximate sequencing of blocks within a phase to enable a better discounting of the block values; b) enable to include varying geometric slope constraints according to the direction considered and c) provide, when possible, pushbacks formed of a single continuous ensemble of blocks.
Complex learning environments that are mediated by technology require distinct concurrent methodologies that reveal when and where learning may occur (Azevedo et al., in press; Lajoie, Gauthier, & Lu, 2009). In this research, we use methods that are rooted in the learning sciences in order to identify, assess, and validate the instructional and learning components of an e-learning environment developed by CAE Corporation, which is aimed to train pilots.
Aviation industry uses flight data recorders (FDR) to monitor a high number of parameters during each flight it operates. It is expected that analyzing this data will provide useful information to airlines for improving flight safety and efficiency. However, this analysis is a challenging task in itself because the amount of accumulated data is enormous and also because it is diverse. To overcome these difficulties, data is first preprocessed (or cleaned) and only significant parameters are kept.
In aviation industry a large flow of data including thousands of parameters are registered by FDRs (Flight Data Recorders). The objective of this project is to use this big data to improve the efficiency and safety of flights. The data is collected and segmented from the raw datasets and then proper data cleaning methods are used to preprocess data. Then, by the help of analytical models we define a baseline for different registered parameters and compare individual flights against the baseline to detect anomalies.
Digital avionics systems of today are designed for the most part with embedded computers. These computers run safety-critical real-time applications such as flight management systems (FMS) and flight control systems (FCS). Even if avionics use conservative technologies, economic concerns are constantly pushing for changes. Space, weight, power and cooling (SWaP-C) considerations are gaining importance in the aviation industry. High fuel and maintenance costs encourage aircraft manufacturers to reduce the footprint of new models.
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