Time Series Analysis Methods And Applications For Flight Data

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Time Series Analysis Methods and Applications for Flight Data

This book focuses on different facets of flight data analysis, including the basic goals, methods, and implementation techniques. As mass flight data possesses the typical characteristics of time series, the time series analysis methods and their application for flight data have been illustrated from several aspects, such as data filtering, data extension, feature optimization, similarity search, trend monitoring, fault diagnosis, and parameter prediction, etc. An intelligent information-processing platform for flight data has been established to assist in aircraft condition monitoring, training evaluation and scientific maintenance. The book will serve as a reference resource for people working in aviation management and maintenance, as well as researchers and engineers in the fields of data analysis and data mining.
Aircraft Accident Investigation Learning from Human and Organizational Factors

Author: ASSOC. PROF. DR. CAPT. BİLAL KILIÇ
language: en
Publisher: Bilal Kilic
Release Date: 2020-11-03
Aircraft Accident Investigation: Learning from Human and Organizational Factors provides a complete overview of the contributing factors to accidents and incidents in aviation and fundamentals of aircraft accident investigation. While the book in your hands may be used in the form of a reference source at universities in terms of its contents, it may also be used in the recurrent trainings of airlines as a supplementary source. It is also a source of reference that may be individually used by those who are interested in aviation for the purpose of learning about the investigation methods and causes of accidents that have been experienced. The accidents covered in the book are as follows: British Airways Flight 38 Birgenair Flight 301 Korean Air Flight 801 Helios Airways Flight 552 Avianca Flight 052 Asiana Airlines Flight 214 Qantas Flight 32 Air France Flight 447 Air Florida Flight 90 Air France Flight 358 Colgan Air Flight 3407 Air Canada Flight 143
Time-of-Flight Cameras

Author: Miles Hansard
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-11-06
Time-of-flight (TOF) cameras provide a depth value at each pixel, from which the 3D structure of the scene can be estimated. This new type of active sensor makes it possible to go beyond traditional 2D image processing, directly to depth-based and 3D scene processing. Many computer vision and graphics applications can benefit from TOF data, including 3D reconstruction, activity and gesture recognition, motion capture and face detection. It is already possible to use multiple TOF cameras, in order to increase the scene coverage, and to combine the depth data with images from several colour cameras. Mixed TOF and colour systems can be used for computational photography, including full 3D scene modelling, as well as for illumination and depth-of-field manipulations. This work is a technical introduction to TOF sensors, from architectural and design issues, to selected image processing and computer vision methods.