Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

Chen, Xiaonan; Yi, Mingxu; Huang, Jun

doi:10.1109/access.2020.3008442

Cited by 9 publications

(1 citation statement)

References 23 publications

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“…While air traffic control (ATC) towers provide detailed information about operating aircraft, more than 97% of the United States airports are not equipped with control towers [3]. These airports typically host general aviation and, in addition to corporate and self-piloted flights, provide the public with vital services, such as aerial firefighting, law enforcement, and aeromedical flights [4], [5]. Accordingly, several attempts have been made to automatically identify operating aircraft, including transponder-based methods; however, they exhibit limited accuracy and require aircraft to be equipped with transponders.…”

Section: Introductionmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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Aircraft identification in airport operations is critical to various applications, including airport planning and environmental studies. Previous research and commercially available systems heavily rely on recognizing aircraft tail numbers using text recognition. However, this approach alone does not provide accurate results in situations when the tail number visibility is reduced or obstructed. Furthermore, general aviation aircraft are harder to identify because they are small in size, and their tail numbers include substantial variations in fonts, sizes, and orientations. To tackle these issues, we propose a two-step computer vision-based aircraft identification method, first identifying the aircraft type and then recognizing the tail number in a probabilistic multi-frame-based (MFB) framework. In the first step, a convolutional neural network (CNN)-based aircraft classifier is customized to decrease the search space in the registration database. In the second step, the identification process is finalized by integrating the text recognition results into the designed probabilistic MFB framework. The proposed method achieves approximately 90% identification accuracy when tested on video data collected from three general aviation airports. This is a significant improvement compared to text recognition alone, which recognizes 67% of the individual tail number characters.INDEX TERMS Intelligent transportation systems, computer vision, airports, aircraft.

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Section: Introductionmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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Parametric cost estimation models of civil aircraft for the preliminary aircraft design phase

Al-Shamma

Ali

2022

Aeronaut. j.

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In the preliminary design phase of aircraft design, estimating the production cost accurately is a challenging task. At this stage, many design parameters that affect the overall cost are still undefined. This paper establishes cost-estimation models for civil, commercial aircraft using a parametric cost analysis (PCA) approach. Aircraft are characterised based on their size, ranging from a wide body to executive jets, into four categories. Key design parameters, such as maximum take-off weight, number of passengers, range, wing area, span, fuselage length, to name a few, are likely to be available in the preliminary design stage and significantly impact the aircraft design. These variables either directly or indirectly affect the overall production cost or performance. The PCA approach includes both correlation and multiple linear regression techniques. The empirical models thus developed were able to predict the aircraft cost with an error of less than ±4% for all aircraft categories considered. Two aircraft in each defined category were not part of the PCA models and were used to verify the models. The proposed models provide the ability to estimate the aircraft cost quickly in the early stages of the preliminary design phase and provide the possibility of performing parametric studies involving the key variables to determine the cost sensitivity to the main design parameters.

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Machine Learning Based Cost prediction for Acquiring New Customers

Prasad¹,

Nanda²,

Darapaneni

et al. 2023

2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

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Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

Cited by 9 publications

References 23 publications

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

Parametric cost estimation models of civil aircraft for the preliminary aircraft design phase

Machine Learning Based Cost prediction for Acquiring New Customers

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