A method for the outlier flights detection of the final approach based on FOQA data

Jiang, Yunpeng; Li, Rui; Le, Ningning; Zheng, Yinger

doi:10.1109/iccasit48058.2019.8973186

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…A data-driven system for outlier fight identifcation was proposed by Jiang et al, focusing on the landing approach of the plane, which has a higher probability of fatalities; frst, three checking points and landing approach output parameters are selected and retrieved from the quick access record (QAR) data in the fight operational quality assurance (FOQA) station in [9]. Second, if discrete parameters are involved in the training datasets, density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced for detection of outliers, and if discrete parameters are not involved in the training datasets, one-class support vector machine (SVM) model is applied.…”

Section: Literature Surveymentioning

confidence: 99%

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

Shetty,

Seema,

Sowmya

et al. 2024

Journal of Engineering

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Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results.

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Section: Literature Surveymentioning

confidence: 99%

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

Shetty,

Seema,

Sowmya

et al. 2024

Journal of Engineering

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“…When an aircraft is in flight, the FDIMU data collector on the aircraft collects thousands of high-quality original parameters on the aircraft bus per second according to the ARINC 429 protocol. The data are acquired in real time on-board the aircraft and downloaded by the airline once the aircraft reaches the destination gate [40,41]. The altitude and speed changes of the flight route are shown in Figure 4.…”

Section: Multi-phase Aircraft Infrared Signaturementioning

confidence: 99%

On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data

Rao

et al. 2023

Applied Sciences

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Frequent aviation safety accidents of civil aircraft misses and crashes lead to an urgent need for flight safety assurance. Due to long-time flights over different backgrounds, accompanied by the changes in flight altitude and speed, it is difficult for a conventional space-based infrared detection system to use a set of fixed parameters to meet the stable detection requirement. To enhance the awareness of civil aircraft surveillance, a real-time parameter optimization method based on Automatic Dependent Surveillance-Broadcast (ADS-B) data is proposed. According to the background spectral characteristics and the real-time flight data, the most reasonable spectral band is analyzed, using the joint signal-to-noise/clutter ratio (JSNCR) as the evaluation criteria. Then, an automatic parameter adjustment is used to maximize the integration time and switch the integration capacitor gear. Numerical simulation results show that the JSNCR increased by 1.16 to 1.31 times, and the corresponding noise equivalent target radiant intensity (NET) reduced from 2.4 W/Sr to 1.2 W/Sr compared with a conventional fixed-parameter detection system. This study lays a solid theoretical foundation for the spectral band analysis of space-based AVD system design. Meanwhile, the proposed method can be used as a standard procedure to improve on-board performance.

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Flight data outlier detection by constrained LSTM-autoencoder

Long

Wang

et al. 2023

Wireless Netw

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A method for the outlier flights detection of the final approach based on FOQA data

Cited by 5 publications

References 3 publications

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data

Flight data outlier detection by constrained LSTM-autoencoder

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