Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise

Tian, Yong; Ye, Bojia; Wan, Lili; Yang, Minhao; Dawei, Xing

doi:10.3390/su11215962

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…ATM systems, if they are to overcome extant and emerging problems, must increase both capacity and efficiency: this, in turn, means high levels of intelligence and automation. Our review [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], demonstrates that the topic of AI in ATM, and the ongoing evolution of this relationship, are of growing importance. For instance, the volume of publications pertinent to this subject has increased by 100% in the last four years alone.…”

Section: Literature Review and Backgroundmentioning

confidence: 93%

Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning

Alharbi

Petrunin

Panagiotakopoulos

2023

Drones

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The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements.

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Section: Literature Review and Backgroundmentioning

confidence: 93%

Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning

Alharbi

Petrunin

Panagiotakopoulos

2023

Drones

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“…This formula calculates that the airport transit time includes two parts: the road service project time and the sinking time. The data information in the table shows that the minimum crossing time required in the document involves a certain sinking time [15]. Therefore, in order to better reduce the spread of mid and downstream aircraft delays caused by forward flight delays, in order to better reduce The spread of delays to midstream and downstream aircraft flights has been adjusted to varying degrees during the remaining time of the sinking.…”

Section: Airport Transit Time Model Based On Bayesian Networkmentioning

confidence: 99%

Research on Flight Adjustment Optimization Based on Flight Delay Forecast

2022

IEIM

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Flight delays are not only a single point effect, but also affect related flights and airports through the air transportation network, causing large-scale flight delays. This article is aimed at Advance control of flight delays, that is, the control of the spread delay of the flight plan, through the delay prediction of the flight, provide advance reference for the subsequent flight plan optimization, on the basis of the flight delay prediction, adjust and optimize the spread delay flight, use The aircraft path spread and delay optimization model based on the column generation algorithm adjusts the delayed flights.

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“…The density-based clustering (DBSCAN) approach [32][33][34][35][36] resolved this type of problem. The clusters are allocated in the dense regions in the data space which separates the lower density of points by regions.…”

Section: Dbscanmentioning

confidence: 99%

The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

Wan

Wang

2020

Agriculture

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The analysis, measurement, and computation of remote sensing images often require enhanced unsupervised/supervised classification approaches. The goal of this study is to have a better understanding of (a) the classification performance of multispectral image and hyperspectral image data; (b) the classification performance of unsupervised and supervised models; and (c) the classification performance of feature selection among different models. More specifically, the multispectral images and hyperspectral images with high spatial resolution are well accepted for improving land use and classification. Hence, this study used multispectral images (WorldView-2) and hyperspectral images (CASI-1500) and focused on the classifiers K-means, density-based spatial clustering of applications with noise (DBSCAN), linear discriminant analysis (LDA), and back-propagation neural network (BPN). Then the feature selection (principle component analysis, PCA) on four classifiers is studied. The results show that the image material of CASI-1500 classification accuracy is slightly better than that of WorldView-2. The overall classification of BPN is the best, the overall data has a κ value of 0.89 and the overall accuracy is 97%. The DBSCAN presents a reality with good accuracy and the integrity of the thematic map. The DBSCAN can attain an accuracy of about 88% and save 95.1% of computational time.

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Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise

Cited by 5 publications

References 12 publications

Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning

Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning

Research on Flight Adjustment Optimization Based on Flight Delay Forecast

The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

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