Related papers: A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil Aviation Over-limit

A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil Aviation Over-limit

URL: http://arxiv.org/abs/2305.04618v1
Date: Mon, 8 May 2023 10:56:06 GMT
Title: A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil Aviation Over-limit
Authors: Yiming Bian
Abstract summary: A real-time warning model for civil aviation over-limit is proposed based on QAR data monitoring. The proposed model achieves an F1 score of 0.991 and an accuracy of 0.978, indicating its effectiveness in real-time warning of civil aviation over-limit.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The issue of over-limit during passenger aircraft flights has drawn increasing attention in civil aviation due to its potential safety risks. To address this issue, real-time automated warning systems are essential. In this study, a real-time warning model for civil aviation over-limit is proposed based on QAR data monitoring. Firstly, highly correlated attributes to over-limit are extracted from a vast QAR dataset using the Spearman rank correlation coefficient. Because flight over-limit poses a binary classification problem with unbalanced samples, this paper incorporates cost-sensitive learning in the LSTM model. Finally, the time step length, number of LSTM cells, and learning rate in the LSTM model are optimized using a grid search approach. The model is trained on a real dataset, and its performance is evaluated on a validation set. The experimental results show that the proposed model achieves an F1 score of 0.991 and an accuracy of 0.978, indicating its effectiveness in real-time warning of civil aviation over-limit.

Related papers

A Practical Approach to using Supervised Machine Learning Models to Classify Aviation Safety Occurrences [0.0]
This paper describes a practical approach of using supervised machine learning (ML) models to classify aviation occurrences into either incident or serious incident categories. Our implementation currently deployed as a ML web application is trained on a dataset derived from publicly available aviation investigation reports.
arXiv Detail & Related papers (2025-04-12T03:46:33Z)
Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties [14.474624795989824]
An innovative machine learning (ML)-enhanced landing scheduling methodology is proposed, aiming to improve automation and safety. ML predictions are then integrated as safety constraints in a time-constrained traveling salesman problem formulation. Case studies demonstrate an average 17.2% reduction in total landing time compared to the First-Come-First-Served (FCFS) rule.
arXiv Detail & Related papers (2023-11-27T17:50:14Z)
Imbalanced Aircraft Data Anomaly Detection [103.01418862972564]
Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task. We propose a Graphical Temporal Data Analysis framework. It consists three modules, named Series-to-Image (S2I), Cluster-based Resampling Approach using Euclidean Distance (CRD) and Variance-Based Loss (VBL)
arXiv Detail & Related papers (2023-05-17T09:37:07Z)
A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset [2.094022863940315]
Anomalies refer to data points or events that deviate from normal and homogeneous events. This study presents a novel framework for time series anomaly detection using a combination of Bi-LSTM architecture and Autoencoder. The Bi-LSTM Autoencoder model achieved a classification accuracy of 96.79% and outperformed more commonly used LSTM Autoencoder models.
arXiv Detail & Related papers (2023-03-17T00:24:28Z)
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data [6.642599588462097]
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. Traditional statistics and machine learning-based approaches in anomaly detection in the IAQ area could not detect anomalies involving the observation of correlations across several data points. We propose a hybrid deep learning model that combines LSTM with Autoencoder for anomaly detection tasks in IAQ to address this issue.
arXiv Detail & Related papers (2022-04-14T01:57:46Z)
DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z)
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series [1.0437764544103274]
This paper introduces SALAD, which is a Self-Adaptive Lightweight Anomaly Detection approach based on a special type of recurrent neural networks called Long Short-Term Memory (LSTM) Experiments based on two real-world open-source time series datasets demonstrate that SALAD outperforms five other state-of-the-art anomaly detection approaches in terms of detection accuracy. In addition, the results also show that SALAD is lightweight and can be deployed on a commodity machine.
arXiv Detail & Related papers (2021-04-19T10:36:23Z)
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation (Extended Version) [64.76619508293966]
This paper proposes a unit-modulus over-the-air computation (UM-AirComp) framework to facilitate efficient edge federated learning. It uploads simultaneously local model parameters and updates global model parameters via analog beamforming. We demonstrate the implementation of UM-AirComp in a vehicle-to-everything autonomous driving simulation platform.
arXiv Detail & Related papers (2021-01-28T15:10:22Z)
Learning summary features of time series for likelihood free inference [93.08098361687722]
We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
arXiv Detail & Related papers (2020-12-04T19:21:37Z)
T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting [65.498967509424]
Air turbulence forecasting can help airlines avoid hazardous turbulence, guide routes that keep passengers safe, maximize efficiency, reduce costs. Traditional forecasting approaches rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. We propose a machine learning based turbulence forecasting system due to two challenges: (1) Complex-temporal correlations, and (2) scarcity, very limited turbulence labels can be obtained.
arXiv Detail & Related papers (2020-10-26T21:14:15Z)
Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
arXiv Detail & Related papers (2020-07-23T13:32:47Z)
Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction [3.59465210252619]
Number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Deep learning-based UAS instantaneous density prediction model is presented.
arXiv Detail & Related papers (2020-03-22T02:40:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.