As deep neural networks (DNNs) are becoming the prominent solution for many
computational problems, the aviation industry seeks to explore their potential
in alleviating pilot workload and in improving operational safety. However, the
use of DNNs in this type of safety-critical applications requires a thorough
certification process. This need can be addressed through formal verification,
which provides rigorous assurances -- e.g.,~by proving the absence of certain
mispredictions. In this case-study paper, we demonstrate this process using an
image-classifier DNN currently under development at Airbus and intended for use
during the aircraft taxiing phase. We use formal methods to assess this DNN's
robustness to three common image perturbation types: noise, brightness and
contrast, and some of their combinations. This process entails multiple
invocations of the underlying verifier, which might be computationally
expensive; and we therefore propose a method that leverages the monotonicity of
these robustness properties, as well as the results of past verification
queries, in order to reduce the overall number of verification queries required
by nearly 60%. Our results provide an indication of the level of robustness
achieved by the DNN classifier under study, and indicate that it is
considerably more vulnerable to noise than to brightness or contrast
perturbations.

使用形式验证方法评估了一个用于飞机滑行阶段的图像分类器深度神经网络 (DNN) 在噪声、亮度和对比度以及它们的组合等三种常见图像扰动类型下的鲁棒性，结果表明该分类器对噪声比亮度和对比度扰动更加脆弱。

安全飞机滑行的跑道物体分类器的稳健性评估

Robustness Assessment of a Runway Object Classifier for Safe Aircraft  Taxiing

The advent of ChatGPT and GPT-4 has captivated the world with large language
models (LLMs), demonstrating exceptional performance in question-answering,
summarization, and content generation. The aviation industry is characterized
by an abundance of complex, unstructured text data, replete with technical
jargon and specialized terminology. Moreover, labeled data for model building
are scarce in this domain, resulting in low usage of aviation text data. The
emergence of LLMs presents an opportunity to transform this situation, but
there is a lack of LLMs specifically designed for the aviation domain. To
address this gap, we propose AviationGPT, which is built on open-source LLaMA-2
and Mistral architectures and continuously trained on a wealth of carefully
curated aviation datasets. Experimental results reveal that AviationGPT offers
users multiple advantages, including the versatility to tackle diverse natural
language processing (NLP) problems (e.g., question-answering, summarization,
document writing, information extraction, report querying, data cleaning, and
interactive data exploration). It also provides accurate and contextually
relevant responses within the aviation domain and significantly improves
performance (e.g., over a 40% performance gain in tested cases). With
AviationGPT, the aviation industry is better equipped to address more complex
research problems and enhance the efficiency and safety of National Airspace
System (NAS) operations.

提供行业专用的 AviationGPT 模型，显著提升在航空领域的性能，解决多种自然语言处理问题，增强国家空中系统运营效率和安全性。

AviationGPT：航空领域的大型语言模型

AviationGPT: A Large Language Model for the Aviation Domain

In recent years, the remarkable progress of Machine Learning (ML)
technologies within the domain of Artificial Intelligence (AI) systems has
presented unprecedented opportunities for the aviation industry, paving the way
for further advancements in automation, including the potential for single
pilot or fully autonomous operation of large commercial airplanes. However, ML
technology faces major incompatibilities with existing airborne certification
standards, such as ML model traceability and explainability issues or the
inadequacy of traditional coverage metrics. Certification of ML-based airborne
systems using current standards is problematic due to these challenges. This
paper presents a case study of an airborne system utilizing a Deep Neural
Network (DNN) for airport sign detection and classification. Building upon our
previous work, which demonstrates compliance with Design Assurance Level (DAL)
D, we upgrade the system to meet the more stringent requirements of Design
Assurance Level C. To achieve DAL C, we employ an established architectural
mitigation technique involving two redundant and dissimilar Deep Neural
Networks. The application of novel ML-specific data management techniques
further enhances this approach. This work is intended to illustrate how the
certification challenges of ML-based systems can be addressed for medium
criticality airborne applications.

近年来，机器学习（ML）技术在人工智能（AI）系统领域取得了显著进展，为航空业带来了前所未有的自动化机会，包括大型商用飞机的单人驾驶或完全自主飞行。然而，ML 技术与现有的空中认证标准存在严重不兼容性，如 ML 模型的可追踪性和可解释性问题，或者传统覆盖度指标的不适用性。当前标准下使用基于 ML 的空中系统的认证存在问题。本文通过一个案例研究，介绍了一个利用深度神经网络（DNN）进行机场标志检测和分类的空中系统。在我们之前的工作基础上，证明了系统符合设计保证等级（DAL）D 的要求，我们将系统升级以满足更严格的设计保证等级 C 的要求。为了实现 DAL C，我们采用了一种已建立的架构缓解技术，涉及两个冗余且不同的深度神经网络。对于这种方法，采用了创新的 ML 特定数据管理技术进行加强。本文旨在说明如何解决中等重要性空中应用中基于 ML 的系统的认证挑战。

跑道标志分类器：一种可通过 DAL C 认证的机器学习系统

Runway Sign Classifier: A DAL C Certifiable Machine Learning System

Demystifying the delay propagation mechanisms among multiple airports is
fundamental to precise and interpretable delay prediction, which is crucial
during decision-making for all aviation industry stakeholders. The principal
challenge lies in effectively leveraging the spatiotemporal dependencies and
exogenous factors related to the delay propagation. However, previous works
only consider limited spatiotemporal patterns with few factors. To promote more
comprehensive propagation modeling for delay prediction, we propose
SpatioTemporal Propagation Network (STPN), a space-time separable graph
convolutional network, which is novel in spatiotemporal dependency capturing.
From the aspect of spatial relation modeling, we propose a multi-graph
convolution model considering both geographic proximity and airline schedule.
From the aspect of temporal dependency capturing, we propose a multi-head
self-attentional mechanism that can be learned end-to-end and explicitly reason
multiple kinds of temporal dependency of delay time series. We show that the
joint spatial and temporal learning models yield a sum of the Kronecker
product, which factors the spatiotemporal dependence into the sum of several
spatial and temporal adjacency matrices. By this means, STPN allows cross-talk
of spatial and temporal factors for modeling delay propagation. Furthermore, a
squeeze and excitation module is added to each layer of STPN to boost
meaningful spatiotemporal features. To this end, we apply STPN to multi-step
ahead arrival and departure delay prediction in large-scale airport networks.
To validate the effectiveness of our model, we experiment with two real-world
delay datasets, including U.S and China flight delays; and we show that STPN
outperforms state-of-the-art methods. In addition, counterfactuals produced by
STPN show that it learns explainable delay propagation patterns.

本文提出了一种新的空时可分离图卷积网络 ——STPN，用于建立多个机场之间航班延误传播的综合模型，并且在美国和中国的航班延误数据上验证了该模型的有效性及优越性。