Recent Advances in Anomaly Detection Methods Applied to Aviation

International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance

Detecting Controllers' Actions in Past Mode S Data by Autoencoder-Based Anomaly Detection

International audienceThe preparation and execution of training simulations for Air Traffic Control (ATC) and pilots requires a significant commitment of operational experts. Such a mobilisation could be alleviated by a decision support tool trained to generate a realistic environment based on historical data. Prior to studying methods able to learn from a dataset of traffic patterns and ATC orders observed in the past, we focus here on the constitution of such a database from a history of trajectories: the difficulty lies in the fact that past flown trajectories are properly regulated, that observed situations may depend on a wide range of potentially unknown factors and that ownership rules apply on parts of the data. We present here a method to analyse flight trajectories, detect unusual flight behaviours and infer ATC actions. When an anomaly is detected, we place the trajectory in context, then assess whether such anomaly could correspond to an ATC action. The trajectory outlier detection method is based on autoencoder Machine Learning models. It determines trajectory outliers and quantifies a level of abnormality, therefore giving hints about the nature of the detected situations. Results obtained on three different scenarios, with Mode S flight data collected over one year, show that this method is well suited to efficiently detect anomalous situations, ranging from classic air traffic controllers orders to more significant deviations. Detecting such situations is not only a necessary milestone for the generation of ATC orders in a realistic environment; this methodology could also be useful in safety studies for anomaly detection and estimation of probabilities of rare events; and in complexity and performance analyses for detecting actions in neighbouring sectors or estimating ATC workload

Evaluation of a Resilience-Driven Operational Concept to Manage Drone Intrusions in Airports

The drone market’s growth poses a serious threat to the negligent, illicit, or non-cooperative use of drones, especially in airports and their surroundings. Effective protection of an airport against drone intrusions should guarantee mandatory safety levels but should also rely on a resilience-driven operational concept aimed at managing the intrusions without necessarily implying the closure of the airport. The concept faces both safety-related and security-related threats and is based on the definitions of: (i) new roles and responsibilities; (ii) a set of operational phases, accomplished by means of specific technological building blocks; (iii) a new operational procedure blending smoothly with existing aerodrome procedures in place. The paper investigates the evaluation of such a resilience-driven operational concept tailored to drone-intrusion features, airport features, and current operations. The proposed concept was evaluated by applying it to a concrete case study related to Milan Malpensa Airport. The evaluation was carried out by real-time simulations and event tree analysis, exploiting the implementation of specific simulation tools and the assessment of resilience-oriented metrics. The achieved results show the effectiveness of the proposed operational concept and elicit further requirements for future counter-drone systems in airports

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

Drone expansion needs to be considered as a menace in cases of negligent, illicit, or non-cooperative use. In the case of airports, a complete protection against drone intrusion should rely on an intrusion management system, aiming at avoiding the closure of the airport. This system requires the setting of proper risk assessment methodologies for airport operations, to explicitly consider the features of drone intrusion, possibly from a quantitative point of view. This work proposes a methodological framework for the risk assessment of drone intrusions in airports, tailored on drone-intrusion features, airport features, and current operations, and considering both safety-related and security-related causes. The framework is based on the combination of model-based and data-driven approaches in order to: (i) estimate an airport vulnerability index, to measure the susceptibility of the airport to drone intrusions, based on reference datasets; (ii) specify a set of event trees to evaluate the risks of the different threat scenarios related to drone intrusions. The proposed methodological framework is applied to a concrete case study, related to Milan Malpensa airport. The achieved results show the effectiveness of the approach and elicit further requirements for counter-drone systems in airports based on the assessed risks

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

Drone expansion needs to be considered as a menace in cases of negligent, illicit, or non-cooperative use. In the case of airports, a complete protection against drone intrusion should rely on an intrusion management system, aiming at avoiding the closure of the airport. This system requires the setting of proper risk assessment methodologies for airport operations, to explicitly consider the features of drone intrusion, possibly from a quantitative point of view. This work proposes a methodological framework for the risk assessment of drone intrusions in airports, tailored on drone-intrusion features, airport features, and current operations, and considering both safety-related and security-related causes. The framework is based on the combination of model-based and data-driven approaches in order to: (i) estimate an airport vulnerability index, to measure the susceptibility of the airport to drone intrusions, based on reference datasets; (ii) specify a set of event trees to evaluate the risks of the different threat scenarios related to drone intrusions. The proposed methodological framework is applied to a concrete case study, related to Milan Malpensa airport. The achieved results show the effectiveness of the approach and elicit further requirements for counter-drone systems in airports based on the assessed risks

Modification des ingestions protéiques par la séance d’hémodialyse : une piste pour lutter contre la dénutrition

Modification des ingestions protéiques par la séance d’hémodialyse : une piste pour lutter contre la dénutrition. 17. réunion commune - société de néphrologie - société francophone de dialys

Impact of age at diagnosis of metastatic breast cancer on overall survival in the real-life ESME metastatic breast cancer cohort

International audienceYoung age is a poor prognostic factor in early stage breast cancer (BC) but its value is less established in metastatic BC (MBC). We evaluated the impact of age at MBC diagnosis on overall survival (OS) across three age groups ( 60 years(y))

Identification of new candidate therapeutic target genes in head and neck squamous cell carcinomas

International audienceBackground: We aimed at identifying druggable molecular alterations at the RNA level from untreated HNSCC patients, and assessing their prognostic significance. Methods: We retrieved 96 HNSCC patients who underwent primary surgery. Realtime quantitative RT-PCR was used to analyze a panel of 42 genes coding for major druggable proteins. Univariate and multivariate analyses were performed to assess the prognostic significance of overexpressed genes. Results: Median age was 56 years [35-78]. Most of patients were men (80%) with a history of alcohol (70.4%) and/or tobacco consumption (72.5%). Twelve patients (12%) were HPV-positive. Most significantly overexpressed genes involved cell cycle regulation (CCND1 [27%], CDK6 [21%]), tyrosine kinase receptors (MET [18%], EGFR [14%]), angiogenesis (PGF [301%], VEGFA [14%]), and immune system (PDL1/CD274 [28%]). PIK3CA expression was an independent prognostic marker, associated with shorter disease-free survival. Conclusions: We identified druggable overexpressed genes associated with a poor outcome that might be of interest for personalizing treatment of HNSCC patien