Natural language processing for aviation safety: Extracting knowledge from publicly-available loss of separation reports

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies

A taxonomical framework of socio-cultural hazards in transport hubs

This article presents a taxonomical framework that supports the considerations of socio-cultural hazards that may affect crowd management in transport hubs, i.e. airports, ports, underground and train stations, both in normal and emergency situations. Such hazards include communication breakdowns with passengers due, for instance, to language barriers; increased potential for revolts, as in stranded passenger situations; misreporting of security threats; and uncooperative behaviour in case of emergencies. Such socio-cultural hazards are not normally considered from the integrated perspective of transport hub operators, e.g. security staff, first responders and service assistants as well as safety and security managers. The present study provides an integrated perspective of these hazards as a means to increase the performance of transport staff members that interact with the public and with passengers on a daily basis. The methodology used to develop the framework comprises: (i) a focus group with relevant experts, (ii) semi-structured interviews at operational facilities with front-end practitioners, and (iii) a review of academic literature and media reports. The framework has also been qualitatively corroborated with transport operators in dedicated interviews and a focus group session. The study identified 10 socio-cultural hazards that were combined into a single framework comprising three high-level sub-categories: (i) crowd–staff interactions, (ii) crowd–crowd interactions, and (iii) crowd–environment interactions. The framework of socio-cultural factors can increase staff’s awareness of relevant socio-cultural hazards, their potential consequences in both normal and emergency situations, and the associated mitigation strategies. In turn, this can increase the quality and continuity of service, safety and security in the management of members of the public and passengers in transport hubs

Remote cable-based video surveillance applications: the AVS-RIO project

This summary aims at presenting an overview of the CEC-ESPRIT AVS-RIO (Advanced Video Surveillance - Cable Television-Based Remote Video surveillance System for Protected sites Monitoring) project. The peculiarity of the project consists both in the video-surveillance application considered (i.e., the monitoring of tourist and naturalistic places), and in the communication mean used for the remote transmission of information from the observed sites to the remote elaboration centre, which is a coaxial cable network for local community TV broadcasting (CATV network). The multimedia information is acquired by video sensors (i.e., TV cameras) and transmitted through the CATV network to a remote control centre, where a PC-based high-performance computing network (HPCN) architecture performs the image processing tasks needed by the implementation of the system functionalities. The functional specifications of the system will be briefly exposed, together with the hardware/software tools needed for the system implementation

Influence of plasma spray parameters on the cracking behavior of yttria stabilized zirconia coatings

Thermal spray coatings enhance the material properties of substrates but this attribute may be negated if the coatings themselves fail. This paper investigates the performance of an important family of coatings as one step in improving the resistance of the coating/substrate system to mechanical failure. Thermal barrier coatings of yttria partially stabilized zirconia (YSZ) with a NiCrAlY bond coat were air plasma sprayed onto mild-steel substrates. The spray process parameters were varied according to top and bond coat thickness, stand off distance, and substrate temperature. These 17 groups of coating types, with six identical samples in each group, were four point bend tested while coupled with an in-situ acoustic emission transducer. Optical microscopy was then used to examine and map the resulting crack patterns. Measurements of the number of cracks, crack separation, delaminating length, and micro-crack density were also made. The combination of this information, along with properties obtained from the four point bend tests and acoustic emission signals, provided insight into the cracking and delaminating processes

Time-invariant filtering and segmentation of SAR images by using mean-field annealing

A stochastic model for restoration of and edge extraction from synthetic aperture radar (SAR) images is presented. This model is based on an observation-prediction model which favors the restoration of piecewise-constant patches separated by long continuous edges. Speckle noise is filtered out by means of an a-priori fixed probability distribution dependent on the number of views. Results on synthetic and real images are reported. To reduce the computational cost, simulated annealing is replaced with a deterministic algorithm based on the weak membrane model and on the mean field equations adapted to SAR

KNOWLEDGE-BASED CONTROL IN MULTISENSOR IMAGE-PROCESSING AND RECOGNITION

An approach to the control of multisensor image processing and recognition based on a suitable representation of control knowledge in symbolic form is presented. A hierarchical organization of control knowledge, corresponding to a decomposition of the image recognition process into subprocesses, is proposed. The knowledge for the control of the low-level and high-level phases is described in detail. The control problem involved in the automatic selection and tuning of image processing algorithms is addressed using data structures representing advised sequences of algorithms, a symbolic representation of quality control, and control strategies with backtracking capabilities. Error handling in the high-level phase is faced by a functional decomposition of the error-handling task into error states and types and by a hierarchical representation of the control knowledge for error detection and recovery. Results obtained in a real-world multisensor application are reported, and the improvement in classification accuracy obtained by the proposed error-handling mechanisms is evaluated