Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

Aircraft Maintenance Routing Problem – A Literature Survey

The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch.

Relationships Under Stress: Relational Outsourcing in the U.S. Airline Industry After the 2008 Financial Crisis

This paper studies how firms restructure their relational contracts in the face of permanent shocks to the value of their relationships. In the context of the U.S. airline industry, we argue that major carriers enter self-enforcing agreements with their outsourced regional partners because a key aspect of airline operations—the exchange of landing slots under adverse weather—is formally noncontractible. We show empirically that major and regional airlines did not terminate their relational contracts after the 2008 crisis but rather, restructured the scope of such contracts in a way that restored their credibility. In particular, we show that a major airline was less likely to continue outsourcing a route to a regional partner after the 2008 crisis the lower the present discounted value of their preexisting relationship and hence, the larger the negative effect of the crisis on the relational contract’s “self-enforcing range.” This paper was accepted by Joshua Gans, business strategy.

Evaluation and mitigation analysis of carbon footprint for an airline operator: Case of Nepal airlines corporation

This paper deals with evaluation and analysis of carbon footprint of an airline operator, Nepal Airlines Corporation (NAC) by using its actual flight and maintenance data from 2016 to 2019. NAC is a multi-fleet operator, of both turboprop and turbofan aircrafts. Carbon footprint in terms of Carbon Dioxide (CO2) emission has been calculated for NAC’s airline operations per individual aircraft, fleet-type and operating sector (i.e., international and domestic), and total ground handling operations. In each of the study years, contribution to NAC’s total CO2 production from its domestic fleet was found out to be very small (below 6% of yearly total), even though its fleet number outnumbered that of international fleet. This indicates better optimization opportunities for international-sector (turbofan) aircrafts than domestic-sector (turboprop) aircrafts. Reductions in fuel on-board as per prescribed levels, better airport slot management and selection of long-haul flight destinations have been identified as potential mitigation strategies for CO2 emission from international sector. Smaller aircrafts operating in domestic sectors are more prone to variations in occupancy rate and as such, NAC could focus on optimizing its commercial strategy to improve its CO2/passenger rate in domestic sector.

Formal modelling and verification of a multi-agent negotiation approach for airline operations control

This paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.