14 research outputs found
Identifying Operational Benefits of the Arrival Management System – A KPI-Based Experimental Method by Evaluating Radar Trajectories
The arrival management (AMAN) system is a decision support tool for air traffic controllers to establish and maintain the landing sequence for arrival aircraft. The original intention of designing the AMAN system is to improve the efficiency of air traffic management (ATM), but few studies are investigating the operational benefits of this system based on key performance indicators (KPIs) and evaluating actual data in a real-time environment. The main purpose of this paper is to propose a KPI based transferable comparative analysis method for identifying the operational benefits of the AMAN through radar trajectories. Firstly, six KPIs are established from a joint study of the mainstream ATM performance frameworks worldwide. Secondly, appropriate evaluation technique approaches are determined according to the characteristics of each KPI. Finally, a Chinese metropolitan airport is taken for the case study, and three periods are defined to form data samples with high similarity for comparative experiments. The results validate the feasibility of the proposed method and find comprehensive performance improvements in arrival operations under the effects of the AMAN system
Benders' decomposition algorithm to solve bi-level bi-objective scheduling of aircrafts and gate assignment under uncertainty
Abstract Management and scheduling of flights and assignment of gates to aircraft play a significant role in improving the procedure of the airport, due to the growing number of flights, decreasing the flight times. This research addresses assigning and scheduling of runways and gates in the main airport simultaneously. Moreover, this research considers the unavailability of runway's constraint and the uncertain parameters relating to both areas of runway and gate assignment. The proposed model is formulated as a comprehensive bi-level bi-objective problem.The leader's objective function minimizes the total waiting time for runways and gates for all aircrafts based on their importance coefficient. Meanwhile, the total distance traveled by all passengers in the airport terminal is minimized by a follower's objective function. To solve the proposed model, the decomposition approach based on Benders' decomposition method is applied. Empirical data are used to show the validation and application of our model. A comparison shows the effectiveness of the proposed model and its significant impact on cost decreasing
Collision Avoidance on Unmanned Aerial Vehicles using Deep Neural Networks
Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently
gained a prominent role in many industries, being widely used not only among enthusiastic
consumers but also in high demanding professional situations, and will have a
massive societal impact over the coming years. However, the operation of UAVs is full
of serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or
randomly thrown objects). These collision scenarios are complex to analyze in real-time,
sometimes being computationally impossible to solve with existing State of the Art (SoA)
algorithms, making the use of UAVs an operational hazard and therefore significantly reducing
their commercial applicability in urban environments. In this work, a conceptual
framework for both stand-alone and swarm (networked) UAVs is introduced, focusing on
the architectural requirements of the collision avoidance subsystem to achieve acceptable
levels of safety and reliability. First, the SoA principles for collision avoidance against
stationary objects are reviewed. Afterward, a novel image processing approach that uses
deep learning and optical flow is presented. This approach is capable of detecting and
generating escape trajectories against potential collisions with dynamic objects. Finally,
novel models and algorithms combinations were tested, providing a new approach for
the collision avoidance of UAVs using Deep Neural Networks. The feasibility of the proposed
approach was demonstrated through experimental tests using a UAV, created from
scratch using the framework developed.Os veículos aéreos não tripulados (VANTs), embora dificilmente considerados uma
nova tecnologia, ganharam recentemente um papel de destaque em muitas indústrias,
sendo amplamente utilizados não apenas por amadores, mas também em situações profissionais
de alta exigência, sendo expectável um impacto social massivo nos próximos
anos. No entanto, a operação de VANTs está repleta de sérios riscos de segurança, como
colisões com obstáculos dinâmicos (pássaros, outros VANTs ou objetos arremessados).
Estes cenários de colisão são complexos para analisar em tempo real, às vezes sendo computacionalmente
impossível de resolver com os algoritmos existentes, tornando o uso de
VANTs um risco operacional e, portanto, reduzindo significativamente a sua aplicabilidade
comercial em ambientes citadinos. Neste trabalho, uma arquitectura conceptual
para VANTs autônomos e em rede é apresentada, com foco nos requisitos arquitetônicos
do subsistema de prevenção de colisão para atingir níveis aceitáveis de segurança e confiabilidade.
Os estudos presentes na literatura para prevenção de colisão contra objectos
estacionários são revistos e uma nova abordagem é descrita. Esta tecnica usa técnicas
de aprendizagem profunda e processamento de imagem, para realizar a prevenção de
colisões em tempo real com objetos móveis. Por fim, novos modelos e combinações de algoritmos
são propostos, fornecendo uma nova abordagem para evitar colisões de VANTs
usando Redes Neurais Profundas. A viabilidade da abordagem foi demonstrada através
de testes experimentais utilizando um VANT, desenvolvido a partir da arquitectura
apresentada
Data bases and data base systems related to NASA's aerospace program. A bibliography with indexes
This bibliography lists 1778 reports, articles, and other documents introduced into the NASA scientific and technical information system, 1975 through 1980
Solving Multi-objective Integer Programs using Convex Preference Cones
Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic