Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Real-time aircraft noise likeness detector

One of the most difficult tasks involved in the process of noise monitoring near airports is related to the automatic detection and classification of aircraft noise events. These tasks can be solved by applying pattern recognition techniques to the audio signal captured by a microphone. But now the problem is caused by the background noise, which is present in real environments. This paper proposes a real-time method for continuously tracking the similarity of the input sound and the aircraft’s sounds. Using these facilities, the monitoring unit will be able to mark aircraft events, or to make measurements only when aircraft sound is louder than background noise. A one-class approach has been applied to this detection-by-classification method. Using the default setup, 93% of the aircraft’s events which held an SNR of 6–8 dB were detected, for 30 different locations with diverse soundscapes

Overview of sensors suitable for active flow control methods

Hlavným cieľom tejto bakalárskej práce bolo vytvorenie prehľadu vyvíjaných a už aplikovaných senzorov pre účely aktívneho riadenia prúdov. Senzory musia splňovať niektoré podmienky, preto výber senzorov bol naviazaný na reálnych výsledkoch testovacích programov, popis ktorých tvorí prvú časť tejto bakalárskej práce. Opis technológie a princíp fungovania senzorov je popísaný v druhej časti tejto práce.The main purpose of this bachelor thesis was to create the overview of the sensors developed for the future active flow control applications and overview the sensors already used in the active flow control applications. The sensors have to fulfil several requirements, so selection for the overview was based on the real flight test programs results, which were described in the first part of the thesis. The sensors technology description and operation principles were included in the second part of the thesis

Stability and Performance Metrics for Adaptive Flight Control

This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the system's characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the system's input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircraft's input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers

Emerging approaches for data-driven innovation in Europe: Sandbox experiments on the governance of data and technology

Europe’s digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

Modeling and identification of power electronic converters

Nowadays, many industries are moving towards more electrical systems and components. This is done with the purpose of enhancing the efficiency of their systems while being environmentally friendlier and sustainable. Therefore, the development of power electronic systems is one of the most important points of this transition. Many manufacturers have improved their equipment and processes in order to satisfy the new necessities of the industries (aircraft, automotive, aerospace, telecommunication, etc.). For the particular case of the More Electric Aircraft (MEA), there are several power converters, inverters and filters that are usually acquired from different manufacturers. These are switched mode power converters that feed multiple loads, being a critical element in the transmission systems. In some cases, these manufacturers do not provide the sufficient information regarding the functionality of the devices such as DC/DC power converters, rectifiers, inverters or filters. Consequently, there is the need to model and identify the performance of these components to allow the aforementioned industries to develop models for the design stage, for predictive maintenance, for detecting possible failures modes, and to have a better control over the electrical system. Thus, the main objective of this thesis is to develop models that are able to describe the behavior of power electronic converters, whose parameters and/or topology are unknown. The algorithms must be replicable and they should work in other types of converters that are used in the power electronics field. The thesis is divided in two main cores, which are the parameter identification for white-box models and the black-box modeling of power electronics devices. The proposed approaches are based on optimization algorithms and deep learning techniques that use non-intrusive measurements to obtain a set of parameters or generate a model, respectively. In both cases, the algorithms are trained and tested using real data gathered from converters used in aircrafts and electric vehicles. This thesis also presents how the proposed methodologies can be applied to more complex power systems and for prognostics tasks. Concluding, this thesis aims to provide algorithms that allow industries to obtain realistic and accurate models of the components that they are using in their electrical systems.En la actualidad, el uso de sistemas y componentes eléctricos complejos se extiende a múltiples sectores industriales. Esto se hace con el propósito de mejorar su eficiencia y, en consecuencia, ser más sostenibles y amigables con el medio ambiente. Por tanto, el desarrollo de sistemas electrónicos de potencia es uno de los puntos más importantes de esta transición. Muchos fabricantes han mejorado sus equipos y procesos para satisfacer las nuevas necesidades de las industrias (aeronáutica, automotriz, aeroespacial, telecomunicaciones, etc.). Para el caso particular de los aviones más eléctricos (MEA, por sus siglas en inglés), existen varios convertidores de potencia, inversores y filtros que suelen adquirirse a diferentes fabricantes. Se trata de convertidores de potencia de modo conmutado que alimentan múltiples cargas, siendo un elemento crítico en los sistemas de transmisión. En algunos casos, estos fabricantes no proporcionan la información suficiente sobre la funcionalidad de los dispositivos como convertidores de potencia DC-DC, rectificadores, inversores o filtros. En consecuencia, existe la necesidad de modelar e identificar el desempeño de estos componentes para permitir que las industrias mencionadas desarrollan modelos para la etapa de diseño, para el mantenimiento predictivo, para la detección de posibles modos de fallas y para tener un mejor control del sistema eléctrico. Así, el principal objetivo de esta tesis es desarrollar modelos que sean capaces de describir el comportamiento de un convertidor de potencia, cuyos parámetros y/o topología se desconocen. Los algoritmos deben ser replicables y deben funcionar en otro tipo de convertidores que se utilizan en el campo de la electrónica de potencia. La tesis se divide en dos núcleos principales, que son la identificación de parámetros de los convertidores y el modelado de caja negra (black-box) de dispositivos electrónicos de potencia. Los enfoques propuestos se basan en algoritmos de optimización y técnicas de aprendizaje profundo que utilizan mediciones no intrusivas de las tensiones y corrientes de los convertidores para obtener un conjunto de parámetros o generar un modelo, respectivamente. En ambos casos, los algoritmos se entrenan y prueban utilizando datos reales recopilados de convertidores utilizados en aviones y vehículos eléctricos. Esta tesis también presenta cómo las metodologías propuestas se pueden aplicar a sistemas eléctricos más complejos y para tareas de diagnóstico. En conclusión, esta tesis tiene como objetivo proporcionar algoritmos que permitan a las industrias obtener modelos realistas y precisos de los componentes que están utilizando en sus sistemas eléctricos.Postprint (published version

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others