468 research outputs found
Contributions to improve the technologies supporting unmanned aircraft operations
Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge.
Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential.
On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle.
This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies
the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir.
Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio.
Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav
Conditional Invertible Generative Models for Supervised Problems
Invertible neural networks (INNs), in the setting of normalizing flows, are a type of unconditional generative likelihood model. Despite various attractive properties compared to other common generative model types, they are rarely useful for supervised tasks or real applications due to their unguided outputs. In this work, we therefore present three new methods that extend the standard INN setting, falling under a broader category we term generative invertible models. These new methods allow leveraging the theoretical and practical benefits of INNs to solve supervised problems in new ways, including real-world applications from different branches of science. The key finding is that our approaches enhance many aspects of trustworthiness in comparison to conventional feed-forward networks, such as uncertainty estimation and quantification, explainability, and proper handling of outlier data
Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies
This book promotes occupants as a focal point for the design process
Predictive Techniques for Scene Understanding by using Deep Learning in Autonomous Driving
La conducción autónoma es considerada uno de los más grandes retos tecnológicos de la actualidad. Cuando los coches autónomos conquisten nuestras carreteras, los accidentes se reducirán notablemente, hasta casi desaparecer, ya que la tecnología estará testada y no incumplirá las normas de conducción, entre otros beneficios sociales y económicos. Uno de los aspectos más críticos a la hora de desarrollar un vehículo autónomo es percibir y entender la escena que le rodea. Esta tarea debe ser tan precisa y eficiente como sea posible para posteriormente predecir el futuro de esta misma y ayudar a la toma de decisiones. De esta forma, las acciones tomadas por el vehículo garantizarán tanto la seguridad del vehículo en sí mismo y sus ocupantes, como la de los obstáculos circundantes, tales como viandantes, otros vehículos o infraestructura de la carretera. En ese sentido, esta tesis doctoral se centra en el estudio y desarrollo de distintas técnicas predictivas para el entendimiento de la escena en el contexto de la conducción autónoma. Durante la tesis, se observa una incorporación progresiva de técnicas de aprendizaje profundo en los distintos algoritmos propuestos para mejorar el razonamiento sobre qué está ocurriendo en el escenario de tráfico, así como para modelar las complejas interacciones entre la información social (distintos participantes o agentes del escenario, tales como vehículos, ciclistas o peatones) y física (es decir, la información geométrica, semántica y topológica del mapa de alta definición) presente en la escena. La capa de percepción de un vehículo autónomo se divide modularmente en tres etapas: Detección, Seguimiento (Tracking), y Predicción. Para iniciar el estudio de las etapas de seguimiento y predicción, se propone un algoritmo de Multi-Object Tracking basado en técnicas clásicas de estimación de movimiento y asociación validado en el dataset KITTI, el cual obtiene métricas del estado del arte. Por otra parte, se propone el uso de un filtro inteligente basado en información contextual de mapa, cuyo objetivo es monitorizar los agentes más relevantes de la escena en el tiempo, representando estos agentes filtrados la entrada preliminar para realizar predicciones unimodales basadas en un modelo cinemático. Para validar esta propuesta de filtro inteligente se usa CARLA (CAR Learning to Act), uno de los simuladores hiperrealistas para conducción autónoma más prometedores en la actualidad, comprobando cómo al usar información contextual de mapa se puede reducir notablemente el tiempo de inferencia de un algoritmo de tracking y predicción basados en métodos físicos, prestando atención a los agentes realmente relevantes del escenario de tráfico. Tras observar las limitaciones de un modelo de predicción basado en cinemática para la predicción a largo plazo de un agente, los distintos algoritmos de la tesis se centran en el módulo de predicción, usando los datasets Argoverse 1 y Argoverse 2, donde se asume que los agentes proporcionados en cada escenario de tráfico ya están monitorizados durante un cierto número de observaciones. En primer lugar, se introduce un modelo basado en redes neuronales recurrentes (particularmente redes LSTM, Long-Short Term Memory) y mecanismo de atención para codificar las trayectorias pasadas de los agentes, y una representación simplificada del mapa en forma de posiciones finales potenciales en la carretera para calcular las trayectorias futuras unimodales, todo envuelto en un marco GAN (Generative Adversarial Network), obteniendo métricas similares al estado del arte en el caso unimodal. Una vez validado el modelo anterior en Argoverse 1, se proponen distintos modelos base (sólo social, incorporando mapa, y una mejora final basada en Transformer encoder, redes convolucionales 1D y mecanismo de atención cruzada para la fusión de características) precisos y eficientes basados en el modelo de predicción anterior, introduciendo dos nuevos conceptos. Por un lado, el uso de redes neuronales gráficas (particularmente GCN, Graph Convolutional Network) para codificar de una forma potente las interacciones de los agentes. Por otro lado, se propone el preprocesamiento de trayectorias preliminares a partir de un mapa con un método heurístico. Gracias a estas entradas y una arquitectura más potente de codificación, los modelos base serán capaces de predecir distintas trayectorias futuras multimodales, es decir, cubriendo distintos posibles futuros para el agente de interés. Los modelos base propuestos obtienen métricas de regresión del estado del arte tanto en el caso multimodal como unimodal manteniendo un claro compromiso de eficiencia con respecto a otras propuestas. El modelo final de la tesis, inspirado en los modelos anteriores y validado en el más reciente dataset para algoritmos de predicción en conducción autónoma (Argoverse 2), introduce varias mejoras para entender mejor el escenario de tráfico y decodificar la información de una forma precisa y eficiente. Se propone incorporar información topológica y semántica de los carriles futuros preliminares con el método heurístico antes mencionado, codificación de mapa basada en aprendizaje profundo con redes GCN, ciclo de fusión de características físicas y sociales, estimación de posiciones finales en la carretera y agregación de su entorno circundante con aprendizaje profundo y finalmente módulo de refinado para mejorar la calidad de las predicciones multimodales finales de un modo elegante y eficiente. Comparado con el estado del arte, nuestro método logra métricas de predicción a la par con los métodos mejor posicionados en el Leaderboard de Argoverse 2, reduciendo de forma notable el número de parámetros y operaciones de coma flotante por segundo. Por último, el modelo final de la tesis ha sido validado en simulación en distintas aplicaciones de conducción autónoma. En primer lugar, se integra el modelo para proporcionar predicciones a un algoritmo de toma de decisiones basado en aprendizaje por refuerzo en el simulador SMARTS (Scalable Multi-Agent Reinforcement Learning Training School), observando en los estudios como el vehículo es capaz de tomar mejores decisiones si conoce el comportamiento futuro de la escena y no solo el estado actual o pasado de esta misma. En segundo lugar, se ha realizado un estudio de adaptación de dominio exitoso en el simulador hiperrealista CARLA en distintos escenarios desafiantes donde el entendimiento de la escena y predicción del entorno son muy necesarios, como una autopista o rotonda con gran densidad de tráfico o la aparición de un usuario vulnerable de la carretera de forma repentina. En ese sentido, el modelo de predicción ha sido integrado junto con el resto de capas de la arquitectura de navegación autónoma del grupo de investigación donde se desarrolla la tesis como paso previo a su implementación en un vehículo autónomo real
Risk Analysis for Smart Cities Urban Planners: Safety and Security in Public Spaces
Christopher Alexander in his famous writings "The Timeless Way of Building" and "A pattern language" defined a formal language for the description of a city. Alexander developed a generative grammar able to formally describe complex and articulated concepts of architecture and urban planning to define a common language that would facilitate both the participation of ordinary citizens and the collaboration between professionals in architectural and urban planning.
In this research, a similar approach has been applied to let two domains communicate although they are very far in terms of lexicon, methodologies and objectives.
These domains are urban planning, urban design and architecture, seen as the first domain both in terms of time and in terms of completeness of vision, and the one relating to the world of engineering, made by innumerable disciplines. In practice, there is a domain that defines the requirements and the overall vision (the first) and a domain (the second) which implements them with real infrastructures and systems.
To put these two worlds seamlessly into communication, allowing the concepts of the first world to be translated into those of the second, Christopher Alexander’s idea has been followed by defining a common language.
By applying Essence, the software engineering formal descriptive theory, using its customization rules, to the concept of a Smart City, a common language to completely trace the requirements at all levels has been defined.
Since the focus was on risk analysis for safety and security in public spaces, existing risk models have been considered, evidencing a further gap also within the engineering world itself. Depending on the area being considered, risk management models have different and siloed approaches which ignore the interactions of one type of risk with the others.
To allow effective communication between the two domains and within the engineering domain, a unified risk analysis framework has been developed.
Then a framework (an ontology) capable of describing all the elements of a Smart City has been developed and combined with the common language to trace the requirements. Following the philosophy of the Vienna Circle, a creative process called Aufbau has then been defined to allow the generation of a detailed description of the Smart City, at any level, using the common language and the ontology above defined.
Then, the risk analysis methodology has been applied to the city model produced by Aufbau.
The research developed tools to apply such results to the entire life cycle of the Smart City. With these tools, it is possible to understand how much a given architectural, urban planning or urban design requirement is operational at a given moment. In this way, the narration can accurately describe how much the initial requirements set by architects, planners and urban designers and, above all, the values required by stakeholders, are satisfied, at any time.
The impact of this research on urban planning is the ability to create a single model between the two worlds, leaving everyone free to express creativity and expertise in the appropriate forms but, at the same time, allowing both to fill the communication gap existing today.
This new way of planning requires adequate IT tools and takes the form, from the engineering side, of harmonization of techniques already in use and greater clarity of objectives. On the side of architecture, urban planning and urban design, it is instead a powerful decision support tool, both in the planning and operational phases.
This decision support tool for Urban Planning, based on the research results, is the starting point for the development of a meta-heuristic process using an evolutionary approach. Consequently, risk management, from Architecture/Urban Planning/Urban Design up to Engineering, in any phase of the Smart City’s life cycle, is seen as an “organism” that evolves.Christopher Alexander nei suoi famosi scritti "The Timeless Way of Building" e "A pattern language" ha definito un linguaggio formale per la descrizione di una città, sviluppando una grammatica in grado di descrivere formalmente concetti complessi e articolati di architettura e urbanistica, definendo un linguaggio comune per facilitare la partecipazione dei comuni cittadini e la collaborazione tra professionisti.
In questa ricerca, un approccio simile è stato applicato per far dialogare due domini sebbene siano molto distanti in termini di lessico, metodologie e obiettivi.
Essi sono l'urbanistica, l'urban design e l'architettura, visti come primo dominio sia in termini di tempo che di completezza di visione, e quello del mondo dell'ingegneria, con numerose discipline. In pratica, esiste un dominio che definisce i requisiti e la visione d'insieme (il primo) e un dominio (il secondo) che li implementa con infrastrutture e sistemi reali.
Per metterli in perfetta comunicazione, permettendo di tradurre i concetti del primo in quelli del secondo, si è seguita l'idea di Alexander definendo un linguaggio.
Applicando Essence, la teoria descrittiva formale dell'ingegneria del software al concetto di Smart City, è stato definito un linguaggio comune per tracciarne i requisiti a tutti i livelli.
Essendo il focus l'analisi dei rischi per la sicurezza negli spazi pubblici, sono stati considerati i modelli di rischio esistenti, evidenziando un'ulteriore lacuna anche all'interno del mondo dell'ingegneria stessa. A seconda dell'area considerata, i modelli di gestione del rischio hanno approcci diversi e isolati che ignorano le interazioni di un tipo di rischio con gli altri.
Per consentire una comunicazione efficace tra i due domini e all'interno del dominio dell'ingegneria, è stato sviluppato un quadro di analisi del rischio unificato.
Quindi è stato sviluppato un framework (un'ontologia) in grado di descrivere tutti gli elementi di una Smart City e combinato con il linguaggio comune per tracciarne i requisiti. Seguendo la filosofia del Circolo di Vienna, è stato poi definito un processo creativo chiamato Aufbau per consentire la generazione di una descrizione dettagliata della Smart City, a qualsiasi livello, utilizzando il linguaggio comune e l'ontologia sopra definita.
Infine, la metodologia dell'analisi del rischio è stata applicata al modello di città prodotto da Aufbau.
La ricerca ha sviluppato strumenti per applicare tali risultati all'intero ciclo di vita della Smart City. Con questi strumenti è possibile capire quanto una data esigenza architettonica, urbanistica o urbanistica sia operativa in un dato momento. In questo modo, la narrazione può descrivere con precisione quanto i requisiti iniziali posti da architetti, pianificatori e urbanisti e, soprattutto, i valori richiesti dagli stakeholder, siano soddisfatti, in ogni momento.
L'impatto di questa ricerca sull'urbanistica è la capacità di creare un modello unico tra i due mondi, lasciando ognuno libero di esprimere creatività e competenza nelle forme appropriate ma, allo stesso tempo, permettendo ad entrambi di colmare il gap comunicativo oggi esistente.
Questo nuovo modo di progettare richiede strumenti informatici adeguati e si concretizza, dal lato ingegneristico, in un'armonizzazione delle tecniche già in uso e in una maggiore chiarezza degli obiettivi. Sul versante dell'architettura, dell'urbanistica e del disegno urbano, è invece un potente strumento di supporto alle decisioni, sia in fase progettuale che operativa.
Questo strumento di supporto alle decisioni per la pianificazione urbana, basato sui risultati della ricerca, è il punto di partenza per lo sviluppo di un processo meta-euristico utilizzando un approccio evolutivo
Measurement of the Triple-Differential Cross-Section for the Production of Multijet Events using 139 fb^{-1} of Proton-Proton Collision Data at \sqrt{s} = 13 TeV with the ATLAS Detector to Disentangle Quarks and Gluons at the Large Hadron Collider
At hadron-hadron colliders, it is almost impossible to obtain pure samples in either quark-
or gluon-initialized hadronic showers as one always deals with a mixture of particle jets.
The analysis presented in this dissertation aims to break the aforementioned degeneracy by
extracting the underlying fractions of (light) quarks and gluons through a measurement of the
relative production rates of multijet events.
A measurement of the triple-differential multijet cross section at a centre-of-mass energy of
13 TeV using an integrated luminosity of 139 fb −1 of data collected with the ATLAS detector
in proton-proton collisions at the Large Hadron Collider (LHC) is presented. The cross section
is measured as a function of the transverse momentum p T , two categories of pseudorapidity
η rel defined by the relative orientation between the jets, as well as a Jet Sub-Structure (JSS)
observable O JSS , sensitive to the quark- or gluon-like nature of the hadronic shower of the two
leading-p T jets with 250 GeV < p T < 4.5 TeV and |η| < 2.1 in the event.
The JSS variables, which have been studied within the context of this thesis, can broadly be
divided into two categories: one set of JSS observables is constructed by iteratively declustering
and counting the jet’s charged constituents; the second set is based on the output predicted by
Deep Neural Networks (DNNs) derived from the “deep sets” paradigm to implement permutation
invariant functions over sets, which are trained to discriminate between quark- and gluon-
initialized showers in a supervised fashion.
All JSS observables are measured based on Inner Detector tracks with p T > 500 MeV
and |η| < 2.5 to maintain strong correlations between detector- and particle-level objects.
The reconstructed spectra are fully corrected for acceptance and detector effects, and the
unfolded cross section is compared to various state-of-the-art parton shower Monte Carlo
models. Several sources of systematic and statistical uncertainties are taken into account that
are fully propagated through the entire unfolding procedure onto the final cross section. The
total uncertainty on the cross section varies between 5 % and 20 % depending on the region of
phase space.
The unfolded multi-differential cross sections are used to extract the underlying fractions
and probability distributions of quark- and gluon-initialized jets in a solely data-driven, model-
independent manner using a statistical demixing procedure (“jet topics”), which has originally
been developed as a tool for extracting emergent themes in an extensive corpus of text-based
documents. The obtained fractions are model-independent and are based on an operational
definition of quark and gluon jets that does not seek to assign a binary label on a jet-to-jet basis,
but rather identifies quark- and gluon-related features on the level of individual distributions,
avoiding common theoretical and conceptional pitfalls regarding the definition of quark and
gluon jets.
The total fraction of gluon-initialized jets in the multijet sample is (IRC-safely) measured
to be 60.5 ± 0.4(Stat) ⊕ 2.4(Syst) % and 52.3 ± 0.4(Stat) ⊕ 2.6(Syst) % in central and forward
region, respectively. Furthermore, the gluon fractions are extracted in several exclusive regions
of transverse momentum
Geometry and Topology in Memory and Navigation
Okinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyGeometry and topology offer rich mathematical worlds and perspectives with which to study and improve our understanding of cognitive function. Here I present the following examples: (1) a functional role for inhibitory diversity in associative memories with graph- ical relationships; (2) improved memory capacity in an associative memory model with setwise connectivity, with implications for glial and dendritic function; (3) safe and effi- cient group navigation among conspecifics using purely local geometric information; and (4) enhancing geometric and topological methods to probe the relations between neural activity and behaviour. In each work, tools and insights from geometry and topology are used in essential ways to gain improved insights or performance. This thesis contributes to our knowledge of the potential computational affordances of biological mechanisms (such as inhibition and setwise connectivity), while also demonstrating new geometric and topological methods and perspectives with which to deepen our understanding of cognitive tasks and their neural representations.doctoral thesi
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