787 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Towards an integrated vulnerability-based approach for evaluating, managing and mitigating earthquake risk in urban areas
Tese de doutoramento em Civil EngineeringSismos de grande intensidade, como aqueles que ocorreram na Turquía-Síria (2023) ou México (2017)
deviam chamar a atenção para o projeto e implementação de ações proativas que conduzam à identificação
de bens vulneráveis. A presente tese propõe um fluxo de trabalho relativamente simples para
efetuar avaliações da vulnerabilidade sísmica à escala urbana mediante ferramentas digitais. Um modelo
de vulnerabilidade baseado em parâmetros é adotado devido à afinidade que possui com o Catálogo Nacional
de Monumentos Históricos mexicano. Uma primeira implementação do método (a grande escala)
foi efetuada na cidade histórica de Atlixco (Puebla, México), demonstrando a sua aplicabilidade e algumas
limitações, o que permitiu o desenvolvimento de uma estratégia para quantificar e considerar as incertezas
epistémicas encontradas nos processos de aquisição de dados. Devido ao volume de dados tratado, foi
preciso desenvolver meios robustos para obter, armazenar e gerir informações. O uso de Sistemas de
Informação Geográfica, com programas à medida baseados em linguagem Python e a distribuição de
ficheiros na ”nuvem”, facilitou a criação de bases de dados de escala urbana para facilitar a aquisição de
dados em campo, os cálculos de vulnerabilidade e dano e, finalmente, a representação dos resultados.
Este desenvolvimento foi a base para um segundo conjunto de trabalhos em municípios do estado de
Morelos (México). A caracterização da vulnerabilidade sísmica de mais de 160 construções permitiu a
avaliação da representatividade do método paramétrico pela comparação entre os níveis de dano teórico
e os danos observados depois do terramoto de Puebla-Morelos (2017). Esta comparação foi a base para
efetuar processos de calibração e ajuste assistidos por algoritmos de aprendizagem de máquina (Machine
Learning), fornecendo bases para o desenvolvimento de modelos de vulnerabilidade à medida (mediante
o uso de Inteligência Artificial), apoiados nas evidências de eventos sísmicos prévios.Strong seismic events like the ones of Türkiye-Syria (2023) or Mexico (2017) should guide our attention
to the design and implementation of proactive actions aimed to identify vulnerable assets. This work is
aimed to propose a suitable and easy-to-implement workflow for performing large-scale seismic vulnerability
assessments in historic environments by means of digital tools. A vulnerability-oriented model based
on parameters is adopted given its affinity with the Mexican Catalogue of Historical Monuments. A first
large-scale implementation of this method in the historical city of Atlixco (Puebla, Mexico) demonstrated its
suitability and some limitations, which lead to develop a strategy for quantifying and involving the epistemic
uncertainties found during the data acquisition process. Given the volume of data that these analyses involve,
it was necessary to develop robust data acquisition, storing and management strategies. The use
of Geographical Information System environments together with customised Python-based programs and
cloud-based distribution permitted to assemble urban databases for facilitating field data acquisition, performing
vulnerability and damage calculations, and representing outcomes. This development was the
base for performing a second large-scale assessment in selected municipalities of the state of Morelos
(Mexico). The characterisation of the seismic vulnerability of more than 160 buildings permitted to assess
the representativeness of the parametric vulnerability approach by comparing the theoretical damage estimations against the damages observed after the Puebla-Morelos 2017 Earthquakes. Such comparison is
the base for performing a Machine Learning assisted process of calibration and adjustment, representing
a feasible strategy for calibrating these vulnerability models by using Machine-Learning algorithms and the
empirical evidence of damage in post-seismic scenarios.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit
Institute for Sustainability and Innovation in Structural Engineering (ISISE), reference UIDB/04029/2020.
This research had financial support provided by the Portuguese Foundation of Science and Technology
(FCT) through the Analysis and Mitigation of Risks in Infrastructures (InfraRisk) program under the PhD
grant PD/BD/150385/2019
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
Structural optimization in steel structures, algorithms and applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
Examining the Relationships Between Distance Education Students’ Self-Efficacy and Their Achievement
This study aimed to examine the relationships between students’ self-efficacy (SSE) and students’ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievement—physical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievement—more time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
Animate Being: Extending a Practice of the Image to New Mediums via Speculative Game Design
This post-disciplinary practice as research thesis examines the potential of Carl Jung's therapeutic method of active imagination as a strategy for engaging with an increasingly complex and interconnected technological reality. Embracing a non-clinical, practice-driven approach, I harness James Hillman’s notion of the image and the imaginal to investigate the interdisciplinary capacity and ethical dimensions of an expansive mode of image-work. My approach to practice theoretically and practically intertwines analytical psychology, feminist worlding and design speculation. Building upon Susan Rowland’s work, I study image-work as an ecological alchemical craft that seeks to matter the immaterial. Through the cyclic iterative design of a video game, I mobilise and respond to image-work as a mode of myth-making that may facilitate dialogue between human and non-human intelligences. Departing from the essentialism of the hero's journey, I adopt Le Guin's Carrier Bag (1986/2019) as a feminist video game form and by utilising the framework of a video game (Bogost, 2007; Flannigan, 2013), the alchemical processes of image-work are transformed into novel interactive game mechanics. The game I design is both a vessel and a portal to an imaginal ecological realm, an open-world, procedurally generated ‘living world’ sandbox exploration game. This game integrates real-time, real-world data streams to invite the non-human to enter into play as player two, facilitating experimentation with possible new forms of cross-species dialogue, collaboration, and healing
Caractérisation mécanique in vivo des tissus mous : application à la peau humaine et la chéloïde
The development of keloids, benign tumors on human skin, is not exclusively due to biological or genetic factors. The presence of anatomical sites favorable to the appearance of these tumors, while others are lacking them, attests to the importance of the mechanical environment of the tissue. The thesis aims to address the problem of keloid growth by developing a patient-specific pipeline, SofTI, based on in vivo experimental measurements and numerical modeling. The objective is to prevent further propagation of keloidic scars via a medical containment solution by identifying optimal material parameters to quantify mechanical stress and map its privileged direction locally. Additionally, the research work introduces MARSAC methodology to characterize the anisotropy in an undamaged skin by estimating Langer's line and stiffness along and across it with an in vivo multi-axial annular suction experiment. The method was used to analyze intra-subject and subject-to-subject variability over a clinical trial.Le développement des chéloïdes, tumeurs bénignes sur la peau humaine, n'est pas exclusivement dû à des facteurs biologiques ou génétiques. La présence de sites anatomiques favorables à l'apparition de ces tumeurs, tandis que d'autres en manquent, atteste de l'importance de l'environnement mécanique du tissu. La thèse vise à résoudre le problème de la croissance des chéloïdes en développant une méthode patient-spécifique, SofTI, basée sur des mesures expérimentales in vivo et une modélisation numérique. L'objectif est de prévenir la propagation des cicatrices chéloïdiennes à l'aide d'une solution médicale de contention en identifiant les paramètres matériau optimaux pour quantifier les contraintes mécaniques et cartographier ses directions privilégiées localement. De plus, le travail de recherche présente la méthodologie MARSAC pour charactériser l'anisotropie dans la peau saine en identifiant la ligne de Langer et la raideur le long et à travers celle-ci partant d'une expérience d'aspiration annulaire multi-axiale in vivo. La méthode a été employée pour analyser la variabilité intra- et inter-sujets sur un essai clinique
Edoardo Benvenuto Prize. Collection of papers
The promotion of studies and research on the science and art of building in their historical development constitutes the objective that the Edoardo Benvenuto Association has set itself, since its establishment, in order to honor the memory of Edoardo Benvenuto (1940-1998). The Association in recent years has achieved interesting results by developing various activities such as: organization of national and international meetings, conferences, study days; collaborations with national and foreign research institutions; promotion of the editorial series “Between Mechanics and Architecture"; activation of the portal Bibliotheca Mechanica Architectonica, first “open source” digitized library dedicated to historical research on mechanical and architectural texts. But perhaps the most qualifying initiative was the institution of the Edoardo Benvenuto Prize, arrived in 2019 in its twelfth edition, reserved for young researchers in the field of historical studies on science and the art of building. The awarding of the Prize takes place after an in-depth examination of the texts received by the Association by an international commission of experts. The purpose of this book is to collect and present the most recent studies and publications produced by the winners of the various editions of the Edoardo Benvenuto Prize
- …