981 resultados para Artificial potential fields


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This article presents recent WMR (wheeled mobile robot) navigation experiences using local perception knowledge provided by monocular and odometer systems. A local narrow perception horizon is used to plan safety trajectories towards the objective. Therefore, monocular data are proposed as a way to obtain real time local information by building two dimensional occupancy grids through a time integration of the frames. The path planning is accomplished by using attraction potential fields, while the trajectory tracking is performed by using model predictive control techniques. The results are faced to indoor situations by using the lab available platform consisting in a differential driven mobile robot

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This research extends a previously developed work concerning about the use of local model predictive control in mobile robots. Hence, experimental results are presented as a way to improve the methodology by considering aspects as trajectory accuracy and time performance. In this sense, the cost function and the prediction horizon are important aspects to be considered. The platformused is a differential driven robot with a free rotating wheel. The aim of the present work is to test the control method by measuring trajectory tracking accuracy and time performance. Moreover, strategies for the integration with perception system and path planning are also introduced. In this sense, monocular image data provide an occupancy grid where safety trajectories are computed by using goal attraction potential fields

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The second scientific meeting of the European systems genetics network for the study of complex genetic human disease using genetic reference populations (SYSGENET) took place at the Center for Cooperative Research in Biosciences in Bilbao, Spain, December 10-12, 2012. SYSGENET is funded by the European Cooperation in the Field of Scientific and Technological Research (COST) and represents a network of scientists in Europe that use mouse genetic reference populations (GRPs) to identify complex genetic factors influencing disease phenotypes (Schughart, Mamm Genome 21:331-336, 2010). About 50 researchers working in the field of systems genetics attended the meeting, which consisted of 27 oral presentations, a poster session, and a management committee meeting. Participants exchanged results, set up future collaborations, and shared phenotyping and data analysis methodologies. This meeting was particularly instrumental for conveying the current status of the US, Israeli, and Australian Collaborative Cross (CC) mouse GRP. The CC is an open source project initiated nearly a decade ago by members of the Complex Trait Consortium to aid the mapping of multigenetic traits (Threadgill, Mamm Genome 13:175-178, 2002). In addition, representatives of the International Mouse Phenotyping Consortium were invited to exchange ongoing activities between the knockout and complex genetics communities and to discuss and explore potential fields for future interactions.

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This article presents recent WMR (wheeled mobile robot) navigation experiences using local perception knowledge provided by monocular and odometer systems. A local narrow perception horizon is used to plan safety trajectories towards the objective. Therefore, monocular data are proposed as a way to obtain real time local information by building two dimensional occupancy grids through a time integration of the frames. The path planning is accomplished by using attraction potential fields, while the trajectory tracking is performed by using model predictive control techniques. The results are faced to indoor situations by using the lab available platform consisting in a differential driven mobile robot

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Vector field formulation based on the Poisson theorem allows an automatic determination of rock physical properties (magnetization to density ratio-MDR-and the magnetization inclination-MI) from combined processing of gravity and magnetic geophysical data. The basic assumptions (i.e., Poisson conditions) are: that gravity and magnetic fields share common sources, and that these sources have a uniform magnetization direction and MDR. In addition, the previously existing formulation was restricted to profile data, and assumed sufficiently elongated (2-D) sources. For sources that violate Poisson conditions or have a 3-D geometry, the apparent values of MDR and MI that are generated in this way have an unclear relationship to the actual properties in the subsurface. We present Fortran programs that estimate MDR and MI values for 3-D sources through processing of gridded gravity and magnetic data. Tests with simple geophysical models indicate that magnetization polarity can be successfully recovered by MDR-MI processing, even in cases where juxtaposed bodies cannot be clearly distinguished on the basis of anomaly data. These results may be useful in crustal studies, especially in mapping magnetization polarity from marine-based gravity and magnetic data. (c) 2007 Elsevier Ltd. All rights reserved.

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Neste tutorial apresentamos uma revisão da deconvolução de Euler que consiste de três partes. Na primeira parte, recordamos o papel da clássica formulação da deconvolução de Euler 2D e 3D como um método para localizar automaticamente fontes de campos potenciais anômalas e apontamos as dificuldades desta formulação: a presença de uma indesejável nuvem de soluções, o critério empírico usado para determinar o índice estrutural (um parâmetro relacionado com a natureza da fonte anômala), a exeqüibilidade da aplicação da deconvolução de Euler a levantamentos magnéticos terrestres, e a determinação do mergulho e do contraste de susceptibilidade magnética de contatos geológicos (ou o produto do contraste de susceptibilidade e a espessura quando aplicado a dique fino). Na segunda parte, apresentamos as recentes melhorias objetivando minimizar algumas dificuldades apresentadas na primeira parte deste tutorial. Entre estas melhorias incluem-se: i) a seleção das soluções essencialmente associadas com observações apresentando alta razão sinal-ruído; ii) o uso da correlação entre a estimativa do nível de base da anomalia e a própria anomalia observada ou a combinação da deconvolução de Euler com o sinal analítico para determinação do índice estrutural; iii) a combinação dos resultados de (i) e (ii), permitindo estimar o índice estrutural independentemente do número de soluções; desta forma, um menor número de observações (tal como em levantamentos terrestres) pode ser usado; iv) a introdução de equações adicionais independentes da equação de Euler que permitem estimar o mergulho e o contraste de susceptibilidade das fontes magnéticas 2D. Na terceira parte apresentaremos um prognóstico sobre futuros desenvolvimentos a curto e médio prazo envolvendo a deconvolução de Euler. As principais perspectivas são: i) novos ataques aos problemas selecionados na segunda parte deste tutorial; ii) desenvolvimento de métodos que permitam considerar interferências de fontes localizadas ao lado ou acima da fonte principal, e iii) uso das estimativas de localização da fonte anômala produzidas pela deconvolução de Euler como vínculos em métodos de inversão para obter a delineação das fontes em um ambiente computacional amigável.

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El objetivo de esta tesis es el desarrollo de un sistema completo de navegación, aprendizaje y planificación para un robot móvil. Dentro de los innumerables problemas que este gran objetivo plantea, hemos dedicado especial atención al problema del conocimiento autónomo del mundo. Nuestra mayor preocupación ha sido la de establecer mecanismos que permitan, a partir de información sensorial cruda, el desarrollo incremental de un modelo topológico del entorno en el que se mueve el robot. Estos mecanismos se apoyan invariablemente en un nuevo concepto propuesto en esta tesis: el gradiente sensorial. El gradiente sensorial es un dispositivo matemático que funciona como un detector de sucesos interesantes para el sistema. Una vez detectado uno de estos sucesos, el robot puede identificar su situación en un mapa topológico y actuar en consecuencia. Hemos denominado a estas situaciones especiales lugares sensorialmente relevantes, ya que (a) captan la atención del sistema y (b) pueden ser identificadas utilizando la información sensorial. Para explotar convenientemente los modelos construidos, hemos desarrollado un algoritmo capaz de elaborar planes internalizados, estableciendo una red de sugerencias en los lugares sensorialmente relevantes, de modo que el robot encuentra en estos puntos una dirección recomendada de navegación. Finalmente, hemos implementado un sistema de navegación robusto con habilidades para interpretar y adecuar los planes internalizados a las circunstancias concretas del momento. Nuestro sistema de navegación está basado en la teoría de campos de potencial artificial, a la que hemos incorporado la posibilidad de añadir cargas ficticias como ayuda a la evitación de mínimos locales. Como aportación adicional de esta tesis al campo genérico de la ciencia cognitiva, todos estos elementos se integran en una arquitectura centrada en la memoria, lo que pretende resaltar la importancia de ésta en los procesos cognitivos de los seres vivos y aporta un giro conceptual al punto de vista tradicional, centrado en los procesos. The general objective of this thesis is the development of a global navigation system endowed with planning and learning features for a mobile robot. Within this general objective we have devoted a special effort to the autonomous learning problem. Our main concern has been to establish the necessary mechanisms for the incremental development of a topological model of the robot’s environment using the sensory information. These mechanisms are based on a new concept proposed in the thesis: the sensory gradient. The sensory gradient is a mathematical device which works like a detector of “interesting” environment’s events. Once a particular event has been detected the robot can identify its situation in the topological map and to react accordingly. We have called these special situations relevant sensory places because (a) they capture the system’s attention and (b) they can be identified using the sensory information. To conveniently exploit the built-in models we have developed an algorithm able to make internalized plans, establishing a suggestion network in the sensory relevant places in such way that the robot can find at those places a recommended navigation direction. It has been also developed a robust navigation system able to navigate by means of interpreting and adapting the internalized plans to the concrete circumstances at each instant, i.e. a reactive navigation system. This reactive system is based on the artificial potential field approach with the additional feature introduced in the thesis of what we call fictitious charges as an aid to avoid local minima. As a general contribution of the thesis to the cognitive science field all the above described elements are integrated in a memory-based architecture, emphasizing the important role played by the memory in the cognitive processes of living beings and giving a conceptual turn in the usual process-based approach.

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In this work, we used direct measurements with the surface force apparatus to determine the pH-dependent electrostatic charge density of a single binding face of streptavidin. Mean field calculations have been used with considerable success to model electrostatic potential fields near protein surfaces, but these models and their inherent assumptions have not been tested directly at the molecular level. Using the force apparatus and immobilized, oriented monolayers of streptavidin, we measured a pI of 5–5.5 for the biotin-binding face of the protein. This differs from the pI of 6.3 for the soluble protein and confirms that we probed the local electrostatic features of the macromolecule. With finite difference solutions of the linearized Poisson–Boltzmann equation, we then calculated the pH-dependent charge densities adjacent to the same face of the protein. These calculated values agreed quantitatively with those obtained by direct force measurements. Although our study focuses on the pH-dependence of surface electrostatics, this direct approach to probing the electrostatic features of proteins is applicable to investigations of any perturbations that alter the charge distribution of the surfaces of immobilized molecules.

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En este trabajo se reflexiona sobre la necesidad de un marco epistemológico inclusivo que aborde la multivocalidad de los procesos históricos objeto de estudio y permita crear relatos históricos más plurales y representativos. Pero el relato sobre el pasado será mejor si, además de ser inclusivo a nivel epistemológico, también contribuye de alguna manera a mejorar la sociedad actual. Por ello se reclama una arqueología que incluya una preocupación axiológica y busque posibles ámbitos de aplicación para los resultados de sus investigaciones. Ejemplificamos esta reivindicación con un caso de estudio sobre cerámica, que subraya que los objetos cotidianos fueron y son utilizados en las estrategias de construcción social de la desigualdad. En este contexto se reclama la toma de consciencia de esta práctica en la actualidad y la renuncia a determinados recursos discursivos. Por ejemplo, se propone retomar el concepto inclusivo, este vez para oponerlo a la significación social del adjetivo exclusivo. Aunque todas estas reflexiones derivan de casos de estudio de arqueología histórica, pueden ser útiles a la arqueología en general, sin sesgo cronológico alguno.

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Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

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CULTURE is an Artificial Life simulation that aims to provide primary school children with opportunities to become actively engaged in the high-order thinking processes of problem solving and critical thinking. A preliminary evaluation of CULTURE has found that it offers the freedom for children to take part in process-oriented learning experiences. Through providing children with opportunities to make inferences, validate results, explain discoveries and analyse situations, CULTURE encourages the development of high-order thinking skills. The evaluation found that CULTURE allows users to autonomously explore the important scientific concepts of life and living, and energy and change within a software environment that children find enjoyable and easy to use.

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A new parameter is introduced: the lightning potential index (LPI), which is a measure of the potential for charge generation and separation that leads to lightning flashes in convective thunderstorms. The LPI is calculated within the charge separation region of clouds between 0 C and 20 C, where the noninductive mechanism involving collisions of ice and graupel particles in the presence of supercooled water is most effective. As shown in several case studies using the Weather Research and Forecasting (WRF) model with explicit microphysics, the LPI is highly correlated with observed lightning. It is suggested that the LPI may be a useful parameter for predicting lightning as well as a tool for improving weather forecasting of convective storms and heavy rainfall.

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Analytical potential energy functions which are valid at all dissociation limits have been derived for the ground states of SO2 and O3. The procedure involves minimizing the errors between the observed vibrational spectra and spectra calculated by a variational procedure. Good agreement is obtained between the observed and calculated spectra for both molecules. Comparisons are made between anharmonic force fields, previously determined from the spectral data, and the force fields obtained by differentiating the derived analytical functions at the equilibrium configurations.

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This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

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L’obiettivo del progetto è stato quello di realizzare ed analizzare aggregati artificiali creati attraverso geopolimerizzazione e macro-incapsulazione di paraffina in aggregati leggeri espansi, discutendo i loro possibili impieghi nelle pavimentazioni stradali. Dopo un'accurata calibrazione delle miscele geopolimeriche, sono stati realizzati degli aggregati artificiali, in seguito caratterizzati in accordo con la norma UNI EN 10343, con l'intento di sostituire materiali stradali vergini. Contemporaneamente, sono stati prodotti aggregati leggeri impregnati di paraffina (PCM), in grado di cambiare fase una volta raggiunti all'incirca i 3 °C, e successivamente rivestiti da due strati di resina poliestere e polvere di granito, denominati PLA: sfruttandone le proprietà termiche, si è valutato il loro possibile utilizzo come soluzione anti-icing. L’ultima fase della ricerca è stata incentrata nella realizzazione di aggregati geopolimerici espansi e molto porosi che potessero contenere una elevata quantità di PCM, sostituendo l'argilla espansa utilizzata nella produzione degli PLA.