66,520 research outputs found

    Inference Over Bayesian Networks for the Diagnosis and Sensory Enhancement of Mobile Robots

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    The thesis contributes with a novel modeling paradigm, a so-called Bayesian sensory architecture, that enables the representation of any robotic sensory system, allowing the identification of anomalies and the recovery from them. The main drawback of this proposal is the potentially high computational cost of inference with Bayesian networks, which is addressed with a novel, approximate algorithm that leverages the structure of the proposed model. Both the sensory architecture and the corresponding inference algorithm are implemented for different robotic tasks, and are validated through different sets of both simulated and real experiments. One of the implementations is aimed at analyzing the performance of the proposed algorithm in terms of error and computation time. The results obtained from the experiments show that the cost of inference is significantly reduced, and that the approximate queries produced still serve to perform sensory diagnosis and recovery adequately. Another implementation is proposed for the problem of robotic navigation in human environments. In this case, the experimental results prove that the use of the architecture manages to increase the safety and efficiency of navigation. Lastly, a new inference approach based on the use of feedforward neural networks is implemented and tested for this problem, showing that it is possible to reduce, even more, the cost of inference with Bayesian networks, enabling real time operation.Mobile robots are nowadays present in countless real-world applications, aiding or substituting human beings in a wide variety of tasks related to scopes as diverse as industrial, military, medical, educational and many others. The use of mobile platforms in all these contexts is revolutionizing their respective fields, overcoming previous limitations and offering new possibilities. However, for a mobile robot to work properly, it is essential that its sensory apparatus provides correct and reliable information, which is often challenging due to the complexity of the physical world and its uncertain nature. To address that, this thesis explores the possibilities of the application of Bayesian networks (BNs) to the problem of sensory diagnosis and enhancement in the context of mobile robotics. Arised from the realm of artificial intelligence, Bayesian networks constitute a rigorous mathematical framework that enables both the integration of heterogeneous sources of information and the reasoning about them while taking their uncertainty into account. The thesis first analyzes different sensory anomalies in mobile robots and the impact of such abnormal behavior on the performance of these platforms. Given the wide variety of existing sensory devices, the analysis is focused on range sensors, since they are essential to many robotic tasks also grounded on probabilistic frameworks such as Bayesian estimators. Specifically, the thesis contributes with a rigorous statistical study of the influence of abnormal range observations on the performance of Bayesian filters, addressing the problem from a generic perspective thanks to the use of BNs. The conclusions obtained serve to illustrate the importance of sensory abnormalities beyond the pervasively studied issue of noisy observations. The treatment of sensory anomalies in mobile robots with Bayesian networks is then addressed

    Développement d’un système intelligent de reconnaissance automatisée pour la caractérisation des états de surface de la chaussée en temps réel par une approche multicapteurs

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    Le rôle d’un service dédié à l’analyse de la météo routière est d’émettre des prévisions et des avertissements aux usagers quant à l’état de la chaussée, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en période hivernale. Il est donc important de définir l’état de chaussée en tout temps. L’objectif de ce projet est donc de développer un système de détection multicapteurs automatisée pour la caractérisation en temps réel des états de surface de la chaussée (neige, glace, humide, sec). Ce mémoire se focalise donc sur le développement d’une méthode de fusion de données images et sons par apprentissage profond basée sur la théorie de Dempster-Shafer. Les mesures directes pour l’acquisition des données qui ont servi à l’entrainement du modèle de fusion ont été effectuées à l’aide de deux capteurs à faible coût disponibles dans le commerce. Le premier capteur est une caméra pour enregistrer des vidéos de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussée qui caractérise chaque état de surface. La finalité de ce système est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps réel afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon précise, le système se présente comme suit :1) une architecture d’apprentissage profond classifiant chaque état de surface à partir des images issues de la vidéo sous forme de probabilités ; 2) une architecture d’apprentissage profond classifiant chaque état de surface à partir du son sous forme de probabilités ; 3) les probabilités issues de chaque architecture ont été ensuite introduites dans le modèle de fusion pour obtenir la décision finale. Afin que le système soit léger et moins coûteux, il a été développé à partir d’architectures alliant légèreté et précision à savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le système a démontré une bonne performance pour la détection des états surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante

    Intra-annual taxonomic and phenological drivers of spectral variance in grasslands

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    According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We tested the seasonal influence of the taxonomic and phenological metrics on spectral variance using linear mixed models. A significant interaction term of percent mature leaves and species diversity was found, with the most parsimonious model explaining 43% of the intra-annual change. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance -species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits

    Multi-taxa spatial conservation planning reveals similar priorities between taxa and improved protected area representation with climate change

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    Protected area (PA) networks have in the past been constructed to include all major habitats, but have often been developed through consideration of only a few indicator taxa or across restricted areas, and rarely account for global climate change. Systematic conservation planning (SCP) aims to improve the efficiency of biodiversity conservation, particularly when addressing internationally agreed protection targets. We apply SCP in Great Britain (GB) using the widest taxonomic coverage to date (4,447 species), compare spatial prioritisation results across 18 taxa and use projected future (2080) distributions to assess the potential impact of climate change on PA network effectiveness. Priority conservation areas were similar among multiple taxa, despite considerable differences in spatial species richness patterns; thus systematic prioritisations based on indicator taxa for which data are widely available are still useful for conservation planning. We found that increasing the number of protected hectads by 2% (to reach the 2020 17% Aichi target) could have a disproportionate positive effect on species protected, with an increase of up to 17% for some taxa. The PA network in GB currently under-represents priority species but, if the potential future distributions under climate change are realised, the proportion of species distributions protected by the current PA network may increase, because many PAs are in northern and higher altitude areas. Optimal locations for new PAs are particularly concentrated in southern and upland areas of GB. This application of SCP shows how a small addition to an existing PA network could have disproportionate benefits for species conservation

    Augmented classification for electrical coil winding defects

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    A green revolution has accelerated over the recent decades with a look to replace existing transportation power solutions through the adoption of greener electrical alternatives. In parallel the digitisation of manufacturing has enabled progress in the tracking and traceability of processes and improvements in fault detection and classification. This paper explores electrical machine manufacture and the challenges faced in identifying failures modes during this life cycle through the demonstration of state-of-the-art machine vision methods for the classification of electrical coil winding defects. We demonstrate how recent generative adversarial networks can be used to augment training of these models to further improve their accuracy for this challenging task. Our approach utilises pre-processing and dimensionality reduction to boost performance of the model from a standard convolutional neural network (CNN) leading to a significant increase in accuracy

    A Hidden Markov Random Field based Bayesian Approach for the Detection of Chromatin Interactions in Hi-C Data

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    Background: This thesis focuses on the statistical analysis of chromatin interactions using Hi-C data produced by Next Generation Sequencing (NGS). Hi-C is a chromosome conformation capture technique (3C-based method) that aims to analyze the spatial organization of chromatin in a cell. Specifically, the method identifies interacting pairs of fragments in the genome. The dataset produced by the Hi-C experiment is in the form of pairs of fragments in a symmetric matrix, which are called contact counts or contact frequency or frequency counts. The counts are genome-wide for every genomic position. There are sources of bias related to the Hi-C data [160], and in this research, we focus on four of them (Distance, GC-content, Transposable elements, and Accessibility). In recent years, attempts have been made at developing computational techniques capable of modelling these sources of bias or detecting significant interactions specifically in Hi-C data. Methods: The present research modelled these sources of bias as covariates within a regression model. Modelling these biases as a regression model allows for a better understanding of their effect on chromatin interactions within the genome. Furthermore, we propose the Potts model [157] which allows us to introduce spatial dependency by borrowing information from neighbouring loci pairs. Also, the introduction of the Potts model allows us to increase the number of components in which we can classify the contact counts from the previously assume two components by existing studies. Finally, we use the deviance information criterion (DIC) to select a preferred distribution for genome-wide analysis. Results: Firstly, we modelled the sources of bias, genomic distances, GC-content, Transposable elements, and Accessibility as a regression model. Our result shows that the genomic distance between interacting loci is the major source of bias and that the effects of the sources of biases depend on the component. Secondly, we assume that the density of the contact frequency first follows a Zero Inflated Poisson (ZIP) and then a Negative Binomial distribution. For the unobserved information, (latent variable), we assume the Potts model. Based on our results, including the calculation of the DIC, the ZIP distribution outperforms the NB distribution. Thirdly, comparative analysis when we assume the number of components to be two and when we assume the number of components to be three using the DIC revealed that increasing the number of components improves the detection of significant information in the Hi-C data. Fourthly, the genome-wide analysis of Drosophila melanogaster reveals that the majority of significant interactions are found within inter-TADs, that is outside TADs of the same anchor, and also the majority of significant interactions are long-range interactions. Conclusion: Our results provide clear evidence that the genome of Drosophila melanogaster can be classified into more than two components, noise and signal interactions, and that in addition to this, the effects of the sources of bias depends on the component

    Ontological Solutions to the Problem of Induction

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    The idea of the uniformity of nature, as a solution to the problem of induction, has at least two contemporary versions: natural kinds and natural necessity. Then there are at least three alternative ontological ideas addressing the problem of induction. In this paper, I articulate how these ideas are used to justify the practice of inductive inference, and compare them, in terms of their applicability, to see whether each of them is preferred in addressing the problem of induction. Given the variety of contexts in which inductive inferences are made, from natural science to social science and to everyday thinking, I suggest that no singular idea is absolutely preferred, and a proper strategy is probably to welcome the plurality of ideas helpful to induction, and to take pragmatic considerations into account, in order to judge in every single case
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