128 research outputs found

    Retrieval of cloud properties from EPIC/DSCOVR

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    This dissertation provides a framework for retrieving cloud macrophysical properties from radiance measurements acquired by the EPIC instrument. It analyses the involved pre-processing steps (registration and degradation correction), speed-up improvements in the radiative transfer model, the determination of the cloud fraction with the OCRA algorithm, and the sensitivity analysis of the ROCINN algorithm to obtain cloud optical thickness and cloud-top height under several sources of uncertainty

    Desarrollo de geotecnologías aplicadas a la inspección y monitorización de entornos industriales

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    Tesis por compendio de publicaciones[ES]El desarrollo tecnológico de las últimas dos décadas ha supuesto un cambio radical que está llevando a un nuevo paradigma en el que se entremezclan el mundo físico y el digital. Estos cambios han influido enormemente en la sociedad, modificando las formas de comunicación, acceso a información, ocio, trabajo, etc. Asimismo, la industria ha adoptado estas tecnologías disruptivas, las cuales están contribuyendo a lograr un mayor control y automatización del proceso productivo. En el ámbito industrial, las tareas de mantenimiento son críticas para garantizar el correcto funcionamiento de una planta o instalación, ya que influyen directamente en la productividad y pueden suponer un elevado costo adicional. Las nuevas tecnologías están posibilitando la monitorización continua y a la inspección automatizada, proporcionando herramientas auxiliares a los inspectores que mejoran la detección de fallos y permiten anticipar y optimizar la planificación de las tareas de mantenimiento. Con el objetivo de desarrollar herramientas que aporten mejoras en las tareas de mantenimiento en industria, la presente tesis doctoral se basa en el estudio de como las geotecnologías pueden aportar soluciones óptimas en la monitorización e inspección. Debido a la gran variedad de entornos industriales, las herramientas de apoyo al mantenimiento deben adaptarse a cada caso en concreto. En este aspecto, y con el fin de demostrar la adaptabilidad de la geomática y las geotecnologías, se han estudiado instalaciones industriales de ámbitos muy diversos, como una sala de máquinas (escenario interior), plantas fotovoltaicas (escenario exterior) y soldaduras (interior y exterior). La escala de los escenarios objeto de estudio ha sido muy variada, desde las escalas más pequeñas, para el estudio de las soldaduras y la sala de máquinas, a las escalas más grandes, en los estudios de evolución de la vegetación y presencia de masas de agua en plantas fotovoltaicas. Las geotecnologías demuestran su versatilidad para trabajar a distintas escalas, con soluciones que permiten un gran detalle y precisión, como la fotogrametría de rango cercano y el sistema de escaneado portátil (Portable Mobile Mapping System - PMMS), y otras que pueden abarcar zonas más amplias del territorio, como es el caso de la teledetección o la fotogrametría con drones. Según lo expuesto anteriormente, el enfoque de la tesis ha sido el estudio de elementos o instalaciones industriales a diferentes escalas. En el primer caso se desarrolló una herramienta para el control de calidad externo de soldaduras utilizando fotogrametría de rango cercano y algoritmos para la detección automática de defectos. En el segundo caso se propuso el uso de un PMMS para optimizar la toma de datos en las tareas de inspección en instalaciones fluidomecánicas. En el tercer caso se utilizó la fotogrametría con drones y la combinación de imágenes RGB y térmicas con algoritmos de visión computacional para la detección de patologías en paneles fotovoltaicos. Finalmente, para la monitorización de la vegetación y la detección de masas de agua en el entorno de plantas fotovoltaicas, se empleó la teledetección mediante el cálculo de índices espectrales. [EN]The technological development of the last two decades has brought about a radical change that is leading to a new paradigm in which the physical and digital worlds are intertwined. These changes have had a great impact on society, modifying communication methods, access to information, leisure, work, etc. In addition, the industry has adopted these disruptive technologies, which are contributing to achieving greater control and automation of the production process. In the industrial sector, maintenance tasks are critical to ensuring the proper operation of a plant or facility, as they directly influence productivity and can involve high additional costs. New technologies are making continuous monitoring and automated inspection possible, providing auxiliary tools to inspectors that improve fault detection and allow for the anticipation and optimization of maintenance task planning. With the aim of developing tools that provide improvements in maintenance tasks in industry, this doctoral thesis is based on the study of how geotechnologies can provide optimal solutions in monitoring and inspection. Due to the great variety of industrial environments, maintenance support tools must adapt to each specific case. In this regard, and in order to demonstrate the adaptability of geomatics and geotechnologies, industrial installations from very diverse areas have been studied, such as a machine room (indoor scenario), photovoltaic plants (outdoor scenario), and welding (indoor and outdoor scenarios). The scale of the studied scenarios has been very varied, ranging from smaller scales for the study of welds and machine rooms, to larger scales in the studies of vegetation evolution and the presence of bodies of water in photovoltaic plants. Geotechnologies demonstrate their versatility to work at different scales, with solutions that allow for great detail and precision, such as close-range photogrammetry and the Portable Mobile Mapping System (PMMS), as well as others that can cover larger areas of the territory, such as remote sensing or photogrammetry with drones. The focus of the thesis has been the study of industrial elements or installations at different scales. In the first case, a tool was developed for external quality control of welding, using close-range photogrammetry and algorithms for automatic defect detection. In the second case, the use of a PMMS is proposed to optimize data collection in fluid-mechanical installation inspection tasks. In the third case, drone photogrammetry and the combination of RGB and thermal images with computer vision algorithms were used for the detection of pathologies in photovoltaic panels. Finally, for the monitoring of vegetation and the detection of water masses in the environment of photovoltaic plants, remote sensing was employed through the calculation of spectral indices

    Research on Calligraphy Evaluation Technology Based on Deep Learning

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    Today, when computer-assisted instruction (CAI) is booming, related research in the field of calligraphy education still hasn’t much progress. This main research for the calligraphy beginners to evaluate their works anytime and anywhere. Author uses the literature research and interview to understand the common writing problems of beginners. Then conducts discussion on these problems, design of solutions, research on algorithms, and experimental verification. Based on the ResNet-50 model, through WeChat applet implements for beginners. The main research contents are as follows: (1) In order to achieve good results in calligraphy judgment, this article uses the ResNet-50 model to judge calligraphy. First, adjust the area of the handwritten calligraphy image as the input of the network to a small block suitable for the network. While training the network, adjust the learning rate, the number of image layers and the number of training samples to achieve the optimal. The research results show that ResNet has certain practicality and reference value in the field of calligraphy judgment. Regarding the possible over-fitting problem, this article proposes to improve the accuracy of the judgment by collecting more data and optimizing the data washing process. (2) Combining the rise of WeChat applets, in view of the current WeChat applet learning platform development process and the problem of fewer functional modules, this paper uses cloud development functions to develop a calligraphy learning platform based on WeChat applets. While simplifying the development process, it ensures that the functional modules of the platform meet the needs of teachers and beginners, it has certain practicality and commercial value. After the development of the calligraphy learning applet is completed, it will be submitted for official

    Rethinking auto-colourisation of natural Images in the context of deep learning

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    Auto-colourisation is the ill-posed problem of creating a plausible full-colour image from a grey-scale prior. The current state of the art utilises image-to-image Generative Adversarial Networks (GANs). The standard method for training colourisation is reformulating RGB images into a luminance prior and two-channel chrominance supervisory signal. However, progress in auto-colourisation is inherently limited by multiple prerequisite dilemmas, where unsolved problems are mutual prerequisites. This thesis advances the field of colourisation on three fronts: architecture, measures, and data. Changes are recommended to common GAN colourisation architectures. Firstly, removing batch normalisation from the discriminator to allow the discriminator to learn the primary statistics of plausible colour images. Secondly, eliminating the direct L1 loss on the generator as L1 will limit the discovery of the plausible colour manifold. The lack of an objective measure of plausible colourisation necessitates resource-intensive human evaluation and repurposed objective measures from other fields. There is no consensus on the best objective measure due to a knowledge gap regarding how well objective measures model the mean human opinion of plausible colourisation. An extensible data set of human-evaluated colourisations, the Human Evaluated Colourisation Dataset (HECD) is presented. The results from this dataset are compared to the commonly-used objective measures and uncover a poor correlation between the objective measures and mean human opinion. The HECD can assess the future appropriateness of proposed objective measures. An interactive tool supplied with the HECD allows for a first exploration of the space of plausible colourisation. Finally, it will be shown that the luminance channel is not representative of the legacy black-and-white images that will be presented to models when deployed; This leads to out-of-distribution errors in all three channels of the final colour image. A novel technique is proposed to simulate priors that match any black-and-white media for which the spectral response is known

    Play Among Books

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    How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books

    Deep Neural Networks and Data for Automated Driving

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    This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above

    Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

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    The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

    Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

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    This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

    Distributed Robotic Vision for Calibration, Localisation, and Mapping

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    This dissertation explores distributed algorithms for calibration, localisation, and mapping in the context of a multi-robot network equipped with cameras and onboard processing, comparing against centralised alternatives where all data is transmitted to a singular external node on which processing occurs. With the rise of large-scale camera networks, and as low-cost on-board processing becomes increasingly feasible in robotics networks, distributed algorithms are becoming important for robustness and scalability. Standard solutions to multi-camera computer vision require the data from all nodes to be processed at a central node which represents a significant single point of failure and incurs infeasible communication costs. Distributed solutions solve these issues by spreading the work over the entire network, operating only on local calculations and direct communication with nearby neighbours. This research considers a framework for a distributed robotic vision platform for calibration, localisation, mapping tasks where three main stages are identified: an initialisation stage where calibration and localisation are performed in a distributed manner, a local tracking stage where visual odometry is performed without inter-robot communication, and a global mapping stage where global alignment and optimisation strategies are applied. In consideration of this framework, this research investigates how algorithms can be developed to produce fundamentally distributed solutions, designed to minimise computational complexity whilst maintaining excellent performance, and designed to operate effectively in the long term. Therefore, three primary objectives are sought aligning with these three stages
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