554 research outputs found

    2017 GREAT Day Program

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    SUNY Geneseoā€™s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    Understanding building and urban environment interactions: An integrated framework for building occupancy modelling

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    Improving building energy efficiency requires accurate modelling and a comprehensive understanding of how occupants use building space. This thesis focuses on modelling building occupancy to enhance the predictive accuracy of occupancy patterns and gain a better understanding of the causal reasons for occupancy behaviour. A conceptual framework is proposed to relax the restriction of isolated building analysis, which accounts for interactions between buildings, its occupants, and other urban systems, such as the effects of transport incidents on occupancy and circulation in buildings. This thesis also presents a counterpart mapping of the framework that elaborates the links between modelling of transport and building systems. To operationalise the proposed framework, a novel modelling approach which has not been used in the current context, called the hazard-based model, is applied to model occupancy from a single building up to a district area. The proposed framework is further adapted to integrate more readily with transport models, to ensure that arrivals and departures to and from the building are consistent with the situation of the surrounding transport systems. The proposed framework and occupancy models are calibrated and validated using Wi-Fi data and other variables, such as transport and weather parameters, harvested from the South Kensington campus of Imperial College London. In addition to calibrating the occupancy model, integrating a travel simulator produces synthetic arrivals into or around the campus, which are further distributed over campus buildings via an adapted technique and feed the occupancy simulations. The model estimation results reveal the causal reasons for or exogenous effects on individual occupancy states. The validation results confirm the ability of the proposed models to predict building occupancy accurately both on average and day by day across the future dataset. Finally, evaluating occupancy simulations for various hypothetical scenarios provides valuable suggestions for efficient building design and facility operation.Open Acces

    Safe navigation and human-robot interaction in assistant robotic applications

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    L'abstract ĆØ presente nell'allegato / the abstract is in the attachmen

    Methods, Models, and Datasets for Visual Servoing and Vehicle Localisation

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    Machine autonomy has become a vibrant part of industrial and commercial aspirations. A growing demand exists for dexterous and intelligent machines that can work in unstructured environments without any human assistance. An autonomously operating machine should sense its surroundings, classify diļ¬€erent kinds of observed objects, and interpret sensory information to perform necessary operations. This thesis summarizes original methods aimed at enhancing machineā€™s autonomous operation capability. These methods and the corresponding results are grouped into two main categories. The ļ¬rst category consists of research works that focus on improving visual servoing systems for robotic manipulators to accurately position workpieces. We start our investigation with the hand-eye calibration problem that focuses on calibrating visual sensors with a robotic manipulator. We thoroughly investigate the problem from various perspectives and provide alternative formulations of the problem and error objectives. The experimental results demonstrate that the proposed methods are robust and yield accurate solutions when tested on real and simulated data. The work package is bundled as a toolkit and available online for public use. In an extension, we proposed a constrained multiview pose estimation approach for robotic manipulators. The approach exploits the available geometric constraints on the robotic system and infuses them directly into the pose estimation method. The empirical results demonstrate higher accuracy and signiļ¬cantly higher precision compared to other studies. In the second part of this research, we tackle problems pertaining to the ļ¬eld of autonomous vehicles and its related applications. First, we introduce a pose estimation and mapping scheme to extend the application of visual Simultaneous Localization and Mapping to unstructured dynamic environments. We identify, extract, and discard dynamic entities from the pose estimation step. Moreover, we track the dynamic entities and actively update the map based on changes in the environment. Upon observing the limitations of the existing datasets during our earlier work, we introduce FinnForest, a novel dataset for testing and validating the performance of visual odometry and Simultaneous Localization and Mapping methods in an un-structured environment. We explored an environment with a forest landscape and recorded data with multiple stereo cameras, an IMU, and a GNSS receiver. The dataset oļ¬€ers unique challenges owing to the nature of the environment, variety of trajectories, and changes in season, weather, and daylight conditions. Building upon the future works proposed in FinnForest Dataset, we introduce a novel scheme that can localize an observer with extreme perspective changes. More speciļ¬cally, we tailor the problem for autonomous vehicles such that they can recognize a previously visited place irrespective of the direction it previously traveled the route. To the best of our knowledge, this is the ļ¬rst study that accomplishes bi-directional loop closure on monocular images with a nominal ļ¬eld of view. To solve the localisation problem, we segregate the place identiļ¬cation from the pose regression by using deep learning in two steps. We demonstrate that bi-directional loop closure on monocular images is indeed possible when the problem is posed correctly, and the training data is adequately leveraged. All methodological contributions of this thesis are accompanied by extensive empirical analysis and discussions demonstrating the need, novelty, and improvement in performance over existing methods for pose estimation, odometry, mapping, and place recognition

    Literacy for digital futures : Mind, body, text

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    The unprecedented rate of global, technological, and societal change calls for a radical, new understanding of literacy. This book offers a nuanced framework for making sense of literacy by addressing knowledge as contextualised, embodied, multimodal, and digitally mediated. In todayā€™s world of technological breakthroughs, social shifts, and rapid changes to the educational landscape, literacy can no longer be understood through established curriculum and static text structures. To prepare teachers, scholars, and researchers for the digital future, the book is organised around three themes ā€“ Mind and Materiality; Body and Senses; and Texts and Digital Semiotics ā€“ to shape readersā€™ understanding of literacy. Opening up new interdisciplinary themes, Mills, Unsworth, and Scholes confront emerging issues for next-generation digital literacy practices. The volume helps new and established researchers rethink dynamic changes in the materiality of texts and their implications for the mind and body, and features recommendations for educational and professional practice

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum
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