4,218 research outputs found

    Flood dynamics derived from video remote sensing

    Get PDF
    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Perceptions, Actors, Innovations

    Get PDF
    With Agenda 2030, the UN adopted wide-ranging Sustainable Development Goals (SDGs) that integrate development and environmental agendas. This book has a unique focus on the political tensions between environmental and socio-economic objectives and advocates for a cooperative shift towards environmentally sound sustainability

    Non-invasive and non-intrusive diagnostic techniques for gas-solid fluidized beds – A review

    Get PDF
    Gas-solid fluidized-bed systems offer great advantages in terms of chemical reaction efficiency and temperature control where other chemical reactor designs fall short. For this reason, they have been widely employed in a range of industrial application where these properties are essential. Nonetheless, the knowledge of such systems and the corresponding design choices, in most cases, rely on a heuristic expertise gained over the years rather than on a deep physical understanding of the phenomena taking place in fluidized beds. This is a huge limiting factor when it comes to the design, the scale-up and the optimization of such complex units. Fortunately, a wide array of diagnostic techniques has enabled researchers to strive in this direction, and, among these, non-invasive and non-intrusive diagnostic techniques stand out thanks to their innate feature of not affecting the flow field, while also avoiding direct contact with the medium under study. This work offers an overview of the non-invasive and non-intrusive diagnostic techniques most commonly applied to fluidized-bed systems, highlighting their capabilities in terms of the quantities they can measure, as well as advantages and limitations of each of them. The latest developments and the likely future trends are also presented. Neither of these methodologies represents a best option on all fronts. The goal of this work is rather to highlight what each technique has to offer and what application are they better suited for

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Geophysical Characterisation and Monitoring of Earth Embankment Dams

    Get PDF
    Geophysics has become fundamental in characterising earth embankment dams and identifying preferential seepage pathways, problem areas, and structural defects. The issue of non-uniqueness is profound in the interpretation of geophysical data, with features often attributed to multiple potential sources. This project tackles this issue by applying a multidisciplinary approach comprising traditional techniques to a study site in South Wales. These techniques comprised ground conductivity, magnetometry, and Electrical Resistivity Tomography (ERT). The computation of normalised chargeability data from an Induced Polarisation (IP) survey, normally used for mineral exploration, was applied to delineate between clay and moisture rich areas. This eliminated the issue of non-uniqueness between these two subsurface conditions. The application of these techniques led to successful characterisation of the embankment in terms of its engineered and natural components and identified a potential seepage pathway attributed to surface waters.The Self-Potential (SP) method was evolved into a monitoring solution, building on the research and development of TerraDat Ltd’s SPiVolt system. A methodology was developed to efficiently fabricate and install an SP monitoring network. SP monitoring confirmed the presence of the preferential seepage pathway hypothesised through the characterisation survey and identified a second pathway through the dam’s core.Dŵr Cymru Welsh Water have since used the results of this project to design a targeted grouting campaign and install surface drainage at the site. Comprehensive understanding of the material composition and temporal variations of subsurface conditions is considered essential for ensuring dam and reservoir owners achieve their aims of climate resilience and asset protection. The geophysical characterisation and monitoring methodology presented in this thesis provides an effective low-cost solution that can be applied to multiple scenarios such as landslide investigations, coal tip stability assessments and other hydrogeological problems

    Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and self-righting

    Full text link
    Despite contending with constraints imposed by the environment, morphology, and physiology, animals move well by physically interactingwith the environment to use and transition between modes such as running, climbing, and self-righting. By contrast, robots struggle to do so in real world. Understanding the principles of how locomotor transitions emerge from constrained physical interaction is necessary for robots to move robustly using similar strategies. Recent studies discovered that discoid cockroaches use and transition between diverse locomotor modes to traverse beams and self-right on ground. For both systems, animals probabilistically transitioned between modes via multiple pathways, while its self-propulsion created kinetic energy fluctuation. Here, we seek mechanistic explanations for these observations by adopting a physics-based approach that integrates biological and robotic studies. We discovered that animal and robot locomotor transitions during beam obstacle traversal and ground self-righting are barrier-crossing transitions on potential energy landscapes. Whereas animals and robot traversed stiff beams by rolling their body betweenbeam, they pushed across flimsy beams, suggesting a concept of terradynamic favorability where modes with easier physical interaction are more likely to occur. Robotic beam traversal revealed that, system state either remains in a favorable mode or transitions to one when energy fluctuation is comparable to the transition barrier. Robotic self-righting transitions occurred similarly and revealed that changing system parameters lowers barriers over which comparable fluctuation can induce transitions. Thetransitionsof animalsin both systems mostly occurred similarly, but sensory feedback may facilitate its beam traversal. Finally, we developed a method to measure animal movement across large spatiotemporal scales in a terrain treadmill.Comment: arXiv admin note: substantial text overlap with arXiv:2006.1271

    Satellite remote sensing of surface winds, waves, and currents: Where are we now?

    Get PDF
    This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields

    Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion

    Full text link
    Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject's body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion system for monitoring vital signs, which can perform consistently well in dynamic scenarios, e.g., when some people move around the subject to be tracked, or a subject waves his/her arms and marches on the spot. Three major processing modules are developed in the system, to enable robust sensing. Firstly, we utilize a camera to assist a mmWave radar to accurately localize the subjects of interest. Secondly, we exploit the calculated subject position to form transmitting and receiving beamformers, which can improve the reflected power from the targets and weaken the impact of dynamic interference. Thirdly, we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD) algorithm to separate the weak vital sign signals from the dynamic ones due to subject's body movement. Experimental results show that, the 90th{^{th}} percentile errors in respiration rate (RR) and heartbeat rate (HR) are less than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute), respectively

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

    Full text link
    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
    • …
    corecore