7,746 research outputs found

    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    Physical phenomena controlling quiescent flame spread in porous wildland fuel beds

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    Despite well-developed solid surface flame spread theories, we still lack a coherent theory to describe flame spread through porous wildland fuel beds. This porosity results in additional complexity, reducing the thermal conductivity of the fuel bed, but allowing in-bed radiative and convective heat transfer to occur. While previous studies have explored the effect of fuel bed structure on the overall fire behaviour, there remains a need for further investigation of the effect of fuel structure on the underlying physical phenomena controlling flame spread. Through an extensive series of laboratory-based experiments, this thesis provides detailed, physics-based insights for quiescent flame spread through natural porous beds, across a range of structural conditions. Measurements are presented for fuel beds representative of natural field conditions within an area of the fire-prone New Jersey Pinelands National Reserve, which compliment a related series of field experiments conducted as part of a wider research project. Additional systematic investigation across a wider range of fuel conditions identified independent effects of fuel loading and bulk density on the spread rate, flame height and heat release rate. However, neither fuel loading nor bulk density alone provided adequate prediction of the resulting fire behaviour. Drawing on existing structural descriptors (for both natural and engineered fuel beds) an alternative parameter ασδ was proposed. This parameter (incorporating the fuel bed porosity (α), fuel element surface-to-volume ratio (σ), and the fuel bed height (δ)) was strongly correlated with the spread rate. One effect of the fuel bed structure is to influence the heat transfer mechanisms both above and within the porous fuel bed. Existing descriptions of radiation transport through porous fuel beds are often predicated on the assumption of an isotropic fuel bed. However, given their preferential angle of inclination, the pine needle beds in this study may not exhibit isotropic behaviour. Regardless, for the structural conditions investigated, horizontal heat transfer through the fuel bed was identified as the dominant heating mechanism within this quiescent flame spread scenario. However, the significance of heat transfer contributions from the above-bed flame generally increased with increasing ασδ value of the fuel bed. Using direct measurements of the heat flux magnitude and effective heating distance, close agreement was observed between experimentally observed spread rates and a simple thermal model considering only radiative heat transfer through the fuel bed, particularly at lower values of ασδ. Over-predictions occurred at higher ασδ values, or where other heat transfer terms were incorporated, which may highlight the need to include additional heat loss terms. A significant effect of fuel structure on the primary flow regimes, both within and above these porous fuel beds, was also observed, with important implications for the heat transfer and oxygen supply within the fuel bed. Independent effects of fuel loading and bulk density on both the buoyant and buoyancy-driven entrainment flow were observed, with a complex feedback cycle occurring between Heat Release Rate (HRR) and combustion behaviour. Generally, increases in fuel loading resulted in increased HRR, and therefore increased buoyant flow velocity, along with an increase in the velocity of flow entrained towards the combustion region. The complex effects of fuel structure in both the flaming and smouldering combustion phases may necessitate modifications to other common modelling approaches. The widely used Rothermel model under-predicted spread rate for higher bulk density and lower ασδ fuel beds. As previously suggested, an over-sensitivity to fuel bed height was observed, with experimental comparison indicating an under-prediction of reaction intensity at lower fuel heights. These findings have important implications particularly given the continuing widespread use of the Rothermel model, which continues to underpin elements of the BehavePlus fire modelling system and the US National Fire Danger Rating System. The physical insights, and modelling approaches, developed for this low-intensity, quiescent flame spread scenario, are applicable to common prescribed fire activities. It is hoped that this work (alongside complimentary laboratory and field experiments conducted by various authors as part of a wider multi-agency project (SERDP-RC2641)) will contribute to the emerging field of prescribed fire science, and help to address the pressing need for further development of fire prediction and modelling tools

    International Conference Shaping light for health and wellbeing in cities

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    The book collects contributions presented during the international conference “Shaping light for health and wellbeing in cities” organized in the framework of the H2020 ENLIGHTENme project. The conference has investigated the multifaceted consequences light has on life in cities, by adopting a multidisciplinary and integrated approach to explore the complexity of challenges urban lighting poses on health and wellbeing, urban realm and social life. Papers cover several disciplines such as clinical and biomedical sciences, ethics and Responsible Research & Innovation, urban planning and architecture, data accessibility and interoperability, as well as social sciences and economics, and provide multifaceted insights that inspire further explorations. Contributions represent a step towards the development of innovative policies for improving health and wellbeing in our cities, addressing indoor and outdoor lighting

    Industry 4.0: product digital twins for remanufacturing decision-making

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    Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a product’s life cycle. The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The model’s architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an asset’s through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored

    The geographies of care and training in the development of assistance dog partnerships

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    Human-assistance-dog partnerships form a significant phenomena that have been overlooked in both animal geographies and disability geographies. By focusing on one Assistance Dogs UK (ADUK) charity, ‘Dog A.I.D’., a charity that helps physically disabled and chronically ill people to train their own pets to be assistance dogs, I detail the intimate entangled lifeworlds that humans and dogs occupy. In doing so, I also dialogue between the sub-disciplinary fields of animal geographies and disability geographies, by exploring two broad thematic areas – embodiment and care. As such, this thesis examines the geographies of assistance dog partnership, the care and training practices involved, the benefits and challenges of sharing a lifeworld with a different species, and the changing relationship from a human-pet bond to a human-assistance-dog partnership. Drawing on lived experience and representations of assistance dog partnerships gathered through qualitative (and quantitative) research methods, including a survey, semi-structured interviews (face-to-face, online, and telephone), video ethnography, and magazine analysis, I contribute to research on the assistance dog partnerships and growing debates around the more-than-human nature of care. The ethnomethodological approach to exploring how training occurs between disabled human and assistance dog is also noteworthy as it centres the lively experiences of practice at work between species. The thesis is organised around interconnected themes: the intimate worlds of assistance dog partnerships, working bodies, and caring relations. These thematics allow for a geographical interpretation into the governance, spatial organisation, and representations of dog assistance partnerships. I also explore the training cultures of Dog A.I.D. whilst also spotlighting the lived experiences of training through the early stages of ‘socialisation’, ‘familiarisation’, ‘life skills training’, through to ‘task work’. Finally, the thesis focuses on the practices of care that characterise the assistance dog partnership, showing how care is provided and received by both human and nonhuman. I pay attention to the complex potentiality of the partnership, illustrating how dogs are trained to assist, but also how dogs appear to embody lively, agentic, moments of care. The thesis contributes original work which speaks to animal and disability geographies and attends to the multiple geographies of care-full cross-species lives

    Hierarchical Strengthening of Polycrystal-Inspired Lattice Materials

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    Architected lattice materials offer excellent specific properties, ideal for high-performance and weight-critical applications. However, high-strength lattice materials also often exhibit a significant and catastrophic post-yielding collapse due to buckling and plastic yielding of struts, leading to a trade-off between the strength and stability, and compromising the energy absorption capacity. The mimicry of crystalline strengthening features in the lattice architecture at the mesoscale offers effective ways of improving the energy absorption capacity and eliminating the post-yield collapse of high-strength architected lattice materials. Such crystal-inspired lattice materials are called meta-crystals. This PhD thesis aims to establish the relationship between the intrinsic crystalline microstructure, the extrinsic architected mesostructure, and the mechanical behaviour of meta-crystals, in particular the separate and synergistic strengthening of intrinsic and extrinsic hierarchical features at different length scales. The mechanical behaviours of metallic polycrystal-like meta-crystals were studied by quasi-static compression experiments with digital image correlation (DIC) analyses, hardness testing, and finite element analysis (FEA). The as-printed and post-processed meso- and microstructures of the meta-crystals were characterised via X-ray computed tomography (x-CT), scanning electron microscope (SEM) imaging, electron backscatter diffraction (EBSD) analysis, electron dispersive x-ray spectroscopy (EDX) analysis, and in the transmission electron microscope (TEM). The meta-crystals investigated contained varying numbers of meta-grains, different strut diameters, were fabricated from different base materials, and were subjected to different post-processing treatments. The experiments were designed to deconvolute the strengthening contributions from the crystalline microstructure and architected features at different length scales, as well as the synergistic strengthening across the hierarchical structures. The study showed that the presence of defects can overwhelm the strengthening contributions from the crystal-inspired architecture and the intrinsic crystalline microstructure, particularly when the base material has low ductility and work hardening. The influence of surface processing defects such as lack-of-fusions is especially detrimental compared to internal porosities and such defects need to be minimised. Excessive effects from the processing defects lead to premature fracture of struts rendering the strengthening architecture ineffective. The influence of defects can be minimised via optimisation of the processing parameters, altering the microstructure, or increasing the strut diameter. The examination of various base materials such as Ti-6Al-4V, 316L, and IN718 meta-crystals highlighted the role of the base material in minimising the influence of the processing defects. Additionally, the base materials’ properties also affected the efficacy of the polycrystal-like architecture, with precipitation-hardenable alloys such as IN718 shown to be the most ideally suited to enable the combined strengthening induced by both the intrinsic and extrinsic features. Optimising the crystalline microstructure of as-printed IN718 enabled meta-crystals with exceptional strength and energy absorption capacity. A theoretical framework for the strength of polycrystal-like meta-crystals was also developed by characterising the hierarchical features of the IN718 meta-crystals, providing a basis for future designs.Open Acces
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