9,755 research outputs found

    Key technologies for safe and autonomous drones

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    Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    Review of Methodologies to Assess Bridge Safety During and After Floods

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    This report summarizes a review of technologies used to monitor bridge scour with an emphasis on techniques appropriate for testing during and immediately after design flood conditions. The goal of this study is to identify potential technologies and strategies for Illinois Department of Transportation that may be used to enhance the reliability of bridge safety monitoring during floods from local to state levels. The research team conducted a literature review of technologies that have been explored by state departments of transportation (DOTs) and national agencies as well as state-of-the-art technologies that have not been extensively employed by DOTs. This review included informational interviews with representatives from DOTs and relevant industry organizations. Recommendations include considering (1) acquisition of tethered kneeboard or surf ski-mounted single-beam sonars for rapid deployment by local agencies, (2) acquisition of remote-controlled vessels mounted with single-beam and side-scan sonars for statewide deployment, (3) development of large-scale particle image velocimetry systems using remote-controlled drones for stream velocity and direction measurement during floods, (4) physical modeling to develop Illinois-specific hydrodynamic loading coefficients for Illinois bridges during flood conditions, and (5) development of holistic risk-based bridge assessment tools that incorporate structural, geotechnical, hydraulic, and scour measurements to provide rapid feedback for bridge closure decisions.IDOT-R27-SP50Ope

    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

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

    Desarrollo de una herramienta integral de gestión de gases de efecto invernadero para la toma de decisión contra el cambio climático a nivel regional y local en la Comunitat Valenciana

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    Tesis por compendio[ES] Actualmente, los responsables de tomar decisiones contra el cambio climático carecen de herramientas para desarrollar inventarios de emisiones de gases de efecto invernadero (GEI) con suficiente rigor científico-técnico y precisión para priorizar e invertir los recursos disponibles de manera eficiente en las medidas necesarias para luchar contra el cambio climático. Por ello, en esta tesis se expone el desarrollo de un sistema de información territorial y sectorial (SITE) para monitorear las emisiones de GEI que sirva como herramienta de gobernanza climática local y regional. SITE combina las ventajas de los enfoques metodológicos descendente o top-down (de arriba hacia abajo) y ascendente o bottom-up (de abajo hacia arriba), para lograr un enfoque híbrido innovador para contabilizar y gestionar de manera eficiente las emisiones de GEI. Por tanto, en esta tesis se definen los diferentes desarrollos metodológicos, tanto generales como específicos de sectores clave del Panel Intergubernamental de Cambio Climático (IPPC) (edificación, transporte, sector forestal, etc.), un desarrollo informático para la parte de SITE que se ejecuta del lado del servidor, que de ahora en adelante denominaremos back-end del sistema, y siete implementaciones como casos de estudio representativos, a diferentes escalas y aplicados sobre diferentes sectores. Estas implementaciones a diferentes escalas y sectores demuestran el potencial del sistema como herramienta de apoyo en la toma de decisión contra el cambio climático a nivel regional y local. Las diferentes implementaciones en casos piloto representativos, tanto a nivel regional en la Comunitat Valenciana como a nivel local en municipios grandes (València) y medianos (Quart de Poblet y Llíria) muestran el potencial de adaptación territorial y sectorial que tiene la herramienta. Las metodologías desarrolladas para los sectores específicos de tráfico rodado, edificación o sector forestal, ofrecen cuantificaciones con una resolución espacial con gran capacidad de optimizar las políticas locales y regionales. Por tanto, la herramienta cuenta con un gran potencial de escalabilidad y gran capacidad de mejora continua mediante la inclusión de nuevos enfoques metodológicos, adaptación de las metodologías a la disponibilidad de datos, metodologías concretas para sectores clave y actualización a las mejores metodologías disponibles derivadas de actividades de investigación de la comunidad científica.[CA] Actualment, els responsables de prendre decisions contra el canvi climàtic no tenen eines per aconseguir inventaris d'emissions de gasos d'efecte hivernacle (GEH) amb prou cientificotècnic rigor, precisió i integritat per invertir els recursos disponibles de manera eficient en les mesures necessàries contra el canvi climàtic. Per això, en aquesta tesis se exposa el desenvolupa un sistema d'informació territorial i sectorial (SITE) per monitoritzar les emissions de GEH com a eina de governança climàtica local i regional. Aquest sistema combina els avantatges dels enfocaments metodològics descendent o top-down (de dalt a baix) i ascendent o bottom-up (de baix a dalt), per aconseguir un enfocament híbrid innovador per comptabilitzar i gestionar de manera eficient les emissions de GEH. Per tant, en aquesta tesi doctoral es descriuen els diferents desenvolupaments metodològics, tant generals com específics de sectors clau del Panel Intergovernamental contra el Canvi Climàtic (edificació, transport, forestal, etc.), un desenvolupament informàtic per al back-end del sistema i set implementacions com a casos d'estudi representatius, a diferents escales, amb els diferents enfocaments metodològics i aplicats sobre diferents sectors. Això queda descrit en sis capítols. Aquestes implementacions a diferents escales i sectors demostren el potencial del sistema com a eina de suport en la presa de decisió contra el canvi climàtic a nivell regional i local. Les diferents implementacions en casos pilot representatius, tant a nivell regional a la Comunitat Valenciana com a nivell local en municipis grans (València) i mitjans (Quart de Poblet i Llíria,) mostren el potencial d'adaptació territorial i sectorial que té l'eina. Les metodologies desenvolupades per als sectors específics de trànsit rodat, edificació i forestal, ofereixen quantificacions amb una resolució espacial amb gran capacitat d'optimitzar les polítiques locals i regionals. Per tant, l'eina compta amb un gran potencial d'escalabilitat i gran capacitat de millora contínua mitjançant la inclusió de nous enfocaments metodològics, adaptació de les metodologies a la disponibilitat de dades, metodologies concretes per a sectors clau, i actualització a les millors metodologies disponibles derivades de activitats de investigació de la comunitat científica.[EN] Currently, regional and local decision-makers lack of tools to achieve greenhouse gases (GHG) emissions inventories with enough rigor, accuracy and completeness in order to prioritize available resources efficiently against climate change. Thus, in this thesis the development of a territorial and sectoral information system (SITE) to monitor GHG emissions as a local and regional climate governance tool is exposed. This system combines the advantages of both, top-down and bottom-up approaches, to achieve an innovative hybrid approach to account and manage efficiently GHG emissions. Furthermore, this thesis defines the methodologies developed, a computer proposal for the back-end of the system and seven implementations as representative case studies at different scales (local and regional level), with the different methodological approaches and applied to different sectors. Thus, these implementations demonstrate the potential of the system as decision-making tool against climate change at the regional and local level as climate governance tool. The different implementations in representative pilot cases, both at the regional level in the Valencian Community and at the local level in large (Valencia) and medium-sized municipalities (Quart de Poblet and Llíria) demonstrate the potential for territorial and sectoral adaptation of the system developed. The methodologies developed for the specific sectors of road transport, building and forestry, offer quantifications with a spatial resolution with a great capacity to optimize local and regional policies. Therefore, the tool has a great potential for scalability and a great capacity for continuous improvement through the inclusion of new methodological approaches, adapting the methodologies to the availability of data, specific methodologies for key sectors, and updating to the best methodologies available in the scientific community.Lorenzo Sáez, E. (2022). Desarrollo de una herramienta integral de gestión de gases de efecto invernadero para la toma de decisión contra el cambio climático a nivel regional y local en la Comunitat Valenciana [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181662TESISCompendi

    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

    Management controls, government regulations, customer involvement: Evidence from a Chinese family-owned business

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    This research reports on a case study of a family-owned elevator manufacturing company in China, where management control was sandwiched between the state policies and global customer production requirements. By analysing the role of government and customer, this thesis aimed to illustrate how management control operated in a family-owned business and to see how and why they do management control differently. In particular, it focused on how international production standards and existing Chinese industry policies translated into a set of the management control practices through a local network within the family-owned business I studied. Based on an ethnographic approach to research, I spent six months in the field, conducted over 30 interviews, several conservations, and reviewed relevant internal documents to understand how management control (MC) techniques with humans cooperated in the company. I also understood how two layers of pressure have shaped company behaviour, and how a company located in a developing country is connecting with global network. I also found there is considerable tension among key actors and investigated how the company responded and managed it. Drawing on Actor Network Theory (ANT), I analysed the interviews from key actors, examined the role of government regulations and customer requirements to see how management control being managed under two layers of pressure, i.e., the government regulations (e.g., labour, tax, environment control) and customer requirement (e.g., quality and production control). Management controls were an obligatory passage point (OPP), and transformation of those elements of Western production requirements and government requirements arrived at the Chinese local factory and influenced management control and budgeting. The findings suggest that management control systems are not only a set of technical procedures, but it is also about managing tensions. This understanding shows a linear perspective on MC practices rather than a social perspective. However, when we use ANT as a theoretical perspective, we see those actors who, being obliged and sandwiched, and controlled by external forces for them to follow. Consequently, human actors must work in an unavoidable OPP. This is the tension they face which constructed mundane practices of MC. Hence, MCs are managing such tensions. This study contributes to management control research by analysing management controls in terms of OPP, extends our understanding by illustrating the role of the government and customers, and our understanding of family-owned business from a management controls perspective in a developing country

    Full stack development toward a trapped ion logical qubit

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    Quantum error correction is a key step toward the construction of a large-scale quantum computer, by preventing small infidelities in quantum gates from accumulating over the course of an algorithm. Detecting and correcting errors is achieved by using multiple physical qubits to form a smaller number of robust logical qubits. The physical implementation of a logical qubit requires multiple qubits, on which high fidelity gates can be performed. The project aims to realize a logical qubit based on ions confined on a microfabricated surface trap. Each physical qubit will be a microwave dressed state qubit based on 171Yb+ ions. Gates are intended to be realized through RF and microwave radiation in combination with magnetic field gradients. The project vertically integrates software down to hardware compilation layers in order to deliver, in the near future, a fully functional small device demonstrator. This thesis presents novel results on multiple layers of a full stack quantum computer model. On the hardware level a robust quantum gate is studied and ion displacement over the X-junction geometry is demonstrated. The experimental organization is optimized through automation and compressed waveform data transmission. A new quantum assembly language purely dedicated to trapped ion quantum computers is introduced. The demonstrator is aimed at testing implementation of quantum error correction codes while preparing for larger scale iterations.Open Acces
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