10,982 research outputs found

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    ICT enabled approach for humanitarian disaster management: a systems perspective

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    Purpose Each stage in disaster management faces different challenges concerning information gathering, sharing, interpretation and dissemination. However, a comprehensive understanding of different information and communication technology (ICT) systems utilised for humanitarian disaster management is limited. Therefore, the paper follows a systems thinking approach to examine ten major man-made and/or natural disasters to comprehend the influence of ICT systems on humanitarian relief operations. Design/methodology/approach A longitudinal, multi-case study captures the use of ICT tools, stakeholders involvement, disaster stages and zones of operations for relief operations over the past two decades. A systems thinking approach is utilised to draw several inferences and develop frameworks. Findings Multiple ICT tools such as geographic information systems, online webpages/search engines, social media, unmanned aerial vehicles/robots and artificial intelligence are used for rapid disaster response and mitigation. Speed and coordination of relief operations have significantly increased in recent years due to the increased use of ICT systems. Research limitations/implications Secondary data on the past ten disasters is utilised to draw inferences. The developed ICT-driven model must be validated during upcoming humanitarian relief operations. Practical implications A holistic understanding of a complex inter-relationship between influential variables (stakeholders, disaster stages, zones of operation, ICT systems) is beneficial for effectively managing humanitarian disasters. Originality/value Broadly classifying the ICT systems into surveillance, decision support and broadcasting systems, a novel ICT-enabled model for humanitarian relief operations is developed

    Printed circuit board coils of multi-track litz structure for 3.3 kW inductive power transfer system

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    This paper presents the optimization procedure of an inductive power transmission (IPT) system which utilizes large size spiral printed circuit board (PCB) coils for high- power transfer. Printed circuit boards for coil assembly provides advantages in the manufacturing process through the use of cost- effective flexible fabrication techniques. Furthermore, this kind of construction offers a low profile device, which is of great interest for applications with space constraints. PCB-based IPT system coils can achieve high energy efficiency by applying litz-structure braiding techniques, as investigated in this work, where the objective was to obtain an optimized balance between the conduc- tion losses and proximity losses associated with the number and dimensions of the traces. Considering the geometrical dimensions and manufacturing constraints, we will proceed to obtain the characteristics of the coil to achieve optimal performance. The estimation of coil losses were in part based on finite element simulations, and the results were conveniently processed with the appropriate mathematical methods. Numerical simulation and experimental results were conducted for validation on a prototype suitable to transfer up to 3.3 kW for a transmitter- receiver distance of 10 cm. In the experimental arrangement, a maximum efficiency in the coils of 93% has been measured, and the overall efficiency of 88% has been reached for the entire IPT system

    Scheduling Classifiers for Real-Time Hazard Perception Considering Functional Uncertainty

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    This paper addresses the problem of real-time classification-based machine perception, exemplified by a mobile autonomous system that must continually check that a designated area ahead is free of hazards. Such hazards must be identified within a specified time. In practice, classifiers are imperfect; they exhibit functional uncertainty. In the majority of cases, a given classifier will correctly determine whether there is a hazard or the area ahead is clear. However, in other cases it may produce false positives, i.e. indicate hazard when the area is clear, or false negatives, i.e. indicate clear when there is in fact a hazard. The former are undesirable since they reduce quality of service, whereas the latter are a potential safety concern. A stringent constraint is therefore placed on the maximum permitted probability of false negatives. Since this requirement may not be achievable using a single classifier, one approach is to (logically) OR the outputs of multiple disparate classifiers together, setting the final output to hazard if any of the classifiers indicates hazard. This reduces the probability of false negatives; however, the trade-off is an inevitably increase in the probability of false positives and an increase in the overall execution time required. In this paper, we provide optimal algorithms for the scheduling of classifiers that minimize the probability of false positives, while meeting both a latency constraint and a constraint on the maximum acceptable probability of false negatives. The classifiers may have arbitrary statistical dependences between their functional behaviors (probabilities of correct identification of hazards), as well as variability in their execution times, characterized by typical and worst-case values

    Integrating materials supply in strategic mine planning of underground coal mines

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    In July 2005 the Australian Coal Industry’s Research Program (ACARP) commissioned Gary Gibson to identify constraints that would prevent development production rates from achieving full capacity. A “TOP 5” constraint was “The logistics of supply transport distribution and handling of roof support consumables is an issue at older extensive mines immediately while the achievement of higher development rates will compound this issue at most mines.” Then in 2020, Walker, Harvey, Baafi, Kiridena, and Porter were commissioned by ACARP to investigate Australian best practice and progress made since Gibson’s 2005 report. This report was titled: - “Benchmarking study in underground coal mining logistics.” It found that even though logistics continue to be recognised as a critical constraint across many operations particularly at a tactical / day to day level, no strategic thought had been given to logistics in underground coal mines, rather it was always assumed that logistics could keep up with any future planned design and productivity. This subsequently meant that without estimating the impact of any logistical constraint in a life of mine plan, the risk of overvaluing a mining operation is high. This thesis attempts to rectify this shortfall and has developed a system to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. At the very worst if a logistics constraint was unsolvable then it could be understood, planned for, and reflected in the mine’s ongoing financial valuations. The system developed in this thesis, using a suite of unique algorithms, is designed to “bolt onto” existing mine plans in the XPAC mine scheduling software package, and identify at a strategic level the number of material delivery loads required to maintain planned productivity for a mining operation. Once an event was identified the system then drills down using FlexSim discrete event simulation to a tactical level to confirm the predicted impact and understand if a solution can be transferred back as a long-term solution. Most importantly the system developed in this thesis was designed to communicate to multiple non-technical stakeholders through simple graphical outputs if there is a risk to planned production levels due to a logistics constraint

    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

    The sustainability impact of a digital circular economy

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    Digital technologies — such as the Internet of Things, big data and advanced analytics, additive manufacturing and 3D printing, blockchain and online platforms — are regarded as key enablers for a circular economy. A systematic literature review and analysis of 48 scientific articles published in the last five years was conducted to identify the first-, second- and third-order sustainability effects of a digital circular economy. Second-order environmental effects such as improved resource efficiency and reduction of emissions, waste and material use in products and production processes are often envisaged. However, limited attention is given to social and economic impact, and rebound effects. Existing literature also lacks a solid assessment of actual (vs expected) impact, and a more balanced consideration of negative (vs positive) effects

    Utilizing digital technologies for waste management

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    This article is aimed at applying a new mechanism for improving an automated control system through methods for creating useful products from waste and developing innovative projects in a digital economy

    Towards Reuse and Recycling of Lithium-ion Batteries: Tele-robotics for Disassembly of Electric Vehicle Batteries

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    Disassembly of electric vehicle batteries is a critical stage in recovery, recycling and re-use of high-value battery materials, but is complicated by limited standardisation, design complexity, compounded by uncertainty and safety issues from varying end-of-life condition. Telerobotics presents an avenue for semi-autonomous robotic disassembly that addresses these challenges. However, it is suggested that quality and realism of the user's haptic interactions with the environment is important for precise, contact-rich and safety-critical tasks. To investigate this proposition, we demonstrate the disassembly of a Nissan Leaf 2011 module stack as a basis for a comparative study between a traditional asymmetric haptic-'cobot' master-slave framework and identical master and slave cobots based on task completion time and success rate metrics. We demonstrate across a range of disassembly tasks a time reduction of 22%-57% is achieved using identical cobots, yet this improvement arises chiefly from an expanded workspace and 1:1 positional mapping, and suffers a 10-30% reduction in first attempt success rate. For unbolting and grasping, the realism of force feedback was comparatively less important than directional information encoded in the interaction, however, 1:1 force mapping strengthened environmental tactile cues for vacuum pick-and-place and contact cutting tasks.Comment: 21 pages, 12 figures, Submitted to Frontiers in Robotics and AI; Human-Robot Interactio

    Warehousing in the UK: Operations, Planning and Decarbonisation - Summary Report

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