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Efficient Large-Scale Semantic Visual Localization in 2D Maps
No full textWith the emergence of autonomous navigation systems, image-based localization is one of the essential tasks to be tackled. However, most of the current algorithms struggle to scale to city-size environments mainly because of the need to collect large (semi-)annotated datasets for CNN training and create databases for test environment of images, key-point level features or image embeddings. This data acquisition is not only expensive and time-consuming but also may cause privacy concerns. In this work, we propose a novel framework for semantic visual localization in city-scale environments which alleviates the aforementioned problem by using freely available 2D maps such as OpenStreetMap. Our method does not require any images or image-map pairs for training or test environment database collection. Instead, a robust embedding is learned from a depth and building instance label information of a particular location in the 2D map. At test time, this embedding is extracted from a panoramic building instance label and depth images. It is then used to retrieve the closest match in the database. We evaluate our localization framework on two large-scale datasets consisting of Cambridge and San Francisco cities with a total length of drivable roads spanning 500 km and including approximately 110k unique locations. To the best of our knowledge, this is the first large-scale semantic localization method which works on par with approaches that require the availability of images at train time or for test environment database creation
FootNet: An Efficient Convolutional Network for Multiview 3D Foot Reconstruction
No full textAutomatic biometric analysis of the human body is normally reserved for expensive customisation of clothing items e.g. for sports or medical purposes. These systems are usually built upon photogrammetric techniques currently requiring a rig and well calibrated cameras. Here we propose building on advancements in deep learning as well as utilising technology present in mobile phones for cheaply and accurately determining biometric data of the foot. The system is designed to run efficiently in a mobile phone app where it can be used in uncalibrated environments and without rigs. By scanning the foot with the phone camera, our system recovers both the 3D shape as well as the scale of the foot, opening the door way for automatic shoe size suggestion. Our contributions are (1) an efficient multiview feed forward neural network capable of inferring foot shape and scale, (2) a system for training from completely synthetic data and (3) a dataset of multiview feet images for evaluation. We fully ablate our system and show our design choices to improve performance at every stage. Our final design has a vertex error of only 1 mm (for 25 cm long synthetic feet) and 4 mm error in foot length on real feet
Graphene coated fabrics by ultrasonic spray coating for wearable electronics and smart textiles
No full textThe seamless incorporation of electronics in textiles have the potential to enable various applications ranging from sensors for the internet of things to personalised medicine and human-machine interfacing. Graphene electronic textiles are a current focus for the research community due to the exceptional electrical and optical properties combined with the high flexibility of this material, which makes it the most effective strategy to achieve ultimate mechanical robustness of electronic devices for textile integrated electronics. An efficient way to create electronic textiles is to fabricate devices directly on the fabric. This can be done by coating the textile fabric with graphene to make it conductive. Here we discuss successful and efficient methods for coating graphene nanoplatelets on textile substrates of nylon, polyester and meta-aramid using ultrasonic spray coating technique. These coatings are characterised by scanning electron microscopy, contact angle and electrical conductivity measurements in order to identify the optimal textile electrode. Our study provides the foundation for the large-area fabrication of graphene electronic textiles
Research on the power sharing capacity of a dual power open-end winding PMSM drive system for vehicular applications
No full textAn open-end winding permanent magnet synchronous motor (OW-PMSM) fed by dual inverter is a competitive option for the drive system of dual power electric vehicles. It allows manageable power flow between two isolated power sources through the motor without requiring a DC/DC converter. Based on the mathematical model of the OW-PMSM and the principles of power sharing, this paper first compares the power sharing capacity among some existing torque regulation algorithms, including unit power factor (UPF) control, maximum torque per ampere (MTPA) control, and constant back electromotive force (CBE) control. Then a control algorithm named minimum voltage vector amplitude (MVVA) is presented, which features covering the maximum motor operating range and the maximum power sharing range under the linear voltage vector distribution. Simulation results confirm the validity of the proposed MVVA control, and demonstrate its advantages in vehicular applications
Impacts of underground climate change on urban geothermal potential: Lessons learnt from a case study in London.
No full textWhile urban underground is being increasingly used for various purposes, two concerns should be addressed with respect to the urban underground climate change: i) how much energy has been stored in urban subsurface due to the heat rejection from underground heated spaces (such as tunnels and basements) and ii) how much of the thermal demand of a city or district can be supplied by harvesting this accumulative thermal energy in the ground. However, our understanding of the temperature rise in the ground and of the geothermal potential of urban subsurface is still limited. This paper quantifies the geothermal potential for a 12 km2 densely populated borough in central London by considering the spatio-temporal temperature variation in the ground owing to continuous rejection of heat into the ground, coupled with the effect of geothermal extraction capacity. A large-scale transient semi-3D geothermal subsurface model of the site is developed, and the thermal interaction between underground heated spaces, geothermal energy extraction systems and the ground and groundwater are simulated. The concurrent heat rejection and extraction processes in the subsurface are computed so that the most influencing parameters of the subsurface on its geothermal potential are identified. Results show that up to 50% of the borough's total heat demand can be supplied via geothermal installations leading to around 33% reduction in CO2 emission. The geothermal extraction efficiency in sand and gravel primarily depends on the ground conditions such as the thickness of the permeable layer and the groundwater flow regime. In impermeable ground such as clay, however, the underground built environment such as heated spaces have shown to have a significant impact on improving the geothermal extraction efficiency
Randomised controlled trial of a just-in-time adaptive intervention (JITAI) smoking cessation smartphone app: The Quit Sense feasibility trial protocol
No full textIntroduction A lapse (any smoking) early in a smoking cessation attempt is strongly associated with reduced success. A substantial proportion of lapses are due to urges to smoke triggered by situational cues. Currently, no available interventions proactively respond to such cues in real time. Quit Sense is a theory-guided just-in-time adaptive intervention smartphone app that uses a learning tool and smartphone sensing to provide in-the-moment tailored support to help smokers manage cue-induced urges to smoke. The primary aim of this randomised controlled trial (RCT) is to assess the feasibility of delivering a definitive online efficacy trial of Quit Sense. Methods and analyses A two-arm parallel-group RCT allocating smokers willing to make a quit attempt, recruited via online adverts, to usual care (referral to the NHS SmokeFree website) or usual care plus Quit Sense. Randomisation will be stratified by smoking rate (<16 vs ≥16 cigarettes/day) and socioeconomic status (low vs high). Recruitment, enrolment, baseline data collection, allocation and intervention delivery will be automated through the study website. Outcomes will be collected at 6 weeks and 6 months follow-up via the study website or telephone, and during app usage. The study aims to recruit 200 smokers to estimate key feasibility outcomes, the preliminary impact of Quit Sense and potential cost-effectiveness, in addition to gaining insights on user views of the app through qualitative interviews. Ethics and dissemination Ethics approval has been granted by the Wales NHS Research Ethics Committee 7 (19/WA/0361). The findings will be disseminated to the public, the funders, relevant practice and policy representatives and other researchers. Trial registration number ISRCTN12326962
Modelling turbine acoustic impedance
No full textWe quantify the sensitivity of turbine acoustic impedance to aerodynamic design parameters. Impedance boundary conditions are an influential yet uncertain parameter in predicting the thermoacoustic stability of gas turbine combustors. We extend the semi-actuator disk model to cambered blades, using non-linear time-domain computations of turbine vane and stage cascades with acoustic forcing for validation data. Discretising cambered aerofoils into multiple disks improves reflection coefficient predictions, reducing error by up to an order of magnitude compared to a flat plate assumption. A parametric study of turbine stage designs using the analytical model shows acoustic impedance is a weak function of degree of reaction and polytropic efficiency. The design parameter with the strongest influence is flow coefficient, followed by axial velocity ratio and Mach number. We provide the combustion engineer with improved tools to predict impedance boundary conditions, and suggest thermoacoustic stability is most likely to be compromised by change in turbine flow coefficient
Adaptive prediction of wall movement during excavation using Bayesian inference
No full textIn underground construction works, uncertainties and insufficient information about the underground environment lead to inaccurate predictions of soil-structure interactions. Supported excavations are often over-designed, which underscores a significant potential for cost optimization. However, the uncertainties exist, and the traditional design process does not allow for leaner designs at the start of the project. The emergence of advanced analysis tools enables the development of an Observational Method based approach for a decision-making process in which data can be best utilized to deliver real value, confidence, and control.An automated back analysis approach based on Bayesian inference is developed in this paper and validated with a synthetic case study. Probabilistic modeling and Markov Chain Monte Carlo simulation are used to deliver estimates of soil parameters for a given a geotechnical model, update the prediction of future excavation stages, and fully quantify uncertainties from the constructed model and measurements. Sensitivity analysis is used for model selection to achieve modeling robustness. The impact of prior engineering knowledge about the soil properties on the precision of the predictions is also examined. This approach significantly improves the efficiency of back analysis in current practice and provides a tool for data-driven decision making of design optimization during construction
Below 1 μV cm-1: Determining the geometrically-saturated critical transport current of a superconducting tape
No full textIn the critical state model, the critical current, Ic, of a superconductor defines the upper limit of dissipation-free current flow. However conventional transport Ic measurements of practical superconductors rely on the detection of a voltage drop along the length of the conductor. This requires that the superconductor has entered the dissipative regime, and hence inherently over-estimates the current at which geometric saturation occurs. Nonetheless, the convenience of the 1 μV cm-1 criterion means that it has become the widely adopted definition of transport Ic. Here, we present an alternative definition for the transport critical current of a superconducting tape under self-field conditions, which is based on the concept of geometric current saturation across the full cross-sectional area. This saturation threshold can be experimentally determined through simple Hall sensor measurements of the evolving perpendicular magnetic field at the tape surface. The surface field exhibits a signature transition when the transport current increases beyond the current-saturation threshold. We present an analytical model which describes this effect and defines the critical saturated current as a function of d2B⊥/dIt finite element (FE) modelling, and then experimentally demonstrated through measurements on a variety of commercial REBCO and Bi-2223 tapes of differing widths. It is found that the 1 μV cm-1 criterion consistently leads to an overestimation of the saturated critical current by ∼15% in REBCO tapes, and up to 30% in a Bi-2223 tape. FE modelling indicates that this overestimate is most prominent in superconductors exhibiting a low n-value (i.e. n ≲ 20). A key advantage of the measurement approach presented here is that it allows the unambiguous measurement of a transport Ic value to be completed at lower currents, without entering the dissipative region in which sample damage can occur. There are also implications as to the correct choice of Ic value which should be employed within the well-known Norris and Brandt equations for AC loss
Thermo-Responsive self-assembly of a dual glucagon-like peptide and glucagon receptor agonist
No full textThe human peptide hormone Oxyntomodulin (Oxm) is known to induce satiety, increase energy expenditure, and control blood glucose in humans, making it a promising candidate for treatment of obesity and/or type 2 diabetes mellitus. However, a pharmaceutical exploitation has thus far been impeded by fast in vivo clearance and the molecule‘s sensitivity to half-life extending structural modifications. We recently showed that Oxm self-assembles into amyloid-like nanofibrils that continuously release active, soluble Oxm in a peptide-deprived environment. S.c. injected Oxm nanofibrils extended plasma exposure from a few hours to five days in rodents, compared to s.c. applied soluble Oxm. Here we show that Oxm fibril elongation kinetics and thermodynamics display a uniquely low temperature optimum compared to previously reported amyloid-like peptide and protein assemblies. Elongation rate is optimal at room temperature, with association rates 2–3 times higher at 25 °C than at ≥37 °C or ≤20 °C. We deduce from a combination of Cryo electron microscopy and spectroscopic methods that Oxm fibrils have a double-layered, triangular cross-section composed of arch-shaped monomers. We suggest a thermodynamic model that links the necessary molecular rearrangements during fibrillation and peptide release to the unique temperature effects in Oxm self-assembly and disassembly