Sphere-Guided Training of Neural Implicit Surfaces

In recent years, surface modeling via neural implicit functions has become one of the main techniques for multi-view 3D reconstruction. However, the state-of-the-art methods rely on the implicit functions to model an entire volume of the scene, leading to reduced reconstruction fidelity in the areas with thin objects or high-frequency details. To address that, we present a method for jointly training neural implicit surfaces alongside an auxiliary explicit shape representation, which acts as surface guide. In our approach, this representation encapsulates the surface region of the scene and enables us to boost the efficiency of the implicit function training by only modeling the volume in that region. We propose using a set of learnable spherical primitives as a learnable surface guidance since they can be efficiently trained alongside the neural surface function using its gradients. Our training pipeline consists of iterative updates of the spheres' centers using the gradients of the implicit function and then fine-tuning the latter to the updated surface region of the scene. We show that such modification to the training procedure can be plugged into several popular implicit reconstruction methods, improving the quality of the results over multiple 3D reconstruction benchmarks

High Precision Astrometric Millimeter VLBI Using a New Method for Atmospheric Calibration

We describe a new method which achieves high precision Very Long Baseline Interferometry (VLBI) astrometry in observations at millimeter wavelengths. It combines fast frequency-switching observations, to correct for the dominant non-dispersive tropospheric fluctuations, with slow source-switching observations, for the remaining ionospheric dispersive terms. We call this method Source-Frequency Phase Referencing. Provided that the switching cycles match the properties of the propagation media, one can recover the source astrometry. We present an analytic description of the two-step calibration strategy, along with an error analysis to characterize its performance. Also, we provide observational demonstrations of a successful application with observations using the Very Long Baseline Array at 86 GHz of the pairs of sources 3C274 & 3C273 and 1308+326 & 1308+328, under various conditions. We conclude that this method is widely applicable to millimeter VLBI observations of many target sources, and unique in providing bona-fide astrometrically registered images and high precision relative astrometric measurements in mm-VLBI using existing and newly built instruments.Comment: Astronomical Journal, accepted for publicatio

Optimisation of residential battery integrated photovoltaics system: analyses and new machine learning methods

Modelling and optimisation of battery integrated photovoltaics (PV) systems require a certain amount of high-quality input PV and load data. Despite the recent rollouts of smart meters, the amount of accessible proprietary load and PV data is still limited. This thesis addresses this data shortage issue by performing data analyses and proposing novel data extrapolation, interpolation, and synthesis models. First, a sensitivity analysis is conducted to investigate the impacts of applying PV and load data with various temporal resolutions in PV-battery optimisation models. The explored data granularities range from 5-second to hourly, and the analysis indicates 5-minute to be the most suitable for the proprietary data, achieving a good balance between accuracy and computational cost. A data extrapolation model is then proposed using net meter data clustering, which can extrapolate a month of 5-minute net/gross meter data to a year of data. This thesis also develops two generative adversarial networks (GANs) based models: a deep convolutional generative adversarial network (DCGAN) model which can generate PV and load power from random noises; a super resolution generative adversarial network (SRGAN) model which synthetically interpolates 5-minute load and PV power data from 30-minute/hourly data. All the developed approaches have been validated using a large amount of real-time residential PV and load data and a battery size optimisation model as the end-use application of the extrapolated, interpolated, and synthetic datasets. The results indicate that these models lead to optimisation results with a satisfactory level of accuracy, and at the same time, outperform other comparative approaches. These newly proposed approaches can potentially assist researchers, end-users, installers and utilities with their battery sizing and scheduling optimisation analyses, with no/minimal requirements on the granularity and amount of the available input data