Next-Generation Rainfall IDF Curves for the Virginian Drainage Area of Chesapeake Bay

Probability-based intensity-duration-frequency IDF curves are needed but currently lacking for Department of Defense DoD to construct and manage its infrastructure in changing climate. The objectives of this project were to 1 develop an innovative approach for considering rainfall non-stationarity in developing such IDF curves and 2 apply this approach to the state of Virginia. In this regard, the observed data on 15-min rainfall at 57 gauges and the precipitations projected by twelve pairs of Regional Climate Model RCM and Global Circulation Model GCM were used. For a given gauge or watershed, in terms of fitting the empirical exceedance probabilities, a best statistical distribution was chosen and then used to create the existing, projected historic, and projected future IDF curves. For a given return period, the projected historic IDF curves were compared with the existing ones to determine the lower and upper limits of the future IDF curve. The most-probable future IDF curve was determined as the average of the twelve curves responding to the GCM-RCM models. In addition, for a given duration and return period, the responding rainfall intensities were used to create a probability-based IDF curve. Further, the areal precipitations for each of the 53 watersheds were used to create the watershed-level future IDF curves. The project results are expected to be a useful and usable tool in guarding against over- or under committing resources

GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression

Neural-network-based approaches recently emerged in the field of data compression and have already led to significant progress in image compression, especially in achieving a higher compression ratio. In the lossless image compression scenario, however, existing methods often struggle to learn a probability model of full-size high-resolution images due to the limitation of the computation source. The current strategy is to crop high-resolution images into multiple non-overlapping patches and process them independently. This strategy ignores long-term dependencies beyond patches, thus limiting modeling performance. To address this problem, we propose a hierarchical latent variable model with a global context to capture the long-term dependencies of high-resolution images. Besides the latent variable unique to each patch, we introduce shared latent variables between patches to construct the global context. The shared latent variables are extracted by a self-supervised clustering module inside the model's encoder. This clustering module assigns each patch the confidence that it belongs to any cluster. Later, shared latent variables are learned according to latent variables of patches and their confidence, which reflects the similarity of patches in the same cluster and benefits the global context modeling. Experimental results show that our global context model improves compression ratio compared to the engineered codecs and deep learning models on three benchmark high-resolution image datasets, DIV2K, CLIC.pro, and CLIC.mobile

Echoes from black bounces surrounded by the string cloud

In the string theory, the fundamental blocks of nature are not particles but one-dimensional strings. Therefore, a generalization of this idea is to think of it as a cloud of strings. Rodrigues et al. embedded the black bounces spacetime into the string cloud, which demonstrates that the existence of the string cloud makes the Bardeen black hole singular, while the black bounces spacetime remains regular. On the other hand, the echoes are the correction to the late stage of the quasinormal ringing for a black hole, which is caused by the deviation of the spacetime relative to the initial black hole spacetime geometry in the near-horizon region. In this work, we study the gravitational wave echoes of black bounces spacetime surrounded by a cloud of strings under scalar field and electromagnetic field perturbation to explore what gravitational effects near-horizon region are caused by string cloud. The ringing of the regular black hole and traversable wormhole with string cloud are presented. Our results demonstrate that the black bounce spacetime with strings cloud is characterized by gravitational wave echoes as it transitions from regular black holes to wormholes, i.e. the echoes signal will facilitate us to distinguish between black holes and the wormholes in black bounces surrounded by the string cloud

Probing hairy black holes caused by gravitational decoupling using quasinormal modes, and greybody bounds

Extended gravitational decoupling can add hair to the black holes in general relativity by adding extra sources. The quasinormal modes of hairy black holes caused by gravitational decoupling for the massless scalar field, electromagnetic field, and axial gravitational perturbation are investigated. The equation of effective potential for three perturbations is derived in hairy black holes spacetime. We also study the time evolution corresponding to the three perturbations, and the quasinormal mode frequencies are calculated using the Prony method through the time-domain profiles. Particularly, we find that the response of hairy black hole spacetime to axial gravitational perturbations is completely different from scalar field and electromagnetic field perturbations, which may be due to the fact that the gravitational radiation produced by the perturbations of the hairy black hole metric itself is much stronger than that of the external field. Furthermore, we have calculated the bounds on this greybody factor and high-energy absorption cross section with the Sinc approximation. The study reveals that the charges ($\alpha$ and $l_0$) generating primary hair contributes positively to the greybody bounds and absorption cross section, whereas the tidal charge $Q$ from the extra sources does the opposite

Visual saliency guided textured model simplification

Mesh geometry can be used to model both object shape and details. If texture maps are involved, it is common to let mesh geometry mainly model object shapes and let the texture maps model the most object details, optimising data size and complexity of an object. To support efficient object rendering and transmission, model simplification can be applied to reduce the modelling data. However, existing methods do not well consider how object features are jointly represented by mesh geometry and texture maps, having problems in identifying and preserving important features for simplified objects. To address this, we propose a visual saliency detection method for simplifying textured 3D models. We produce good simplification results by jointly processing mesh geometry and texture map to produce a unified saliency map for identifying visually important object features. Results show that our method offers a better object rendering quality than existing methods

Text-Guided Neural Image Inpainting

Image inpainting task requires filling the corrupted image with contents coherent with the context. This research field has achieved promising progress by using neural image inpainting methods. Nevertheless, there is still a critical challenge in guessing the missed content with only the context pixels. The goal of this paper is to fill the semantic information in corrupted images according to the provided descriptive text. Unique from existing text-guided image generation works, the inpainting models are required to compare the semantic content of the given text and the remaining part of the image, then find out the semantic content that should be filled for missing part. To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet). Firstly, a dual multimodal attention mechanism is designed to extract the explicit semantic information about the corrupted regions, which is done by comparing the descriptive text and complementary image areas through reciprocal attention. Secondly, an image-text matching loss is applied to maximize the semantic similarity of the generated image and the text. Experiments are conducted on two open datasets. Results show that the proposed TDANet model reaches new state-of-the-art on both quantitative and qualitative measures. Result analysis suggests that the generated images are consistent with the guidance text, enabling the generation of various results by providing different descriptions. Codes are available at https://github.com/idealwhite/TDANetComment: ACM MM'2020 (Oral). 9 pages, 4 tables, 7 figure

Design of high-performance electric machine for hybrid vehicle

This is a Final-Year Project; Designing a high-performance motor for hybrid vehicles. Electrical and hybrid vehicles are gaining traction to promote a cleaner and greener environment with permanent magnet-based machines widely adopted for its high torque density and efficiency. However, the price volatility and environmental implications from permanent magnets, which are obtain from deep earth mining, causes significant concern for long term sustainability. A Switched reluctance motor has a simple and low-cost construction which does not employ the use of permanent magnets. The Switched reluctance motor is presented in this report as an alternative high-performance motor for a hybrid vehicle with various optimisation methodology for torque ripple reductions, that it most commonly suffers from.Bachelor of Engineering (Electrical and Electronic Engineering