316 research outputs found

    Component Segmentation of Engineering Drawings Using Graph Convolutional Networks

    Full text link
    We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.Comment: Preprint accepted to Computers in Industr

    Multidimensional Time Series Methods for Economics and Finance

    Get PDF
    Questa tesi mira ad affrontare le questioni inferenziali e interpretative nei modelli ad alta dimensione e multidimensionali nel contesto dell'Economia e della Finanza. La crescente integrazione economica e finanziaria ha reso di fondamentale importanza considerare i Paesi e i Mercati Finanziari come un'unica, grande e interconnessa entitĂ . Le principali sfide indotte da questo quadro riguardano la stima e l'interpretazione di ampi Panel data, in cui le unitĂ  possono essere rappresentate da paesi o attivitĂ  finanziarie, osservate attraverso diversi indicatori nel tempo. Questa tesi propone tecniche di stima Bayesiana per nuovi modelli matriciali e tensoriali e utilizza tecniche della Teoria dei Grafi per facilitare l'interpretazione di network ad alta dimensione. I contributi sono presentati in tre capitoli. Nel Capitolo 2, vengono proposti approcci della Teoria dei Grafi per studiare le strutture e le interazioni in Network direzionali e pesati. Nel Capitolo 3, viene proposto un approccio Bayesiano di variable selection per gestire il problema della sovrapparametrizzazione nei modelli di Autorregressione Matriciale di grandi dimensioni. Nel Capitolo 4, viene esplorata la relazione dinamica tra rendimenti, volatilitĂ  e sentiment nel settore delle criptovalute attraverso un modello Autoregressivo Matriciale, che rappresenta il primo tentativo di considerare i dati sugli asset finanziari come strutture multidimensionali.This thesis aims to address the inferential and interpretational issues in high and multi-dimensional models in the context of Economics and Finance. The growing economic and financial integration has made imperative the need to conceive Countries and Financial Markets as a single, large, interconnected entity. The main challenges induced by this framework concern the estimation and interpretation of large panels, where units can be represented by countries or assets, observed via several indicators across time. This thesis proposes Bayesian estimation techniques for novel matrix and tensor-valued models and employs new methodological tools from Graph Theory to facilitate interpretation of high-dimensional networks. The contributions are presented in three chapters. In Chapter 2, Graph Theory approaches are proposed to study the structures and interactions of weighted directed networks of multivariate time series observations/relationships. In Chapter 3, a Bayesian variable selection approach is proposed to handle the over-parametrization problem in large Matrix Autoregressive models. In Chapter 4, the dynamic relationship among returns, volatility, and sentiment in the cryptocurrency class is explored through a Bayesian Matrix Autoregressive model, which is the first attempt to consider financial asset data as multi-dimensional structures

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Spatio-temporal analysis of coastal sediment erosion in Cape Town through remote sensing and geoinformation science

    Get PDF
    Coastal erosion can be described as the landward or seaward propagation of coastlines. Coastal processes occur over various space and time scales, limiting in-situ approaches of monitoring change. As such it is imperative to take advantage of multisensory, multi-scale and multi-temporal modern spatial technologies for multi-dimensional coastline change monitoring. The research presented here intends to showcase the synergy amongst remote sensing techniques by showcasing the use of coastal indicators towards shoreline assessment over the Kommetjie and Milnerton areas along the Cape Town coastline. There has been little progress in coastal studies in the Western Cape that encompass the diverse and dynamic aspects of coastal environments and in particular, sediment movement. Cape Town, in particular; is socioeconomically diverse and spatially segregated, with heavy dependence on its 240km of coastline. It faces sea level rise intensified by real-estate development close to the high-water mark and on reclaimed land. Spectral indices and classification techniques are explored to accommodate the complex bio-optical properties of coastal zones. This allows for the segmentation of land and ocean components to extract shorelines from multispectral Landsat imagery for a long term (1991-2021) shoreline assessment. The DSAS tool used these extracted shorelines to quantify shoreline change and was able to determine an overall averaged erosional rate of 2.56m/yr. for Kommetjie and 2.35m/yr. for Milnerton. Beach elevation modelling was also included to evaluate short term (2016-2021) sediment volumetric changes by applying Differential Interferometry to Sentinel-1 SLC data and the Waterline method through a combination of Sentinel -1 GRD and tide gauge data. The accuracy, validation and correction of these elevation models was conducted at the pixel level by comparison to an in-field RTK GPS survey used to capture the current state of the beaches. The results depict a sediment deficit in Kommetjie whilst accretion is prevalent along the Milnerton coastline. Shoreline propagation and coastal erosion quantification leads to a better understanding of geomorphology, hydrodynamic and land use influences on coastlines. This further informs climate adaptation strategies, urban planning and can support further development of interactive coastal information systems

    Digital twin development for improved operation of batch process systems

    Get PDF

    Machine Learning and Its Application to Reacting Flows

    Get PDF
    This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

    Statistical learning of random probability measures

    Get PDF
    The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle
    • 

    corecore