7,569 research outputs found
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowenās model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engineās failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbineās failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbineās CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo SimĆ£o Diniz Dalmolin
Structure and adsorption properties of gas-ionic liquid interfaces
Supported ionic liquids are a diverse class of materials that have been considered
as a promising approach to design new surface properties within solids for gas
adsorption and separation applications. In these materials, the surface morphology and
composition of a porous solid are modified by depositing ionic liquid. The resulting
materials exhibit a unique combination of structural and gas adsorption properties
arising from both components, the support, and the liquid. Naturally, theoretical and
experimental studies devoted to understanding the underlying principles of exhibited
interfacial properties have been an intense area of research. However, a complete
understanding of the interplay between interfacial gas-liquid and liquid-solid
interactions as well as molecular details of these processes remains elusive.
The proposed problem is challenging and in this thesis, it is approached from
two different perspectives applying computational and experimental techniques. In
particular, molecular dynamics simulations are used to model gas adsorption in films
of ionic liquids on a molecular level. A detailed description of the modeled systems is
possible if the interfacial and bulk properties of ionic liquid films are separated. In this
study, we use a unique method that recognizes the interfacial and bulk structures of
ionic liquids and distinguishes gas adsorption from gas solubility. By combining
classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous
support.
The developed approach was applied to a range of ionic liquids that feature
different interaction behavior with gas and porous support. Using molecular
simulations with interfacial analysis, it was discovered that gas adsorption capacity
can be directly related to gas solubility data, allowing the development of a predictive
model for the gas adsorption performance of ionic liquid films. Furthermore, it was
found that this CO2 adsorption on the surface of ionic liquid films is determined by the
specific arrangement of cations and anions on the surface. A particularly important
result is that, for the first time, a quantitative relation between these structural and
adsorption properties of different ionic liquid films has been established. This link
between two types of properties determines design principles for supported ionic
liquids.
However, the proposed predictive model and design principles rely on the
assumption that the ionic liquid is uniformly distributed on the surface of the porous
support. To test how ionic liquids behave under confinement, nitrogen physisorption
experiments were conducted for microā and mesopore analysis of supported ionic
liquid materials. In conjunction with mean-field density functional theory applied to
the lattice gas and pore models, we revealed different scenarios for the pore-filling
mechanism depending on the strength of the liquid-solid interactions.
In this thesis, a combination of computational and experimental studies provides
a framework for the characterization of complex interfacial gas-liquid and liquid-solid
processes. It is shown that interfacial analysis is a powerful tool for studying
molecular-level interactions between different phases. Finally, nitrogen sorption
experiments were effectively used to obtain information on the structure of supported
ionic liquids
Optical coherence tomography methods using 2-D detector arrays
Optical coherence tomography (OCT) is a non-invasive, non-contact optical technique that allows cross-section imaging of biological tissues with high spatial resolution, high sensitivity and high dynamic range. Standard OCT uses a focused beam to illuminate a point on the target and detects the signal using a single photodetector. To acquire transverse information, transversal scanning of the illumination point is required. Alternatively, multiple OCT channels can be operated in parallel simultaneously; parallel OCT signals are recorded by a two-dimensional (2D) detector array. This approach is known as Parallel-detection OCT. In this thesis, methods, experiments and results using three parallel OCT techniques, including full -field (time-domain) OCT (FF-OCT), full-field swept-source OCT (FF-SS-OCT) and line-field Fourier-domain OCT (LF-FD-OCT), are presented. Several 2D digital cameras of different formats have been used and evaluated in the experiments of different methods. With the LF-FD-OCT method, photography equipment, such as flashtubes and commercial DSLR cameras have been equipped and tested for OCT imaging. The techniques used in FF-OCT and FF-SS-OCT are employed in a novel wavefront sensing technique, which combines OCT methods with a Shack-Hartmann wavefront sensor (SH-WFS). This combination technique is demonstrated capable of measuring depth-resolved wavefront aberrations, which has the potential to extend the applications of SH-WFS in wavefront-guided biomedical imaging techniques
Computational analysis of single-cell dynamics: protein localisation, cell cycle, and metabolic adaptation
Cells need to be able to adapt quickly to changes in nutrient availability in their environment in order to survive. Budding yeasts constitute a convenient model to study how eukaryotic cells respond to sudden environmental change because of their fast growth and relative simplicity. Many of the intracellular changes needed for adaptation are spatial and transient; they can be captured experimentally using ļ¬uorescence time-lapse microscopy. These data are limited when only used for observation, and become most powerful when they can be used to extract quantitative, dynamic, single-cell information.
In this thesis we describe an analysis framework heavily based on deep learning methods that allows us to quantitatively describe diļ¬erent aspects of cellsā response to a new environment from microscopy data. chapter 2 describes a start-to-ļ¬nish pipeline for data access and preprocessing, cell segmentation, volume and growth rate estimation, and lineage extraction. We provide benchmarks of run time and describe how to speed up analysis using parallelisation. We then show how this pipeline can be extended with custom processing functions, and how it can be used for real-time analysis of microscopy experiments.
In chapter 3 we develop a method for predicting the location of the vacuole and nucleus from bright ļ¬eld images. We combine this method with cell segmentation to quantify the timing of three aspects of the cellsā response to a sudden nutrient shift: a transient change in transcription factor nuclear localisation, a change in instantaneous growth rate, and the reorganisation of the plasma membrane through the endocytosis of certain membrane proteins. In particular, we quantify the relative timing of these processes and show that there is a consistent lag between the perception of the stress at the level of gene expression and the reorganisation of the cell membrane.
In chapter 4 we evaluate several methods to obtain cell cycle phase information in a label-free manner. We begin by using the outputs of cell segmentation to predict cytokinesis with high accuracy. We then predict cell cycle phase at a higher granularity directly from bright ļ¬eld images. We show that bright ļ¬eld images contain information about the cell cycle which is not visible by eye. We use these methods to quantify the relationship between cell cycle phase length and growth rate.
Finally, in chapter 5 we look beyond microscopy to the bigger picture. We sketch an abstract description of how, at a genome-scale, cells might choose a strategy for adapting to a nutrient shift based on limited, noisy, and local information. Starting from a constraint-based model of metabolism, we propose an algorithm to navigate through metabolic space using only a lossy encoding of the full metabolic network. We show how this navigation can be used to adapt to a changing environment, and how its results diļ¬er from the global optimisation usually applied to metabolic models
Excess foundry sand characterization and experimental investigation in controlled low-strength material and hot-mixing asphalt
This report provides technical data regarding the reuse of excess foundry sand. The report addresses three topics: a statistically sound evaluation of the characterization of foundry sand, a laboratory investigation to qualify excess foundry sand as a major component in controlled low-strength material (CLSM), and the identification of the best methods for using foundry sand as a replacement for natural aggregates for construction purposes, specifically in asphalt paving materials. The survival analysis statistical technique was used to characterize foundry sand over a full spectrum of general chemical parameters, metallic elements, and organic compounds regarding bulk analysis and leachate characterization. Not limited to characterization and environmental impact, foundry sand was evaluated by factor analyses, which contributes to proper selection of factor and maximization of the reuse marketplace for foundry sand. Regarding the integration of foundry sand into CLSM, excavatable CLSM and structural CLSM containing different types of excess foundry sands were investigated through laboratory experiments. Foundry sand was approved to constitute a major component in CLSM. Regarding the integration of foundry sand into asphalt paving materials, the optimum asphalt content was determined for each mixture, as well as the bulk density, maximum density, asphalt absorption, and air voids at Nini, Ndes, and Nmax. It was found that foundry sands can be used as an aggregate in hot-mix asphalt production, but each sand should be evaluated individually. Foundry sands tend to lower the strength of mixtures and also may make them more susceptible to moisture damage. Finally, traditional anti-stripping additives may decrease the moisture sensitivity of a mixture containing foundry sand, but not to the level allowed by most highway agencies.Structural Engineerin
Three decades of statistical pattern recognition paradigm for SHM of bridges
This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordBridges play a crucial role in modern societies, regardless of their culture, geographical location, or economic development. The safest, economical, and most resilient bridges are those that are well managed and maintained. In the last three decades, structural health monitoring (SHM) has been a promising tool in management activities of bridges as potentially it permits one to perform condition assessment to reduce uncertainty in the planning and designing of maintenance activities as well as to increase the service performance and safety of operation. The general idea has been the transformation of massive data obtained from monitoring systems and numerical models into meaningful information. To deal with large amounts of data and perform the damage identification automatically, SHM has been cast in the context of the statistical pattern recognition (SPR) paradigm, where machine learning plays an important role. Meanwhile, recent technologies have unveiled alternative sensing opportunities and new perspectives to manage and observe the response of bridges, but it is widely recognized that bridge SHM is not yet fully capable of producing reliable global information on the presence of damage. While there have been multiple review studies published on SHM and vibration-based structural damage detection for wider scopes, there have not been so many reviews on SHM of bridges in the context of the SPR paradigm. Besides, some of those reviews become obsolete quite fast, and they are usually biased towards applications falling outside of bridge engineering. Therefore, the main goal of this article is to summarize the concept of SHM and point out key developments in research and applications of the SPR paradigm observed in bridges in the last three decades, including developments in sensing technology and data analysis, and to identify current and future trends to promote more coordinated and interdisciplinary research in the SHM of bridges
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