20,949 research outputs found

    Deep Dynamic Cloud Lighting

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    Sky illumination is a core source of lighting in rendering, and a substantial amount of work has been developed to simulate lighting from clear skies. However, in reality, clouds substantially alter the appearance of the sky and subsequently change the scene's illumination. While there have been recent advances in developing sky models which include clouds, these all neglect cloud movement which is a crucial component of cloudy sky appearance. In any sort of video or interactive environment, it can be expected that clouds will move, sometimes quite substantially in a short period of time. Our work proposes a solution to this which enables whole-sky dynamic cloud synthesis for the first time. We achieve this by proposing a multi-timescale sky appearance model which learns to predict the sky illumination over various timescales, and can be used to add dynamism to previous static, cloudy sky lighting approaches.Comment: Project page: https://pinarsatilmis.github.io/DDC

    Digital Image Correlation for Measuring Full-Field Residual Stresses in Wire and Arc Additive Manufactured Components

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    This study aims to demonstrate the capability of the digital image correlation (DIC) technique for evaluating full-field residual stresses in wire and arc-additive-manufactured (WAAM) components. Investigations were carried out on WAAM steel parts (wall deposited on a substrate) with two different wall heights: 24 mm and 48 mm. Mild steel solid wire AWS ER70S-6 was used to print WAAM walls on substrates that were rigidly clamped to H-profiles. DIC was used to monitor the bending deformation of WAAM parts during unclamping from the H-profiles, and residual stresses were calculated from the strain field captured during unclamping. Residual stresses determined from the proposed DIC-based method were verified with an analytical model and validated by the results from established residual stress measurement techniques, i.e., the contour method and X-ray diffraction.</jats:p

    VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs

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    We introduce VIVE3D, a novel approach that extends the capabilities of image-based 3D GANs to video editing and is able to represent the input video in an identity-preserving and temporally consistent way. We propose two new building blocks. First, we introduce a novel GAN inversion technique specifically tailored to 3D GANs by jointly embedding multiple frames and optimizing for the camera parameters. Second, besides traditional semantic face edits (e.g. for age and expression), we are the first to demonstrate edits that show novel views of the head enabled by the inherent properties of 3D GANs and our optical flow-guided compositing technique to combine the head with the background video. Our experiments demonstrate that VIVE3D generates high-fidelity face edits at consistent quality from a range of camera viewpoints which are composited with the original video in a temporally and spatially consistent manner.Comment: CVPR 2023. Project webpage and video available at http://afruehstueck.github.io/vive3

    Computertomographie-basierte Bestimmung von Aortenklappenkalk und seine Assoziation mit Komplikationen nach interventioneller Aortenklappenimplantation (TAVI)

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    Background: Severe aortic valve calcification (AVC) has generally been recognized as a key factor in the occurrence of adverse events after transcatheter aortic valve implantation (TAVI). To date, however, a consensus on a standardized calcium detection threshold for aortic valve calcium quantification in contrast-enhanced computed tomography angiography (CTA) is still lacking. The present thesis aimed at comparing two different approaches for quantifying AVC in CTA scans based on their predictive power for adverse events and survival after a TAVI procedure.   Methods: The extensive dataset of this study included 198 characteristics for each of the 965 prospectively included patients who had undergone TAVI between November 2012 and December 2019 at the German Heart Center Berlin (DHZB). AVC quantification in CTA scans was performed at a fixed Hounsfield Unit (HU) threshold of 850 HU (HU 850 approach) and at a patient-specific threshold, where the HU threshold was set by multiplying the mean luminal attenuation of the ascending aorta by 2 (+100 % HUAorta approach). The primary endpoint of this study consisted of a combination of post-TAVI outcomes (paravalvular leak ≥ mild, implant-related conduction disturbances, 30-day mortality, post-procedural stroke, annulus rupture, and device migration). The Akaike information criterion was used to select variables for the multivariable regression model. Multivariable analysis was carried out to determine the predictive power of the investigated approaches.   Results: Multivariable analyses showed that a fixed threshold of 850 HU (calcium volume cut-off 146 mm3) was unable to predict the composite clinical endpoint post-TAVI (OR=1.13, 95 % CI 0.87 to 1.48, p=0.35). In contrast, the +100 % HUAorta approach (calcium volume cut-off 1421 mm3) enabled independent prediction of the composite clinical endpoint post-TAVI (OR=2, 95 % CI 1.52 to 2.64, p=9.2x10-7). No significant difference in the Kaplan-Meier survival analysis was observed for either of the approaches.   Conclusions: The patient-specific calcium detection threshold +100 % HUAorta is more predictive of post-TAVI adverse events included in the combined clinical endpoint than the fixed HU 850 approach. For the +100 % HUAorta approach, a calcium volume cut-off of 1421 mm3 of the aortic valve had the highest predictive value.Hintergrund: Ein wichtiger Auslöser von Komplikationen nach einer Transkatheter-Aortenklappen-Implantation (TAVI) sind ausgeprägte Kalkablagerung an der Aortenklappe. Dennoch erfolgte bisher keine Einigung auf ein standardisiertes Messverfahren zur Quantifizierung der Kalklast der Aortenklappe in einer kontrastverstärkten dynamischen computertomographischen Angiographie (CTA). Die vorliegende Dissertation untersucht, inwieweit die Wahl des Analyseverfahrens zur Quantifizierung von Kalkablagerungen in der Aortenklappe die Prognose von Komplikationen und der Überlebensdauer nach einer TAVI beeinflusst.   Methodik: Der Untersuchung liegt ein umfangreicher Datensatz von 965 Patienten mit 198 Merkmalen pro Patienten zugrunde, welche sich zwischen 2012 und 2019 am Deutschen Herzzentrum Berlin einer TAVI unterzogen haben. Die Quantifizierung der Kalkablagerung an der Aortenklappe mittels CTA wurde einerseits mit einem starren Grenzwert von 850 Hounsfield Einheiten (HU) (HU 850 Verfahren) und andererseits anhand eines individuellen Grenzwertes bemessen. Letzterer ergibt sich aus der HU-Dämpfung in dem Lumen der Aorta ascendens multipliziert mit 2 (+100 % HUAorta Verfahren). Der primäre klinische Endpunkt dieser Dissertation besteht aus einem aus sechs Variablen zusammengesetzten klinischen Endpunkt, welcher ungewünschte Ereignisse nach einer TAVI abbildet (paravalvuläre Leckage ≥mild, Herzrhythmusstörungen nach einer TAVI, Tod innerhalb von 30 Tagen, post-TAVI Schlaganfall, Ruptur des Annulus und Prothesendislokation). Mögliche Störfaktoren, die auf das Eintreten der Komplikationen nach TAVI Einfluss haben, wurden durch den Einsatz des Akaike Informationskriterium ermittelt. Um die Vorhersagekraft von Komplikationen nach einer TAVI durch beide Verfahren zu ermitteln, wurde eine multivariate Regressionsanalyse durchgeführt.   Ergebnisse: Die multivariaten logistischen Regressionen zeigen, dass die Messung der Kalkablagerungen anhand der HU 850 Messung (Kalklast Grenzwert von 146 mm3) die Komplikationen und die Überlebensdauer nicht vorhersagen konnten (OR=1.13, 95 % CI 0.87 bis 1.48, p=0.35). Die nach dem +100 % HUAorta Verfahren (Kalklast Grenzwert von 1421 mm3) individualisierte Kalkmessung erwies sich hingegen als sehr aussagekräftig, da hiermit Komplikationen nach einer TAVI signifikant vorhergesagt werden konnten (OR=2, 95 % CI 1.52 bis 2.64, p=9.2x10-7). In Hinblick auf die postoperative Kaplan-Meier Überlebenszeitanalyse kann auch mit dem +100 % HUAorta Verfahren keine Vorhersage getroffen werden.   Fazit: Aus der Dissertation ergibt sich die Empfehlung, die Messung von Kalkablagerungen nach dem +100 % HUAorta Verfahren vorzunehmen, da Komplikationen wesentlich besser und zuverlässiger als nach der gängigen HU 850 Messmethode vorhergesagt werden können. Für das +100 % HUAorta Verfahren lag der optimale Kalklast Grenzwert bei 1421 mm3

    Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC

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    The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this final state has yielded some of the most precise measurements of the particle. As measurements of the Higgs boson become increasingly precise, greater import is placed on the factors that constitute the uncertainty. Reducing the effects of these uncertainties requires an understanding of their causes. The research presented in this thesis aims to illuminate how uncertainties on simulation modelling are determined and proffers novel techniques in deriving them. The upgrade of the FastCaloSim tool is described, used for simulating events in the ATLAS calorimeter at a rate far exceeding the nominal detector simulation, Geant4. The integration of a method that allows the toolbox to emulate the accordion geometry of the liquid argon calorimeters is detailed. This tool allows for the production of larger samples while using significantly fewer computing resources. A measurement of the total Higgs boson production cross-section multiplied by the diphoton branching ratio (σ × Bγγ) is presented, where this value was determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement with the Standard Model prediction. The signal and background shape modelling is described, and the contribution of the background modelling uncertainty to the total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production mechanism. A method for estimating the number of events in a Monte Carlo background sample required to model the shape is detailed. It was found that the size of the nominal γγ background events sample required a multiplicative increase by a factor of 3.60 to adequately model the background with a confidence level of 68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate, 0.5 billion additional simulated events were produced, substantially reducing the background modelling uncertainty. A technique is detailed for emulating the effects of Monte Carlo event generator differences using multivariate reweighting. The technique is used to estimate the event generator uncertainty on the signal modelling of tHqb events, improving the reliability of estimating the tHqb production cross-section. Then this multivariate reweighting technique is used to estimate the generator modelling uncertainties on background V γγ samples for the first time. The estimated uncertainties were found to be covered by the currently assumed background modelling uncertainty

    Learning disentangled speech representations

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    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

    Underwater optical wireless communications in turbulent conditions: from simulation to experimentation

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    Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel. This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated. Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link. Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions

    An investigation of the geothermal potential of the Upper Devonian sandstones beneath eastern Glasgow

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    The urban development of the city of Glasgow is a consequence of its economic development, in part fuelled by local coalfields which exploited rocks in the same sedimentary basin within which geothermal resources in flooded abandoned mine workings, and deeper hot sedimentary aquifers (HSA), are present. This creates an opportunity to provide geothermal heating to areas of dense urban population with high heat demand. The depth of the target HSA geothermal resource, in Upper Devonian aged sandstones of the Stratheden Group, beneath eastern Glasgow was determined by gravity surveying and structural geological modelling. The estimated depth of the geothermal resource ranged from c.1500-2000 m, in the eastward deepening sedimentary basin. To reliably estimate the temperature of the geothermal resource, rigorous corrections to account for the effects of palaeoclimate and topography on heat flow were applied to boreholes in the Greater Glasgow area. The mean regional corrected heat flow was calculated as 75.7 mW m-2, an increase of 13.8 mW m-2 from the uncorrected value of 61.9 mW m-2, emphasising the extent to which heat flow was previously underestimated. Extrapolation of the geothermal gradient, calculated from the mean regional corrected heat flow, results in aquifer temperatures of c. 64-79 °C at depths of c.1500-2000 m beneath eastern Glasgow. The geothermal resource may, therefore, be capable of supporting a wide variety of direct heat use applications if sufficient matrix permeability or fracture networks are present. However, diagenetic effects such as quartz and carbonate cementation were found to restrict the porosity in Upper Devonian sandstones in a borehole and outcrop analogue study. These effects may likewise reduce porosity and intergranular permeability in the target aquifer, although this crucial aspect cannot be fully understood without deep exploratory drilling. To quantify the magnitude of the deep geothermal resource, the indicative thermal power outputs of geothermal doublet wells located in Glasgow’s East End were calculated for the first time, with outputs ranging from 1.3-2.1 MW dependent upon the aquifer depth. This, however, is predicated upon an aquifer permeability of c. 40 mD, which if reduced to 10 mD or less due to the effects of diagenesis, significantly reduces the thermal power outputs to 230-390 kW. The lack of assured project-success, given uncertainties related to the aquifer properties at depth, coupled with high capital costs of drilling, pose barriers to the development of deep geothermal energy in Glasgow. Further investigation of the economic viability of geothermal exploration, and alternative technological solutions is therefore required to mitigate the technical and economic risks. However, if sufficient matrix permeability or fracture networks are present at depth in the Upper Devonian sandstone sequence, then the potential contribution that geothermal energy could make to meeting local heat demand, reducing greenhouse gas emissions, and addressing the ‘energy trilemma’ in Glasgow is significant

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    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 Rowen’s 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 engine’s 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 turbine’s 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 turbine’s 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
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