Electromechanics of an Ocean Current Turbine

The development of a numeric simulation for predicting the performance of an Ocean Current Energy Conversion System is presented in this thesis along with a control system development using a PID controller for the achievement of specified rotational velocity set-points. In the beginning, this numeric model is implemented in MATLAB/Simulink® and it is used to predict the performance of a three phase squirrel single-cage type induction motor/generator in two different cases. The first case is a small 3 meter rotor diameter, 20 kW ocean current turbine with fixed pitch blades, and the second case a 20 meter, 720 kW ocean current turbine with variable pitch blades. Furthermore, the second case is also used for the development of a Voltage Source Variable Frequency Drive for the induction motor/generator. Comparison among the Variable Frequency Drive and a simplified model is applied. Finally, the simulation is also used to estimate the average electric power generation from the 720 kW Ocean Current Energy Conversion System which consists of an induction generator and an ocean current turbine connected with a shaft which modeled as a mechanical vibration system

Electromechanics of an Ocean Current Turbine

The development of a numeric simulation for predicting the performance of an Ocean Current Energy Conversion System is presented in this thesis along with a control system development using a PID controller for the achievement of specified rotational velocity set-points. In the beginning, this numeric model is implemented in MATLAB/Simulink® and it is used to predict the performance of a three phase squirrel single-cage type induction motor/generator in two different cases. The first case is a small 3 meter rotor diameter, 20 kW ocean current turbine with fixed pitch blades, and the second case a 20 meter, 720 kW ocean current turbine with variable pitch blades. Furthermore, the second case is also used for the development of a Voltage Source Variable Frequency Drive for the induction motor/generator. Comparison among the Variable Frequency Drive and a simplified model is applied. Finally, the simulation is also used to estimate the average electric power generation from the 720 kW Ocean Current Energy Conversion System which consists of an induction generator and an ocean current turbine connected with a shaft which modeled as a mechanical vibration system

Learning to detect video events from zero or very few video examples

In this work we deal with the problem of high-level event detection in video. Specifically, we study the challenging problems of i) learning to detect video events from solely a textual description of the event, without using any positive video examples, and ii) additionally exploiting very few positive training samples together with a small number of ``related'' videos. For learning only from an event's textual description, we first identify a general learning framework and then study the impact of different design choices for various stages of this framework. For additionally learning from example videos, when true positive training samples are scarce, we employ an extension of the Support Vector Machine that allows us to exploit ``related'' event videos by automatically introducing different weights for subsets of the videos in the overall training set. Experimental evaluations performed on the large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for publicatio

Impact of synchronous condensers on transmission line protection in scenarios with high penetration of renewable energy sources

The objective of the studies presented in this paper aims to demonstrate that the deployment and operational control of synchronous condensers in the GB transmission system can mitigate a part of the challenges associated with the high penetration of renewable energy sources. These include the decline of short circuit level and the subsequent impact on transmission line protection schemes. The case studies include scenarios such as transmission-level faults, fault level calculation and assessment of distance protection performance. The results and observations included in the paper aim to highlight the means and supporting evidence for the benefits of synchronous condensers in the view of a fully de-carbonised power system

HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces

In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synthesize realistic facial images, yet producing reenacted faces that are prone to significant visual artifacts, especially under the challenging condition of extreme head pose changes, or requiring expensive few-shot fine-tuning to better preserve the source identity characteristics. We propose to address these limitations by leveraging the photorealistic generation ability and the disentangled properties of a pretrained StyleGAN2 generator, by first inverting the real images into its latent space and then using a hypernetwork to perform: (i) refinement of the source identity characteristics and (ii) facial pose re-targeting, eliminating this way the dependence on external editing methods that typically produce artifacts. Our method operates under the one-shot setting (i.e., using a single source frame) and allows for cross-subject reenactment, without requiring any subject-specific fine-tuning. We compare our method both quantitatively and qualitatively against several state-of-the-art techniques on the standard benchmarks of VoxCeleb1 and VoxCeleb2, demonstrating the superiority of our approach in producing artifact-free images, exhibiting remarkable robustness even under extreme head pose changes. We make the code and the pretrained models publicly available at: https://github.com/StelaBou/HyperReenact .Comment: Accepted for publication in ICCV 2023. Project page: https://stelabou.github.io/hyperreenact.github.io/ Code: https://github.com/StelaBou/HyperReenac

HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces

In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synthesize realistic facial images, yet producing reenacted faces that are prone to significant visual artifacts, especially under the challenging condition of extreme head pose changes, or requiring expensive few-shot fine-tuning to better preserve the source identity characteristics. We propose to address these limitations by leveraging the photorealistic generation ability and the disentangled properties of a pretrained StyleGAN2 generator, by first inverting the real images into its latent space and then using a hypernetwork to perform:(i) refinement of the source identity characteristics and (ii) facial pose re-targeting, eliminating this way the dependence on external editing methods that typically produce artifacts. Our method operates under the one-shot setting (ie, using a single source frame) and allows for cross-subject reenactment, without requiring any subject-specific fine-tuning. We compare our method both quantitatively and qualitatively against several state-of-the-art techniques on the standard benchmarks of VoxCeleb1 and VoxCeleb2, demonstrating the superiority of our approach in producing artifact-free images, exhibiting remarkable robustness even under extreme head pose changes

Non-unit protection for HVDC grids : an analytical approach for wavelet transform-based schemes

Speed and selectivity of DC fault protection are critical for High-Voltage DC (HVDC) grids and present significant technical and economic challenges. Therefore, this paper proposes a non-unit protection solution that detects and discriminates DC faults based on frequency domain analysis of the transient period of DC faults. The representation of a generic HVDC grid section and the corresponding DC-side fault signatures in the frequency domain form the basis of a generalized approach for analytically designing a protection scheme based on Wavelet Transform (WT). The proposed solution is adaptive within its design stage and offers general applicability and immunity to system changes, while the protection settings are configured for optimized performance. The scheme is validated through offline simulations in PSCAD/EMTDC and the technical feasibility of the algorithm in the real world is demonstrated through the use of real-time digital simulation (using RTDS) and Hardware-in-the-Loop (HIL) testing. Both offline and real-time simulations demonstrate that the scheme is able to detect and discriminate between internal and external faults at a significantly high speed, while remaining sensitive to high impedance faults and robust to external disturbances and outside noise

Fault location in DC microgrids based on a multiple capacitive earthing scheme

This paper presents a new method for locating faults along feeders in a DC microgrid using a multiple capacitive earthing scheme. During fault conditions, capacitors within the earthing scheme are charging by transient currents that correlate to the fault distance and resistance. Therefore, by assessing the response of the capacitive earthing scheme during the fault, the distance to fault is estimated. The proposed method uti- lizes instantaneous current and voltage measurements (obtained from the feeder terminals and earthing capacitors) applied to an analytical mathematical model of the faulted feeder. The proposed method has been found to accurately estimate the fault position along the faulted feeder and systematic evaluation has been carried out to further scrutinize its performance under different loading scenarios and highly-resistive faults. Addition- ally, the performance and practical feasibility of the proposed method has been experimentally validated by developing a low- voltage laboratory prototype and testing it under a series of test conditions

HyperReenact : one-shot reenactment via jointly learning to refine and retarget faces

In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synthesize realistic facial images, yet producing reenacted faces that are prone to significant visual artifacts, especially under the challenging condition of extreme head pose changes, or requiring expensive few-shot fine-tuning to better preserve the source identity characteristics. We propose to address these limitations by leveraging the photorealistic generation ability and the disentangled properties of a pretrained StyleGAN2 generator, by first inverting the real images into its latent space and then using a hypernetwork to perform: (i) refinement of the source identity characteristics and (ii) facial pose re-targeting, eliminating this way the dependence on external editing methods that typically produce artifacts. Our method operates under the one-shot setting (i.e., using a single source frame) and allows for cross-subject reenactment, without requiring any subject-specific fine-tuning. We compare our method both quantitatively and qualitatively against several state-of-the-art techniques on the standard benchmarks of VoxCeleb1 and VoxCeleb2, demonstrating the superiority of our approach in producing artifact-free images, exhibiting remarkable robustness even under extreme head pose changes. We make the code and the pretrained models publicly available at: https://github.com/StelaBou/HyperReenact

The prognostic value of P53 in patients with prostate cancer after radical prostatectomy

Objective: This study assesses the correlation of p53 immunoreactivity andp21 immunoreactivity with biochemical recurrence after radicalprostatectomy.Materials and Methods: P53 protein expression and p21 were evaluated on84 archival paraffin-embedded radical prostatectomy specimens. Patientswere divided into 2 groups for p533: patients with low (38/84, 45%) andpatients with high (46/84, 55%) p53 immunoreactivity and into 5 groups forp21. Patients with expression of p21<1%, patients with p21 expressionbetween 1 and 5%, patients with p21 expression between 5 and 10%,patients with p21 expression between 10 and 20% and finally patients withp21 expression over 20%. The results were correlated with Gleason score,DNA ploidy, stage and serum PSA. Kaplan-Meier biochemical recurrence freesurvival and Cox hazard-regression model were used for analysis.Results: Multivariate analysis revealed p53, DNA ploidy, Gleason score andstage to be independent prognostic factors in the order they are presented.Kaplan-Meier analysis showed a statistically significant difference inbiochemical recurrence when p53 high expression and DNA aneuploidy werecombined. P21 and PSA level according the previus analysis were notindependent prognostic factors. Conclusion: The results of this study suggest that stratification for p53expression, p21 expression and DNA ploidy status can provide additionalprognostic information for patients with prostate carcinoma after radicalprostatectomy