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Publications

2023

Simulating the GB power system frequency during underfrequency events 2018–19

Authors
Christian Cooke; Ben Mestel;

Publication
Energy Systems

Abstract
Abstract Lightning hit a transmission power line outside London, England on 9 August 2019. There followed a loss of power from a cascade of generator outages that exceeded contingency reserves, leading to an exceptional fall in grid frequency causing widespread transport disruptions and the disconnection of over 1 m households. Simulating such events typically involves a system of differential equations representing the overall generation and load present at the time. A standard model based on the swing equation assumes unlimited capacity in aggregated resources, and the availability of these services throughout the duration of the frequency deviation. In simulating the effect of outages on the GB Grid frequency on 2019/8/9, the effect of limiting these services to the capacity of resources engaged during the event is examined. It is shown that by taking these refinements into account the timing and extent of the frequency nadir can be successfully estimated. Insight is gained into the responses of various grid characteristics and how they interact with unplanned generation imbalances. Using this adapted model, further events on the GB grid are examined to validate the influence of these features. With the model’s effectiveness validated, novel mitigating measures to preserve the stability of a low-inertia grid can be evaluated.

2023

Drilling Parameters in the Evaluation of Rock Mass Quality

Authors
Pereira, M; Fernandes, I; Moura, R; Plasencia, N;

Publication
Advances in Science, Technology and Innovation

Abstract

2023

PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric Face Recognition

Authors
Neto, PC; Sequeira, AF; Cardoso, JS; Terhörst, P;

Publication
IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Workshops, Vancouver, BC, Canada, June 17-24, 2023

Abstract
In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct. Since many biometric systems operate in critical decision-making processes, such as in forensics investigations, accurately and reliably stating the matching confidence becomes of high importance. Previous works on biometric confidence estimation can well differentiate between high and low confidence, but lack interpretability. Therefore, they do not provide accurate probabilistic estimates of the correctness of a decision. In this work, we propose a probabilistic interpretable comparison (PIC) score that accurately reflects the probability that the score originates from samples of the same identity. We prove that the proposed approach provides optimal matching confidence. Contrary to other approaches, it can also optimally combine multiple samples in a joint PIC score which further increases the recognition and confidence estimation performance. In the experiments, the proposed PIC approach is compared against all biometric confidence estimation methods available on four publicly available databases and five state-of-the-art face recognition systems. The results demonstrate that PIC has a significantly more accurate probabilistic interpretation than similar approaches and is highly effective for multi-biometric recognition. The code is publicly-available1. © 2023 IEEE.

2023

A Year Embedded in the Crypto-NFT Space

Authors
Baquero, C;

Publication
COMMUNICATIONS OF THE ACM

Abstract

2023

Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models

Authors
Moreira, G; Magalhães, SA; dos Santos, FN; Cunha, M;

Publication
IECAG 2023

Abstract

2023

Keck Adaptive Optics Current and Future Roles as an ELT Pathfinder

Authors
Wizinowich P.; Cetre S.; Chin J.; Correia C.; Gers L.; Guthery C.; Karkar S.; Kwok S.H.; Lilley S.; Lyke J.; Marin E.; Ragland S.; Richards P.; Service M.; Surendran A.; Tsubota K.; Wetherell E.; Bottom M.; Chun M.; Dekany R.; Do T.; Fassnacht C.; Fitzgerald M.; Ghez A.; Hinz P.; Jensen-Clem B.; Jones T.; de Kleer K.; Liu M.C.; Lu J.; Mather J.; Mawet D.; Millar-Blanchaer M.; Pasquale B.; Peretz E.; Sallum S.; Treu T.; Wright S.;

Publication
7th Adaptive Optics for Extremely Large Telescopes Conference, AO4ELT7 2023

Abstract
The segmented nature of the 10-m Keck telescopes combined with facility-class AO systems and science instruments, and a history of science-driven upgrades to these systems, offers a uniquely powerful pathfinder for future AO science facilities on the segmented ELTs. Keck’s 2035 Strategic Vision includes visible, high contrast and ground layer AO facilities all of which could support ELT AO pathfinding. Keck’s pathfinder strength is not just demonstrating new techniques or technologies but developing them into operational science capabilities. For example, since first Keck AO science in 1999, Keck has successfully implemented three generations of sodium-wavelength lasers and is currently implementing its third generation of real-time controller (this time GPU-based). Current pathfinder-related developments include laser tomography, near-infrared low order wavefront sensing and PSF-reconstruction for high Strehl ratio and high sky coverage on the Keck I AO system. Current AO-based primary mirror phasing techniques under development include the use of Zernike, pyramid and phase diversity techniques. High-contrast AO developments include near-infrared pyramid wavefront sensing, on-sky phase diversity, speckle nulling and predictive wavefront control. Another pathfinder development is the NASA Goddard-led ORCAS satellite to provide a bright artificial point source for AO-correction. A fast, visible science camera has been implemented in support of ORCAS, demonstrating 15 mas FWHM, and, in a further move toward the visible, ALPAO is developing a 2.5 mm spacing, 60x60 actuator deformable mirror for Keck. In addition, three new AO science instruments are planned: Liger as a prototype of TMT’s IRIS, HISPEC which is the same as TMT’s MODHIS (based on KPIC’s science success), and SCALES.

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