Ciclogénesis explosivas en el sector Euro-Atlántico: estudio de su dinámica a gran escala y variabilidad

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física de la Tierra, Astronomía y Astrofísica I (Geofísica y Meteorología) (Astronomía y Geodesia), leída el 27-11-2015.Depto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEunpu

Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking

A general method of anomaly detection from time-correlated sensor data is disclosed. Multiple time-correlated signals are received. Their cross-signal behavior is compared against a fixed library of invariants. The library is constructed during a training process, which is itself data-driven using the same time-correlated signals. The method is applicable to a broad class of problems and is designed to respond to any departure from normal operation, including faults or events that lie outside the training envelope

A sequential Bayesian approach to online power quality anomaly segmentation

Increased observability on power distribution networks can reveal signs of incipient faults which can develop into costly and unexpected plant failures. While low-cost sensing and communications infrastructure is facilitating this, it is also highlighting the complex nature of fault signals, a challenge which entails precisely extracting anomalous regions from continuous data streams before classifying the underlying fault signature. Doing this incorrectly will result in capture of uninformative data. Extraction processes can be confounded by operational noise on the network including harmonics produced by embedded generation. In this paper, an online model is proposed. Our Bayesian Changepoint Power Quality anomaly Segmentation allows automated segmentation of anomalies from continuous current waveforms, irrespective of noise. Demonstration of the effectiveness of the proposed technique is carried out with operational field data as well as a challenging simulated network, highlighting the ability to accommodate noise from typical network penetration levels of power electronic devices

Chirality scenario of the spin-glass ordering

Detailed account is given of the chirality scenario of experimental spin-glass transitions. In this scenario, the spin glass order of weakly anisotropic Heisenberg-like spin-glass magnets including canonical spin glasses are essentially chirality driven. Recent numerical and experimental results are discussed in conjunction with this scenario.Comment: Submitted to J. Phys. Soc. Japan "Special Issue on Frustration

A Systematic Review for Transformer-based Long-term Series Forecasting

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field