2063 research outputs found
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Condition monitoring of mooring systems for Floating Offshore Wind Turbines using Convolutional Neural Network framework coupled with Autoregressive coefficients
Full text linkThis research presents a novel approach proposed for the monitoring of mooring systems in Floating Offshore Wind Turbines (FOWTs), employing a combination of Convolutional Neural Networks (CNNs) and Auto-Regressive (AR) models. CNN finds broad application in monitoring intricate structures, as they adeptly handle noisy response data without necessitating profound domain expertise. The precision of CNNs relies on the extraction of meaningful features from input data, necessitating meticulous data curation and labeling for optimal computational efficiency and accurate estimation. Emphasis is placed on the preference for feature-rich small datasets over voluminous yet sparse datasets, aiming to enable CNNs to discern crucial patterns more effectively and mitigate issues such as overfitting and extensive preprocessing. The novelty of the proposed approach lies in the integration of AR models, which serve to compress data and enhance damage-sensitive characteristics in the input for CNNs. This integration involves deploying regression models fitted to historical responses, parameterized with AR coefficients sensitive to damage, and further classifying severity using CNNs. The sequential nature of this approach addresses challenges such as vanishing/exploding gradients, particularly for extended historical data, while also attenuating the impact of noise and irrelevant information through data compression. The study explores the effectiveness of the coupled AR-CNN method in monitoring FOWT mooring lines, with a specific focus on two levels of damage identification: detection with classification and damage severity across diverse damage and operational scenarios. The modified methodology exhibits superior outcomes by conducting a performance analysis against traditional CNNs and other machine-learning methods, highlighting the potential of the AR-CNN strategy to improve the precision of FOWT mooring line condition monitoring. These findings underscore the AR-CNN strategy's potential to enhance the accuracy of FOWT mooring line condition monitoring
Revisiting excitation force estimation in WECs: On the (mis)use of structure-based estimation approaches
No full textWave excitation force (torque) estimators, vital in wave energy systems, generally combine the nominal representation of a wave energy converter (WEC) with an excitation force (perturbation) model. Thus, this model-based estimation approach, grounded in the internal model principle, often employs two perturbation models: (i) the harmonic oscillator structure, prevalent in literature, assuming sinusoidal signals; and (ii) the integrator (random walk) scheme, assuming unit step-like signals. These models comprehensively represent a specific family of estimators, as discussed in this study. However, both models may struggle to capture the irregular (stochastic) nature of ocean waves. This study challenges the prevailing assumption that the harmonic oscillator structure, selected for its resemblance to ocean wave oscillations, is inherently the optimal choice. This study provides a rigorous discussion on convergence conditions. Thus, is shown that, while the harmonic oscillator can be highly effective under specific conditions, the random walk structure, despite its simplicity, can surpass the performance of the harmonic oscillator scheme. Formal proofs support this argument, emphasising the effectiveness of the harmonic oscillator can be guaranteed with periodic signals
Evolutionary Virus Pandemics: From Modeling and Simulations to Society
No full textThis chapter provides an introduction to the contents of this edited volume which is devoted to the modeling and simulation of mutating virus pandemics in a globally interconnected world. First, we report on the motivations and objectives of the edited book which are consistent with the idea that mathematical models should go beyond deterministic population dynamics by considering multiscale and heterogeneous features of the complex system under consideration. The second part provides a brief introduction to the contents of the chapters that follow this editorial introduction. This is then followed by an outlook on research perspectives.Maira Aguiar acknowledges the financial support by the Ministerio de Ciencia
e Innovacion (MICINN) of the Spanish Government through the Ramon y Ca-
jal grant RYC2021-031380-I
Multi-task Online Learning for Probabilistic Load Forecasting
Full text linkLoad forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions, buildings). Techniques
based on multi-task learning obtain predictions by leveraging consumption patterns from the historical load demand of multiple
entities and their relationships. However, existing techniques cannot effectively assess inherent uncertainties in load demand or account for dynamic changes in consumption patterns. This paper proposes a multi-task learning technique for online and
probabilistic load forecasting. This technique provides accurate probabilistic predictions for the loads of multiple entities by leveraging their dynamic similarities. The method’s performance is evaluated using datasets that register the load demand of
multiple entities and contain diverse and dynamic consumption patterns. The experimental results show that the proposed
method can significantly enhance the effectiveness of current multi-task learning approaches across a wide variety of load
consumption scenarios.PID2022-137063NB-I00
CNS2022-135203
CEX2021-001142-S
European Union “NextGenerationEU”/PRT
Contextual Semantics Machinery
Full text linkThe study establishes a formal connection between quantum contextuality and interactive computation.Future and Emerging Technologies (FET) Programme within the Seventh Framework Programme (FP7
Heisenberg-Limited Quantum Lidar for Joint Range and Velocity Estimation
Full text linkWe propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velocity estimations are inversely proportional to the squared number of signal photons, simultaneously attaining the Heisenberg limit. This is achieved by engineering the multiphoton squeezed state of the temporal modes and adopting standard homodyne detection. To assess the robustness of the quantum protocol, we incorporate photon losses and detuning of the homodyne receiver. Our findings reveal a quantum advantage over the best-known classical strategy across a wide range of round-trip transmissivities. Particularly, the quantum advantage is substantial for sufficiently small losses, even when compared to the optimal - potentially unattainable - classical performance limit. The quantum advantage also extends to the practical case where quantum engineering is done on top of a strong classical coherent state with watts of power. This, together with the robustness against losses and the feasibility of the measurement with state-of-the-art technology, make the protocol highly promising for near-term implementation.The
authors acknowledge support from EU FET Open project EPIQUS (899368) and HORIZON-CL4- 2022-QUANTUM01-SGA project 101113946 OpenSuperQ-Plus100 of the EU Flagship on Quantum Technologies, the Spanish Ram´on y Cajal Grant RYC-2020-030503-I, project Grant No. PID2021-125823NA-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A
way of making Europe” and “ERDF Invest in your Future”, and from the IKUR Strategy under the collaboration agreement between Ikerbasque Foundation and BCAM on behalf of the Department of Education of the Basque Government. This project has also received support from the Spanish Ministry of Economic Affairs and Digital Transformation through the QUAN-
TUM ENIA project call - Quantum Spain, and by the EU through the Recovery, Transformation and Resilience Plan - NextGenerationEU. M.R. acknowledges support from UPV/EHU PhD Grant PIF21/289. QZ acknowledges NSF CAREER Award CCF-2240641, National Science Foundation OMA-2326746, ONR Grant No. N00014-23-1-2296, Cisco Systems, Inc. and Hal-
liburton Company
LANDIS’ CONJECTURE: A SURVEY
No full textWe survey Kondrat'ev--Landis' conjecture, providing an up-to-date account of the main advances and describing the techniques developed. We complement the overview with references and formulations of the problem in further closely connected contexts
The Tustin Method for approximating eigenvalues of delay systems
Full text linkNumerical approximation of the eigenvalues of Delay Differential Equations (DDEs) is an active field of research due to its impact in the modelling of several processes of industrial interest. In this work we introduce the Tustin Method, a new numerical method for Linear Time-Invariant (LTI) systems based on the Cayley transform. We compare the performance of the Tustin Method with the Semi-discretization method for computing eigenvalues using the Arnoldi iteration, and for constructing stability charts. We find that the Tustin method is a promising alternative when combined with the Arnoldi iteration. Also, we rigorously prove that, in the limit of increasing precision, the Tustin method approaches all the eigenvalues. We supplement our work with a multiscale algorithm for determining the stability boundary
Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem
Full text linkThe Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic algorithms, such as adaptive neighborhood search (ANS) and variable neighborhood descent (VND), mainly focus on optimizing the current solution without considering potential long-term consequences, which may easily get stuck in local optima and limit the overall performance. Thus, we propose a multi-agent deep Q-network-based metaheuristic algorithm (MDQN-MA) for NRP to harness the strengths of various metaheuristics. Each agent encapsulates a metaheuristic algorithm, where its available actions represent different perspectives of the problem environment. By combining their strengths and various perspectives, these agents can work collaboratively to navigate and search for a broader range of potential solutions effectively. Furthermore, to improve the performance of an individual agent, we model its neighborhood search as a Markov Decision Process model and integrate a deep Q-network to consider long-term impacts for its neighborhood sequential decision-making. The experimental results clearly show that an individual agent in MDQN-MA can outperform ANS and VND, and multiple agents in MDQN-MA even perform better, achieving the best results among metaheuristic algorithms on the Second International Nurse Rostering Competition dataset
Deep Neural Network for damage detection in Infante Dom Henrique bridge using multi-sensor data
Full text linkThis paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto.
The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale SHM application.
We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them.
An autoencoder-based Deep Neural Network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability.
The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements.
Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination.
To test the performance of the methodology in detecting the presence of damage, we employ a Finite Element model to calculate the relative change in the structural response induced by damage at four locations.
These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements.
We analyze the Receiver Operating Curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage.
Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources.
When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases compared to using local variables only.
The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to precission values for the four considered test damage scenarios.
Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.HAZITEK programme (ERROTAID project) and TCRINI project (KK-2023-0029)
European Horizon (HE) with LIASON project (GA 101103698), and FUTURAL project (101083958