An FBG staged monitoring method for carbon fiber reinforced plastics composite fracture status based on modulus/strain wave coupling property

From the sensitivity of the FBG center wavelength changing with the macro-elastic modulus and the instantaneous fracture strain wave on the surface of carbon fiber reinforced plastics (CFRP) composite, we investigate the correlation between the macro-elastic modulus (the changing rate of the FBG center wavelength during the stretching process) and the fracture status of CFRP specimen. An FBG staged monitoring method based on modulus/strain wave coupling properties designed to monitor tensile fracture state of composite has been proposed. By monitoring the change of macro-elastic modulus during the stretching process, the damage state of composite in a macro perspective is obtained; when the internal damage reaches a critical state, the fracture distribution status of CFRP specimen is captured by monitoring the strain wave response induced by stress relaxation in different locations. Simulated analysis and experimental results in this paper show that the proposed FBG staged monitoring method can achieve the identification of the damage state and the breakage position of CFRP composite effectively, with a good prospect

Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach

Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network seamlessly with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model with respect to predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 2000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.</p

Empirical Analysis of Reputation-aware Task Delegation by Humans from a Multi-agent Game (Extended Abstract)

ABSTRACT What are the strategies people adopt when deciding how to delegated tasks to agents when the agents&apos; reputation and productivity information is available? How effective are these strategies under different conditions? These questions are important since they have significant implications to the ongoing research of reputation aware task delegation in multi-agent systems (MASs). In this paper, we conduct an empirical study to address the aforementioned research questions by providing a gamified mechanism for people to report the reputation-aware task delegation strategies they adopt. The findings from this empirical study may help MAS researchers develop multi-agent trust evaluation testbeds with more realistic simulated human behaviours