An important issue in designing the structures of rubble-mound breakwaters,is to estimate the stability number of its armor block.Most of the traditional stability analysis methods are not compatible enough to handle ...An important issue in designing the structures of rubble-mound breakwaters,is to estimate the stability number of its armor block.Most of the traditional stability analysis methods are not compatible enough to handle the obscurities,indistintness and uncertainties of this field.The relations between stability number,damage level and other stability variables can better be modeled using the advanced techniques of machine learning(ML)algorithms.In this prospect,three new ML models consisting of two ensemble learning models;Random Forest,Gradient Boosting and one fully connected deep artificial neural net-work based prediction model have been presented in this study.Using the ensemble learning models a detailed feature analysis has been introduced here,to understand the feature importances of stability variables on the stability number.To the best of the author’s knowledge,these have never been used in this field of stability analysis of rubble-mound breakwaters.Outperforming all of the conventional methods,the proposed study has delivered the highest level of accuracy as 99%,in the prediction of the stability number.Also,the proposed ML models are found to perform better,in dealing with the com-plex non-linearities related to this field.The feature analysis gives a meaningful insight into the dataset.Therefore,this study can be a useful alternative approach for the designers of the rubble-mound break-waters.展开更多
We investigate the spin wave （SW） modes in high-aspect-ratio single-crystal ferromagnetic nanowires （FMNWs） using an all-optical time-resolved magnetooptical Kerr effect （TR-MOKE） microscope. The precessional ma...We investigate the spin wave （SW） modes in high-aspect-ratio single-crystal ferromagnetic nanowires （FMNWs） using an all-optical time-resolved magnetooptical Kerr effect （TR-MOKE） microscope. The precessional magnetization dynamics in such FMNWs unveil the presence of uniform and quantized SW modes that can be tuned by varying the bias magnetic field （H）. The frequencies of the modes are observed to decrease systematically with a decreasing magnetic field, and the number of modes in the spectrum is reduced from four to three for H 〈 0.7 kOe. To understand these results, we perform micromagnetic simulations that reveal the presence of edge, standing wave, and uniform SW modes in the nanowires （NWs）. Our simulations clearly show how the standing wave and uniform SW modes coalesce to form a single mode with uniform precession over the entire NW for H 〈 0.7 kOe, reproducing the experimentally observed reduction in modes. Our study elucidates the possibility of manipulating the SW modes in magnetic nanostructures, which is useful for applications in magnonic and spintronic devices.展开更多
文摘An important issue in designing the structures of rubble-mound breakwaters,is to estimate the stability number of its armor block.Most of the traditional stability analysis methods are not compatible enough to handle the obscurities,indistintness and uncertainties of this field.The relations between stability number,damage level and other stability variables can better be modeled using the advanced techniques of machine learning(ML)algorithms.In this prospect,three new ML models consisting of two ensemble learning models;Random Forest,Gradient Boosting and one fully connected deep artificial neural net-work based prediction model have been presented in this study.Using the ensemble learning models a detailed feature analysis has been introduced here,to understand the feature importances of stability variables on the stability number.To the best of the author’s knowledge,these have never been used in this field of stability analysis of rubble-mound breakwaters.Outperforming all of the conventional methods,the proposed study has delivered the highest level of accuracy as 99%,in the prediction of the stability number.Also,the proposed ML models are found to perform better,in dealing with the com-plex non-linearities related to this field.The feature analysis gives a meaningful insight into the dataset.Therefore,this study can be a useful alternative approach for the designers of the rubble-mound break-waters.
文摘We investigate the spin wave （SW） modes in high-aspect-ratio single-crystal ferromagnetic nanowires （FMNWs） using an all-optical time-resolved magnetooptical Kerr effect （TR-MOKE） microscope. The precessional magnetization dynamics in such FMNWs unveil the presence of uniform and quantized SW modes that can be tuned by varying the bias magnetic field （H）. The frequencies of the modes are observed to decrease systematically with a decreasing magnetic field, and the number of modes in the spectrum is reduced from four to three for H 〈 0.7 kOe. To understand these results, we perform micromagnetic simulations that reveal the presence of edge, standing wave, and uniform SW modes in the nanowires （NWs）. Our simulations clearly show how the standing wave and uniform SW modes coalesce to form a single mode with uniform precession over the entire NW for H 〈 0.7 kOe, reproducing the experimentally observed reduction in modes. Our study elucidates the possibility of manipulating the SW modes in magnetic nanostructures, which is useful for applications in magnonic and spintronic devices.
