No abstract

This paper investigates the time-varying formation problem for general linear multi-agent systems using distributed event-triggered control strategy. Different from the previous works, to achieve the desired time-varying formation, a distributed control scheme is designed in an event-triggered way, in which for each agent the controller is triggered only at its own event times. The interaction topology among agents is assumed to be switching. The common Lyapunov function as well as Riccati inequality is applied to solve the time-varying formation problem. Moreover, the Zeno behavior of triggering time sequences can be excluded for each agent. Finally, a simulation example is presented to illustrate the effectiveness of the theoretical results.

The role of the substrate on the morphology of nanometer size clusters fabricated by buffer layer assisted growth (BLAG) was studied using scanning tunneling microscopy. Clusters of Fe and Co were deposited on Ag(111), Cu(100), Rh(111), and Pt(111) surfaces using identical BLAG parameters, which are temperature, as well as metal and buffer layer coverage. Semi‐hemispherical clusters are found on Ag(111) and Cu(100), while flat monolayer high islands are observed on Rh(111) and Pt(111) due to complete wetting. The results of this study are in agreement with the common notion that BLAG is useful to deposit clusters of virtually any material on any substrate, however, the shape, size, and lateral distribution of the resulting clusters depend strongly on the substrate surface free energy and diffusion processes.

The generalized linear model (GLM) is a widely used method in traditional automobile insurance loss prediction. Ensemble learning algorithms have recently shown promising results in the realm of automobile insurance, providing a new option for loss prediction. In the age of big data, how to predict loss in automobile insurance more accurately is an urgent problem to be solved. Stacking is a hot issue in ensemble learning that has been effectively used in many fields, but few researchers have applied it to the field of automobile insurance. In this research, Stacking was innovatively introduced into loss prediction for automobile insurance to solve this problem. Three datasets related to automobile insurance were used. Adopting the Synthetic Minority Oversampling Technique for class balance, a model of claim occurrence was established using four methods, namely logistic regression in GLM and bagging, boosting, stacking in ensemble learning.The area under the receiver operating characteristic curve values and F1-scores achieved with the four methods were then compared to assess classification performance. Ensemble algorithms were used to rank the importance of features in the FRE dataset. Finally, we combined probability with the bonus-malus system to formulate a fairer transfer strategy. The results showed that the proposed approach performed better than the other methods on all datasets, with significantly enhanced prediction accuracy.