The novel coronavirus pneumonia (COVID-19) has raged in many countries around the world. In the process of fighting against the COVID-19, unprecedented large-scale epidemic data have been produced such as case data, spatio-temporal data, public opinion data and so on. The increasingly complex data poses a significant challenge to understand. A two-level interactive visualization system named COVID-19Vis is proposed in this paper, which collects epidemic data from multiple sources and provides an interactive mode of multi-graph linkage. Users can not only easily analyze and interpret the spatial-temporal characteristics and potential rules of the epidemic, but also find the relationship between policy, online public opinion and the development of the epidemic situation. Through a large number of visualization effects and user feedback, the effectiveness and practicability of the COVID-19Vis are further verified.
The brake system has the characteristics of multi-component, multi working conditions and complex degradation process, which brings great challenges to its health condition assessment. As it is difficult for a single brake agent to make a comprehensive and accurate health condition assessment, a health condition assessment model based on multi-agent federated learning is proposed in this paper. The different agents train their brake health data under different working conditions and states, which ensures the accuracy of health condition assessment and the safety of data of each agent. Aiming at the problem that it is difficult to determine the credibility of agent data in the process of federated learning, a credibility of agent data scheme based on evidence theory is proposed, which not only reduces the cost of calculation and communication, but also further improves the accuracy of health condition assessment. Simulation results show that the scheme not only has higher prediction accuracy, but also can ensure the security of agent data compared with the conventional centralized training scheme.
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