Search

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to cross-device FL for millions or billions of devices presents many risks, including performance loss, developer inertia, poor user experience, and unexpected application failures. In addition, the corresponding infrastructure, development costs, and return on investment are difficult to estimate. In this paper, we present a device-cloud collaborative FL platform that integrates with an existing machine learning platform, providing tools to measure real-world constraints, assess infrastructure capabilities, evaluate model training performance, and estimate system resource requirements to responsibly bring FL into production. We also present a decision workflow that leverages the FL-integrated platform to comprehensively evaluate the trade-offs of cross-device FL and share our empirical evaluations of business-critical machine learning applications that impact hundreds of millions of users., Comment: Preprint for MLSys 2023

Qianhai, the early Qing order that forced people in southeast coast to evacuate the coast to a distance of ten to eighteen miles inland in order to eliminate anti-Qing activities, is an exc...

The emphasis of this paper lies in the development of an efficient approach to reproduce the behaviors of the scramjet-powered hypersonic system with high fidelity. The modeling of the dual-mode scramjet powered hypersonic vehicle dynamics with shock interaction, Ram-to-Scram transition, and finite rate chemistry reaction is firstly introduced. The structure of surrogate model is identified with the implement of iterative fractional factorial design (IFFD). In order to declare the reliability of the surrogate models, ν-gap metric is applied to distinguish the difference among these surrogate models in terms of closed-loop performance. The results show that the influence of Mach number on the aerodynamics should not be overlooked, and the effect of propulsion system to the aerodynamic pitch moment is dramatic. Further, the partial Kriging model appears to have the closest plants throughout the flight envelope compared with the full Kriging model and polynomials model. Nevertheless, considering the briefness of analytical expression, the polynomials model may be an alternative approach for design-oriented modeling.

In the light of the broad range of hypersonic flight conditions, a design method of switching control based on guardian maps is proposed to guarantee the global stability within the flight envelope. First, a linear parameter varying (LPV) model of a hypersonic vehicle is established over a range of hypersonic flight conditions. The parameters of controller are determined by pole placement technology with the target pole locations at the boundaries of the parameters variation range. Second, a new range of flight conditions that ensures system stability with current controller is analyzed based on guardian maps. The execution of the iterative process is used to determinate the set of controller parameters overall the flight envelope with initial target pole locations. By switching controller parameters the closed-loop performance is guaranteed over the entire flight envelope. The simulation shows that the proposed design method has the ability to ensure the global stability over the flight envelope, and achieving good tracking performance.