The ensemble Kalman filter is now an important component of ensemble forecasting. While using the linear relationship between the observation and state variables makes it applicable for large systems, relying on linearity introduces nonnegligible bias since the true distribution will never be Gaussian. This paper analyzes the bias of the ensemble Kalman filter from a statistical perspective and proposes a debiasing method called the nonlinear ensemble adjustment filter. This new filter transforms the forecast ensemble in a statistically principled manner so that the updated ensemble has the desired mean and variance. It is also easily localizable and, hence, potentially useful for large systems. Its performance is demonstrated and compared with other Kalman filter and particle filter variants through various experiments on the Lorenz-63 and Lorenz-96 systems. The results show that the new filter is stable and accurate for challenging situations such as nonlinear, high-dimensional systems with sparse observations.

Two MOF-74 analogs with OH groups on 1D channel surfaces have been synthesized through multi-component self-assembly at room temperature. Their guest-free forms demonstrate a potential luminescent probe or sensor for small molecules, and OH-MOF-74 (2a) also showed exceptional fluorescence quenching and enhancement behavior for different types of aromatic molecules.

Recently various versions of ensemble Kalman filters (EnKFs) have been proposed and studied. This work concerns, in a mathematically rigorous manner, the relative performance of two major versions of EnKF when the forecast ensemble is non-Gaussian. The approach is based on the stability of the filtering methods against small model violations, using the expected squared L 2 distance as a measure of the deviation between the updated distributions. Analytical and experimental results suggest that both stochastic and deterministic EnKFs are sensitive to the violation of the Gaussian assumption, while the stochastic filter is relatively more stable than the deterministic filter under certain circumstances, especially when there are wild outliers. These results not only agree with previous empirical studies, but also suggest a natural choice of a free parameter in the square root Kalman filter algorithm.

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Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method.

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This preliminary study for the first time investigated the feasibility of tomographic monitoring of flames in porous media, in which the cross-sectional profiles of flames inside a porous medium were imaged by electrical capacitance tomography (ECT). The relationship between the flame ionization and relative permittivity was established as the basis for ECT imaging of flames. Image reconstruction algorithms were discussed and an online iterative method OIOR was selected for image reconstruction. Experimental measurements were carried out and images of the flames were reconstructed. The shape, size and motion of the flames in a porous block were clearly monitored. Also the images correspond clearly to the variations of the combustion intensity. The feasibility of ECT monitoring of flames in porous media is proven by this study.