Lithium-ion batteries have become an important power source in low-carbon transportation energy, and the safe operation and remaining useful life prediction are of great significance. Aiming at the shortcomings of existing methods, such as low prediction accuracy and a short prediction period, this paper proposes a real-time update high-order extended Kalman filter method based on fixed-step life prediction for vehicle lithium batteries based on the principle of combining models and data. First, the state model describing the parameters in the dynamic energy attenuation model is established, and the energy attenuation model is regarded as the observation model of the system to meet the requirements of establishing the Kalman filter. Secondly, the multi-step prediction equation of the state model is established by iterative recursion. At the same time, the multi-step prediction equation between the existing energy output value and the future output value is established based on the multi-dimensional Taylor network (MTN). The multiplicative noise term introduced in the dynamic modeling process is regarded as the hidden variable of the system to meet the requirements of establishing the multi-step linear predictive Kalman filter. Finally, the effectiveness of the new method is verified by digital simulation examples.
Due to the low activity and poor selectivity of current artificial nitrogen fixation catalysts, there is an urgent need to develop efficient and environmentally friendly Electrochemical ammonia synthesis (EAS) electrocatalysts....
Due to the low activity and poor selectivity of current artificial nitrogen fixation catalysts, there is an urgent need to develop efficient and environmentally friendly Electrochemical ammonia synthesis (EAS) electrocatalysts. Electrochemical ammonia synthesis is considered an environmentally friendly and sustainable method for artificial nitrogen fixation. Herein, Fe2O3 nanoparticles assembled on MoSe2 (Fe2O3/MoSe2) were first developed and regarded as an efficient electrocatalytic nitrogen fixation catalyst with high electroactive. The Fe2O3/MoSe2 composites exhibited excellent NRR activity with an NH3 yield of 55.52 µg∙h− 1∙mg− 1 at -0.5 V and Faradaic efficiency of 9.6% at -0.6 V vs. RHE. Notably, the Fe2O3/MoSe2 composites exhibited excellent stability and durability in recycling tests. Density functional theory (DFT) calculations revealed that the interfacial charge transport from Fe2O3 to MoSe2 could significantly enhance the Electrochemical nitrogen reduction reaction (NRR) activity of Fe2O3/MoSe2 by promoting the conductivity of Fe2O3/MoSe2 and reducing the free energy barrier for the rate-determining of *N2 to *N2H formation step. This work provides a promising avenue for the green synthesis of NH3.
As the acquisition and application of color images become more and more extensive, color face recognition technology has also been vigorously developed, especially the recognition methods based on convolutional neural network, which have excellent performance. However, with the increasing depth and complexity of network models, the number of calculated parameters also increases, which means the training of most high-performance models depends on large-scale samples and expensive equipment. Therefore, the key to the current research is to realize a lightweight model while ensuring the recognition accuracy. At present, PCANet, a typical lightweight framework for deep learning, has achieved good results in most of the image recognition tasks, but its recognition accuracy for color face images, especially under occlusion, still needs to be improved. Therefore, a color occlusion face recognition method based on quaternion non-convex sparse constraint mechanism is proposed in this paper. Firstly, a quaternion non-convex sparse principal component analysis network model was constructed based on Lp regularization of strong sparsity. Secondly, the fixed point iteration method and coordinate descent method were established to solve the non-convex optimization problem. Finally, the occlusion recognition performance of the proposed method was verified on Georgia Tech, Color FERET, AR, and LFW-A Color face datasets.
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