A novel fireworks factor and improved elite strategy based on back propagation neural networks for state-of-charge estimation of lithium-ion batteries.

The state of charge (SOC) of Lithium-ion battery is one of the key parameters of the battery management system. In the SOC estimation algorithm, the Back Propagation (BP) neural network algorithm is easy to converge to the local optimal solution, which leads to the problem of low accuracy based on the BP network. It is proposed that the Fireworks Elite Genetic Algorithm (FEG-BP) is used to optimize the BP neural network, which can not only solve the problem of the traditional neural network algorithm that is easy to fall into the local maximum optimal solution but also solve the limitation of the traditional neural network algorithm. The searchability of the improved algorithm has been significantly enhanced, and the error has become smaller and the propagation speed is faster. Combining the experimental data of charging and discharging, the proposed FEG-BP neural network is compared with the traditional genetic neural network algorithm (GA-BP), and the results are analyzed. The results show that the standard BP neural network genetic algorithm predicts error within 7%, while FEG-BP reduces the error to within 3%

An improved rainflow algorithm combined with linear criterion for the accurate li-ion battery residual life prediction.

Li-ion battery health assessment has been widely used in electric vehicles, unmanned aerial vehicle and other fields. In this paper, a new linear prediction method is proposed. By weakening the sensitivity of the Rainflow algorithm to the peak data, it can be applied to the field of battery, and can accurately count the number of Li-ion battery cycles, and skip the cumbersome link of parameter identification. Then, a linear criterion is proposed based on the idea of proportion, which makes the life prediction of Li-ion battery linear. Under the verification of multiple sets of data, the prediction error of this method is kept within 2.53%. This method has the advantages of high operation efficiency and simple operation, which provides a new idea for battery life prediction in the field of electric vehicles and aerospace

Breviscapine alleviates MPP+-induced damage and apoptosis of SH-SY5Y cells by activating Nrf2 pathway

Purpose: To investigate the role and mechanism of action of breviscapine (Brp) in 1-methyl-4- phenylpyridinium ion (MPP+)-induced cell injury in human neuroblastoma cell line, SH-SY5Y.Methods: The injury on SH-SY5Y cells was induced using MPP+. Cell viability and apoptotic ability were determined by CCK8 assay and Annexin V/PI staining, respectively. Protein expressions of nuclear factor E2-related factor 2 (Nrf2) and its related downstream proteins - hemeoxygenase 1(HO-1) and NAD(P)H-quinoneoxido reductase 1(NQO1), were determined using Western blotting.Results: Brp dose-dependently attenuated MPP+ induced reduction in the viability of SH SY5Y cells, but alleviated MPP+-induced oxidative stress (OS) and cell injury, as evidenced by the levels of reactive oxygen species (ROS), tyrosine hydroxylase (TH), lactic dehydrogenase (LDH), and dopaminetransporter (DAT) (p < 0.05). Brp decreased the amount of apoptotic cells induced by MPP+, as well as the protein levels of Bax and cleaved-caspase 3, and also induced the activation of Nrf2 signaling pathway (p < 0.05).Conclusion: Brp alleviates MPP+-induced cellular damage and cell apoptosis in SH-SY5Y cells by activating Nrf2 pathway. Thus, Brp is a potential therapeutic candidate for the treatment of PD

Reserve price of risk-averse search engine in keyword auctions with advertisers’ endogenous investment

Motivated by vigorous development of keyword auctions, this paper analyzes the reserve price policies in keyword auction with advertisers’ endogenous investment and risk-averse search engine. We explore advertisers’ optimal investment and equilibrium bidding strategies and derive the determination functions where utility-maximizing reserve price and efficient reserve price which maximizes the social welfare satisfy respectively. The results show that advertisers’ equilibrium bidding is monotonously increasing in bidders’ valuations, the number of advertisers, as well as the reserve price. Meanwhile, advertisers’ optimal investment is negatively correlated with reserve price and the number of advertisers. By numerical examples, the utility-maximizing reserve price decreases with the risk aversion parameter and the number of advertisers. Search engine’s expected utility increases with risk aversion parameter and decreases with the number of advertisers. Moreover, we declare that search engine can use reserve price as a regulatory tool to increase the utility. But there is an upper bound on search engine’s utility. It is interesting to find the efficient reserve price equals to zero. Hence there is a trade-off between total efficiency and search engine’s utility by search engine that has incentive to withhold reserve price that would benefit social welfare

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

Over the past few decades, video quality assessment (VQA) has become a valuable research field. The perception of in-the-wild video quality without reference is mainly challenged by hybrid distortions with dynamic variations and the movement of the content. In order to address this barrier, we propose a no-reference video quality assessment (NR-VQA) method that adds the enhanced awareness of dynamic information to the perception of static objects. Specifically, we use convolutional networks with different dimensions to extract low-level static-dynamic fusion features for video clips and subsequently implement alignment, followed by a temporal memory module consisting of recurrent neural networks branches and fully connected (FC) branches to construct feature associations in a time series. Meanwhile, in order to simulate human visual habits, we built a parametric adaptive network structure to obtain the final score. We further validated the proposed method on four datasets (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) to test the generalization ability. Extensive experiments have demonstrated that the proposed method not only outperforms other NR-VQA methods in terms of overall performance of mixed datasets but also achieves competitive performance in individual datasets compared to the existing state-of-the-art methods