Calibration Method of Magnetometer Based on BP Neural Network

Due to the influence of processing technology and environmental factors, there are errors in attitude measurement with the three-axis magnetometer, and the change of parameters during the operation of the magnetometer in orbit will have a great impact on the measurement accuracy. This paper studies the calibration method of magnetometer based on BP neural network, which reduces the influence of model error on calibration accuracy. Firstly, the error model of the magnetometer and the structural characteristics of the BP neural network are analyzed. Secondly, the number of hidden layers and hidden nodes is optimized. To avoid the problem of slow convergence and low accuracy of basic BP algorithm, this paper uses the Levenberg Marquardt backpropagation training method to improve the training speed and prediction accuracy and realizes the on-orbit calibration of magnetometer through online training of the neural network. Finally, the effectiveness of the method is verified by numerical simulation. The results show that the neural network designed in this paper can effectively reduce the measurement error of magnetometer, while the online training can effectively reduce the error caused by the change of magnetometer parameters, and reduce the measurement error of magnetometer to less than 10 nT.

Metal oxyacid salts-confined pyrolysis towards hierarchical porous metal oxide@carbon(MO@C)composites as lithium-ion battery anodes

Transition metal oxides(TMOs)have been thought of potential anodic materials for lithium-ion batteries(LIBs)owing to their intriguing properties.However,the limited conductivity and drastic volume change still hinder their practical applications.Herein,a metal oxyacid salts-confined pyrolysis strategy is proposed to construct hierarchical porous metal oxide@carbon(MO@C,MO=MoO_(2),V_(2)O_(5),WO_(3))composites for solving the aforementioned problems.A water-evaporation-induced self-assembly mechanism has been put forward for fabricating the MO@polyvinyl pyrrolidone(PVP)@SiO_(2)precursors.After the following pyrolysis and etching process,small MO nanoparticles can be successfully encapsulated in the hierarchical porous carbon framework.Profiting from the synergistic effect of MO nanoparticles and highly conductive carbon framework,MO@C composites show excellent electrochemical properties.For example,the as-obtained MoO_(2)@C composite exhibits a large discharge capacity(1513.7 mAh·g^(−1)at 0.1 A·g^(−1)),good rate ability(443.5 mAh·g^(−1)at 5.0 A·g^(−1)),supernal long-lived stability(669.1 mAh·g^(−1)after 1000 cycles at 1.0 A·g^(−1)).This work will inspire the design of novel anode materials for high-performance LIBs.

Highly Regioselective Sequential 1,1-Dihydrosilylation of Terminal Aliphatic Alkynes with Primary Silanes

Summary of main observation and conclusion A regioselective double 1,1-hydrosilylation of terminal aliphatic alkynes with primary silanes catalyzed by one cobalt catalyst has been developed. gem-Bis(dihydrosilyl)alkanes containing four silicon-hydrogen bonds are efficiently constructed in an atom-economical man ner. Tolerated substrates include simplest alkyne-ethyne, a complicated drug derivative and various functi on alized terminal aliphatic alkynes. Asymmetric approach using two catalysts is achieved with excelle nt enan tioselectivities to access corresp on ding chiral products. The tra nsformations of Si—H bonds into Si—C, Si—O, and Si —F bonds and the synthesis of enantioriched a-hydroxysilane show synthetic utility.

One Earth-Abundant Cobalt Catalyst for Alkynes Double Hydrosilylation:Efficient Synthesis of Gem-bis(silanes)

Geminal bis(silanes) are unique orga no sila ne species containing one sp^3-carbon attached with two silyl groups, which have emerged as attractive synthetic building blocks.[1] In spite of the usefulness, the scarcity of synthetic methods to approach gem-bis(silanes) extremely restricts their potentialities for broader applications.