Li dendrites and electrolyte leakage are common causes of Li-ion battery failure.H_(2),generated by Li dendrites,and electrolyte vapors have been regarded as gas markers of the early safety warning of Li-ion batteries...Li dendrites and electrolyte leakage are common causes of Li-ion battery failure.H_(2),generated by Li dendrites,and electrolyte vapors have been regarded as gas markers of the early safety warning of Li-ion batteries.SnO_(2)-based gas sensors,widely used for a variety of applications,are promising for the early safety detection of Li-ion batteries,which are necessary and urgently required for the development of Li-ion battery systems.However,the traditional SnO_(2)sensor,with a single signal,cannot demonstrate intelligent multi-gas recognition.Here,a single dual-mode(direct and alternating current modes)SnO_(2)sensor demonstrates clear discrimination of electrolyte vapors and H_(2),released in different states of Li-ion batteries,together with principal component analysis(PCA)analysis.This work provides insight into the intelligent technology of single gas sensors.展开更多
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method...Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.展开更多
基金supported by the Zhejiang Science and Technology Foundation(Grant No.LQ20F040006)。
文摘Li dendrites and electrolyte leakage are common causes of Li-ion battery failure.H_(2),generated by Li dendrites,and electrolyte vapors have been regarded as gas markers of the early safety warning of Li-ion batteries.SnO_(2)-based gas sensors,widely used for a variety of applications,are promising for the early safety detection of Li-ion batteries,which are necessary and urgently required for the development of Li-ion battery systems.However,the traditional SnO_(2)sensor,with a single signal,cannot demonstrate intelligent multi-gas recognition.Here,a single dual-mode(direct and alternating current modes)SnO_(2)sensor demonstrates clear discrimination of electrolyte vapors and H_(2),released in different states of Li-ion batteries,together with principal component analysis(PCA)analysis.This work provides insight into the intelligent technology of single gas sensors.
文摘Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.
