The authors reported a facile method for the synthesis of manganese dioxide without any template and catalyst at a low-temperature. The prepared sample was characterized with X-ray diffraction（XRD）, scanning electro...The authors reported a facile method for the synthesis of manganese dioxide without any template and catalyst at a low-temperature. The prepared sample was characterized with X-ray diffraction（XRD）, scanning electron microscopy（SEM）, Brunauer-Emmett-Teller（BET） surface analysis, Fourier transform infrared（FTIR） spectrometry, cyclic voltammetry, alternative current（AC） impedance test and battery discharge test. It is found that the prepared sample belongs to α-MnO2 and has a microsphere morphology and a large BET surface area. The electrochemical characterization indicates that the prepared sample displays a larger electrochemical capacitance than the commercial electrolytic manganese dioxides（EMD） in Na2SO4 solution, and exhibits larger discharge capacity than EMD, especially at a high rate discharge condition when it is used as cathode of alkaline Zn/MnO2 battery.展开更多
针对煤矿瓦斯浓度的预测的问题,以亭南煤矿正常生产期间302工作面的监测数据为研究背景,采用深度学习技术LSTM(Long Short Time Memory,长短时记忆网络)建立瓦斯预测模型,研究与设计了基于LSTM的煤矿瓦斯预测预警系统。LSTM网络针对时...针对煤矿瓦斯浓度的预测的问题,以亭南煤矿正常生产期间302工作面的监测数据为研究背景,采用深度学习技术LSTM(Long Short Time Memory,长短时记忆网络)建立瓦斯预测模型,研究与设计了基于LSTM的煤矿瓦斯预测预警系统。LSTM网络针对时间序列数据具有较强的建模能力,能够实现信息的长期依赖,自动挖掘数据之间潜在的关联关系。采集煤矿正常生产期间的瓦斯监测数据作为训练数据,利用深度学习框架TensorFlow进行算法的仿真,并研究了不同时间步长、网络深度下的LSTM以及多信息融合对瓦斯预测模型性能的影响。实验结果在1 000条测试数据集上获得了3.61%平均相对偏差,LSTM瓦斯预测模型具有较高的准确度,泛化能力强。在系统研究与设计中,使用Spring,SpringMVC和Hibernate框架按照适应性、易用性、可扩展性等原则对系统进行了设计。系统部署阶段,将训练好的LSTM瓦斯预测模型部署在TensorFlow Serving服务器中,对外提供服务,实现了煤矿瓦斯预警系统,增强了煤矿瓦斯监控系统的预警能力,提高了煤炭企业安全生产管理水平,具有一定的实用价值。展开更多
基金Supported by the National Natural Science Foundation of China(No.20873046)the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.200805740004)+1 种基金the Natural Science Foundation of Guangdong Province,China(No.10351063101000001)the Fund of Guangdong Province Cooperation of Producing, Studying and Researching,China (No.2011B090400317)
文摘The authors reported a facile method for the synthesis of manganese dioxide without any template and catalyst at a low-temperature. The prepared sample was characterized with X-ray diffraction（XRD）, scanning electron microscopy（SEM）, Brunauer-Emmett-Teller（BET） surface analysis, Fourier transform infrared（FTIR） spectrometry, cyclic voltammetry, alternative current（AC） impedance test and battery discharge test. It is found that the prepared sample belongs to α-MnO2 and has a microsphere morphology and a large BET surface area. The electrochemical characterization indicates that the prepared sample displays a larger electrochemical capacitance than the commercial electrolytic manganese dioxides（EMD） in Na2SO4 solution, and exhibits larger discharge capacity than EMD, especially at a high rate discharge condition when it is used as cathode of alkaline Zn/MnO2 battery.
文摘针对煤矿瓦斯浓度的预测的问题,以亭南煤矿正常生产期间302工作面的监测数据为研究背景,采用深度学习技术LSTM(Long Short Time Memory,长短时记忆网络)建立瓦斯预测模型,研究与设计了基于LSTM的煤矿瓦斯预测预警系统。LSTM网络针对时间序列数据具有较强的建模能力,能够实现信息的长期依赖,自动挖掘数据之间潜在的关联关系。采集煤矿正常生产期间的瓦斯监测数据作为训练数据,利用深度学习框架TensorFlow进行算法的仿真,并研究了不同时间步长、网络深度下的LSTM以及多信息融合对瓦斯预测模型性能的影响。实验结果在1 000条测试数据集上获得了3.61%平均相对偏差,LSTM瓦斯预测模型具有较高的准确度,泛化能力强。在系统研究与设计中,使用Spring,SpringMVC和Hibernate框架按照适应性、易用性、可扩展性等原则对系统进行了设计。系统部署阶段,将训练好的LSTM瓦斯预测模型部署在TensorFlow Serving服务器中,对外提供服务,实现了煤矿瓦斯预警系统,增强了煤矿瓦斯监控系统的预警能力,提高了煤炭企业安全生产管理水平,具有一定的实用价值。
