针对矿井巷道环境内存在大量的设备和设施,会造成电磁波传播的NLOS(non line of sight)时延、对矿井TOA(time of arrival)定位精度产生不利影响,根据成因将巷道电磁波传播NLOS时延分为随机NLOS时延和固定NLOS时延,分析了两类NLOS时延造...针对矿井巷道环境内存在大量的设备和设施,会造成电磁波传播的NLOS(non line of sight)时延、对矿井TOA(time of arrival)定位精度产生不利影响,根据成因将巷道电磁波传播NLOS时延分为随机NLOS时延和固定NLOS时延,分析了两类NLOS时延造成测距误差的特点。为了分步抑制两类NLOS时延造成的TOA测距定位误差,提出基于改进均值滤波和参数拟合的矿井TOA几何定位算法。针对巷道随机NLOS时延造成的以脉冲形式存在的TOA测距误差,提出基于偏差值丢弃的加权均值滤波算法加以抑制;进而提出依据定位区域巷道固定NLOS时延参数拟合方法,用以抑制其造成的TOA测距正向偏移误差,最后采用几何方法进行目标位置的估计。实验结果表明,提出的方法对巷道NLOS时延造成的TOA定位误差具有显著的抑制作用,能够保证矿井TOA定位的精度。展开更多
Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 20...Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.展开更多
文摘针对矿井巷道环境内存在大量的设备和设施,会造成电磁波传播的NLOS(non line of sight)时延、对矿井TOA(time of arrival)定位精度产生不利影响,根据成因将巷道电磁波传播NLOS时延分为随机NLOS时延和固定NLOS时延,分析了两类NLOS时延造成测距误差的特点。为了分步抑制两类NLOS时延造成的TOA测距定位误差,提出基于改进均值滤波和参数拟合的矿井TOA几何定位算法。针对巷道随机NLOS时延造成的以脉冲形式存在的TOA测距误差,提出基于偏差值丢弃的加权均值滤波算法加以抑制;进而提出依据定位区域巷道固定NLOS时延参数拟合方法,用以抑制其造成的TOA测距正向偏移误差,最后采用几何方法进行目标位置的估计。实验结果表明,提出的方法对巷道NLOS时延造成的TOA定位误差具有显著的抑制作用,能够保证矿井TOA定位的精度。
基金Soft Science Research Project in Shanxi Province of China(2017041030-5)Science Fund Projects in North University of China(XJJ2016037)
文摘Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.
