An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in...An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in which both the access point(AP)and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss,meanwhile compromise between hardware complexity and system performance.Based on the sparse scattering nature of the mmWave channel,the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure.A CANDECOMP/PARAFAC(CP)decomposition-based method is proposed for time-varying channel parameter extraction,including angles of departure/arrival(AoDs/AoAs),Doppler shift,time delay and path gain.Then leveraging the estimates of channel parameters,a nonlinear weighted least-square problem is proposed to recover the location accurately,heading and velocity of vehicles.Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMOOFDM V2I systems.展开更多
抽采后残余瓦斯的存在对于矿井生产依然具有危险性,研究残余瓦斯的赋存规律及其预测是十分必要的。分析了淮北煤田许疃煤矿3 2 33采区地质条件,通过断层分维、煤层底板构造曲率和煤层倾角等指标的计算和统计,并分别赋予0.35,0,35和0.30...抽采后残余瓦斯的存在对于矿井生产依然具有危险性,研究残余瓦斯的赋存规律及其预测是十分必要的。分析了淮北煤田许疃煤矿3 2 33采区地质条件,通过断层分维、煤层底板构造曲率和煤层倾角等指标的计算和统计,并分别赋予0.35,0,35和0.30的权重,计算得到研究区的构造指数及其分布,根据选取的42组数据,讨论了构造指数、煤层埋深、煤厚和原煤瓦斯含量等影响因素对抽采残余瓦斯赋存的影响,运用多元线性回归方法,拟合了瓦斯含量损失与构造指数、煤层埋深、煤厚等影响因素指标之间的相关关系,运用BP人工神经网络模型研究了预测抽采后瓦斯含量损失的可行性。结果表明:构造指数可以更精确地定量表征矿井构造复杂程度。瓦斯含量损失的主要影响因素为构造指数、煤层埋深、煤厚和原煤瓦斯含量。瓦斯含量损失总体上与构造指数呈负相关,而与其他因素的指标均呈正相关。经过数理统计的 F 检验, F =20.82> F 0.01 (3,38)=4.35,故多元线性回归的结果是显著的,表明瓦斯含量损失与各影响因素指标之间具有较密切的内在联系,其中构造指数对瓦斯含量损失的影响程度最大,煤层埋深影响程度最小,煤厚的影响程度介于构造指数与煤层埋深之间。以瓦斯含量损失为输出指标,以构造指数、埋深、煤厚和原始瓦斯含量为输入指标,建立了4×10×1结构的BP人工神经网络模型,模型经过学习训练后预测精度高,相对误差为1.19%~1.34%,表明可以运用人工神经网络模型预测未采区抽采后的瓦斯含量损失,残余瓦斯含量即为原煤瓦斯含量减去瓦斯含量损失,故可以间接预测抽采后残余瓦斯含量。展开更多
To break through the restrictions of traditional organizational forms,systems,and mechanisms and quickly respond to the innovative development requirements of CASC,the innovation team has gradually become a crucial or...To break through the restrictions of traditional organizational forms,systems,and mechanisms and quickly respond to the innovative development requirements of CASC,the innovation team has gradually become a crucial organizational form within CASC.One of the biggest differences between the innovation team and traditional orga-nizational structure lies in knowledge heterogeneity.Existing studies present different conclusions on the relationship between knowledge heterogeneity and innovation performance,which should be analyzed according to specific situ-ations.Therefore,this paper takes the innovation team of CASC as the research object to conduct an empirical study on 186 team members,propose conceptual models and hypotheses,and study the relationship among knowledge heterogeneity,knowledge sharing,and innovation performance.The research results indicate that the two dimensions of knowledge heterogeneity—explicit knowledge heterogeneity and implicit knowledge heterogeneity—are beneficial to innovation performance when they are to a great extent.Knowledge sharing plays a partially mediating role between knowledge heterogeneity and collaborative innovation performance.It reveals the influence of knowledge heterogene-ity on innovation performance in the innovation team of CASC,aiming to provide a certain reference for the establish-ment and development of CASC’s innovation team.展开更多
文摘An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in which both the access point(AP)and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss,meanwhile compromise between hardware complexity and system performance.Based on the sparse scattering nature of the mmWave channel,the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure.A CANDECOMP/PARAFAC(CP)decomposition-based method is proposed for time-varying channel parameter extraction,including angles of departure/arrival(AoDs/AoAs),Doppler shift,time delay and path gain.Then leveraging the estimates of channel parameters,a nonlinear weighted least-square problem is proposed to recover the location accurately,heading and velocity of vehicles.Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMOOFDM V2I systems.
文摘抽采后残余瓦斯的存在对于矿井生产依然具有危险性,研究残余瓦斯的赋存规律及其预测是十分必要的。分析了淮北煤田许疃煤矿3 2 33采区地质条件,通过断层分维、煤层底板构造曲率和煤层倾角等指标的计算和统计,并分别赋予0.35,0,35和0.30的权重,计算得到研究区的构造指数及其分布,根据选取的42组数据,讨论了构造指数、煤层埋深、煤厚和原煤瓦斯含量等影响因素对抽采残余瓦斯赋存的影响,运用多元线性回归方法,拟合了瓦斯含量损失与构造指数、煤层埋深、煤厚等影响因素指标之间的相关关系,运用BP人工神经网络模型研究了预测抽采后瓦斯含量损失的可行性。结果表明:构造指数可以更精确地定量表征矿井构造复杂程度。瓦斯含量损失的主要影响因素为构造指数、煤层埋深、煤厚和原煤瓦斯含量。瓦斯含量损失总体上与构造指数呈负相关,而与其他因素的指标均呈正相关。经过数理统计的 F 检验, F =20.82> F 0.01 (3,38)=4.35,故多元线性回归的结果是显著的,表明瓦斯含量损失与各影响因素指标之间具有较密切的内在联系,其中构造指数对瓦斯含量损失的影响程度最大,煤层埋深影响程度最小,煤厚的影响程度介于构造指数与煤层埋深之间。以瓦斯含量损失为输出指标,以构造指数、埋深、煤厚和原始瓦斯含量为输入指标,建立了4×10×1结构的BP人工神经网络模型,模型经过学习训练后预测精度高,相对误差为1.19%~1.34%,表明可以运用人工神经网络模型预测未采区抽采后的瓦斯含量损失,残余瓦斯含量即为原煤瓦斯含量减去瓦斯含量损失,故可以间接预测抽采后残余瓦斯含量。
文摘To break through the restrictions of traditional organizational forms,systems,and mechanisms and quickly respond to the innovative development requirements of CASC,the innovation team has gradually become a crucial organizational form within CASC.One of the biggest differences between the innovation team and traditional orga-nizational structure lies in knowledge heterogeneity.Existing studies present different conclusions on the relationship between knowledge heterogeneity and innovation performance,which should be analyzed according to specific situ-ations.Therefore,this paper takes the innovation team of CASC as the research object to conduct an empirical study on 186 team members,propose conceptual models and hypotheses,and study the relationship among knowledge heterogeneity,knowledge sharing,and innovation performance.The research results indicate that the two dimensions of knowledge heterogeneity—explicit knowledge heterogeneity and implicit knowledge heterogeneity—are beneficial to innovation performance when they are to a great extent.Knowledge sharing plays a partially mediating role between knowledge heterogeneity and collaborative innovation performance.It reveals the influence of knowledge heterogene-ity on innovation performance in the innovation team of CASC,aiming to provide a certain reference for the establish-ment and development of CASC’s innovation team.
