Intelligence at either the material or metamaterial level is a goal that researchers have been pursuing.From passive to active,metasurfaces have been developed to be programmable to dynamically and arbitrarily manipul...Intelligence at either the material or metamaterial level is a goal that researchers have been pursuing.From passive to active,metasurfaces have been developed to be programmable to dynamically and arbitrarily manipulate electromagnetic(EM)wavefields.However,the programmable metasurfaces require manual control to switch among different functionalities.Here,we put forth a smart metasurface that has self-adaptively reprogrammable functionalities without human participation.The smart metasurface is capable of sensing ambient environments by integrating an additional sensor(s)and can adaptively adjust its EM operational functionality through an unmanned sensing feedback system.As an illustrative example,we experimentally develop a motion-sensitive smart metasurface integrated with a three-axis gyroscope,which can adjust self-adaptively the EM radiation beams via different rotations of the metasurface.We develop an online feedback algorithm as the control software to make the smart metasurface achieve single-beam and multibeam steering and other dynamic reactions adaptively.The proposed metasurface is extendable to other physical sensors to detect the humidity,temperature,illuminating light,and so on.Our strategy will open up a new avenue for future unmanned devices that are consistent with the ambient environment.展开更多
基金supported in part by the National Key Research and Development Program of China(2017YFA0700201,2017YFA0700202,and 2017YFA0700203)in part by the National Natural Science Foundation of China(61631007,61571117,61501112,61501117,61522106,61731010,61735010,61722106,61701107,and 61701108)+1 种基金in part by the 111 Project(111-2-05)in part by the Fund for International Cooperation and Exchange of the National Natural Science Foundation of China(61761136007).
文摘Intelligence at either the material or metamaterial level is a goal that researchers have been pursuing.From passive to active,metasurfaces have been developed to be programmable to dynamically and arbitrarily manipulate electromagnetic(EM)wavefields.However,the programmable metasurfaces require manual control to switch among different functionalities.Here,we put forth a smart metasurface that has self-adaptively reprogrammable functionalities without human participation.The smart metasurface is capable of sensing ambient environments by integrating an additional sensor(s)and can adaptively adjust its EM operational functionality through an unmanned sensing feedback system.As an illustrative example,we experimentally develop a motion-sensitive smart metasurface integrated with a three-axis gyroscope,which can adjust self-adaptively the EM radiation beams via different rotations of the metasurface.We develop an online feedback algorithm as the control software to make the smart metasurface achieve single-beam and multibeam steering and other dynamic reactions adaptively.The proposed metasurface is extendable to other physical sensors to detect the humidity,temperature,illuminating light,and so on.Our strategy will open up a new avenue for future unmanned devices that are consistent with the ambient environment.
文摘为实现对瓦斯浓度(体积分数)的准确预测,基于海量煤矿瓦斯监测数据和多元分布滞后模型(MDL)建立了多变量瓦斯浓度时间序列预测模型.基于惩罚最小二乘法和自回归的思想,提出了新的变量选择和定阶方法——Adjust Group最小绝对值压缩与选择(LASSO)方法.该方法以岭估计及局部二次近似迭代算法实现了预测模型的构建,通过有效选取具有解释性的自变量子集,提高模型的解释性,采用广义交叉检验准则(GCV)确定惩罚参数,并通过分组惩罚来实现变量筛选与滞后变量的定阶.结果表明:Adjust Group LASSO方法预测得到的残差平方和为0.433 0,具有较高的精度,能够较好的预测工作面瓦斯浓度的动态变化,与LASSO、最小角回归算法(LARS)以及其他瓦斯预测常用方法相比,大大提高了预测的准确性.
