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设计了一种用于快速气相色谱(Fast gas chromatography,FGC)的新型控制系统。该控制器主要由色谱柱温度控制系统、自动进样及气路压力控制系统组成。其温度控制范围为30~160℃,升温速度约为3℃/s,温度控制精度为±0.5℃,载气压力控制范围为0~0.5 MPa。将本控制器应用于自制快速色谱,并用色谱对由直链正构烷烃(C1~C8)以及甲苯9种物质组成的标准样品进行测试。结果显示,色谱能在100 s内将此9种物质完全分开。 相似文献
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为解决电磁逆散射问题,提出了一种实时逆散射方法,该方法利用支持向量机(SVM)将逆散射问题转化为一个回归估计问题. 基于SVM的电磁逆散射方法成功地解决了逆散射问题中的非线性和不适定性.利用穿墙问题测试了该方法的可行性和有效性, 测试结果表明,不论是无噪声还是有噪声的情况,该方法都能很好地对墙后目标进行探测与定位.此外, 在穿墙环境下用SVM预测模型讨论了接收天线的采样位置数对预测结果的影响.最后对多源设置下的预测误差进行了分析和研究, 研究表明,相比于单源情况多源设置有利于对墙后目标的识别. 相似文献
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In this work,we propose an all-dielectric frequency selective surface(FSS) composed of periodically placed highpermittivity dielectric resonators and a three-dimensional(3D) printed supporter.Mie resonances in the dielectric resonators offer strong electric and magnetic dipoles,quadrupoles,and higher order terms.The re-radiated electric and magnetic fields by these multipoles interact with the incident fields,which leads to total reflection or total transmission in some special frequency bands.The measured results of the fabricated FSS demonstrate a stopband fractional bandwidth(FBW)of 22.2%,which is consistent with the simulated result. 相似文献
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为了重建二维有耗色散介质的电参数分布,基于Debye模型,应用泛函分析和变分法,提出一种时域逆散射新方法.该方法首先以最小二乘准则构造目标函数,将逆问题表示为约束最小化问题,接着应用罚函数法转化为无约束最小化问题,然后基于变分计算导出闭式的Lagrange函数关于特征参数的Fréchet导数,最后借助梯度算法和时域有限差分法迭代反演Debye模型参数.为了对抗噪声污染和逆问题的病态特性,采用了一阶Tikhonov正则化方法.数值应用中,利用Polak-Ribière-Polyak非线性共轭梯度法,对二维乳 相似文献
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A novel method based on the relevance vector machine(RVM) for the inverse scattering problem is presented in this paper.The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered.The nonlinearity is embodied in the relation between the scattered field and the target property,which can be obtained through the RVM training process.Besides,rather than utilizing regularization,the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output.Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy,convergence,robustness,generalization,and improved performance in terms of sparse property in comparison with the support vector machine(SVM) based approach. 相似文献
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