We study a supergravity D-term chaotic inflationary model, in the context of the braneworld scenario, in particular we consider the Randal1-Sundrum model type 2. Using the latest release from the combination of WMAP9,...We study a supergravity D-term chaotic inflationary model, in the context of the braneworld scenario, in particular we consider the Randal1-Sundrum model type 2. Using the latest release from the combination of WMAP9, eCMB, BAO, and Ho, we show that the inflation observables depend only on the number ore-folds N. We also derive all known spectrum inflationary parameters, which are widely consistent with WMAP9 data for a particular choice of values N specially for the scalar spectral index ns and the ratio r. However, the running of the scalar spectral index dns/dlnk is now excluded from the range given by the latest observational measurements.展开更多
文摘We study a supergravity D-term chaotic inflationary model, in the context of the braneworld scenario, in particular we consider the Randal1-Sundrum model type 2. Using the latest release from the combination of WMAP9, eCMB, BAO, and Ho, we show that the inflation observables depend only on the number ore-folds N. We also derive all known spectrum inflationary parameters, which are widely consistent with WMAP9 data for a particular choice of values N specially for the scalar spectral index ns and the ratio r. However, the running of the scalar spectral index dns/dlnk is now excluded from the range given by the latest observational measurements.
文摘针对基于显性知识的智能制造缺陷检测机制在工程实践中日益凸显的若干缺陷,提出了一种基于机器视觉和深度残差收缩网络(deep residual shrinkage networks,D-RSN)的智能制造缺陷检测方法,并进行了先验环境下的仿真验证。首先利用互补金属氧化物半导体(complementary metal oxide semiconductor,CMOS)相机集群搭建快速机器视觉图像获取装置,形成融合前置训练集和后置测试集的图像特征数据池;然后利用D-RSN对数据池前置训练集进行图像缺陷特征隐性知识学习辨识,构建时间正序下的图像缺陷特征全息感知机制;最后利用深度长短期记忆(deep long short-term memory,D-LSTM)神经网络对数据池后置测试集进行图像缺陷自主检测,借助图像缺陷定位及分类函数输出检测结果。选取某医用外科口罩智能制造生产线为工程实践验证载体,对模型进行了工程应用实践验证,结果表明:所提方法较好地改善了基于显性知识的智能制造缺陷检测机制在工程实践中日益凸显的若干缺陷,可以自主学习辨识图像缺陷特征隐性知识,大幅度提高了智能制造缺陷检测有效率,图像缺陷检测均值有效率达98.37%,符合医用外科口罩智能制造生产线国检要求。