基于支持向量机的低分辨核磁共振氢谱法快速预测喷气燃料密度

密度是喷气燃料生产、储运、加注过程中重要的质量指标。为了实现喷气燃料密度的快速、准确测定，提出了一种基于支持向量机（SVM）的低分辨核磁共振氢谱法预测喷气燃料密度。采集喷气燃料样品密度数据和低分辨核磁共振氢谱，利用网格寻优算法和训练集内部五折交叉验证方法优化建模参数。以均方误差和相关系数为指标，通过正交试验对影响模型性能的数据预处理方法进行优化，并对谱图特征的提取方式进行考察。在优化条件下建立的定量分析模型比偏最小二乘法模型预测准确度更高。所建模型对6个油样的预测值与标准值的相对偏差均低于0.3%，模型可应用于喷气燃料密度的快速分析。     

Optical Neural Network Architecture for Deep Learning with Temporal Synthetic Dimension

The physical concept of synthetic dimensions has recently been introduced into optics. The fundamental physics and applications are not yet fully understood, and this report explores an approach to optical neural networks using synthetic dimension in time domain, by theoretically proposing to utilize a single resonator network, where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension.The set of pulses in each roundtrip therefore provides the sites ...