Fermi-LAT LCR provides continuous and regularly sampled gamma-ray light curves, spanning about 14 yr, for a large sample of blazars. The log-normal flux distribution and linear rms–flux relation of the light curves f...Fermi-LAT LCR provides continuous and regularly sampled gamma-ray light curves, spanning about 14 yr, for a large sample of blazars. The log-normal flux distribution and linear rms–flux relation of the light curves for a few Fermi blazars have been examined in previous studies. However, the probability that blazars exhibit the log-normal flux distribution and linear rms–flux relation in their gamma-ray light curves has not been systematically explored.In this study, we comprehensively research the distribution of γ-ray flux and the statistical characteristics on a large sample of 1414 variable blazars from the Fermi-LAT LCR catalog, including 572 FSRQs, 477 BL Lacs, and 365BCUs, and statistically compare their flux distributions with normal and log-normal distributions. The results indicate that the probability of not rejecting log-normal is 42.05% for the large sample, and there is still a 2.05%probability of not rejecting normality, based on the joint of Kolmogorov–Smirnov, Shapiro–Wilk, and Normality tests. We further find that the probability that BL Lacs conform to the log-normal distribution is higher than that of FSRQs. Besides, after removing sources with less than 200 data points from this large sample, a sample of 549blazars, which is still a large sample compared to the previous studies, was obtained. Based on dividing the light curves into segments every 20 points(or 40 points, or one year), we fitted the linear rms–flux relation of these three different sets and found that the Pearson correlation coefficients are all close to 1 for most blazars. This result indicates a strong linear correlation between the rms and the flux of these 549 blazars. The log-normal distribution and linear rms–flux relation indicate that the variability of the γ-ray flux for most blazars is a non-linear and multiplicative process.展开更多
为了提高纯电动汽车电驱总成的故障诊断准确率,提出了一种基于粒子群优化(Particle Swarm Optimizing,PSO)算法的改进BP(Improved Back Propagation,IBP)神经网络(PSO-IBP)故障诊断方法。应用线性整流单元(Rectified Linear Unit,ReLU)...为了提高纯电动汽车电驱总成的故障诊断准确率,提出了一种基于粒子群优化(Particle Swarm Optimizing,PSO)算法的改进BP(Improved Back Propagation,IBP)神经网络(PSO-IBP)故障诊断方法。应用线性整流单元(Rectified Linear Unit,ReLU)作为BP神经网络的激活函数,通过粒子群优化算法对BP神经网络权值和阈值进行动态寻优,构建PSO-IBP模型。通过采集纯电动汽车电驱总成故障数据,分别对PSO-IBP神经网络模型、BP神经网络模型和概率神经网络(Probabilistic Neural Network,PNN)模型进行训练与仿真,结果表明,相比于BP神经网络方法及概率神经网络方法,基于PSO-IBP神经网络模型的纯电动汽车电驱总成故障诊断方法具有更高的准确率。展开更多
基金funded by the National Natural Science Foundation of China (grants 12063007 and 11863007)。
文摘Fermi-LAT LCR provides continuous and regularly sampled gamma-ray light curves, spanning about 14 yr, for a large sample of blazars. The log-normal flux distribution and linear rms–flux relation of the light curves for a few Fermi blazars have been examined in previous studies. However, the probability that blazars exhibit the log-normal flux distribution and linear rms–flux relation in their gamma-ray light curves has not been systematically explored.In this study, we comprehensively research the distribution of γ-ray flux and the statistical characteristics on a large sample of 1414 variable blazars from the Fermi-LAT LCR catalog, including 572 FSRQs, 477 BL Lacs, and 365BCUs, and statistically compare their flux distributions with normal and log-normal distributions. The results indicate that the probability of not rejecting log-normal is 42.05% for the large sample, and there is still a 2.05%probability of not rejecting normality, based on the joint of Kolmogorov–Smirnov, Shapiro–Wilk, and Normality tests. We further find that the probability that BL Lacs conform to the log-normal distribution is higher than that of FSRQs. Besides, after removing sources with less than 200 data points from this large sample, a sample of 549blazars, which is still a large sample compared to the previous studies, was obtained. Based on dividing the light curves into segments every 20 points(or 40 points, or one year), we fitted the linear rms–flux relation of these three different sets and found that the Pearson correlation coefficients are all close to 1 for most blazars. This result indicates a strong linear correlation between the rms and the flux of these 549 blazars. The log-normal distribution and linear rms–flux relation indicate that the variability of the γ-ray flux for most blazars is a non-linear and multiplicative process.
文摘为了提高纯电动汽车电驱总成的故障诊断准确率,提出了一种基于粒子群优化(Particle Swarm Optimizing,PSO)算法的改进BP(Improved Back Propagation,IBP)神经网络(PSO-IBP)故障诊断方法。应用线性整流单元(Rectified Linear Unit,ReLU)作为BP神经网络的激活函数,通过粒子群优化算法对BP神经网络权值和阈值进行动态寻优,构建PSO-IBP模型。通过采集纯电动汽车电驱总成故障数据,分别对PSO-IBP神经网络模型、BP神经网络模型和概率神经网络(Probabilistic Neural Network,PNN)模型进行训练与仿真,结果表明,相比于BP神经网络方法及概率神经网络方法,基于PSO-IBP神经网络模型的纯电动汽车电驱总成故障诊断方法具有更高的准确率。
