全矢深度学习在轴承故障诊断中的应用

为了应对日趋庞杂的故障监测系统数据,针对单通道信号存在的信息遗漏以及传统智能诊断手工提取特征的复杂性和不通用性,提出了全矢深度学习滚动轴承智能诊断方法。首先,用全矢谱融合双通道的振动信号,得到全矢融合后的主振矢数据,克服了单通道振动信号信息不完整的缺点;然后,在此基础上构建全矢深度神经网络,结合稀疏编码和去噪编码算法,自适应地提取故障特征。最后,使用反向传播算法进行网络参数整体微调。试验结果表明,该方法能够自适应地提取更为有效的故障特征,提高了故障诊断准确率和稳定性,改善了传统方法的复杂流程

过滤、吸附及灭活病毒的纳米催化材料的快速筛选方法

本发明涉及催化材料，具体地说是一种过滤、吸附及灭活病毒的纳米催化材料的快速筛选方法，具体操作为：1)以100～1000个碱基的核酸分子片段作为探针，采用喷洒或浸泡的方式将核酸分子吸附于催化剂上，然后用不少于探针原液9倍的水进行洗脱，筛选出吸附能力强的、即吸附量为原液重量60％)以上的催化剂；2)将上述步骤选取的催化剂对目标病毒进行吸附、洗脱，对催化剂用水或生理盐水一次或多次洗脱，然后对洗脱液中的病毒进行传统的活性检测；筛选出对病毒的过滤、吸附及灭活作用好的、即吸附量为原液重量95％的催化剂。本发明首先以核酸分子作为探针，建立了对纳米催化材料吸附性能和抗水性能进行快速筛选和评价的方法。带填

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies