立方相InGaN的稳态和瞬态光学特性研究

用光荧光和时间分辨光谱技术研究了MEB生长立方In_(1-x)Ga_(1-x)N(x=0.150.26)外延材料的稳态和瞬态发光特性。实验表明InGaN发光主要来自局域激子发光，局域化是由合金无序造成的，相应局域化能量为60meV左右。荧光衰退呈现双指数特性，快过程(50ps,12K)是自由激子的快速驰豫引起的，而慢过程(200～270ps,12K)则对应局域激子发光，其荧光寿命随温度缓变反映了激子发光的强局域性质

One-dimensional multi-scale domain adaptive network for bearing-fault diagnosis under varying working conditions

Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.</p

Static analysis and neural network based software failure prediction technique construction method

本发明提供一种基于静态分析和神经网络的软件故障预测技术的构建方法,步骤如下：1、搜集被诊断软件的有效故障，加入到创建的故障案例库；2、统计软件各历史版本的有效故障的次数；3、使用静态分析工具扫描软件源代码，输出复杂度度量值；4、进行相关性分析，计算故障次数与度量值的显著性水平；5、选出与故障次数具有显著相关性的复杂度度量值；6、构建网络训练输入输出矩阵和预测输入矩阵；7、构建BP神经网络；8、完成网络训练，构建故障预测系统；9、神经网络预测，预测新版本的故障数量。通过上述步骤，可以完成对基于静态分析和BP神经网络的软件故障预测技术的构建。本发明能帮助开发者预测可能发生的软件故障，具有实用价值

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