A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.     

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.     

Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.     

基于PCA-BP神经网络的光谱消光法颗粒粒径反演算法研究

光谱消光法广泛应用于颗粒粒径测量领域,在利用光谱消光法对颗粒粒径进行反演的过程中,由于颗粒的消光系数存在理论复杂、计算繁琐、收敛速度慢以及求解不稳定等问题,很大程度上影响了整个反演过程的快速性和准确性。且在众多波长的消光数据中,存在较多重复冗余的信息,也很大程度上增加了反演算法的时间。针对光谱消光法粒径反演算法计算繁琐、反演效率低的问题,提出了基于主成分分析（PCA）和BP神经网络的光谱消光颗粒粒径分析方法。基于Mie散射理论对不同粒径、不同波长下的光谱消光值进行了仿真计算,通过对光谱消光数据集的主成分分析及各个波长综合载荷系数的计算,实现了最优特征波长的选取,利用降维后的光谱消光数据训练了PCA-BP神经网络模型,并利用该网络模型计算了粒径颗粒分布。通过仿真计算,比较了PCA-BP神经网络模型与传统的BP神经网络模型的预测精度,并分析了波长数目对两种神经网络模型预测结果的影响。针对训练得到的PCA-BP神经网络模型开展光谱消光法粒径参数反演算法的验证实验,搭建了光谱消光法颗粒粒径参数测量实验系统,测量了粒径范围在0.5～9.7 μm内的6种不同粒径参数的聚苯乙烯标准颗粒。仿真和实验结果表明：基于主成分分析方法可确定各个波长向量之间的相关性,利用综合载荷系数选取最优特征波长对应的消光值对整体的光谱数据具有较好的代表性,可实现光谱数据的降维。相比传统的BP神经网络模型,基于PCA-BP神经网络模型的颗粒粒径分布的分析方法预测精度更高,对于较分散颗粒系的分布参数的预测有更加明显的优势。而且,被选取的波长数较少时,PCA-BP神经网络模型依然有较高的预测精度。利用训练好的PCA-BP神经网络模型对颗粒粒径参数进行实验验证,预测结果可瞬时输出,颗粒粒径分布误差在5%以内,验证了该算法的可行性。     

钕敏化多层壳纳米结构的增强型染料敏化上转换发光

对于稀土离子掺杂的上转换发光,由于稀土离子吸收截面小、吸收范围窄,导致其发光强度受限.最近,在稀土上转换纳米粒子的表面连接近红外染料分子敏化发光,被证实是提高上转换发光强度的有效策略.然而,将染料分子连接经典的稀土Yb掺杂纳米粒子,并不能有效利用染料分子的敏化能力.针对这一问题,本文通过高温热分解法成功制备了Nd3+敏...     

变形镜部分驱动器断电失效对校正性能的影响

变形镜在长期工作的过程中,压电陶瓷驱动器因累积疲劳效应会导致其失效,从而导致校正性能的降低.从变形镜的影响函数出发,将失效驱动器的电压置零,采用有限元方法建立变形镜疲劳失效模型,重点分析畸变波前的形态分布、入射光束的类型和驱动器的排布方式等对校正能力的影响.实验结果表明,在部分驱动器失效的情况下,变形镜应当根据高斯型随...     

自旋轨道耦合Su-Schrieffer-Heeger原子链系统的电子输运特性

在Su-Schrieffer-Heeger (SSH)原子链中,电子在胞内和胞间的跳跃依赖于其自旋时,即SSH原子链存在自旋轨道耦合作用时,存在不同缠绕数的非平庸拓扑边缘态.如何探测自旋轨道耦合SSH原子链不同缠绕数的边缘态是一个重要问题.本文在紧束缚近似下研究了自旋轨道耦合SSH原子链的非平庸拓扑边缘态性质及其零能附近的电子输运特性.研究发现四重和二重简并边缘态的缠绕数分别为2和1;并且仅当源极入射电子的自旋被极化(铁磁电极)时,自旋轨道耦合SSH原子链在零能附近的电子输运特性才能反映其边缘态的能谱特性.尤其是,随着自旋轨道耦合SSH原子链与左、右导线之间的耦合强度由弱到强改变,对于缠绕数为2的四重简并边缘态,入射电子在零能附近的透射峰数目将从4个变为0;而对于缠绕数为1的二重简并边缘态情形,其透射峰数目将从2个变为0.因此,在源极为铁磁电极的情形下,通过观察自旋轨道耦合SSH原子链在零能附近电子共振透射峰的数目随着其与左、右导线之间耦合强度的变化,来探测其不同缠绕数的边缘态.上述结果为基于电子输运特性探测自旋轨道耦合SSH原子链不同拓扑性质的边缘态提供了一种可选择的理论方案.     

Network-Based Driving Force of National Economic Development: A Social Capital Perspective

Network science has been widely applied in theoretical and empirical studies of global value chain (GVC), and many related articles have emerged, forming many more mature and complete analytical frameworks. Among them, the GVC accounting method based on complex network theory is different from the mainstream economics in both research angle and content. In this paper, we build up global industrial value chain network (GIVCN) models based on World Input–Output Database, introduce the theoretical framework of Social Capital, and define the network-based indicators with economic meanings. Second, we follow the econometric framework to analyze the hypothesis and test whether it is true. Finally, we study how the three types of capital constituted by these indicators interact with each other, and discuss their impact on the social capital (economic development level, i.e., GDP). The results prove that the structural capital (industrial status) has a positive impact on the social capital; the relational capital (industrial correlation) has a positive impact on both social capital and structural capital; the cognitive capital (industrial structure) has a small impact on the social capital, structural capital, and relational capital.     

O4对利用天顶散射光法测量大气组分含量的影响研究

通过实验证明是否考虑O₄的存在对利用天顶散射光测量大气组分含量的结果存在影响.介绍了天顶散射光的观测原理、实验仪器、测量方法、光谱处理以及浓度反演过程.用具体的实验数据说明考虑O₄在实验波段内的吸收能提高NO₂和O₃浓度反演结果的准确性.同时将观测得到的臭氧垂直柱密度与美国TOMS观测数据进行了对比. 关键词： 4')" href="#">O₄ 臭氧 天顶散射光 DOAS     

金属磁记忆效应的第一性原理计算与实验研究

为研究铁磁材料应力集中区域金属磁记忆信号的产生机理及其变化规律, 采用基于密度泛函理论的第一性原理平面波赝势法, 建立了磁记忆效应的磁力学模型; 计算分析了力与磁记忆自发漏磁信号的定量变化关系. 研究结果表明:力作用导致晶格畸变是磁记忆自发漏磁信号产生的根本原因; 常温下, 磁记忆信号随应力近似线性变化的规律与X70钢管水压爆破实验结果具有很好的一致性. 研究结果有助于金属磁记忆检测机理的研究. 关键词： 金属磁记忆 第一性原理 漏磁信号