强流脉冲离子束辐照靶材烧蚀效应二维数值研究

对强流脉冲离子束(IPIB)辐照Ti靶的烧蚀效应进行了二维数值研究.得到了表面烧蚀物质随脉冲时间的变化关系.得出TEMP Ⅱ 型加速器产生的脉冲束流辐照靶材时引起的汽、液化均是从表面开始、并且汽化过程中表面物质被层层烧蚀的结论.同时，得到中心区的平均烧蚀速度为10m/s 数量级，它远小于产生的烧蚀等离子体的喷发速度.得到脉冲期间靶材内部不同位置烧蚀斑痕形状的时间演化过程，以及束流中含有的离子种类分额不同时IPIB辐照过程产生的不同效果. 关键词： 强流脉冲离子束 靶 烧蚀过程 二维数值模拟     

激光驻波场中钠原子沉积图样的理论研究

原子在近共振激光驻波场中受光的作用力汇聚沉积，形成以激光半波长为周期的光栅结构. 利用半经典模型对钠原子束在不饱和驻波场中的运动行为进行了理论研究，用数值方法模拟出在考虑横向温度、纵向速度和自由飞程后钠原子沉积的光栅结构，通过半高宽、对比度对最终沉积图样进行表征，分析了这些因素对沉积的影响，为正在开展的原子光刻实验提供了必要的理论依据. 关键词： 原子沉积 半经典模型 激光驻波场     

Synthesis and luminescence of a novel conjugated europium complex with 6-parachloroaniline carbonyl 2-pyridine carboxylic acid

A novel organic ligand, 6-parachloroaniline carbonyl 2-pyridine carboxylic acid, and the corresponding europium complex, tris(6-parachloroaniline carbonyl-2-pyridine carboxylate) europium (III) have been designed and synthesized. The results showed that the synthesized product was a conjugated complex, emitting remarkable strong red luminescence, and was a good red luminescence material with good thermal stability. The ⁵D₀ lifetime of Eu³⁺ in the complex was examined using time-resolved spectroscopic analysis. The lifetime values for 1.0×10⁻⁵ mol/l ethanol solution of the complex and for the complex solid were 0.49±0.01 and 1.94±0.01 ms, respectively.     

内毒素肺损伤对多形核白细胞化学发光的影响

本文研究了绵羊内毒素肺损伤对全血多形核白细胞(PMN)化学发光的影响;比较了正常混合血浆正常自体血浆和自体致伤血浆调理酵母多糖对发光的影响;观察了致伤后的血浆和肺淋巴液的发光促进活性.结果表明,内毒素致伤后,PMN的化学发光峰时无显著变化,而峰值于4h前呈直线上升,4h达高峰,而后逐渐减弱,但至24h仍明显大于伤前值.三种不同血浆调理酵母多糖对PMN发光无明显影响.致伤后期(2h后)的血浆和肺淋巴液无发光促进活性.这些结果提示,一次性微量的大肠杆菌内毒素对PMN的发光具有强烈和持久的促进作用,而对血浆的调理能力无明显影响.     

用峰面积判据改善Delsuc法预测的谱

基于Prony算法的Delsuc谱预测是目前NMR LPSVD(Linear Prediction SingularValue Decomposition)法中比较实用的方法。但在高噪声的条件下,时有失峰和假峰出现且要事先知道谱的峰数。本文通过跟踪迭代过程发现Delsuc法的判别因子r_(mis) 很难选定。如果它太小会失峰,而过大又会出现假峰。同时,由于噪声的影响,弱信号的采样点之间的相关性比强信号小,从而在迭代过程中会增加数值不稳定性。文中引入峰面积判据来改善Delsuc法,经模拟与实验均证明结果有效。     

矩形均匀电子束在激光介质中的能量沉积的模拟计算

本文主要论述了0.5MeV,单能垂直入射的电子束在三种不同密度的氟化氪准分子激光介质中的能量沉积。计算是采用Monte Carlo方法的MCSED程序,光轴方向采用周期性边界条件,因此能够给出平行于和垂直于电子束入射方向的沉积能量的空间分布。本文给出了在三种不同密度下的出射电子的角分布。用该程序对垂直入射的,初始能量为1MeV的电子在半无限大Al靶中的沉积能量的计算结果与ONETRAN程序的结果及实验结果的比较表明MCSED程序的计算结果是可靠的。     

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。