基于马赫曾德尔干涉原理的优化双锥结构应变温度同步传感

设计了一种基于光纤马赫曾德尔干涉仪的优化双凹锥结构。该结构通过单模光纤和保偏光纤之间使用不充分的电弧放电熔接制作而成,可以实现应变和温度的同步测量.凹锥中部的球形纤芯可以进一步地调控包层和纤芯中的光能量分布,经优化几何参数后的结构可以获得16 dB的干涉条纹消光比,大于相同参数下的双凹锥结构。传感实验表明所提出的结构在0～244.35με和25～50℃的范围内分别具有±1.616με和±0.79°C的高分辨率。由于交叉敏感导致两个参数的测量误差均小于1×10~(-3)%.这种结构为同时测量应变和温度提供了一种有效的方法,可应用于精密仪器测量中.     

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.     

Cu过量对Cu1+x Al1-x O2(0≤x≤0.04)薄膜结构与光电性能的影响

以单相多晶Cu1+x Al1-x O2陶瓷做靶材,采用射频磁控溅射方法在石英衬底上沉积了Cu过量的Cu1+x Al1-x O2(0≤x≤0.04)薄膜.通过X射线衍射(XRD)、紫外吸收光谱以及电导率的测试,表征了不同含Cu量Cu1+x Al1-x O2薄膜的结构与光电性能.结果表明,沉积态薄膜经退火处理后,由非晶转变为具有铜铁矿结构的纯相Cu1+x Al1-x O2;退火态薄膜在可见光区域的平均透过率约为55;,平均可见光透过率不受Cu含量的影响;退火态薄膜样品的室温电导率随Cu含量的增加而增大,Cu1.04 Al0.96 O2的室温电导率最高,为1.22×10-2 S/cm;在近室温区(200～300 K),退火态薄膜均很好地符合Arrhenius热激活模式.     

通用法制备石墨烯/硫化铜微米花和石墨烯/硫化亚锡 微米花及在水污染处理中的应用

以L-组氨酸辅助和微波加热相结合,得到了石墨烯/硫化铜微米花和石墨烯/硫化亚锡微米花的通用制备技术.通过扫描电子显微镜(SEM)、透射电子显微镜(TEM)、X射线衍射分析(XRD)、傅里叶变换红外光谱仪(FTIR)、X光电子能谱(XPS)等手段对样品进行了形貌观察和物相分析,并阐明了获得石墨烯/硫化铜花瓣和石墨烯/硫化亚锡花瓣的形成机理.对其光催化性能的研究结果表明,在可见光照射下,石墨烯/硫化铜花瓣和石墨烯/硫化亚锡花瓣能够降解溶液中83.5;(40 min内)和90.4;(50 min内)的甲基橙,表现出优异的可见光催化效果.     

茴拉西坦/介孔二氧化硅的制备及释药性能

为了提高难溶性药物的溶出速率,采用水热合成法制备了MCM-41和SBA-15两种介孔二氧化硅载体材料,利用浸渍法将模型药茴拉西坦负载于两种载体上.利用扫描电子显微镜(SEM)、高分辨透射电子显微镜(HRTEM)、小角X射线衍射(XRD)、N2吸附-脱附、红外光谱(IR)、差热-热重(DTA-TG)对载药前后载体材料的表面形貌、粒径、孔径、孔容、比表面积、晶胞参数、骨架结构等进行测试,结果表明MCM-41和SBA-15具有相同的二维六方孔道结构,孔径及孔容分别为3.49 nm和6.67 nm,载药后的载药量分别为17;和22;,药物以非晶态装载于介孔孔道中没有影响介孔二氧化硅的骨架结构,但使介孔孔容及比表面积都有所降低.通过在不同pH溶出介质中负载于载体上的茴拉西坦与原料药晶体的溶出比较发现,两种载体均能够显著提高茴拉西坦的溶出速率.结合材料表征、载药量及溶出度综合比较,SBA-15载体的载药及释药性能均优于MCM-41载体.     

La1-x CaxAl0.8Cr0.2O3/CaZrO3(0≤x≤0.2)复相陶瓷材料发射率的研究

以La2O3、Al2O3、ZrO2、Cr2O3、CaCO3为原料,通过高温固相反应法制备La1-xCaxAl0.8 Cr0.2O3/CaZrO3(0≤x≤0.2)复相陶瓷材料,分别通过XRD、SEM和EDS等仪器对试样的物相组成和显微结构进行分析,利用紫外可见近红外分光光度计测量其红外吸收曲线.结果 表明:当CaZrO3理论生成量和La1-xCaxAl0.8Cr0.2O3的摩尔比小于0.20时复相陶瓷材料在0.76～2.5 μm的发射率较La0.8 Ca0.2Al0.8Cr0.2O3得到进一步提高,当CaZrO3理论生成量和La1-xCaxAl0.8Cr0.2O3的摩尔比为0.04时发射率最佳.     

小比距离密闭空腔爆炸爆后气体温度和压力测量技术研究

为实现空腔爆炸温度、压力变化趋势的准确测量,基于铠装K型热电偶和压力变送器,建立密闭空腔爆后气体温度、压力测量系统。设计密封隔热防护装置,将传感器的敏感端与信号调理模块分别安装在两个密封腔内,有效提高了传感器在大当量爆炸冲击条件下的存活率。在0.86m/kg^1/3比距离密闭空腔大当量爆炸条件下,对传感器及防护装置的性能进行考核验证,爆后测量采集到了有效的气体温度及压力变化历程,且传感器状态能够最终恢复至正常状态。测试结果表明,使用密封隔热安装的K型热电偶和压力变送器可以满足小比距离密闭空腔爆后气体静态温度、压力测量需求。     

基于多重子域代理模型的结构可靠性分析

基于合理子域概念,构建多重代理模型。多重代理模型在合理子域内采用经典响应面模型,在合理子域外采用经典Kriging模型,能够充分利用两种经典模型各自的优势。多重代理模型能够合理规避经典响应面模型中响应函数采取事先假定形式带来的可靠度评估风险,同时能够有效避免经典Kriging模型中试验点组合爆炸的问题。相比经典响应面模型,多重代理模型所需试验点数量并未增加,计算效率与经典响应面模型大体相同。算例表明,本文方法的计算结果与MCS的计算结果几乎一致,具有较好的计算精度。     

基于Hopf-Cole变换的Burgers方程初边值问题的Sinc-Galerkin法

利用Sinc-Galerkin法数值求解Burgers方程的初边值问题。首先,用Hopf-Cole变换将二阶非线性的Burgers方程变换为二阶线性方程,同时把第一类边界条件变为第二类边界条件。时间上的导数采用θ加权格式离散,空间导数采用Sinc-Galerkin法离散,端点处分别引入权函数处理变换后的第二类边界条件。最后,通过数值算例验证了Sinc-Galerkin法的指数收敛性,与精确解相比,本文构造的数值格式精度高,能够有效捕捉激波等物理现象。     

Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.