基于双槽式孔板组合和神经网络技术的凝析天然气流量计开发

A slotted orifice has many superiorities over a standard orifice. For single-phase flow measurement, its flow coefficient is insensitive to the upstream velocity profile. For two phase flow measurement, various characteristics of its differential pressure （DP） are stable and closely correlated with the mass flow rate of gas and liquid. The complex relationships between the signal features and the two-phase flow rate are established through the use of a back propagation （BP） neural network. Experiments were carried out in the horizontal tubes with 50ram inner diameter, ooerated with water flow rate in the range of 0.2m^3·h^-1 to 4m3·h^-1, gas flow rate in the range of 100m^3·h^-1 to 1000m^3·h^-1, and pressure at 400kPa and 850kPa respectively, where the temperature is ambient temperature. This article includes the principle of wet gas meter development, the experimental matrix, the signal processing techniques and the achieved results. On the basis of the results it is suggested that the slotted orifice couple with a trained neural network may provide a simple but efficient solution to the wet gas meter development.     

中国干混砂浆的应用研究概况

从干混砂浆的性能、组分以及聚合物干粉对其性能影响和作用机理等方面论述了干混砂浆在中国的研究进展.中国国内目前关于干混砂浆的理论研究主要集中在聚合物共混改性水泥砂浆,无机矿物外加剂和聚合物种类、成分、掺量等对水泥砂浆性能影响方面. 干混砂浆所用聚合物种类和掺量对干混砂浆的各种性能影响显著.对聚合物的作用机理研究几乎还是空白.     

Raman spectra of high‐density,low‐density,and linear low‐density polyethylene pellets and prediction of their physical properties by multivariate data analysis

Raman spectra have been measured for pellets of five samples of high‐density polyethylene (HDPE), seven samples of low‐density polyethylene (LDPE), and six samples of linear low‐density polyethylene (LLDPE). The obtained Raman spectra have been compared to find out characteristic Raman bands of HDPE, LDPE, and LLDPE. Principal component analysis (PCA) was applied to the Raman spectra in the 1600–650 cm^?1 region after multiplicative scatter correction (MSC) to discriminate the Raman spectra of the three different PE species. They are classified into three groups by a score plot of PCA factor 1 vs. 2. HDPE with high density and high crystallinity gives high scores on the factor 1 axis, while LDPE with low density and low crystallinity yields negative scores on the same axis. It seems that factor 1 reflects the density or crystallinity. A PC weight loadings plot for factor 1 shows six upward peaks corresponding to the bands arising from the crystalline parts or all‐trans ? (CH₂)_n? groups and seven downward peaks ascribed to the bands of the amorphous or anisotropic regions and those arising from the short branches. Partial least‐squares (PLS‐1) regression was applied to the Raman spectra after MSC to propose calibration models that predict the density, crystallinity, and melting points of the polyethylenes. The correlation coefficient was calculated to be 0.9941, 0.9800, and 0.9709 for the density, crystallinity, and melting point, respectively, and their root‐mean‐square error of cross validation (RMSECV) was found to be 0.0015, 3.3707, and 2.3745, respectively. The loadings plot of factor 2 for the prediction of melting point is largely different from those for the prediction of density and crystallinity. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 443–448, 2002     

黑骨藤化学成分及药理作用研究进展

黑骨藤是苗族地区的常用药,主治风湿关节痛、跌打损伤、月经不调等症。为了更好开发和利用其资源,文章综述了近年来黑骨藤化学成分及药理作用研究方面的进展,并对其发展趋势和前景作了展望。     

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional,time‐dependent Processes

High‐dimensional and time‐dependent data pose significant challenges to Statistical Process Monitoring. Most of the high‐dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as nonadaptive and adaptive. Nonadaptive methods include the static PCA approach and Dynamic Principal Component Analysis (DPCA) for data with autocorrelation. Methods, such as DPCA with Decorrelated Residuals, extend DPCA to further reduce the effects of autocorrelation and cross‐correlation on the monitoring statistics. Recursive Principal Component Analysis and Moving Window Principal Component Analysis, developed for nonstationary data, are adaptive. These fundamental methods will be systematically compared on high‐dimensional, time‐dependent processes (including the Tennessee Eastman benchmark process) to provide practitioners with guidelines for appropriate monitoring strategies and a sense of how they can be expected to perform. The selection of parameter values for the different methods is also discussed. Finally, the relevant challenges of modeling time‐dependent data are discussed, and areas of possible further research are highlighted. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1478–1493, 2016     

基于主成分分析法的X射线焊缝缺陷图像增强与分割算法

为提高X射线图像缺陷自动识别的能力与图像分割的效果,提出了一种基于主成分分析法的X射线焊缝缺陷图像增强与分割算法。该算法首先通过计算图像的协方差矩阵特征值与其对应的特征向量,并根据特征向量分布,选择感兴趣区域即图像中的焊缝部分,从而减少图像处理的计算量;其次通过分析特征值累计百分比和试验结果,筛选出最佳的特征向量,对图像进行基于主成分的重构;最后采用Otsu阈值分割法,对重构后的图像进行分割。试验结果表明,该算法在对比度低、噪声严重的X射线缺陷图像分割中有很好的应用效果。     

从系统总体看现代引信爆炸元件的设计问题

从引信系统总体的角度研究了爆炸元件的总体结构设计，分析了影响爆炸元件针刺感度、威力、作用时间的因素。提出小型针刺起爆元件应采用美国M55雷管结构；在保证安全的情况下，针刺感度宜高，应选用太安作雷管猛炸药装药；考虑引信隔爆要求时，应选用无气体延期药。同时提出减少性能散布应是引信爆炸元件的发展方向。     

基于鲁棒主成分分析的红外图像小目标检测

鲁棒的小目标检测是红外目标搜索与跟踪的关键技术,提出一种改进的单帧红外图像小目标检测算法。该方法将原始红外图像通过预处理变换到新的红外块图像模式,在红外块图像上,将红外图像小目标检测问题转换为低秩矩阵和稀疏矩阵分离的鲁棒主成分分析(RPCA)问题。考虑到红外图像中噪声和杂波的存在,用交替方向方法求解带噪声的RPCA问题,获得稀疏目标图像,并对获得的稀疏目标图像采用简单的图像分割算法进行目标检测。对空天、海天、天云、海面4种不 同场景的红外图像小目标检测,进行仿真实验,结果验证了所提出算法的有效性。     

基于本体和知识组件的高低机设计方法研究

为了提高高低机设计质量和效率,提出一种基于本体和知识组件的高低机设计方法。利用本体和知识组件开展高低机设计知识的表示与建模,构建高低机设计知识库,在明确基于本体和知识组件的高低机设计方法的基础上,以Java为平台开发了高低机设计专用系统,并以某高低机的设计实例进行验证。验证结果表明：该方法及开发的设计系统具有可行性和有效性,能够按照本体中既定设计流程,依次调用对应知识组件,实时提示相关设计知识,辅助设计人员完成繁琐与重复的计算工作。     

不同产地皱盖疣柄牛肝菌中矿质元素 含量测定及特征分析

采用微波消解-电感耦合等离子体发射光谱法测定5 个产地皱盖疣柄牛肝菌中10 种矿质元素含量,并用SPSS软件进行方差分析和主成分分析。结果显示：该方法标准物质（GBW 07605）回收率在91%～101%之间,检出限在0.000 1～0.616 5 mg/kg范围,表明该方法准确、可靠;皱盖疣柄牛肝菌中Mg、Cu、Ca、Na、Zn等矿质元素的含量较高,不同产地皱盖疣柄牛肝菌中矿质元素含量具有差异;主成分分析的前3 个主成分累积贡献率达到87.87%,能表达大部分信息,主成分综合得分可以反映出不同产地皱盖疣柄牛肝菌对10 种矿质元素的富集情况。