铁电薄膜存储器底电极Pt/Ti的制备及性能研究

应用射频磁控溅射方法 ,在Si基片上采用不同的溅射工艺制备了铁电薄膜底电极Pt/Ti,并分析了在不同温度下退火后材料的电导率性能和粘结力性能 ,从理论和实验上分析了不同的溅射工艺和退火处理对底电极性能的影响。     

生物质中元素分布特征的聚类分析研究

测定了稻草秆、小麦秆、玉米秆、棉花秆、大豆秆、玉米芯、花生壳和柳树、松树、桦树、杨树、杉树 1 2个农业生物质和林业生物质种类 2 7个样品中主要元素 K,Ca,Na,Mg,Al,Fe,P和痕量元素 As,Cr,Co,Cu,Cd,Mn,Mo,Ni,Pb,Ti,Zn,V,Si,Ba,Be,Se,Sb,Ge,Hg的含量 ,并用SPSS聚类分析法分别进行了 R型和 Q型聚类研究 ,对这些元素在生物质中的分布特征进行了研究 .结果显示 :杨木、杉木和桦木与松木和柳木归成一大类 ,经济类作物黄豆秆和棉花秆分别归类后又归入一大类 ;农业生物质稻草秆和小麦秆、玉米秆被归入一类 ;虽然 2 7种生物质样品分别取自我国东北、华东、华北和西南地区 ,在地理位置上相距极远 ,但样品聚类谱系图表明 ,元素的分布特征与生物质的种类的关系显著 ,与产地的关系不明显 ,即元素的分布特征受种类的影响大 ,而受生长环境、气候、土壤条件的影响小 .     

Exploration of Linked Anomalies in Sensor Data for Suspicious Behavior Detection

We present a visual analytics system to understand the operation data of a company, GAStech, from IEEE VAST Challenge 2016. The data include proximity data recording the locations and movements of employees, and heating, ventilation, and air conditioning (HVAC) data recording the environmental conditions in the building. Analyzing the data to detect the suspicious behaviors of some disgruntled employees is of special interest. Our system provides coordinated multiple views to visualize the proximity data and the HVAC data over time. Visual hints and comparisons are designed for users to identify abnormal patterns and compare them. Furthermore, the system automatically detects and correlates the anomalies in the data. We provide use cases to demonstrate the effectiveness of our system.     

基于气体穿透效果数值模拟的气体辅助注射成型工艺优化

对气体辅助注射成型工艺进行分析，阐述了气体辅助注射成型的关键技术要求在分析熔料流动、气体穿透物理模型的基础上，探讨了气体辅助注射成型数值分析的实现原理运用MPI／Gas模块对一T型支架进行了气体辅助注射成型CAE分析，模拟不同工艺条件下的气体穿透效果，确定了合理的工艺参数。     

Integrating a membrane and a fluidized pellet reactor for removing hardness: effects of NOM and phosphate

A novel treatment process combining a membrane with outside-in flow configuration and a fluidized pellet rector for removing hardness was studied. The effects of influent water quality, e.g., concentrations of natural organic matter (NOM) and phosphate along with the hydrophobicity of NOM, on the hardness removal efficiency of the novel process were investigated. The hardness removal efficiency decreases with increasing NOM concentration. However, the NOM removal efficiency is independent of NOM concentration and is related to the hydrophobicity of NOM samples. Raw water containing phosphate has adverse impact on the hardness removal efficiency, which decreases from 85% for treating water containing no phosphate to only 38% and 14% for water containing 0.33 mg/L and 1.63 mg/L of phosphate, respectively.     

氯碱整流新技术应用动态

概述了整流系统的配置，全数字化的计算机控制和监测系统，整流装置的电源和负载模式，整流辅助设备的新产品，新技术以及谐波抑制措施等新技术在工业上的应用现状。     

聚烯烃接枝马来酸酐接枝率的测定—聚乙烯接枝

以非均相化学接枝法和红外光谱法相结合的方法绘制了定量校正曲线,可用于红外光谱法测定聚乙烯接枝马来酸酐的接枝率。该方法简便快速,准确可靠,重复性好。     

脂肪族聚酯熔点的预估——对D．W．Vankrevelen经验公式的修正

对D．W．Vankrevelen经验公式进行了修正,并对脂肪族聚酯的熔点进行了计算。结果表明,修正后的公式比原公式准确性星著提高,可对脂肪族聚酯熔点进行预估。     

发用防晒成分芦荟甙的提取

芦荟甙不仅具有防晒功效,而且还具有润泽、软化头发,防止断发、护发止痒的功效.设计正交实验,用乙醇水溶液回流溶出芦荟甙,用减压蒸馏的方法得到产品,并找出最佳反应条件.用溴水和红外光谱定性分析,证明了所得产品为芦荟甙.将芦荟甙加入洗发香波中,使用效果良好.实验方法操作简单、可行性高,为芦荟的提取研究提供数据.     

Shrinkage and warpage prediction of injection‐molded thin‐wall parts using artificial neural networks

This study demonstrates the successful use of back‐propagation artificial neural networks (BPANNs) in predicting the shrinkage and warpage of injection‐molded thin‐wall parts. The effects of structural parameters of a BPANN on the predictionaccuracy and the capability of a BPANN in determining the optimal process condition are also discussed. The training and testing data are obtained experimentally based on a Taguchi L₂₇ (3¹³) test schedule. The results show that the trained BPANN can successfully predict the shrinkage and warpage of injection‐molded thin‐wall parts. Comparing the prediction accuracies of the trained BPANN and C‐Mold software, it is noted that the trained BPANN predicts more accurately. In terms of determining the optimal process condition for minimizing the shrinkage and warpage of injected thin‐wall parts, the trained BPANN is also shown to give a better optimal process condition than Taguchi's method. Polym. Eng. Sci. 44:2029–2040, 2004. © 2004 Society of Plastics Engineers.