Sn-doped α-Fe2O3 patulous nanotubes for high performance formaldehyde sensing

SnO₂-doped α-Fe₂O₃ patulous microtubes (SFPNs) are synthesized by an electrospinning method. The as-synthesized materials are characterized by scanning electron microscope, X-ray powder diffraction and energy dispersive spectroscopy. The gas sensing results show SFPNs possess an excellent sensing property to formaldehyde. The response value of SFPNs gas sensor to 50 ppm formaldehyde is 25.4 at 220 ℃. The lowest detecting limit of 1 ppm formaldehyde is 3.2. Response and recovery characteristic curves of SFPNs gas sensors to 1, 2, 3, 5, 5, 3, 2 and 1 ppm formaldehyde are also tested. The results show a good reversibility and repeatability of SFPNs gas sensors. The sensor exhibits a high selectivity in the presence of acetone, ethanol, toluene, ammonia, hydrogen, carbon monoxide and butane. Moreover, the sensor has a good long-time stability.     

基于有限元方法的直流输电接地极多层土壤地电位分布计算

高压直流输电单极-大地回路方式运行时入地电流会在接地极周围的土壤中产生强电场,会对接地极附近电网的电力设备和地下金属管线等产生影响。针对接地极周围实际土壤的分布情况,建立了典型土壤模型和多层土壤模型,采用有限元法对典型接地极的地电位分布进行计算。比较了采用典型土壤模型和采用多层土壤模型下的直流输电接地极的接地特性以及对地面电位分布的影响。计算结果表明,在极址附近,接地极所在层土壤电阻率对接地电阻和跨步电压的影响较大,在离接地极数十公里范围内,深层土壤电阻率对地表电位的影响较大。     

CO2探测仪信息处理系统的设计与实现

鉴于CO2气体对气候的重大影响,世界各国针对CO2气体提出了多种遥感观测方法,并发射了多颗光学遥感探测仪器.CO2探测仪是基于大气分子吸收池原理,通过测量CO2和Q的吸收光谱来对大气中的CO2和O2进行观测,提供精细光谱测量结果,而信息处理系统是CO2探测仪获取高光谱分辨率和高信噪比光谱信息的关键部分.详细阐述了信息处理系统的硬件电路结构和软件系统结构,以可编程逻辑器件FPGA为核心进行信息处理系统的设计,完成了对信号的放大、相关双采样、量化、采集和传输等重要功能控制.实验结果表明,信息处理系统输出信噪比达到63.6 dB,图像清晰,工作稳定可靠,实时传输数据稳定,完全满足预期设计,为最终获取大气光谱信息奠定了坚实的基础.     

计及相量测量单元信息不可观性的强迫功率振荡扰动源定位

强迫功率振荡的扰动源定位是低频振荡诊断与分析中的重要问题。目前,利用相量测量单元(PMU)的数据进行扰动源位置识别已引起了一定的关注,但相关研究成果大多未考虑PMU尚无法完整配置的现状,因而难以在实际电力系统中推广应用。为此,提出了一种计及PMU信息不可观性的强迫功率扰动源定位方法,并从扰动源定位的角度出发,提出了PMU分区配置的基本要求。仿真结果表明,该方法可在PMU信息不可观的情况下准确识别扰动源位置。     

Poly(ether ether ketone)/wrapped graphite nanosheets with poly(ether sulfone) composites: Preparation,mechanical properties,and tribological behavior

Antiwear composites with extraordinary tribological performances and good mechanical/thermal properties were developed by the dispersion of poly(ether sulfone) (PES) wrapped graphite nanosheets (GNSs) inside a poly(ether ether ketone) (PEEK) matrix via melt blending. The tribological behaviors and the mechanical/thermal properties of the composites were carefully investigated. Compared with pure PEEK and PEEK/GNS composites, the PEEK/wrapped GNS composites exhibited considerable enhancements in those performances; these were attributed to the eliminated layer of PES; this elimination not only eliminated the GNS aggregation inside the PEEK matrix for homogeneous distribution inside the PEEK matrix but also enhanced the interfacial adhesion between the PEEK and wrapped GNSs. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41728.     

基于BP神经网络和模糊推理系统的短时交通流预测

本文研究短时交通流预测。短时交通流预测是智能交通系统研究和实践的必要基础。本文提出和建立了一个短时交通流量预测模型,该模型利用一个基于规则的模糊系统,非线性地组合BP神经网络模型和自适应卡尔曼滤波模型的交通流量预测结果,使得短时交通流量的预测结果更加准确可靠。该模型将传统方法和人工智能方法有机结合,一方面,利用人工神经网络强大的动态非线性映射能力,从而提高预测精度;另一方面,充分发挥卡尔曼滤波的静态线性稳定性,解决了单独使用BP神经网络进行预测时识别率不理想和可信度不高的问题。实验结果表明,本文提出的短时交通流预测模型具有较高的准确度和可靠度。