新型环氧树脂泡沫塑料制备及其性能研究

采用两步法制备环氧(EP)树脂泡沫塑料,并对其结构和性能进行了表征。结果表明,EP固化完全,观察不到827cm-1处环氧基的特征峰。所得的EP泡沫塑料为闭孔结构,泡孔均匀,密度最低可达0.14g/cm3,玻璃化转变温度为107℃,起始分解温度为352.5℃。     

Room temperature ferromagnetism of Ni, (Ni, Li), (Ni, N)-doped ZnO thin films

Ni-doped ZnO thin films (Ni concentration up to 10 mol%) were generated on Si (100) substrates by a sol-gel technique. The films showed wurtzite structure and no other phase was found. The chemical state of Ni was found to be bivalent by X-ray photoelectron spectroscopy. The results of magnetic measurements at room temperature indicated that the films were ferromagnetic, and magnetic moment decreased with rise of Ni concentration. The magnetization of Ni (10 mol%)-doped ZnO film annealed in nitrogen was low...     

并联安装通风机对锅炉供风系统改造

根据蒸汽锅炉通风系统通风机设备老化、送风量不能满足锅炉燃烧需要等存在的问题,分析问题原因,采取措施进行改造。     

基于自适应加权融合的标准铜柱鉴选研究

介绍了标准铜柱在塑性测压校准系统的重要作用及其鉴选方法。为提高测压铜柱受压后变形量测量准确性,提出了用自适应加权融合算法应用于变形量的融合计算,利用所提供的数据融合出标准偏差最小的数据融合值,以3.5×8.75、4×6.5、8×13测压铜柱实测数据为例,比较分析了各型号铜柱采用自适应加权融合算法与平均估算法、贝赛尔公式计算法的有效性和融合精度的差异,从理论和实践两个方面验证了该方法的有效性,提高了融合精度。     

“自动控制理论”的课程设置和改革

针对自动控制理论教改问题,从理论教学和实践性教学角度来具体阐述理论基础、辅助分析设计、平台实验的必要性和它们之间的关系;然后从上述三个方面提出了对自动控制理论课程教学的几点思考和举措。     

风力发电发展政策及电价机制的研究

介绍了基于市场化条件的英国可再生能源发展的政策机制,以英国电力市场为例,重点分析了配额制(非化石燃料公约)与竞价制(可再生能源义务令)两种体制下影响风力发电电价的因素.结合英国风电市场,讨论了中国风力发电的发展障碍,提出中国发展可再生能源发电应注意的问题及存在的机遇,并提出建议.     

一种可编程电压基准源设计与实现

提出了一种新的基于改进的电流模式带隙基准源的可编程基准源的设计与实现方法.电路采用Chartered 0.35μm 工艺仿真并流片.测试结果表明,温度变化范围为0～100℃,温度系数为±36.3ppm/℃(VID=11110).电源电压变化范围为2.7～5V,其相对变化值为5mV.当VID0频率为125kHz时,瞬态响应最大毛刺幅度约为20mV.5位VID码不同的输入状态,输出基准电压从1.1变到1.85V,变化步长为25mV.     

碳酸盐岩裂缝性地层气液重力置换实验

碳酸盐岩裂缝性地层在钻进过程中常出现由气液重力置换导致的溢漏同存现象,造成井下复杂、井控困难和储层伤害。笔者建立了气液重力置换室内实验装置,可模拟溢漏同存工况,具有裂缝尺寸、钻井液性能等关键参数可调、数据自动采集与分析、置换过程可视化等特点。基于该装置模拟了气液重力置换现象,可观察到裂缝中存在明显的气液分界面。通过改变裂缝宽度、裂缝钻开程度、钻井液黏度、井底压力等参数,研究了裂缝性地产气液重力置换特点。研究结果表明：漏失速率随裂缝缝宽和裂缝钻开程度的增加而增加,且对裂缝宽度更为敏感,而钻井液黏度增大对气液重力置换的抑制效果明显。基于室内实验结果,提出了气液重力置换的预防措施,可为碳酸盐岩裂缝性地层安全钻井施工提供参考。     

Self‐Healing,Flexible, and Tailorable Triboelectric Nanogenerators for Self‐Powered Sensors based on Thermal Effect of Infrared Radiation

Self‐healing triboelectric nanogenerators (TENGs) with flexibility, robustness, and conformability are highly desirable for promising flexible and wearable devices, which can serve as a durable, stable, and renewable power supply, as well as a self‐powered sensor. Herein, an entirely self‐healing, flexible, and tailorable TENG is designed as a wearable sensor to monitor human motion, with infrared radiation from skin to promote self‐healing after being broken based on thermal effect of infrared radiation. Human skin is a natural infrared radiation emitter, providing favorable conditions for the device to function efficiently. The reversible imine bonds and quadruple hydrogen bonding (UPy) moieties are introduced into polymer networks to construct self‐healable electrification layer. UPy‐functionalized multiwalled carbon nanotubes are further incorporated into healable polymer to obtain conductive nanocomposite. Driven by the dynamic bonds, the designed and synthesized materials show excellent intrinsic self‐healing and shape‐tailorable features. Moreover, there is a robust interface bonding in the TENG devices due to the similar healable networks between electrification layer and electrode. The output electric performances of the self‐healable TENG devices can almost restore their original state when the damage of the devices occurs. This work presents a novel strategy for flexible devices, contributing to future sustainable energy and wearable electronics.     

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.