Microstructure and Properties of Co2+: ZnAl2O4/SiO2 Nanocomposite Glasses Prepared by Sol–Gel Method

Transparent bulk Co²⁺: ZnAl₂O₄/SiO₂ nanocomposites containing nanocrystalline Co²⁺: ZnAl₂O₄ dispersed in silica glass matrix were obtained by the sol–gel method. The gels of composition 89SiO₂–6Al₂O₃–5ZnO− x CoO ( x =0.2, 0.4, 0.6, 0.8, 1.0) (mol%) were prepared at room temperature by using two different aluminum salts, aluminum nitrate and aluminum alkoxide (aluminum-iso-propoxide, Al(OPrⁱ)₃), as starting materials. The transparent gels were converted to the crystalline phase of gahnite by heating above 900°C. The microstructural evolution of gels was characterized. The effect of Co²⁺ concentration on spectroscopic properties was also discussed. Co²⁺: ZnAl₂O₄ nanocrystals dispersed in the SiO₂-based glass are formed at lower heat-treatment temperature and shorter heating time by using Al(OPrⁱ)₃ as raw material.     

组合KdV方程的Hamilton系统

本文根据KdV方程的Hamilton系统,构造并证明了组合KdV方程的Hamilton系统。     

基于通道注意力机制和U-net的医学图像分割方法

随着人工智能和医学大数据的发展,基于深度学习的医学图像分割技术因具有重要的应用价值和前景,已经成为目前的研究热点.为了增强特征图的语义信息,在U-net网络的基础上引入通道注意力机制,对U-net生成的特征逐通道进行压缩,将压缩后的特征逐通道计算权重,然后将该权重与原始特征相乘得出最终的特征.通过在两个不同器官的医学图像数据集上进行实验,Dice系数相较于原始U-net网络分别提高了2.7％和1.8％,验证了该方法的可行性和有效性.     

Augmented Online Sequential Quaternion Extreme Learning Machine

Neural Processing Letters - Online sequential extreme learning machine (OS-ELM) is one of the most popular real-time learning strategy for feedforward neural networks with single hidden layer due...     

Visualizing and understanding graph convolutional network

Multimedia Tools and Applications - The graph convolutional network (GCN), which can handle graph-structured data, is enjoying great interest in recent years. However, while GCN achieved remarkable...     

基于布谷鸟搜索优化神经网络的锂电池荷电状态预测

锂电池荷电状态(SOC)的预测是电动汽车锂电池管理系统中最为关键的技术之一;为实现对SOC的高精度的预测,提岀了一种基于布谷鸟搜索算法(CS)优化的误差反向传播(BP)神经网络的锂电池SOC预测方法,该方法的核心难点之一,在于优化BP神经网络的初始权值和阈值,从而可以改善易陷入局部最优的情况,减小算法对初始值的依赖;Matlab仿真结果表明,CS—BP神经网络算法的均方根误差值比BP算法的均方根误差值平均降低了0.010 6,CS—BP算法具有更高的预测精度和极强的泛化性能.     

基于供应链管理的装备维修器材保障流程再造

现行装备维修器材保障业务流程繁杂、各环节独立、数据来源科学性不强,并且存在不必要的物流活动,需要进行调整优化。以供应链管理理论为依据,参照供应链管理在实践中的应用,从删除冗余环节、优化有用流程、提高信息化程度等方面,提出流程再造策略,即以信息系统为中心,以信息流为基础,改进物流配送,提高装备维修器材保障效益。     

煤粉源集合体对水力压裂效果的影响

煤粉颗粒在压裂液中发生"造浆"及"聚集"作用是影响煤层气藏水力压裂效果的关键.构造煤粉源集合体对压裂效果影响显著,研究结果表明:原位状态下,煤粉以构造煤粉源和原生裂缝煤粉源集合体两种形式赋存.前者体积在20~30m3之间,呈顺层和断层两种产状,其明显受煤储层小微构造的控制;后者规模很小,主要分布在内生裂隙缝间,其受煤变质程度和煤岩类型影响.煤层气完井期间,井筒附近构造煤坍塌扩径,构造煤粉源卸压膨胀,煤粉颗粒启动难度降低;压裂期间,在压裂液动压、紊流搅动作用下,煤粉颗粒均匀混合在流体中形成浆体;排采期间,回排的压裂液中煤粉含量较高,导致气井近井部位压裂裂缝及炮眼较早地堵塞,气井减产.而原生裂缝内煤粉在压裂液中通过颗粒疏水相互作用发生聚集,在压裂裂缝内形成堵塞,但堵塞程度较轻.稳定压裂液注入排量、减小流速,利用煤储层小微构造解剖技术预测构造煤粉源集合体发育部位能够降低近井部位堵塞的风险,改善水力压裂效果.     

Numerical Simulation of Energy Saving Potential in Dispersing Process of Blasted Smoke in Mines

The dispersing process of the blasted smoke in underground mines by ventilation is very important for the safety of personnel, and its energy consumption is also worthy of concern. In this study, the impact of supply air flow rate on the decay process of harmful gases and fan energy, and the role of harmful gases buried by blasted pile in the smoke dispersing process is analyzed with three-dimensional numerical simulation. According to the results, air flow facilitates quick smoke dispersion and the gas emits from the blasted pile is only significant at the beginning of ventilation. It is thus recommended that large amount of air supply is taken to disperse the smoke quickly until the concentration of harmful gases reaches relevant standards, and reduce the amount of air in the remain time with frequency conversion technology to save energy. In the given study, 12 m/s air flow rate is taken for dispersing smoke and 3 m/s air flow rate is taken for the waiting time. The energy consumption for ventilation is reduced by about 50％. In addition, due to the gases in the blasted pile remains very high concentration after the dispersing process, it is recommended to keep suitable amount of ventilation in the consequent process.     

LS—SVM算法在光伏短期功率预测中的应用

以光伏阵列为研究对象,分析了辐照强度、温度以及日类型对光伏阵列出力的影响。建立了光伏短期功率预测最小二乘支持向量机LS-SVM模型。依据实验数据对模型进行了验证计算．并与BP神经网络模型做了比较,其中LS-SVM模型最大相对误差值为10．54％,平均绝对百分比误差（MAPE）为8．18％,绝对误差平方和平均值的均方根（RMSE）为0．4884,表明模型预测值离散化程度较小,所有预测点均与实际值非常接近,模型具有较好的拟合效果和泛化能力．可以有效地预测短期光伏发电功率。