Sealing behaviour of glass-based composites for oxygen transport membranes

In this study, seven different filler materials in different proportions were added to a Ba-Ca-Si glass matrix “H” to investigate new sealant with higher thermal expansion coefficient (CTE) value and good sealing performance for application in oxygen transport membrane (OTM). SrTi_0.75Fe_0.25O_3-δ (STF25) was used as an OTM, and the sealing partners were ferritic steel Aluchrom and pre-oxidized Aluchrom. Compatibility tests were carried out to investigate the feasibility of the composites. Higher CTE values were found in dilatometer tests on composite samples by adding 40 wt% Ag (HAg40) and 30 wt% Ni-Cr (HNC30). Gas-tightness measurements of sandwiched samples produced appropriate helium leakage rates in the range of 10^?6 mbar·l·s^?1. Sealing behaviour of sealants HAg40 and HNC30 were investigated by joining STF25 and as-delivered/pre-oxidized Aluchrom together. Scanning electron microscopy (SEM) on cross-sections of the joints revealed a homogeneous microstructure and good adherence of the glass sealants to support metals and STF25.     

A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition

Neural Computing and Applications - In real-world application of affective brain–computer interface (aBCI), individual differences across subjects and non-stationary characteristics of...     

多频段脑功能网络融合的阿尔茨海默病分类

将图论与机器学习方法相结合的阿尔茨海默病计算机辅助中,脑网络的构建大多是基于滤波去噪后的全频段BOLD信号匹配,忽略了不同脑活动信息的差异.因此,本文提出了一种多频段脑功能网络融合模型.首先将离散小波变换应用于BOLD信号中,得到不同频域下的体素信号,而后计算同频信号的相关性,获取不同频段下相关矩阵.而后计算所有矩阵的网络特征,在特征选择后基于SVM对患者进行分类.从实验结果可以看出,分频下的脑功能网络特征与未分频网络相比能在一定程度上提高分类的准确性;体素级网络由于可以更加详细的表达脑网络的变化,其分类效果要优于脑区级.     

基于随机经济模型预测控制的电热综合能源系统运行优化

北方供暖地区存在大量热电联产机组,其"以热定电"的运行特性限制了机组调峰能力.同时,随着我国能源替代的提出以及风电的快速发展,建立包含风电的电热综合能源系统是降低电热耦合以及系统运行成本的重要手段.因此,建立电热综合能源系统模型,在满足用户电热负荷需求的基础上,综合考虑风电不确定性以及系统动态过程中的经济性,提出一种基于情景树结构的随机经济模型预测控制策略.为了降低风电功率随机性干扰和风电功率变化难以描述的问题,提出采用一种改进的非线性变换方法建立风电功率非高斯分布模型,在此基础上建立基于情景树结构的风电功率变化显式表达结构,在保证鲁棒性的同时降低了优化过程中风电随机性的干扰.最后,通过累计节约成本的复合单步增长率指标验证所提策略的有效性.     

Intelligent fault diagnosis for rolling bearings based on graph shift regularization with directed graphs

Graph shift regularization is a new and effective graph-based semi-supervised classification method, but its performance is closely related to the representation graphs. Since directed graphs can convey more information about the relationship between vertices than undirected graphs, an intelligent method called graph shift regularization with directed graphs (GSR-D) is presented for fault diagnosis of rolling bearings. For greatly improving the diagnosis performance of GSR-D, a directed and weighted k-nearest neighbor graph is first constructed by treating each sample (i.e., each vibration signal segment) as a vertex, in which the similarity between samples is measured by cosine distance instead of the commonly used Euclidean distance, and the edge weights are also defined by cosine distance instead of the commonly used heat kernel. Then, the labels of samples are considered as the graph signals indexed by the vertices of the representation graph. Finally, the states of unlabeled samples are predicted by finding a graph signal that has minimal total variation and satisfies the constraint given by labeled samples as much as possible. Experimental results indicate that GSR-D is better and more stable than the standard convolutional neural network and support vector machine in rolling bearing fault diagnosis, and GSR-D only has two tuning parameters with certain robustness.     

基于CPU-GPU异构体系结构的并行字符串相似性连接方法

相似性连接技术在数据清洗、数据集成等领域中具有重要意义,近年来引起了学术界的广泛关注.随着数据量的不断增大、数据处理实时性的要求逐渐提高以及处理器性能提升瓶颈的出现,传统的串行相似性连接方法已经不能满足当前大数据处理的需求.近些年,GPU作为协处理器在机器学习等领域取得了良好的加速效果,因此基于GPU的并行算法开始成为解决各类性能问题的有效解决方案.为此,提出了基于CPU-GPU异构体系的并行相似性连接方法.首先,方法使用GPU构建倒排索引,索引采用SoA(struct of arrays)结构,从而解决了传统索引结构在并行模式下读写效率低的问题.其次,针对串行算法的性能问题,提出基于过滤验证框架的并行双重长度过滤算法,其中利用前缀过滤和构建好的倒排索引提升过滤效果.方法中相似度精确计算验证过程使用CPU计算执行,从而充分利用CPU-GPU的异构计算资源.最后,在多个数据集上进行实验验证性能.通过与串行相似性连接算法进行对比,实验结果表明所提出方法相对于已有方法具有更好的过滤效果和更低的索引生成代价,并在相似性连接上具有更好的性能和良好的加速比.     

时序图节点嵌入策略的研究

相较于传统的图数据分析方法,图嵌入算法是一种面向图节点的新型图数据分析策略.其旨在通过将图节点向量化表达,进而在节点向量基础上利用神经网络相关技术更有效的进行图数据分析或挖掘工作,如在节点分类、链接预测及交通流预测等经典问题上取得效果显著.虽然研究者们在图嵌入方面已取得了诸多成果,但是面向时序图的节点嵌入问题却未被充分重视,本文便是在先前研究工作的基础上,结合信息在时序图中的传播特性,提出了一种对时序图节点进行自适应嵌入表达的方法ATGEB （Adaptive Temporal Graph Embedding）.首先,为了解决不同类型时序图节点活跃程度不同的问题,通过设计一种自适应方式对其活跃时刻进行聚类.而后,在此基础上设计一种游走模型用以保存节点对之间的时间关系,并将节点游走序列保存在一种双向多叉树上进而可以更快速的得到节点时间相关的游走序列.最后,在基于节点游走特性和图拓扑结构的基础上,对节点向量进行重要节点采样,以便在尽可能短的时间内训练出满足需求的网络模型.通过充分的实验证明,本文面向时序图的嵌入策略相较于现流行的嵌入方法,在时序图时序中节点间时序可达性检测以及节点分类等问题上得出了更好的实验效果.     

基于多块信息提取的AUV资源勘查系统故障检测

针对"潜龙二号"AUV在实际航行过程中,资源勘查系统传感器数据具有多重变量相关性、故障类型多样、受运行状态和环境变化影响数值变化大以及噪声强等问题,提出一种新的基于多块信息提取的主元分析(PCA)故障检测方法.首先,针对变量之间的多重相关性,通过滑窗和相关系数的方法提取变量间相关性信息;然后,根据变化率在不同运行状态和环境下基本稳定的特点,对于不同类型故障,分别提取变化率信息和变化率信息的各阶统计量累积误差信息;最后,基于提取的特征信息建立3个子块,对每个子块分别建立PCA模型并进行检测,将检测的结果通过中值滤波去噪后,用贝叶斯推断进行融合.通过对"潜龙二号"实际运行数据进行检测,验证所提方法的有效性.     

多飞行机器人吊运系统研究进展及挑战

多飞行机器人吊运系统是指由多个旋翼飞行机器人、吊绳及单个悬挂负载共同构成的具有空中操作能力的一类新型机电系统,具有灵活性强、地域可达性好、运输便捷、载荷能力强等显著优点.多飞行机器人吊运系统应用广泛,但是其建模与控制还存在诸多挑战.旋翼飞行机器人自身是一种复杂的欠驱动非线性系统,随着吊绳和负载的引入,系统的耦合性、欠驱动特性和非线性也会随之增加.为了全面对多飞行机器人吊运系统的研究进行综述,首先介绍了多飞行机器人吊运系统的常见构型,并对比分析了其优缺点;其次,从动力学建模、协调控制、实验三个方面介绍了多飞行机器人吊运系统的研究现状与挑战;最后,凝练了现存的关键科学问题,对其潜在的应用领域和未来的研究工作进行了探讨与展望.     

Commutation Torque Ripple Suppression in Three Phase Brushless DC Motor Using Open-end Winding

Three phase brushless DC motor has the characteristics of high power density, simple structure and excellent speed regulation performance, which is widely used in the field of electric drive. Open-end winding brushless DC motor has no neutral point, and each phase winding is electrically isolated, which can realize the independent control of each phase winding. Compared with Y-connected brushless DC motor, the open-end winding brushless DC motor has a wider range of speed regulation under the same voltage, and has certain fault-tolerant performance, which is suitable for the application of low voltage and large current. Compared with the multiphase fault-tolerant motor, open-end winding brushless DC motor has simpler structure and higher winding utilization rate. However, the open-end winding brushless DC motor still has torque ripple, among which the cogging torque is related to the motor body structure, while commutation torque ripple is affected by the mutual inductance of the winding and the current freewheeling loop. The commutation process of the open-winding brushless DC motor is analyzed, and it is concluded that the current change rates of the turn-off phase and turn-on phase are equal under the ideal back electromotive force(back-EMF), which can ensure that the non-commutation phase current and the electromagnetic torque kept constant. The overlapping commutation with phase current closed-loop method is proposed to suppress the commutation torque ripple of open-end winding BLDC motor, and the applied voltage on turn-off phase and delayed time of turn-off phase in overlapping commutation are derived, the sliding mode observer is used to calculate the electromagnetic torque. MATLAB simulation and experimental results verify the effectiveness of this method.