基于高频损耗比的干式空心电抗器制造缺陷检测方法及应用探讨

本文通过分析电抗器因制造工艺导致少匝和极限工艺偏差时600Hz损耗,提出高频损耗可作为电抗器制造缺陷的检测手段及其适用范围,并以600Hz的实测损耗与理论损耗之比是否超过1.5作为判据,以同组任两台损耗之比是否超过1.5作为辅助判据,同时列举应用实例.     

基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

Enhanced relative permittivity in niobium and europium co-doped TiO2 ceramics

The appearance of colossal permittivity materials broadened the choice of materials for energy-storage applications. In this work, colossal permittivity in ceramics of TiO₂ co-doped with niobium and europium ions ((Eu_0.5Nb_0.5)_xTi_1-xO₂ ceramics) was reported. A large permittivity (ε_r ～ 2.01?×?10⁵) and a low dielectric loss (tanδ ～ 0.095) were observed for (Eu_0.5Nb_0.5)_xTi_1-xO₂ (x?=?1%) ceramics at 1?kHz. Moreover, two significant relaxations were observed in the temperature dependence of dielectric properties for (Eu, Nb) co-doped TiO₂ ceramics, which originated from defect dipoles and electron hopping, respectively. The low dielectric loss and high relative permittivity were ascribed to the electron-pinned defect-dipoles and electrons hopping. The (Eu_0.5Nb_0.5)_xTi_1-xO₂ ceramic with great colossal permittivity is one of the most promising candidates for high-energy density storage applications.     

GK1C型内燃机车频繁停车现象探讨与解决方案

运用过程中各类相关因子的变化,会使得机车柴油机配气相位出现一定程度变化,采取某种措施调整或是处理可以解决动力不足的问题。     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

Role of Co in formation of Ni-Ti clusters in maraging stainless steel

The effect of Co addition on the formation of Ni-Ti clusters in maraging stainless steel was studied by three dimensional atom probe (3DAP) and first-principles calculation. The cluster analysis based on the maximum separation approach showed an increase in size but a decrease in density of Ni-Ti clusters with increasing the Co content. The first-principles calculation indicated weaker Co-Ni (Co-Ti) interactions than Co-Ti (Fe-Ti) interactions, which should be the essential reason for the change of distribution characteristics of Ni-Ti clusters in bcc Fe caused by Co addition.