民机试飞规划与管理数据库设计研究

针对国内在民机试飞规划与管理工作中所涉及的各类数据及其之间的关联进行了深入地分析。所涉及的数据资源涵盖了从飞行试验的需求确认至试验规划编制这一完整过程。根据分析结果,以ER图的形式描述了民机试飞规划与管理数据库设计的概念数据模型。并结合关系型数据库模型,简述了数据库设计工作中由概念数据模型向逻辑数据模型转换的方式。     

某型涡轴发动机研制过程中尾喷管漏油机理分析

某型涡轴发动机在研制过程中屡次出现尾喷管漏油故障。在滑油系统原理分析的基础上,建立滑油泄漏故障树,对泄漏原因逐层剖析,并结合分解检查、零件计量等手段对发动机各相关部件和滑油泵进行详细分析,确定故障原因。针对影响因素,提出相应的整改措施。     

基于AI+神经网络的数字化故障电弧识别方案研究

线路故障电弧的特征随着电力电子技术的提高更趋复杂，传统的故障电弧识别方式已无法应对新型用电系统。对比传统故障电弧识别方案，研究故障电弧产生类型和新型检测技术，提出基于谐波因素、总谐波畸变率、电流零休时间、电流变化速率、电流周期性等典型时域、频域特征的AI+神经网络的数字化故障电弧识别方案。该方案运用回归算法向量机对特征图进行分类处理，通过正向传输运算、反向传输运算和迭代回归运算三个步骤执行卷积神经网络，频繁迭代回归以获得最佳特征识别图。本文以调光器的故障电弧识别为例，对故障识别方案进行验证，结果表明采用此方案可实现更高效、更精准、更稳定地识别故障电弧。     

抽油机故障诊断的分布驱动主动学习算法

抽油机示功图直观显示了抽油机工作情况，但实际工况情况呈现典型的长尾分布特性，类别严重不平衡。传统方法无法准确识别小类别工况，也无法获得井下工作状态准确识别。针对这一问题，提出一种基于分布驱动的多类别长尾数据代价敏感主动学习算法（Cost-sensitive active learning algorithm based on distribution -driven multi-class long-tailed data， CALA）。首先，考虑数据分布特性，以最小化代价为优化目标确定数据的最佳聚类簇数；其次，通过加入预分类误差代价来更新之前得到的最佳聚类簇数；然后，构建集成分类模型作为分类器；最后，通过迭代来平衡数据分布。采用某油田真实的示功图数据进行测试，显著性实验分析证明CALA在小类别工况诊断上具有更好的性能。     

Blind identification of space–time block codes based on deep learning

Deep Learning(DL) has important applications to both commercial and military communications, such as software-defined radio, cognitive radio and spectrum surveillance. While DL has been intensively studied for modulation recognition, there are very few investigations for blind identification of Space-Time Block Codes(STBCs). This paper proposes a Residual Network(RN)-based model for identifying 6 kinds of STBC signals with a single receiving antenna, including the same length of coding matrix. I...     

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

The aerodynamic test in the pulse combustion wind tunnel is very important for the design, evaluation and optimization of aerodynamic characteristics of the hypersonic aircraft. The test accuracy even affects the success or failure of hypersonic aircraft development. In the aerodynamic test of pulse combustion wind tunnel, the aerodynamic signal is disturbed by the inertial force signal, which seriously affects the test accuracy of aerodynamic force. Aiming at the above problems, this paper innovatively proposes an aerodynamic intelligent identification method, that is the transfer learning network based on adaptive Empirical Modal Decomposition (EMD) and Soft Thresholding (TLN-AE&ST). Compared with the existing aerodynamic intelligent identification model based on deep learning technology, this study introduces the transfer learning idea into the aerodynamic intelligent identification model for the first time. The TLN-AE&ST effectively alleviates the problem of scarcity of training samples for intelligent models due to the high cost of wind tunnel tests, and provides a new idea for further implementation of deep learning technology in the field of wind tunnel aerodynamic testing. And this study designed residual attention block with soft threshold and dense block with adaptive EMD in TLN-AE&ST model. Residual attention block with soft threshold module can more effectively suppress the influence of instrument noise signal on model training effect. Dense block with adaptive EMD makes the deep learning model no longer a black box to a certain extent, and has certain physical significance. Finally, a series of wind tunnel tests were carried out in the Φ = 2.4 m pulse combustion wind tunnel of China Aerodynamic Research and Development Center to verify the effectiveness of TLN-AE&ST.     

Integration of Landsat-8 and Sentinel-1 dataset to extract geological lineaments in complex formations of Tepal mountain area,Shahrood, north Iran

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map.