一种求非线性规划全局最小解的算法

在评述了近２０年来发展的全局最优化方法之后，提出了一种求解全局最优化问题的算法，即从一个求得的局部最小解点出发，去解一个最大化问题，这个最大化问题是构造一个辅助函数去寻求一个更好的局部最优解，这样就产生一个局部最小解序列，得后得到全局最小解，另外还有了全局收敛性定理，也给出了数值例子。     

Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the ‘‘long life and high reliability" mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing(EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology,which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature(SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis(LDA). Finally, Random Forest(RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one.     

Experimental investigation on dynamic response of flat blades with underplatform dampers

One test rig with three blades and two Under-Platform Dampers (UPDs) is established to better understand the dynamical behavior of blades with UPDs. A pre-loaded spring is used to simulate the centrifugal load acting on the damper, thereby achieving continuous adjustment of the pressing load. UPDs with different forms, sizes and materials are carefully designed as experimental control groups. Noncontact measurement via a laser Doppler velocimeter is employed and contact excitation which is performed by an electromagnetic exciter is adopted to directly obtain the magnitude of the excitation load by a force sensor mounted on the excitation rod. Particular attention is paid to the influence of the contact status of the contact surfaces, e.g. the pressure-sensitive paper is used to measure the effective contact area of the UPDs. The experimental variables are selected as the centrifugal force, the amplitude of the excitation force, the damper mass, the effective contact area, and the damper material. The Frequency Response Function (FRF) of the blade under different experimental parameters is obtained by slow frequency sweep under sinusoidal excitation to study the influence of each parameter on the dynamic characteristics of the blade and the mechanism analysis is carried out combined with the experimental results.     

专科学生英语学习动机研究

学习动机与学生的学习态度、学习策略及学习成绩是紧密相关的.它直接制约着学习的积极性,极大地影响着学生的外语学习效果.本文介绍了对大学生英语学习动机研究的过程及其教学启示.     

ESP与高职高专英语教学

高职高专英语在理论上被界定为专门用途英语(ESP)分支下,介于学术英语(EAP)与职业英语(EOP)之间的业务用途英语(EPP).本文从理论上探讨了ESP与高职高专英语教学之间的关系.     

基于融合伽马变换全卷积神经网络的火星地貌分割方法

为了让火星巡视器在有限的寿命内能获得更多的科学产出,需要提高巡视器在火星表面的自主通过能力,而火星表面地貌类型是评估巡视器可通过性的重要信息。因此,提出了融合伽马变换的全卷积神经网络(FCN)火星表面地貌分割算法。首先,考虑对巡视器通过性的影响,确认地貌分类的类型,基于好奇号拍摄的火星地表图像Mars32K数据库,构建地貌分割数据集;其次,使用自适应伽马变换(AGT)对灰度单一的火星图像进行预处理,减弱了光照等因素干扰;最后,利用数据集训练一个FCN,以实现对火星表面地貌的分割预测。仿真结果表明:网络测试准确率达到83.03%,地形分割预测结果可靠,验证了方法的可行性。     

商务英语专业实习基地建设的探索与思考

学生在真实的工作环境实习是落实素质教育,培养学生创新精神和实践能力的基本要求.实习基地则是学生扩展视野、增长见识、了解社会的重要平台.外语系高度重视作为专业建设重要组成部分的实习基地建设,并进行了大胆的探索和尝试,积累了成功的经验,也发现了一些问题.本文回顾了基地建设的背景和意义,就遇到的问题进行了分析与探讨.     

Aero-engine fault diagnosis applying new fast support vector algorithm

A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.     

喷管流场和羽流场的N－S数值模拟

利用时间推进的方法，通过求解可压缩的、雷诺平均的Ｎ－Ｓ方程，并利用Ｂａｌｄｗｉｎ－Ｌｏｍａｘ两层代数湍流模型和Ｐｒａｎｄｔｌ混合长度模型，数值模拟了飞机和火箭发动机喷管的内流和羽流，获得了喷管内流场和羽流场的参数分布，计算结果与试验结果及国外研究成果一致。网格划分采用了自适应网格技术。     

Partition method for improvement of piecewise linear model with full envelope covered

Since ambient conditions vary in a wide range within the full flight envelope, the existing piecewise linear model (PLM), which is based on sea-level static condition with use of corrected parameters for other points in the flight envelope, cannot meet the accuracy for replacing the nonlinear model. To obtain more accurate linear models, a method of partitioning the flight envelope over a grid of Mach number and altitude boxes was suggested. Then, a set of linear models for a given operating condition was selected by picking the nearest (Mach number, altitude) box in the flight envelope. Through the selected set of linear models, interpolating for power level based on a weighted sum of corrected rotor speeds can obtain a linear model with acceptable accuracy. Simulation results of different points within the full flight envelope showed that the maximum error between nonlinear model and the existing PLM was more than 50%, while the maximum error between nonlinear model and the improved PLM was within 8%. It is concluded that the improved PLM performs accurately, especially under the non-standard conditions. In addition, the improved PLM can satisfy the real-time requirement better than the existing PLM.