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371.
《中国航空学报》2020,33(4):1218-1227
The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10−5) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications. 相似文献
372.
高凤香 《西安航空技术高等专科学校学报》2012,(4):7-10
基于辩证系统思维引领,科学界定高校思想政治教育工作队伍整体优化的内涵,创新开启立足于微观、中观和宏观三个不同层面的多维研究视角,将思想政治教育工作队伍理论概括为三个不同层面上的"三位一体"化大军,并通过确定建构理想目标、把握发展趋势、整合资源力量、追求最高境界的进路,将高校思想政治教育工作队伍整体优化的理论研究引向深入,促使高校思想政治教育工作实效性进一步增强。 相似文献
373.
以词块教学理论为依据,分析在听说教学中进行词块训练的可行性。研究词块训练在听说课堂中的具体实施过程,并通过问卷调查和访谈结果,论证了听说课上引入词块理念能提高英语学习者听说能力从而提出词块学习对语言习得的有效性。 相似文献
374.
Ali K Abed Rami Qahwaji Ahmed Abed 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2021,67(8):2544-2557
In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS). 相似文献
375.
何树勋 《西安航空技术高等专科学校学报》2013,(4):64-67
针对学院2011级196名学生自主学习情况进行了实证调查,发现学生外语学习和自主学习策略各要素的使用情况,在此基础上提出了一些符合中国文化及学生自身特点、适合不同任务的自主学习策略,以期达到提高学生外语学习效果的目的。 相似文献
376.
提升合成孔径雷达(SAR)图像质量以增强其可判读性,一直是SAR图像处理中的关键问题。近年来,深度学习在光学图像处理中取得显著的成功,并逐步应用到SAR图像质量提升领域。对深度学习在SAR图像质量提升中的关键应用进行综述,对深度学习在SAR图像质量提升中采用的典型网络进行了介绍,并从SAR图像旁瓣抑制、超分辨(SR)处理和图像融合3个方面对深度学习的应用进行阐述。最后,分析与探讨了基于深度学习的SAR图像质量提升中的关键问题及进一步研究方向。 相似文献
377.
王璞尔 《长沙航空职业技术学院学报》2011,11(2):22-24
高职学生在英语学习过程中普遍存在着焦虑情绪,一定程度上阻碍了学生英语水平的提高.对焦虑情绪对英语学习的影响、成因和如何预防及解决学生的焦虑情绪进行分析,以期对解决学生的焦虑情绪提供一些建议. 相似文献
378.
针对组合动力水平起飞可重复使用运载器,开展了上升段轨迹优化模型设计与轨迹优化方法研究。首先,针对跨大空/速域飞行须采用多种动力形式协调工作这一问题,考虑动力/气动/轨迹/指标间的复杂耦合关系,建立了运载器动力和气动模型。其次,为降低轨迹优化问题的求解难度,设计了一种全新的飞行剖面,实现了关键优化参数的提取和攻角约束的自动满足,减少了优化算法需要处理的约束数量。然后,提出了一种改进的粒子群优化(PSO)算法完成求解;在收敛性分析的基础上,引入强化学习机制对PSO寻优过程进行自主智能控制,从本质上提升了PSO算法的求解效率。最后通过数学仿真验证了方法的正确性和有效性。 相似文献
379.
鉴于高校教师师德建设的重要性研究,在现代化视域下限定其论域边界,分析其与其他范畴的联系和区别,高校教师师德建设的具体路径。 相似文献
380.
《中国航空学报》2020,33(3):1016-1025
Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems (a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR. 相似文献