全文获取类型
收费全文 | 66篇 |
免费 | 13篇 |
国内免费 | 7篇 |
专业分类
航空 | 54篇 |
航天技术 | 10篇 |
综合类 | 12篇 |
航天 | 10篇 |
出版年
2024年 | 1篇 |
2023年 | 4篇 |
2022年 | 7篇 |
2021年 | 1篇 |
2020年 | 3篇 |
2019年 | 1篇 |
2018年 | 1篇 |
2017年 | 2篇 |
2016年 | 2篇 |
2015年 | 4篇 |
2013年 | 2篇 |
2012年 | 4篇 |
2011年 | 9篇 |
2010年 | 5篇 |
2009年 | 2篇 |
2008年 | 9篇 |
2007年 | 4篇 |
2006年 | 10篇 |
2005年 | 2篇 |
2004年 | 4篇 |
2003年 | 1篇 |
2002年 | 2篇 |
1999年 | 2篇 |
1998年 | 2篇 |
1994年 | 1篇 |
1992年 | 1篇 |
排序方式: 共有86条查询结果,搜索用时 15 毫秒
81.
82.
《中国航空学报》2022,35(12):253-265
To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%. 相似文献
83.
84.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(1):946-963
In this paper, we implement the AdaBoost algorithm to optimize the classifications results of precipitations intensities carried out by One versus All strategy using Support Vector Machine (OvA-SVM). The model developed which combines the AdaBoost algorithm with a multiclass SVM is applied to images from the MSG (Meteosat Second Generation) satellite. Other variants to build multiclass SVMs, such as the OvO-SVM (One versus One SVM), SBT-SVM (Slant Binary Tree SVM) and DDAG-SVM (Decision Directed Acyclic Graph) are also implemented on which we tested the AdaBoost algorithm. The study showed that the AdaBoost algorithm performed better in the case of the OvA-SVM variant compared to the other variants.In order to evaluate the elaborated model, some classification techniques, such as the ECST Enhanced Convective Stratiform Technique (ECST), the SART where the Support vector machine, Artificial neural network and Random forest classifiers are combined, the Convective/Stratiform Rain Area Delineation Technique (CS-RADT) and the Random Forest technique (RFT) are applied. The classification results obtained show that AdaBoost with OvA-SVM (AdaOvA-SVM) presents very interesting performances where the evaluation parameters POD, POFD, FAR, BIAS, CSI and PC indicate the values 95.2%, 12.4%, 14.7%, 0.9, 88.1% and 96.5% respectively. Indeed, the AdaOvA-SVM technique has surpassed the CS-RADT, ECST and RFT techniques. As for the comparison with the SART, we noted that OvA-SVM presents very close results. The same trend was also observed when estimating precipitation. At the end of this study, it is shown that the AdaBoost algorithm performs better on a weak classifier or on a strong classifier operating in an unfavorable environment. 相似文献
85.
《中国航空学报》2023,36(2):213-228
Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models. 相似文献
86.
热端部件散热是众多空天设备的关键技术。表面温度分布是散热设计中用到的重要信息,常规的解析建模手段和机器学习方法均无法有效地表达此类高维信息。近年来兴起的图像深度学习算法是解决表面温度信息预测的有效手段。然而,现有的基于大数据的深度学习方法往往对于物理数据和小样本数据不适用,体现为泛化精度差、数据兼容性差、可解释性差。因此,有必要结合传热的先验知识发展物理启发的新型深度学习算法,以增强高自由度、高复杂度散热对象上的设计能力。本文基于卷积算子和有限差分求解方式的类比关系,提出了一种物理启发式的循环卷积神经网络。以横向出流的冲击冷却为例,开展了变计算域大小、变工况、变尺寸的批量数值模拟,获取了冲击冷却关键特征的小样本图像数据。进一步通过神经网络的训练,构建了多参数、大范围内有较好拟合能力的温度、传热系数、压力代理模型。研究结果表明,本文提出的物理启发神经网络模型,对于计算域大小没有限制,可以统一表达不同空间范围内获取的物理数据的共性规律。模型的各类超参设定均具有明确的物理意义,且与经典的微分方程求解理论有一定的类比关系,增强了神经网络调参的方向性。通过传热物理规律与黑箱模型的融合,本文实现了小样本多参数物理数据的共性建模。该方法可以迅速重构热端部件的高维分布信息,可服务于热端部件的快速分析设计以及优化。 相似文献