排序方式: 共有9条查询结果,搜索用时 265 毫秒
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介绍星用电容器选用要点、失效实例,以及如何提高固但、液但、陶瓷电容器的应用可靠性。还谈及四个问题:①固钽不能用在电源滤波器中;②因钽的可靠性与使用线路阻抗有关,如果线路阻抗越小则可靠性越差;③液钽易发生振动失效,有些在振动激励下可能成为瞬息电势源(间歇短路);④多层陶瓷电容器的低压失效。分析了这些特殊失效现象及失效机理,提出可能采用的筛选方法。 相似文献
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Xiujie Jiang Zhihua Wang Huixian Sun Xiaomin Chen Tianlin Zhao Guanghua Yu Changyi Zhou 《Acta Astronautica》2009,65(9-10):1500-1505
More and more plastic encapsulated microcircuits (PEMs) are used in space missions to achieve high performance. Since PEMs are designed for use in terrestrial operating conditions, the successful usage of PEMs in space harsh environment is closely related to reliability issues, which should be considered firstly. However, there is no ready-made methodology for PEMs in space applications. This paper discusses the reliability for the usage of PEMs in space. This reliability analysis can be divided into five categories: radiation test, radiation hardness, screening test, reliability calculation and reliability assessment. One case study is also presented to illuminate the details of the process, in which a PEM part is used in a joint space program Double-Star Project between the European Space Agency (ESA) and China. The influence of environmental constrains including radiation, humidity, temperature and mechanics on the PEM part has been considered. Both Double-Star Project satellites are still running well in space now. 相似文献
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《中国航空学报》2022,35(9):49-57
Deep learning has been fully verified and accepted in the field of electromagnetic signal classification. However, in many specific scenarios, such as radio resource management for aircraft communications, labeled data are difficult to obtain, which makes the best deep learning methods at present seem almost powerless, because these methods need a large amount of labeled data for training. When the training dataset is small, it is highly possible to fall into overfitting, which causes performance degradation of the deep neural network. For few-shot electromagnetic signal classification, data augmentation is one of the most intuitive countermeasures. In this work, a generative adversarial network based on the data augmentation method is proposed to achieve better classification performance for electromagnetic signals. Based on the similarity principle, a screening mechanism is established to obtain high-quality generated signals. Then, a data union augmentation algorithm is designed by introducing spatiotemporally flipped shapes of the signal. To verify the effectiveness of the proposed data augmentation algorithm, experiments are conducted on the RADIOML 2016.04C dataset and real-world ACARS dataset. The experimental results show that the proposed method significantly improves the performance of few-shot electromagnetic signal classification. 相似文献
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