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基于并联深度学习网络的雷达有源干扰智能识别方法 被引量:5
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作者 姜正云 舒汀 +1 位作者 何劲 郁文贤 《现代雷达》 CSCD 北大核心 2021年第10期9-14,共6页
针对传统的雷达有源干扰识别方法存在特征参数对干扰样式敏感,识别准确率不高等问题,提出了一种基于深度学习的雷达有源干扰智能识别方法,设计了一种残差网络(ResNet)和长短时间记忆网络(LSTM)相并联的新型网络结构。该方法基于多维度... 针对传统的雷达有源干扰识别方法存在特征参数对干扰样式敏感,识别准确率不高等问题,提出了一种基于深度学习的雷达有源干扰智能识别方法,设计了一种残差网络(ResNet)和长短时间记忆网络(LSTM)相并联的新型网络结构。该方法基于多维度信息联合处理,可提高干扰识别的稳健性。通过外场试验,对常规的6种雷达有源干扰样式进行识别性能验证,识别准确率达到94.80%,证明了该文的方法具有较好的工程应用前景。 展开更多
关键词 残差网络 长短时间记忆网络 并联网络 雷达有源干扰识别 实测数据验证
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Investigation of Flow in Data Rack
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作者 Manoch Lukas Novotny Jan Novakova Ludmila 《Journal of Civil Engineering and Architecture》 2012年第12期1685-1694,共10页
The main purpose of this paper was to set up a functioning numerical model of data rack verified by an experimental measurement. The numerical model will serve for the flow simulation inside the data rack. For the aim... The main purpose of this paper was to set up a functioning numerical model of data rack verified by an experimental measurement. The numerical model will serve for the flow simulation inside the data rack. For the aim of experimental verification of the server model, a PIV (particle image velocimetry) method was used. The server model was projected based on the original Rack Workstation Dell Precision 17,5400 (2U rack space). The flow rate in each channel was implemented with the help of pressure loss, which was set up so that the server flow rate corresponded with the measured values. The verification of the correct functioning of the numerical model of the server was carded out together with verifying the numerical model of a small data rack. The experiment was compared with the numerical model for the case of data rack (12U rack space) fitted with two 2U rack workstations Dell Precision R5400, on which the simulation of several phases of the entire data rack with given configuration was carried out. 展开更多
关键词 Data rack PIV numerical simulation flow field.
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