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1. 发明申请

WO2016000133A1 METHOD AND SYSTEM OF EFFICIENCY EVALUATION OF RCAES SYSTEM 审中-公开
标题翻译： RCAES系统效率评估方法与系统
公开(公告)号：WO2016000133A1
公开(公告)日：2016-01-07
申请号：PCT/CN2014/081173
申请日：2014-06-30
申请人： TSINGHUA UNIVERSITY
发明人： MEI, Shengwei , CHEN, Laijun , LIU, Feng , LIU, Bin , WANG, Cheng , WANG, Zhaojian
IPC分类号： F04B41/02
CPC分类号： F01K13/003 , F01K3/02 , F01K7/16 , F01K13/006 , F02C6/16 , Y02E60/15
摘要： An efficiency evaluation method of an RCAES system is disclosed, and the method includes calculating electric energy charged by an electric power system in a compression process (201), calculating electric energy discharged to the electric power system in an expansion process (202), and calculating a ratio of the electric energy discharged in the expansion process to that charged in the compression process, and taking the ratio as an efficiency of the whole RCAES system (203); wherein gas in operation is ideal gas, air mass flow rates in the compression and expansion processes are known and constant in operation, an isothermal model is adopted for the CASV of which the temperature is the same with ambient circumstances, and the temperature and pressure of compressed air after throttling become constant. A corresponding system is also disclosed.
摘要翻译：公开了一种RCAES系统的效率评估方法，该方法包括计算在压缩处理（201）中由电力系统充电的电能，计算在膨胀过程（202）中向电力系统排放的电能，以及计算膨胀过程中放电的电能与在压缩过程中放电的电能的比率，并将该比率作为整个RCAES系统的效率（203）; 其中运行中的气体是理想的气体，压缩和膨胀过程中的空气质量流量是已知的并且在运行中是恒定的，对于与环境条件相同的CASV，采用等温模型，并且温度和压力节流后的压缩空气变得恒定。还公开了相应的系统。

2. 发明公开

EP3388978A1 IMAGE CLASSIFICATION METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM 有权
公开(公告)号：EP3388978A1
公开(公告)日：2018-10-17
申请号：EP16871963.1
申请日：2016-05-23
申请人： Tencent Technology (Shenzhen) Company Limited , Tsinghua University
发明人： XU, Kun , GUO, Xiaowei , HUANG, Feiyue , ZHANG, Ruixin , WANG, Juhong , HU, Shimin , LIU, Bin
IPC分类号： G06K9/62
CPC分类号： G06K9/6267 , G06K9/46 , G06K9/6262 , G06K9/6273 , G06K9/6292 , G06K9/66 , G06N3/0454
摘要： An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of different neural network models, to obtain data output by a plurality of non-input layers specified by each neural network model to generate a plurality of corresponding image features; respectively inputting the plurality of image features into a linear classifier, configured to determine a preset class, corresponding to each neural network model, to obtain a corresponding probability that the to-be-classified image includes an object image of the preset class, the linear classifier being obtained by training according to a feature extracted by a corresponding neural network model from a corresponding training image; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.

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