{"title":"基于神经网络的表面波天线金属单元方向图设计","authors":"Jiashu Yang, K. Tong","doi":"10.1109/iWEM52897.2022.9993476","DOIUrl":null,"url":null,"abstract":"This work presents a surface wave antenna metallic cell pattern prediction method which can be generated based on the required far-field radiation pattern by the mean of applying Wasserstein generative adversarial network (WGAN) and bi-directional gated recurrent unit (Bi-GRU) neural network models. The predicted metallic cell pattern has been 3D-modelled in CST and the radiation pattern shows less than 1 dBi variation level from the desired input radiation pattern.","PeriodicalId":433151,"journal":{"name":"2022 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Surface Wave Antenna Metallic Cell Pattern Design Using Neural Network Method\",\"authors\":\"Jiashu Yang, K. Tong\",\"doi\":\"10.1109/iWEM52897.2022.9993476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents a surface wave antenna metallic cell pattern prediction method which can be generated based on the required far-field radiation pattern by the mean of applying Wasserstein generative adversarial network (WGAN) and bi-directional gated recurrent unit (Bi-GRU) neural network models. The predicted metallic cell pattern has been 3D-modelled in CST and the radiation pattern shows less than 1 dBi variation level from the desired input radiation pattern.\",\"PeriodicalId\":433151,\"journal\":{\"name\":\"2022 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iWEM52897.2022.9993476\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iWEM52897.2022.9993476","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This work presents a surface wave antenna metallic cell pattern prediction method which can be generated based on the required far-field radiation pattern by the mean of applying Wasserstein generative adversarial network (WGAN) and bi-directional gated recurrent unit (Bi-GRU) neural network models. The predicted metallic cell pattern has been 3D-modelled in CST and the radiation pattern shows less than 1 dBi variation level from the desired input radiation pattern.