| 基于TCN和Transformer的鸡胚心跳混淆信号分类方法 |
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| 引用本文： | 耿磊,吴寒冰,张芳,肖志涛,李晓捷.基于TCN和Transformer的鸡胚心跳混淆信号分类方法[J].农业机械学报,2023,54(8):296-308. |
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| 作者姓名： | 耿磊 吴寒冰 张芳 肖志涛 李晓捷 |
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| 作者单位： | 天津工业大学 |
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| 基金项目： | 天津市科技计划项目（20YDTPJC00110） |
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| 摘 要： | 鸡蛋胚胎培养法是制备禽流感疫苗常用的方法，快速准确地对鸡蛋胚胎进行成活性分类并将死胚从活胚中尽早剔除可以有效避免因胚胎死亡导致的细菌或霉菌污染，对孵化效率的提高有着重要意义。目前，主要以鸡胚心跳信号作为分辨死胚和活胚的依据。然而，鸡蛋活胚在注入禽流感病毒96 h后，其心跳信号特征介于普通活胚和死胚之间，易与死胚混淆，本文将该类数据称为鸡胚心跳混淆信号，单独作为一类加入数据集，将原本死胚、活胚二分类改为死胚、普通活胚和96 h活胚三分类，根据信号特征设计了绝对值均值标准化预处理方法，增强原始数据特征以提升数据可分类性，并针对全局特征和细节特征提出了一种基于时间卷积网络(Temporal convolutional network, TCN)和Transformer的残差结构浅层双分支网络结构(Residual fully temporal convolutional with transformer network, RFTNet)。实验结果表明，本文提出的三分类绝对值均值标准化预处理方法和RFTNet双分支网络在鸡胚混淆数据集分类任务中展现出良好性能，检测准确率高达99.75%。此外，...
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| 关 键 词： | 鸡胚成活性分类 鸡胚心跳混淆信号 绝对值均值标准化 时间卷积网络 Transformer |
| 收稿时间： | 2023/1/11 0:00:00 |
Classification Method of Heartbeat Confusion Signals of Hatching Eggs Based on TCN and Transformer |
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| GENG Lei,WU Hanbing,ZHANG Fang,XIAO Zhitao,LI Xiaojie.Classification Method of Heartbeat Confusion Signals of Hatching Eggs Based on TCN and Transformer[J].Transactions of the Chinese Society of Agricultural Machinery,2023,54(8):296-308. |
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| Authors: | GENG Lei WU Hanbing ZHANG Fang XIAO Zhitao LI Xiaojie |
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| Affiliation: | Tiangong University |
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| Abstract: | The egg embryo culture method is commonly used for the preparation of avian influenza vaccines. The rapid and accurate classification of hatching eggs into active and early removal of dead embryos from live embryos can effectively avoid bacterial or mycobacterial contamination due to embryo death and it is of great importance for the improvement of hatching efficiency. Currently, the heartbeat signal of chicken embryos is mainly used as the basis for distinguishing dead embryos from live embryos. However, after 96 h of avian influenza virus injection, the heartbeat signal of live egg embryos is between that of ordinary live embryos and dead embryos, which is easily confused with dead embryos. This type of data is called chicken embryo heartbeat confusion signal, and is added to the data set as a separate category. The original dual classification of dead embryos and live embryos was changed to a triple classification of dead embryos, ordinary live embryos and 96 hour live embryos. An absolute average value normalization preprocessing method was proposed based on confusing heartbeat signals of hatching eggs, to enhance the original data features and improve the classifiability of the data. A shallow dual branch network structure residual fully temporal convolutional with transformer network (RFTNet) with residual structure was proposed based on temporal convolutional network (TCN) and transformer for global features and detail features. The experimental results showed that the three-classification absolute average value normalization preprocessing method and RFTNet two-branch network proposed demonstrated good performance in the classification task of hatching eggs confusion dataset with a detection accuracy of 99.75%. In addition, the three evaluation indexes of detection accuracy, recall rate and F1 score reached 99.75%, 99.74% and 99.7%, respectively, further verifying the effectiveness of the method. |
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| Keywords: | hatching eggs activity classification heartbeat confusion signals of hatching eggs absolute average value normalization TCN Transformer |
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