Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
Taking the real part and the imaginary part of complex sound pressure of the sound field as features,a transfer learning model is constructed.Based on the pre-training of a large amount of underwater acoustic data in ...Taking the real part and the imaginary part of complex sound pressure of the sound field as features,a transfer learning model is constructed.Based on the pre-training of a large amount of underwater acoustic data in the preselected sea area using the convolutional neural network(CNN),the few-shot underwater acoustic data in the test sea area are retrained to study the underwater sound source ranging problem.The S5 voyage data of SWellEX-96 experiment is used to verify the proposed method,realize the range estimation for the shallow source in the experiment,and compare the range estimation performance of the underwater target sound source of four methods:matched field processing(MFP),generalized regression neural network(GRNN),traditional CNN,and transfer learning.Experimental data processing results show that the transfer learning model based on residual CNN can effectively realize range estimation in few-shot scenes,and the estimation performance is remarkably better than that of other methods.展开更多
外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模...外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模型(invasive alien plant fine-grained named entity recognition model based on multi-feature fusion,IAPMFF)。首先,采用RoBERTa(Robustly optimized BERT approach,RoBERTa)预训练模型为基础架构,通过构建领域专用词典并通过词汇特征向量融合,增强模型对低频词及专业术语的表征能力;其次,设计双通道特征提取层,利用双向长短时记忆网络(Bi-directional long-short term memory,BiLSTM)提取长序列语义特征,结合卷积残差结构(convolution residual structure,CRS)捕获更多细粒度特征;然后,设计分层特征融合机制,通过多头自注意力机制加权融合两种特征向量,构建多维度语义表征;最后,采用条件随机场(conditional random field,CRF)进行序列解码优化。基于专家知识,构建包含24类细粒度实体标签的外来入侵植物命名实体识别数据集。试验表明,IAP-MFF模型在外来入侵植物命名实体识别数据集上取得91.51%精确率、92.51%召回率和92.01%的F1值,较基线模型分别提升4.40、3.39、3.91个百分点,显著改善了小样本细粒度实体的识别效果。在Weibo、Resume公共数据集上F1值分别达到72.75%和97.15%,表明了模型的泛化性和优越性能。IAP-MFF模型通过融合包含领域知识在内的多种特征,有效提升实体识别精度与泛化能力,为外来入侵植物知识图谱构建奠定技术基础。展开更多
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金supported by the National Natural Science Foundation of China(1197428611904274)+1 种基金the Shaanxi Young Science and Technology Star Program(2021KJXX-07)the fundamental research funding for characteristic disciplines(G2022WD0235)。
文摘Taking the real part and the imaginary part of complex sound pressure of the sound field as features,a transfer learning model is constructed.Based on the pre-training of a large amount of underwater acoustic data in the preselected sea area using the convolutional neural network(CNN),the few-shot underwater acoustic data in the test sea area are retrained to study the underwater sound source ranging problem.The S5 voyage data of SWellEX-96 experiment is used to verify the proposed method,realize the range estimation for the shallow source in the experiment,and compare the range estimation performance of the underwater target sound source of four methods:matched field processing(MFP),generalized regression neural network(GRNN),traditional CNN,and transfer learning.Experimental data processing results show that the transfer learning model based on residual CNN can effectively realize range estimation in few-shot scenes,and the estimation performance is remarkably better than that of other methods.
文摘外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模型(invasive alien plant fine-grained named entity recognition model based on multi-feature fusion,IAPMFF)。首先,采用RoBERTa(Robustly optimized BERT approach,RoBERTa)预训练模型为基础架构,通过构建领域专用词典并通过词汇特征向量融合,增强模型对低频词及专业术语的表征能力;其次,设计双通道特征提取层,利用双向长短时记忆网络(Bi-directional long-short term memory,BiLSTM)提取长序列语义特征,结合卷积残差结构(convolution residual structure,CRS)捕获更多细粒度特征;然后,设计分层特征融合机制,通过多头自注意力机制加权融合两种特征向量,构建多维度语义表征;最后,采用条件随机场(conditional random field,CRF)进行序列解码优化。基于专家知识,构建包含24类细粒度实体标签的外来入侵植物命名实体识别数据集。试验表明,IAP-MFF模型在外来入侵植物命名实体识别数据集上取得91.51%精确率、92.51%召回率和92.01%的F1值,较基线模型分别提升4.40、3.39、3.91个百分点,显著改善了小样本细粒度实体的识别效果。在Weibo、Resume公共数据集上F1值分别达到72.75%和97.15%,表明了模型的泛化性和优越性能。IAP-MFF模型通过融合包含领域知识在内的多种特征,有效提升实体识别精度与泛化能力,为外来入侵植物知识图谱构建奠定技术基础。
