The translation activity is a process of the interlinguistic transmission of information realized by the information encoding and decoding.Encoding and decoding,cognitive practices operated in objective contexts,are i...The translation activity is a process of the interlinguistic transmission of information realized by the information encoding and decoding.Encoding and decoding,cognitive practices operated in objective contexts,are inevitably of selectivity ascribing to the restriction of contextual reasons.The translator as the intermediary agent connects the original author(encoder)and the target readers(decoder),shouldering the dual duties of the decoder and the encoder,for which his subjectivity is irrevocably manipulated by the selectivity of encoding and decoding.展开更多
with the development of 5G,the future wireless communication network tends to be more and more intelligent.In the face of new service de-mands of communication in the future such as super-heterogeneous network,multipl...with the development of 5G,the future wireless communication network tends to be more and more intelligent.In the face of new service de-mands of communication in the future such as super-heterogeneous network,multiple communication sce-narios,large number of antenna elements and large bandwidth,new theories and technologies of intelli-gent communication have been widely studied,among which Deep Learning(DL)is a powerful technology in artificial intelligence(AI).It can be trained to con-tinuously learn to update the optimal parameters.This paper reviews the latest research progress of DL in in-telligent communication,and emphatically introduces five scenarios including Cognitive Radio(CR),Edge Computing(EC),Channel Measurement(CM),End to end Encoder/Decoder(EED)and Visible Light Com-munication(VLC).The prospect and challenges of further research and development in the future are also discussed.展开更多
石质文物假山长期曝露于室外,受多源因素影响易形成不均匀沉降,因此假山沉降传感器监测与长时精准预测对石质文物保护十分必要。现有沉降长时预测方法难以有效解决噪声和瞬时波动造成的精度降低与应用可靠性问题。为此,本文提出一种融...石质文物假山长期曝露于室外,受多源因素影响易形成不均匀沉降,因此假山沉降传感器监测与长时精准预测对石质文物保护十分必要。现有沉降长时预测方法难以有效解决噪声和瞬时波动造成的精度降低与应用可靠性问题。为此,本文提出一种融合多源因素的编码器-解码器沉降长时预测模型。在多源因素编码器中设计动态多源因素融合模块将深度特征进行融合并实时计算沉降、温度、振动、裂缝等多源因素与目标数据的动态相关性;在时域增强解码器中构建多头自适应平滑模块,通过多头注意力的方法自适应学习各时间步的平滑指数,保留时间序列长期趋势,减少传感器带来的噪声和瞬时波动。本模型以环秀山庄沉降监测系统的实测数据集进行验证,结果表明该模型相较于基线方法在评价指标均方根误差(Root Mean Squared Error,RMSE)指标、平均绝对误差(Mean Absolute Error,MAE)指标以及连续排序概率评分(Continuous Ranked Probability Score,CRPS)最高分别提升了19.1%、19%以及16.3%,且符合实际应用需求。展开更多
文摘The translation activity is a process of the interlinguistic transmission of information realized by the information encoding and decoding.Encoding and decoding,cognitive practices operated in objective contexts,are inevitably of selectivity ascribing to the restriction of contextual reasons.The translator as the intermediary agent connects the original author(encoder)and the target readers(decoder),shouldering the dual duties of the decoder and the encoder,for which his subjectivity is irrevocably manipulated by the selectivity of encoding and decoding.
基金the National Nat-ural Science Foundation of China under Grant No.62061039Postgraduate Innovation Project of Ningxia University No.JIP20210076Key project of Ningxia Natural Science Foundation No.2020AAC02006.
文摘with the development of 5G,the future wireless communication network tends to be more and more intelligent.In the face of new service de-mands of communication in the future such as super-heterogeneous network,multiple communication sce-narios,large number of antenna elements and large bandwidth,new theories and technologies of intelli-gent communication have been widely studied,among which Deep Learning(DL)is a powerful technology in artificial intelligence(AI).It can be trained to con-tinuously learn to update the optimal parameters.This paper reviews the latest research progress of DL in in-telligent communication,and emphatically introduces five scenarios including Cognitive Radio(CR),Edge Computing(EC),Channel Measurement(CM),End to end Encoder/Decoder(EED)and Visible Light Com-munication(VLC).The prospect and challenges of further research and development in the future are also discussed.
文摘石质文物假山长期曝露于室外,受多源因素影响易形成不均匀沉降,因此假山沉降传感器监测与长时精准预测对石质文物保护十分必要。现有沉降长时预测方法难以有效解决噪声和瞬时波动造成的精度降低与应用可靠性问题。为此,本文提出一种融合多源因素的编码器-解码器沉降长时预测模型。在多源因素编码器中设计动态多源因素融合模块将深度特征进行融合并实时计算沉降、温度、振动、裂缝等多源因素与目标数据的动态相关性;在时域增强解码器中构建多头自适应平滑模块,通过多头注意力的方法自适应学习各时间步的平滑指数,保留时间序列长期趋势,减少传感器带来的噪声和瞬时波动。本模型以环秀山庄沉降监测系统的实测数据集进行验证,结果表明该模型相较于基线方法在评价指标均方根误差(Root Mean Squared Error,RMSE)指标、平均绝对误差(Mean Absolute Error,MAE)指标以及连续排序概率评分(Continuous Ranked Probability Score,CRPS)最高分别提升了19.1%、19%以及16.3%,且符合实际应用需求。
