As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with h...As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with high accuracy is an important topic.The PM_(2.5) monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and predictions.Therefore,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_(2.5) concentration prediction models across different monitoring stations.We chose the northern slope of the Tianshan Mountains as the study area and took the January−December in 2019 as the research period.On the basis of data from 21 PM_(2.5) monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely GCN−TCN−AR(where GCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM_(2.5) concentrations on the northern slope of the Tianshan Mountains.The GCN−TCN−AR model is composed of an improved GCN model,a TCN model,and an AR model.The results revealed that the R2 values predicted by the GCN−TCN−AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28μg/m^(3),respectively.The performance of the GCN−TCN−AR model was also compared with the currently neural network models,including the GCN−TCN,GCN,TCN,Support Vector Regression(SVR),and AR.The GCN−TCN−AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM_(2.5)concentrations.This study revealed the significant spatiotemporal variations of PM_(2.5)concentrations.First,the PM_(2.5) concentrations exhibited clear seasonal fluctuations,with higher levels typically observed in winter and differences presented between months.Second,the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM_(2.5) concentrations,with a noticeable geographical clustering of pollutions.Understanding the variations in PM_(2.5) concentrations is highly important for the sustainable development of ecological environment in arid areas.展开更多
针对自然语言处理中传统因果关系抽取主要用基于模式匹配的方法或机器学习算法进行抽取,结果准确率较低,且只能抽取带有因果提示词的显性因果关系问题,提出一种使用大规模的预训练模型结合图卷积神经网络的算法BERT-GCN.首先,使用BERT(b...针对自然语言处理中传统因果关系抽取主要用基于模式匹配的方法或机器学习算法进行抽取,结果准确率较低,且只能抽取带有因果提示词的显性因果关系问题,提出一种使用大规模的预训练模型结合图卷积神经网络的算法BERT-GCN.首先,使用BERT(bidirectional encoder representation from transformers)对语料进行编码,生成词向量;然后,将生成的词向量放入图卷积神经网络中进行训练;最后,放入Softmax层中完成对因果关系的抽取.实验结果表明,该模型在数据集SEDR-CE上获得了较好的结果,且针对隐式的因果关系效果也较好.展开更多
目前现有问答系统模型大多数都采用模板匹配的方式进行推理,对问题推理不够充分,因此,提出基于认知图谱的问答系统推理模型。依据专业领域知识作为知识源构建本体;并基于该认知图谱构建了"问题-关系"一对一的认知图谱问答系...目前现有问答系统模型大多数都采用模板匹配的方式进行推理,对问题推理不够充分,因此,提出基于认知图谱的问答系统推理模型。依据专业领域知识作为知识源构建本体;并基于该认知图谱构建了"问题-关系"一对一的认知图谱问答系统模型。最后通过将问答问题划分为简单问题与复杂问题分别对问题进行处理,其中简单问题运用BERT+CRF(Bidirectional Encoder Representations from Transformers+Conditional Random Field)模型进行模板匹配;针对复杂问题运用Node2vec生成子图后用GCN(Graph Convolutional Network)推理模型进行推理,将得出的答案作为输出结果。最后对所提出的模型通过井下作业领域进行了实验,结果表明认知图谱问答模型优于其他算法模型。展开更多
基金supported by the Program of Support Xinjiang by Technology(2024E02028,B2-2024-0359)Xinjiang Tianchi Talent Program of 2024,the Foundation of Chinese Academy of Sciences(B2-2023-0239)the Youth Foundation of Shandong Natural Science(ZR2023QD070).
文摘As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with high accuracy is an important topic.The PM_(2.5) monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and predictions.Therefore,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_(2.5) concentration prediction models across different monitoring stations.We chose the northern slope of the Tianshan Mountains as the study area and took the January−December in 2019 as the research period.On the basis of data from 21 PM_(2.5) monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely GCN−TCN−AR(where GCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM_(2.5) concentrations on the northern slope of the Tianshan Mountains.The GCN−TCN−AR model is composed of an improved GCN model,a TCN model,and an AR model.The results revealed that the R2 values predicted by the GCN−TCN−AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28μg/m^(3),respectively.The performance of the GCN−TCN−AR model was also compared with the currently neural network models,including the GCN−TCN,GCN,TCN,Support Vector Regression(SVR),and AR.The GCN−TCN−AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM_(2.5)concentrations.This study revealed the significant spatiotemporal variations of PM_(2.5)concentrations.First,the PM_(2.5) concentrations exhibited clear seasonal fluctuations,with higher levels typically observed in winter and differences presented between months.Second,the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM_(2.5) concentrations,with a noticeable geographical clustering of pollutions.Understanding the variations in PM_(2.5) concentrations is highly important for the sustainable development of ecological environment in arid areas.
文摘针对自然语言处理中传统因果关系抽取主要用基于模式匹配的方法或机器学习算法进行抽取,结果准确率较低,且只能抽取带有因果提示词的显性因果关系问题,提出一种使用大规模的预训练模型结合图卷积神经网络的算法BERT-GCN.首先,使用BERT(bidirectional encoder representation from transformers)对语料进行编码,生成词向量;然后,将生成的词向量放入图卷积神经网络中进行训练;最后,放入Softmax层中完成对因果关系的抽取.实验结果表明,该模型在数据集SEDR-CE上获得了较好的结果,且针对隐式的因果关系效果也较好.
文摘目前现有问答系统模型大多数都采用模板匹配的方式进行推理,对问题推理不够充分,因此,提出基于认知图谱的问答系统推理模型。依据专业领域知识作为知识源构建本体;并基于该认知图谱构建了"问题-关系"一对一的认知图谱问答系统模型。最后通过将问答问题划分为简单问题与复杂问题分别对问题进行处理,其中简单问题运用BERT+CRF(Bidirectional Encoder Representations from Transformers+Conditional Random Field)模型进行模板匹配;针对复杂问题运用Node2vec生成子图后用GCN(Graph Convolutional Network)推理模型进行推理,将得出的答案作为输出结果。最后对所提出的模型通过井下作业领域进行了实验,结果表明认知图谱问答模型优于其他算法模型。
