The warming and thawing of permafrost are the primary factors that impact the stability of embankments in cold regions.However,due to uncertainties in thermal boundaries and soil properties,the stochastic modeling of ...The warming and thawing of permafrost are the primary factors that impact the stability of embankments in cold regions.However,due to uncertainties in thermal boundaries and soil properties,the stochastic modeling of thermal regimes is challenging and computationally expensive.To address this,we propose a knowledge-integrated deep learning method for predicting the stochastic thermal regime of embankments in permafrost regions.Geotechnical knowledge is embedded in the training data through numerical modeling,while the neural network learns the mapping from the thermal boundary and soil property fields to the temperature field.The effectiveness of our method is verified in comparison with monitoring data and numerical analysis results.Experimental results show that the proposed method achieves good accuracy with small coefficient of variation.It still provides satisfactory accuracy as the coefficient of variation increases.The proposed knowledge-integrated deep learning method provides an efficient approach to predict the stochastic thermal regime of heterogeneous embankments.It can also be used in other permafrost engineering investigations that require stochastic numerical modeling.展开更多
To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation met...To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.展开更多
基金funding support from the National Natural Science Foundation of China(Grant Nos.42277161,42230709).
文摘The warming and thawing of permafrost are the primary factors that impact the stability of embankments in cold regions.However,due to uncertainties in thermal boundaries and soil properties,the stochastic modeling of thermal regimes is challenging and computationally expensive.To address this,we propose a knowledge-integrated deep learning method for predicting the stochastic thermal regime of embankments in permafrost regions.Geotechnical knowledge is embedded in the training data through numerical modeling,while the neural network learns the mapping from the thermal boundary and soil property fields to the temperature field.The effectiveness of our method is verified in comparison with monitoring data and numerical analysis results.Experimental results show that the proposed method achieves good accuracy with small coefficient of variation.It still provides satisfactory accuracy as the coefficient of variation increases.The proposed knowledge-integrated deep learning method provides an efficient approach to predict the stochastic thermal regime of heterogeneous embankments.It can also be used in other permafrost engineering investigations that require stochastic numerical modeling.
文摘To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.
