针对传统数值预报模式计算时间长和计算资源消耗大的问题,以及现有深度学习预报方法在温度预报结果上不精确,且预测结果模糊的问题,提出了一个新的温度预报模型。首先,设计了一个时空信息捕捉模块,将该模块捕获的长期依赖信息,作为扩散...针对传统数值预报模式计算时间长和计算资源消耗大的问题,以及现有深度学习预报方法在温度预报结果上不精确,且预测结果模糊的问题,提出了一个新的温度预报模型。首先,设计了一个时空信息捕捉模块,将该模块捕获的长期依赖信息,作为扩散模型的生成条件,赋予扩散模型预报的能力;其次,设计了一个新的平衡损失函数,同时保护了扩散模型的生成能力和时空信息捕捉模块对时空信息的捕捉能力;最后,基于美国国家环境预报中心的再分析数据进行预报,与现有的深度学习方法相比,所提模型预报结果的质量在均方误差(mean square error,MSE)上降低了17.3%,在均方根误差(root mean square error,RMSE)上降低了9.14%,在峰值信噪比(peak signal to noise ratio,PSNR)上提升了5.1%。改进的扩散模型能有效地捕捉时空依赖的关系,有效地进行时空序列预测,效果优于其他对比方法。展开更多
Rock condition perception based on tunnel boring machine(TBM)data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency.The prediction of TBM boring responses(i.e...Rock condition perception based on tunnel boring machine(TBM)data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency.The prediction of TBM boring responses(i.e.,torque and total thrust of the cutterhead)largely determines the reliability of rock condition perception.In this paper,a new architecture of a two-dimensional convolutional neural network(2D-CNN)with a dual-input strategy is proposed to predict the TBM responses.The TBM Lot 3 of the Yinsong project in Jilin province,China,is taken as the case study in this paper.Two types of models that follow different learning strategies are compared:one is defined as the point model,which only learns data of the stable phase,and the other is defined as the line model,which learns data from both the loading and stable boring phases.The line model is further improved by the weighted loss function method.The results indicate that the strategy of learning data from both the loading phase and stable boring phase and increasing the weight of samples from the stable phase is shown to be optimal in predicting TBM boring responses.In terms of learning strategies,the line model can learn the influence of active control parameters on passive response parameters,but the point model cannot.In terms of machine learning algorithms,2D-CNN has the best performance,with R2 values of 0.865 and 0.923 for torque and total thrust,respectively.The proposed line model can overcome the problem that the traditional model failed to learn the influence of control parameters.Such a model can provide a solid base for the timely optimization of the control parameters in TBM boring process.展开更多
文摘针对传统数值预报模式计算时间长和计算资源消耗大的问题,以及现有深度学习预报方法在温度预报结果上不精确,且预测结果模糊的问题,提出了一个新的温度预报模型。首先,设计了一个时空信息捕捉模块,将该模块捕获的长期依赖信息,作为扩散模型的生成条件,赋予扩散模型预报的能力;其次,设计了一个新的平衡损失函数,同时保护了扩散模型的生成能力和时空信息捕捉模块对时空信息的捕捉能力;最后,基于美国国家环境预报中心的再分析数据进行预报,与现有的深度学习方法相比,所提模型预报结果的质量在均方误差(mean square error,MSE)上降低了17.3%,在均方根误差(root mean square error,RMSE)上降低了9.14%,在峰值信噪比(peak signal to noise ratio,PSNR)上提升了5.1%。改进的扩散模型能有效地捕捉时空依赖的关系,有效地进行时空序列预测,效果优于其他对比方法。
基金supported by the National Key R&D Program of China(Grant No.2022YFE0200400)the Natural Science Foundation of China(Grant No.52025094)+1 种基金In addition,we sincerely give our thanks to the data support from the National Program on Key Basic Research Project(973 Program,Grant No.2015CB058100)of China,China Railway Engineering Equipment Group Corporationthe Survey and Design Institute of Water Conservancy of Jilin Province,China.
文摘Rock condition perception based on tunnel boring machine(TBM)data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency.The prediction of TBM boring responses(i.e.,torque and total thrust of the cutterhead)largely determines the reliability of rock condition perception.In this paper,a new architecture of a two-dimensional convolutional neural network(2D-CNN)with a dual-input strategy is proposed to predict the TBM responses.The TBM Lot 3 of the Yinsong project in Jilin province,China,is taken as the case study in this paper.Two types of models that follow different learning strategies are compared:one is defined as the point model,which only learns data of the stable phase,and the other is defined as the line model,which learns data from both the loading and stable boring phases.The line model is further improved by the weighted loss function method.The results indicate that the strategy of learning data from both the loading phase and stable boring phase and increasing the weight of samples from the stable phase is shown to be optimal in predicting TBM boring responses.In terms of learning strategies,the line model can learn the influence of active control parameters on passive response parameters,but the point model cannot.In terms of machine learning algorithms,2D-CNN has the best performance,with R2 values of 0.865 and 0.923 for torque and total thrust,respectively.The proposed line model can overcome the problem that the traditional model failed to learn the influence of control parameters.Such a model can provide a solid base for the timely optimization of the control parameters in TBM boring process.
