The test section’s Mach number in wind tunnel testing is a significant metric for evaluating system performance.The quality of the flow field in the wind tunnel is contingent upon the system's capacity to maintai...The test section’s Mach number in wind tunnel testing is a significant metric for evaluating system performance.The quality of the flow field in the wind tunnel is contingent upon the system's capacity to maintain stability across various working conditions.The process flow in wind tunnel testing is inherently complex,resulting in a system characterized by nonlinearity,time lag,and multiple working conditions.To implement the predictive control algorithm,a precise Mach number prediction model must be created.Therefore,this report studies the method for Mach number prediction modelling in wind tunnel flow fields with various working conditions.Firstly,this paper introduces a continuous transonic wind tunnel.The key physical quantities affecting the flow field of the wind tunnel are determined by analyzing its structure and blowing process.Secondly,considering the nonlinear and time-lag characteristics of the wind tunnel system,a CNN-LSTM model is employed to establish the Mach number prediction model by combining the 1D-CNN algorithm with the LSTM model,which has long and short-term memory functions.Then,the attention mechanism is incorporated into the CNN-LSTM prediction model to enable the model to focus more on data with greater information importance,thereby enhancing the model's training effectiveness.The application results ultimately demonstrate the efficacy of the proposed approach.展开更多
方面级情感分析(aspect based sentiment analysis,ABSA)是自然语言处理领域的一个重要任务,其目标是对句子中给定的方面词进行情感极性的判断。目前,最先进的ABSA模型采用图神经网络处理句子的语义信息和句法结构。然而,这些方法对句...方面级情感分析(aspect based sentiment analysis,ABSA)是自然语言处理领域的一个重要任务,其目标是对句子中给定的方面词进行情感极性的判断。目前,最先进的ABSA模型采用图神经网络处理句子的语义信息和句法结构。然而,这些方法对句法依赖树蕴含的信息使用不足,不仅缺少对外部知识的挖掘,而且忽略了对模型引入上下文噪声的消除。针对这些问题,提出了一种知识增强的双通道多头图卷积神经网络。该模型建立了基于语义的多头图卷积网络和基于句法的多头图卷积网络,利用外部情感知识以及句法依赖距离重构句法依赖树,使模型充分融入外部知识。同时采用自注意力机制构建动态语义图并过滤引入噪声,从而更多地关注方面词。模型在3个公开基准数据集Rest14、Lap14、Twitter上的准确率分别达到了87.57%、82.34%、77.75%,显著优于基线模型。展开更多
基金funded by the National Natural Science Foundation of China(No.61503069)the Fundamental Research Funds for the Central Universities(N150404020).
文摘The test section’s Mach number in wind tunnel testing is a significant metric for evaluating system performance.The quality of the flow field in the wind tunnel is contingent upon the system's capacity to maintain stability across various working conditions.The process flow in wind tunnel testing is inherently complex,resulting in a system characterized by nonlinearity,time lag,and multiple working conditions.To implement the predictive control algorithm,a precise Mach number prediction model must be created.Therefore,this report studies the method for Mach number prediction modelling in wind tunnel flow fields with various working conditions.Firstly,this paper introduces a continuous transonic wind tunnel.The key physical quantities affecting the flow field of the wind tunnel are determined by analyzing its structure and blowing process.Secondly,considering the nonlinear and time-lag characteristics of the wind tunnel system,a CNN-LSTM model is employed to establish the Mach number prediction model by combining the 1D-CNN algorithm with the LSTM model,which has long and short-term memory functions.Then,the attention mechanism is incorporated into the CNN-LSTM prediction model to enable the model to focus more on data with greater information importance,thereby enhancing the model's training effectiveness.The application results ultimately demonstrate the efficacy of the proposed approach.
文摘方面级情感分析(aspect based sentiment analysis,ABSA)是自然语言处理领域的一个重要任务,其目标是对句子中给定的方面词进行情感极性的判断。目前,最先进的ABSA模型采用图神经网络处理句子的语义信息和句法结构。然而,这些方法对句法依赖树蕴含的信息使用不足,不仅缺少对外部知识的挖掘,而且忽略了对模型引入上下文噪声的消除。针对这些问题,提出了一种知识增强的双通道多头图卷积神经网络。该模型建立了基于语义的多头图卷积网络和基于句法的多头图卷积网络,利用外部情感知识以及句法依赖距离重构句法依赖树,使模型充分融入外部知识。同时采用自注意力机制构建动态语义图并过滤引入噪声,从而更多地关注方面词。模型在3个公开基准数据集Rest14、Lap14、Twitter上的准确率分别达到了87.57%、82.34%、77.75%,显著优于基线模型。
