目的:探讨不同水平射箭运动员撒放前的晃动及其传递特征。方法:选取20名男子职业射箭运动员,根据运动员技术等级分为高水平组(n=9)和低水平组(n=11),同步采集命中10环的运动学和动力学数据,运用神经Granger因果模型分析持弓臂及压力中心...目的:探讨不同水平射箭运动员撒放前的晃动及其传递特征。方法:选取20名男子职业射箭运动员,根据运动员技术等级分为高水平组(n=9)和低水平组(n=11),同步采集命中10环的运动学和动力学数据,运用神经Granger因果模型分析持弓臂及压力中心(center of pressure,COP)晃动传递特征。结果:高水平组持弓臂肩关节左右和上下方向、腕关节左右方向的晃动及COP晃动幅度等指标均小于低水平组(P<0.05)。低水平组前后方向的晃动由腕关节向肩关节传递,肩关节向COP传递,左右方向的晃动由腕、肘关节传递至肩关节;高水平组左右方向的晃动由COP传递至持弓臂各关节。结论:高水平射箭运动员持弓臂和整体的晃动均更小,且晃动由整体向持弓臂传递;低水平运动员的晃动多由远端关节向近端关节传递。展开更多
Most existing parametric approaches for detecting linear or nonlinear Granger causality(GC)face challenges in estimating appropriate time delays,a critical factor for accurate GC detection.This issue becomes particula...Most existing parametric approaches for detecting linear or nonlinear Granger causality(GC)face challenges in estimating appropriate time delays,a critical factor for accurate GC detection.This issue becomes particularly pronounced in nonlinear complex systems,which are often opaque and consist of numerous components or variables.In this paper,we propose a novel temporal convolutional network(TCN)-based end-to-end GC detection approach called the interpretable temporal convolutional framework(ITCF).Unlike conventional deep learning models,which act like a“black box”and are difficult to analyse the interactions between variables,the proposed ITCF is able to detect both linear and nonlinear GC and automatically estimate time delay during the multivariant time series prediction.Specifically,GC is obtained by employing the least absolute shrinkage and selection operator(Lasso)regression during the prediction of multivariate time series using TCN.Then,time delays can be estimated by interpreting the TCN kernels.We propose a convolutional hierarchical group Lasso(cHGL),a hierarchical regularisation approach to effectively utilise temporal information within each TCN channel for enhanced GC detection.Additionally,as far as we are concerned,this paper is the first to integrate the Iterative Soft-Thresholding Algorithm into the backpropagation of TCN to optimise the proposed cHGL,which enables causal channel selection and induces sparsity within each TCN channel to remove redundant temporal information,ultimately creating an end-to-end GC detection framework.The testing results of four experiments,involving two simulations and two real data,demonstrate that the proposed ITCF,in comparison with state-ofthe-art,offers a more reliable estimation of GC relationships in complex systems featuring intricate dynamics,limited data lengths,or numerous variables.展开更多
Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,th...Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.展开更多
Granger因果关系在时间序列建模中具有重要意义,但传统方法存在难以捕捉复杂非线性关系等诸多局限性。本文在相关工作的基础上提出GCGAN模型,首次将生成对抗网络应用于时间序列中的Granger因果关系发现。通过设计多头生成器的架构建模...Granger因果关系在时间序列建模中具有重要意义,但传统方法存在难以捕捉复杂非线性关系等诸多局限性。本文在相关工作的基础上提出GCGAN模型,首次将生成对抗网络应用于时间序列中的Granger因果关系发现。通过设计多头生成器的架构建模目标序列,在训练时对生成器施加稀疏诱导惩罚项,并在提取因果关系矩阵时引入阈值方法,GCGAN能够精准找寻高维时间序列中的复杂Granger因果关系。本研究为时间序列Granger因果关系发现提供了新的深度学习范式。Granger causality plays a significant role in time series modeling. However, traditional methods struggle with capturing complex nonlinear relationships among others limitations. Building on previous studies, this paper introduces the GCGAN model, marking the first application of Generative Adversarial Networks (GANs) to Granger causality discovery in time series. By designing a multi-head generator architecture to model target sequences, imposing sparse inducing penalties on the generator during training, and introducing a threshold method when extracting the causality matrix, GCGAN can accurately identify complex Granger causalities in high-dimensional time series. This study provides a novel deep learning paradigm for the discovery of Granger causality in time series.展开更多
摘要目的:探讨不同水平射箭运动员撒放前的晃动及其传递特征。方法:选取20名男子职业射箭运动员,根据运动员技术等级分为高水平组(n=9)和低水平组(n=11),同步采集命中10环的运动学和动力学数据,运用神经Granger因果模型分析持弓臂及压力中心(center of pressure,COP)晃动传递特征。结果:高水平组持弓臂肩关节左右和上下方向、腕关节左右方向的晃动及COP晃动幅度等指标均小于低水平组(P<0.05)。低水平组前后方向的晃动由腕关节向肩关节传递,肩关节向COP传递,左右方向的晃动由腕、肘关节传递至肩关节;高水平组左右方向的晃动由COP传递至持弓臂各关节。结论:高水平射箭运动员持弓臂和整体的晃动均更小,且晃动由整体向持弓臂传递;低水平运动员的晃动多由远端关节向近端关节传递。
摘要Most existing parametric approaches for detecting linear or nonlinear Granger causality(GC)face challenges in estimating appropriate time delays,a critical factor for accurate GC detection.This issue becomes particularly pronounced in nonlinear complex systems,which are often opaque and consist of numerous components or variables.In this paper,we propose a novel temporal convolutional network(TCN)-based end-to-end GC detection approach called the interpretable temporal convolutional framework(ITCF).Unlike conventional deep learning models,which act like a“black box”and are difficult to analyse the interactions between variables,the proposed ITCF is able to detect both linear and nonlinear GC and automatically estimate time delay during the multivariant time series prediction.Specifically,GC is obtained by employing the least absolute shrinkage and selection operator(Lasso)regression during the prediction of multivariate time series using TCN.Then,time delays can be estimated by interpreting the TCN kernels.We propose a convolutional hierarchical group Lasso(cHGL),a hierarchical regularisation approach to effectively utilise temporal information within each TCN channel for enhanced GC detection.Additionally,as far as we are concerned,this paper is the first to integrate the Iterative Soft-Thresholding Algorithm into the backpropagation of TCN to optimise the proposed cHGL,which enables causal channel selection and induces sparsity within each TCN channel to remove redundant temporal information,ultimately creating an end-to-end GC detection framework.The testing results of four experiments,involving two simulations and two real data,demonstrate that the proposed ITCF,in comparison with state-ofthe-art,offers a more reliable estimation of GC relationships in complex systems featuring intricate dynamics,limited data lengths,or numerous variables.
摘要Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.
摘要Granger因果关系在时间序列建模中具有重要意义,但传统方法存在难以捕捉复杂非线性关系等诸多局限性。本文在相关工作的基础上提出GCGAN模型,首次将生成对抗网络应用于时间序列中的Granger因果关系发现。通过设计多头生成器的架构建模目标序列,在训练时对生成器施加稀疏诱导惩罚项,并在提取因果关系矩阵时引入阈值方法,GCGAN能够精准找寻高维时间序列中的复杂Granger因果关系。本研究为时间序列Granger因果关系发现提供了新的深度学习范式。Granger causality plays a significant role in time series modeling. However, traditional methods struggle with capturing complex nonlinear relationships among others limitations. Building on previous studies, this paper introduces the GCGAN model, marking the first application of Generative Adversarial Networks (GANs) to Granger causality discovery in time series. By designing a multi-head generator architecture to model target sequences, imposing sparse inducing penalties on the generator during training, and introducing a threshold method when extracting the causality matrix, GCGAN can accurately identify complex Granger causalities in high-dimensional time series. This study provides a novel deep learning paradigm for the discovery of Granger causality in time series.