A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experime...A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.展开更多
In this paper, a new spatial coherence model of seismic ground motions is proposed by a fitting procedure. The analytical expressions of modal combination （correlation） coefficients of structural response are develo...In this paper, a new spatial coherence model of seismic ground motions is proposed by a fitting procedure. The analytical expressions of modal combination （correlation） coefficients of structural response are developed for multi-support seismic excitations. The coefficients from both the numerical integration and analytical solutions are compared to verify the accuracy of the solutions. It is shown that the analytical expressions of numerical modal combination coefficients are of high accuracy. The results of random responses of an example bridge show that the analytical modal combination coefficients developed in this paper are accurate enough to meet the requirements needed in practice. In addition, the computational efficiency of the analytical solutions of the modal combination coefficients is demonstrated by the response computation of the example bridge. It is found that the time required for the structural response analysis by using the analytical modal combination coefficients is less than 1/20 of that using numerical integral methods.展开更多
随着自动驾驶、智能导航等领域的快速发展,对时空轨迹预测的准确性和鲁棒性的要求不断提高。传统轨迹预测方法主要依赖运动历史数据,忽略了环境中的语义信息,在复杂场景下往往难以取得理想的预测效果。对轨迹预测领域相关研究进行综述,...随着自动驾驶、智能导航等领域的快速发展,对时空轨迹预测的准确性和鲁棒性的要求不断提高。传统轨迹预测方法主要依赖运动历史数据,忽略了环境中的语义信息,在复杂场景下往往难以取得理想的预测效果。对轨迹预测领域相关研究进行综述,特别是基于空间语义分析的轨迹预测研究进展。重点探讨了视觉语言模型(Vision Language Model,VLM)和大语言模型(Large Language Model,LLM)在轨迹预测方面的应用,介绍了多种基于空间语义分析的轨迹预测模型。通过实验结果分析发现,VLM和LLM能够显著提升轨迹预测的准确率。基于空间语义分析的轨迹预测方法未来将考虑多模态融合、提升模型架构、提高推理速度等方向,以进一步提升大规模轨迹预测的性能。展开更多
基金China Postdoctoral Science Foundation Under Grant No. 2004035215 Jiangsu Planned Projects for Postdoctoral Research Funds 2004 Aeronautical Science Research Foundation Under Grant No. 04152065
文摘A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.
基金National Natural Science Foundation of China Under Grant No. 50478112
文摘In this paper, a new spatial coherence model of seismic ground motions is proposed by a fitting procedure. The analytical expressions of modal combination （correlation） coefficients of structural response are developed for multi-support seismic excitations. The coefficients from both the numerical integration and analytical solutions are compared to verify the accuracy of the solutions. It is shown that the analytical expressions of numerical modal combination coefficients are of high accuracy. The results of random responses of an example bridge show that the analytical modal combination coefficients developed in this paper are accurate enough to meet the requirements needed in practice. In addition, the computational efficiency of the analytical solutions of the modal combination coefficients is demonstrated by the response computation of the example bridge. It is found that the time required for the structural response analysis by using the analytical modal combination coefficients is less than 1/20 of that using numerical integral methods.
文摘随着自动驾驶、智能导航等领域的快速发展,对时空轨迹预测的准确性和鲁棒性的要求不断提高。传统轨迹预测方法主要依赖运动历史数据,忽略了环境中的语义信息,在复杂场景下往往难以取得理想的预测效果。对轨迹预测领域相关研究进行综述,特别是基于空间语义分析的轨迹预测研究进展。重点探讨了视觉语言模型(Vision Language Model,VLM)和大语言模型(Large Language Model,LLM)在轨迹预测方面的应用,介绍了多种基于空间语义分析的轨迹预测模型。通过实验结果分析发现,VLM和LLM能够显著提升轨迹预测的准确率。基于空间语义分析的轨迹预测方法未来将考虑多模态融合、提升模型架构、提高推理速度等方向,以进一步提升大规模轨迹预测的性能。
