A physical value mapping （PVM） algorithm based on finite element mesh from the stamped part in stamping process to the product is presented, In order to improve the efficiency of the PVM algorithm, a search way from...A physical value mapping （PVM） algorithm based on finite element mesh from the stamped part in stamping process to the product is presented, In order to improve the efficiency of the PVM algorithm, a search way from the mesh of the product to the mesh of the stamped part will be adopted. At the same time, the search process is divided into two steps： entire search （ES） and local search （LS）, which improve the searching efficiency. The searching area is enlarged to avoid missing projection elements in ES process. An arc-length method is introduced in LS process. The validity is confirmed by the results of the complex industry-forming product.展开更多
The polynomial matrix using the block coefficient matrix representation auto-regressive moving average(referred to as the PM-ARMA)model is constructed in this paper for actively controlled multi-degree-of-freedom(MDOF...The polynomial matrix using the block coefficient matrix representation auto-regressive moving average(referred to as the PM-ARMA)model is constructed in this paper for actively controlled multi-degree-of-freedom(MDOF)structures with time-delay through equivalently transforming the preliminary state space realization into the new state space realization.The PM-ARMA model is a more general formulation with respect to the polynomial using the coefficient representation auto-regressive moving average(ARMA)model due to its capability to cope with actively controlled structures with any given structural degrees of freedom and any chosen number of sensors and actuators.(The sensors and actuators are required to maintain the identical number.)under any dimensional stationary stochastic excitation.展开更多
An analysis has been conducted of the multi-hierarchical structure and jump of temperature variation for the globe, China and Yunnan Province over the past 100 years using an auto-adaptive, multi-resolution data filte...An analysis has been conducted of the multi-hierarchical structure and jump of temperature variation for the globe, China and Yunnan Province over the past 100 years using an auto-adaptive, multi-resolution data filter set up in You, Lin and Deng (1997). The result is shown below in three aspects. (l1 The variation of global temperature in this period is marked by warming on a large scale and can be divided into three stages of being cold (prior to 1919), warm (between 1920 and 1978) and warmer (since 1 979). Well-defined jumps are with the variation in correspondence with the hierarchical evolution on such scale, occurring in 1920 and 1979 when there is the most substantial jump towards warming. For the evolution on smaller scales, however, the variation has shown more of alternations of cold and warm temperatures. The preceding hierarchical structure and warming jump are added with new ones. (2) The trend in which temperature varies is much the same for China and the Yunnan Province, but it is not consistent with that globally, the largest difference being that a weak period of cold temperature in 1955 - 1978 across the globe was suspended in 1979 when it jumped to a significant warming,while a period of very cold temperature in 1955 - 1986 in China and Yunnan was not followed by warming in similar extent until 1987. (3) Though there are consistent hierarchical structure and jumping features throughout the year in Yunnan, significant changes with season are also present and the most striking difference is that temperature tends to vary consistently with China in winter and spring but with the globe in summer and fall.展开更多
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
Auto CAD软件应用及其专用周课程具有很强的实践性与应用性，为了进一步提高学生机械零件图识图能力与绘图能力，本文探索了在Pro-E三维实体模型的指导下，对二维零件图结构、形状、特征的详细地分析的教学方法。课堂教学结果表明：增...Auto CAD软件应用及其专用周课程具有很强的实践性与应用性，为了进一步提高学生机械零件图识图能力与绘图能力，本文探索了在Pro-E三维实体模型的指导下，对二维零件图结构、形状、特征的详细地分析的教学方法。课堂教学结果表明：增强了同学们机械零件的识图能力，提高了同学们机械零件的绘图能力。展开更多
基金This project is supported by National Natural Science Foundation ofChina(No.l9832020) and National Outstanding Youth Science Foundation ofChina(No.10125208).
文摘A physical value mapping （PVM） algorithm based on finite element mesh from the stamped part in stamping process to the product is presented, In order to improve the efficiency of the PVM algorithm, a search way from the mesh of the product to the mesh of the stamped part will be adopted. At the same time, the search process is divided into two steps： entire search （ES） and local search （LS）, which improve the searching efficiency. The searching area is enlarged to avoid missing projection elements in ES process. An arc-length method is introduced in LS process. The validity is confirmed by the results of the complex industry-forming product.
基金The project supported by the National Natural Science Foundation of China(50278054)
文摘The polynomial matrix using the block coefficient matrix representation auto-regressive moving average(referred to as the PM-ARMA)model is constructed in this paper for actively controlled multi-degree-of-freedom(MDOF)structures with time-delay through equivalently transforming the preliminary state space realization into the new state space realization.The PM-ARMA model is a more general formulation with respect to the polynomial using the coefficient representation auto-regressive moving average(ARMA)model due to its capability to cope with actively controlled structures with any given structural degrees of freedom and any chosen number of sensors and actuators.(The sensors and actuators are required to maintain the identical number.)under any dimensional stationary stochastic excitation.
文摘An analysis has been conducted of the multi-hierarchical structure and jump of temperature variation for the globe, China and Yunnan Province over the past 100 years using an auto-adaptive, multi-resolution data filter set up in You, Lin and Deng (1997). The result is shown below in three aspects. (l1 The variation of global temperature in this period is marked by warming on a large scale and can be divided into three stages of being cold (prior to 1919), warm (between 1920 and 1978) and warmer (since 1 979). Well-defined jumps are with the variation in correspondence with the hierarchical evolution on such scale, occurring in 1920 and 1979 when there is the most substantial jump towards warming. For the evolution on smaller scales, however, the variation has shown more of alternations of cold and warm temperatures. The preceding hierarchical structure and warming jump are added with new ones. (2) The trend in which temperature varies is much the same for China and the Yunnan Province, but it is not consistent with that globally, the largest difference being that a weak period of cold temperature in 1955 - 1978 across the globe was suspended in 1979 when it jumped to a significant warming,while a period of very cold temperature in 1955 - 1986 in China and Yunnan was not followed by warming in similar extent until 1987. (3) Though there are consistent hierarchical structure and jumping features throughout the year in Yunnan, significant changes with season are also present and the most striking difference is that temperature tends to vary consistently with China in winter and spring but with the globe in summer and fall.
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
