提出了一种保持生理特征的交互式人脸编辑方法。采用控制点分层策略,即以用户直接操作的控制点对(称为主控制点对)为输入层,其他控制点对(称为次控制点对)为输出层,建立人工神经网络;然后采用误差反向传播法(Error Back Propagation)学...提出了一种保持生理特征的交互式人脸编辑方法。采用控制点分层策略,即以用户直接操作的控制点对(称为主控制点对)为输入层,其他控制点对(称为次控制点对)为输出层,建立人工神经网络;然后采用误差反向传播法(Error Back Propagation)学习,从而建立主、次控制点之间的约束关系;最后通过输出层将编辑信息在模型中进行插值。该编辑结果可以应用到具有相同拓扑的任意人脸模型上。实验结果表明,采用分层控制的方法不仅保持了编辑操作的方便性、精确性,同时还保持了人脸生理特征的真实性。展开更多
A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant inf...A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.展开更多
文摘提出了一种保持生理特征的交互式人脸编辑方法。采用控制点分层策略,即以用户直接操作的控制点对(称为主控制点对)为输入层,其他控制点对(称为次控制点对)为输出层,建立人工神经网络;然后采用误差反向传播法(Error Back Propagation)学习,从而建立主、次控制点之间的约束关系;最后通过输出层将编辑信息在模型中进行插值。该编辑结果可以应用到具有相同拓扑的任意人脸模型上。实验结果表明,采用分层控制的方法不仅保持了编辑操作的方便性、精确性,同时还保持了人脸生理特征的真实性。
基金The National Natural Science Foundation of China(No.60443007,50390063).
文摘A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.
