A bionic shoulder joint with three degree-of-freedom(DOF)driven by pneumatic muscle actuator is proposed and its corresponding kinematic model is established.The bionic shoulder is optimized by particle swam optimizat...A bionic shoulder joint with three degree-of-freedom(DOF)driven by pneumatic muscle actuator is proposed and its corresponding kinematic model is established.The bionic shoulder is optimized by particle swam optimization(PSO)with the fitness standards that the requirements of rotation indexes are met and the fluctuation of motion is kept in the lowest resolution in a pneumatic muscle actuator range.Simulation considering rotation indexes only(first simulation)is compared with the one considering both rotation indexes and motion resolution(second simulation)subsequently.Mounting position of the pneumatic muscle actuators in bionic shoulder is optimized after initializing the same condition in simulations.Results show that the fluctuations of parameters are consistent,and the parameters of the first simulation have good convergence than those of the second one.With the increase of stretch rate of the pneumatic muscle actuator,the needed length of fixed link in the center of static platform decreases in optimization.展开更多
为了推动大数据技术在制造车间的应用,针对复杂产品晶圆制造过程中海量制造数据时序性、强噪音影响加工周期预测精度的问题,提出考虑特征学习的改进粒子群优化长短期记忆网络(improved particle swarm optimization-long short term mem...为了推动大数据技术在制造车间的应用,针对复杂产品晶圆制造过程中海量制造数据时序性、强噪音影响加工周期预测精度的问题,提出考虑特征学习的改进粒子群优化长短期记忆网络(improved particle swarm optimization-long short term memory,IPSO-LSTM)的加工周期预测方法。采用降噪自编码器和稀疏自编码器联合构建深度自编码器,增强特征学习能力和抗噪能力;运用IPSO优化LSTM参数,克服时间依赖性,提升预测模型性能。实例验证了所提方法的预测精度优于传统机器学习方法,其平均绝对误差低于3%;并分析特征学习方法的有效性,将支持向量回归和多层感知器等传统方法加入特征学习方法,R^(2)分别提高了1.46%、1.05%,为晶圆加工周期的有效预测提供新的解决方法。展开更多
基金supported by the National Natural Science Foundation of China(No.51405229)the Natural Science Foundation of Jiangsu Province of China (No. BK20151470)the NUAA Fundamental Research Fund(No.NS2013049)
文摘A bionic shoulder joint with three degree-of-freedom(DOF)driven by pneumatic muscle actuator is proposed and its corresponding kinematic model is established.The bionic shoulder is optimized by particle swam optimization(PSO)with the fitness standards that the requirements of rotation indexes are met and the fluctuation of motion is kept in the lowest resolution in a pneumatic muscle actuator range.Simulation considering rotation indexes only(first simulation)is compared with the one considering both rotation indexes and motion resolution(second simulation)subsequently.Mounting position of the pneumatic muscle actuators in bionic shoulder is optimized after initializing the same condition in simulations.Results show that the fluctuations of parameters are consistent,and the parameters of the first simulation have good convergence than those of the second one.With the increase of stretch rate of the pneumatic muscle actuator,the needed length of fixed link in the center of static platform decreases in optimization.
文摘为了推动大数据技术在制造车间的应用,针对复杂产品晶圆制造过程中海量制造数据时序性、强噪音影响加工周期预测精度的问题,提出考虑特征学习的改进粒子群优化长短期记忆网络(improved particle swarm optimization-long short term memory,IPSO-LSTM)的加工周期预测方法。采用降噪自编码器和稀疏自编码器联合构建深度自编码器,增强特征学习能力和抗噪能力;运用IPSO优化LSTM参数,克服时间依赖性,提升预测模型性能。实例验证了所提方法的预测精度优于传统机器学习方法,其平均绝对误差低于3%;并分析特征学习方法的有效性,将支持向量回归和多层感知器等传统方法加入特征学习方法,R^(2)分别提高了1.46%、1.05%,为晶圆加工周期的有效预测提供新的解决方法。
