Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality cata...Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.展开更多
为抑制色噪声对多信号分类(multiple signal classification,MUSIC)算法估计性能的影响,提出利用延时相关消除色噪声的二阶预处理方法.以L阶滑动平均(moving average,MA)模型为色噪声模型,利用大于L阶的延时相关函数为0的特点去除背景...为抑制色噪声对多信号分类(multiple signal classification,MUSIC)算法估计性能的影响,提出利用延时相关消除色噪声的二阶预处理方法.以L阶滑动平均(moving average,MA)模型为色噪声模型,利用大于L阶的延时相关函数为0的特点去除背景色噪声.为增加对多个入射信号成功分辨的概率,基于信号子空间估计稳健的特点,构造基于信号子空间加权的谱函数.提出的新算法无阵列孔径损失,不增加运算复杂度,因此,在色噪声背景下的实时波达方向(direction of arrival,DOA)估计中有较好应用价值.仿真实验表明,所提方法可有效抑制色噪声,在同样的估计条件下,该算法表现出较好的估计性能,且不增加计算量.展开更多
The remanufacturing system is remolding the manufacturing industry by bringing scrapped products back to such a condition that reintegrated performance is just as good as new.The remanufacturing environment is feature...The remanufacturing system is remolding the manufacturing industry by bringing scrapped products back to such a condition that reintegrated performance is just as good as new.The remanufacturing environment is featured by a far deeper level of uncertainty than new manufacturing,such as probabilistic routing files,and highly variable processing time.The stochastic disturbances result in the production bottlenecks,which constrain the productivity of the job shop.The uncertainties in the remanufacturing process cause the bottlenecks to shift when the workshop is processing.Considering this outstanding problem,many researchers try to optimize the production process to mitigate dynamic bottlenecks toward a balanced state.This paper proposes a data-driven method to predict bottlenecks in the remanufacturing system with multi-variant uncertainties.Firstly,discrete event simulation technology is applied to establish a simulation model of the remanufacturing production line and calculate the bottleneck index to identify bottlenecks.Secondly,a data-driven method,auto-regressive moving average(ARMA)model is employed to predict the bottlenecks in the system based on real-time data captured by the Arena software.Finally,the proposed prediction method is verified on real data from the automobile engine remanufacturing production line.展开更多
文摘Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.
文摘为抑制色噪声对多信号分类(multiple signal classification,MUSIC)算法估计性能的影响,提出利用延时相关消除色噪声的二阶预处理方法.以L阶滑动平均(moving average,MA)模型为色噪声模型,利用大于L阶的延时相关函数为0的特点去除背景色噪声.为增加对多个入射信号成功分辨的概率,基于信号子空间估计稳健的特点,构造基于信号子空间加权的谱函数.提出的新算法无阵列孔径损失,不增加运算复杂度,因此,在色噪声背景下的实时波达方向(direction of arrival,DOA)估计中有较好应用价值.仿真实验表明,所提方法可有效抑制色噪声,在同样的估计条件下,该算法表现出较好的估计性能,且不增加计算量.
基金Projects(51975099,51775086)supported by the Natural Science Foundation of China。
文摘The remanufacturing system is remolding the manufacturing industry by bringing scrapped products back to such a condition that reintegrated performance is just as good as new.The remanufacturing environment is featured by a far deeper level of uncertainty than new manufacturing,such as probabilistic routing files,and highly variable processing time.The stochastic disturbances result in the production bottlenecks,which constrain the productivity of the job shop.The uncertainties in the remanufacturing process cause the bottlenecks to shift when the workshop is processing.Considering this outstanding problem,many researchers try to optimize the production process to mitigate dynamic bottlenecks toward a balanced state.This paper proposes a data-driven method to predict bottlenecks in the remanufacturing system with multi-variant uncertainties.Firstly,discrete event simulation technology is applied to establish a simulation model of the remanufacturing production line and calculate the bottleneck index to identify bottlenecks.Secondly,a data-driven method,auto-regressive moving average(ARMA)model is employed to predict the bottlenecks in the system based on real-time data captured by the Arena software.Finally,the proposed prediction method is verified on real data from the automobile engine remanufacturing production line.