In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an ar...In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.展开更多
Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average （ARIMA） model and exponential smoothing model are used to predict the...Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average （ARIMA） model and exponential smoothing model are used to predict the throughput in a single cell and whole region in an LTE network. The experimental results show that these two models perform differently in both scenarios. The ARIMA model is better than the exponential smoothing model for predicting throughput on weekdays in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput on weekends in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput in a single cell. In these two LTE network scenarios, throughput prediction based on traffic time series leads to more efficient resource management and better QoS.展开更多
Data-driven techniques could be used to enhance decision-making capacity of breeders and farmers.We used an RGB camera on an unmanned aerial vehicle(UAV)to collect time series data on sugar beet canopy coverage(CC)and...Data-driven techniques could be used to enhance decision-making capacity of breeders and farmers.We used an RGB camera on an unmanned aerial vehicle(UAV)to collect time series data on sugar beet canopy coverage(CC)and canopy height(CH)from small-plot breeding fields including 20 genotypes per season over 3 seasons.Digital orthomosaic and digital surface models were created from each flight and were converted to individual plot-level data.Plot-level data including CC and CH were calculated on a per-plot basis.A multiple regression model was fitted,which predicts root weight(RW)(r=0.89,0.89,and 0.92 in the 3 seasons,respectively)and sugar content(SC)(r=0.79,0.83,and 0.77 in the 3 seasons,respectively)using individual time point CC and CH data.Individual CC and CH values in late June tended to be strong predictors of RW and SC,suggesting that early season growth is critical for obtaining high RW and SC.Coefficient of parentage was not a strong factor influencing SC.Integrals of CC and CH time series data were calculated for genetic analysis purposes since they are more stable over multiple growing seasons.Calculations of general combining ability and specific combining ability in F1 offspring demonstrate how growth curve quantification can be used in diallel cross analysis and yield prediction.Our simple yet robust solution demonstrates how state-of-the-art remote sensing tools and basic analysis methods can be applied to small-plot breeder fields for selection purpose.展开更多
基金National High Technology Research and Development Program of China(No.2007AA04Z109)Open Research Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology at Huazhong University of Science and Technology,China(No.DMETKF2009006)
文摘In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.
文摘Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average （ARIMA） model and exponential smoothing model are used to predict the throughput in a single cell and whole region in an LTE network. The experimental results show that these two models perform differently in both scenarios. The ARIMA model is better than the exponential smoothing model for predicting throughput on weekdays in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput on weekends in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput in a single cell. In these two LTE network scenarios, throughput prediction based on traffic time series leads to more efficient resource management and better QoS.
基金partially supported by CREST(JPMJCR1512)and the AIPAccelerationResearch(JPMJCR21U3)of JST.
文摘Data-driven techniques could be used to enhance decision-making capacity of breeders and farmers.We used an RGB camera on an unmanned aerial vehicle(UAV)to collect time series data on sugar beet canopy coverage(CC)and canopy height(CH)from small-plot breeding fields including 20 genotypes per season over 3 seasons.Digital orthomosaic and digital surface models were created from each flight and were converted to individual plot-level data.Plot-level data including CC and CH were calculated on a per-plot basis.A multiple regression model was fitted,which predicts root weight(RW)(r=0.89,0.89,and 0.92 in the 3 seasons,respectively)and sugar content(SC)(r=0.79,0.83,and 0.77 in the 3 seasons,respectively)using individual time point CC and CH data.Individual CC and CH values in late June tended to be strong predictors of RW and SC,suggesting that early season growth is critical for obtaining high RW and SC.Coefficient of parentage was not a strong factor influencing SC.Integrals of CC and CH time series data were calculated for genetic analysis purposes since they are more stable over multiple growing seasons.Calculations of general combining ability and specific combining ability in F1 offspring demonstrate how growth curve quantification can be used in diallel cross analysis and yield prediction.Our simple yet robust solution demonstrates how state-of-the-art remote sensing tools and basic analysis methods can be applied to small-plot breeder fields for selection purpose.
