Compared to fixed virtual window algorithm （FVWA）, the dynamic virtual window algorithm （DVWA） determines the length of each virtual container according to the sizes of goods of each order, which saves space of vi...Compared to fixed virtual window algorithm （FVWA）, the dynamic virtual window algorithm （DVWA） determines the length of each virtual container according to the sizes of goods of each order, which saves space of virtual containers and improves the picking efficiency. However, the interval of consecutive goods caused by dispensers on conveyor can not be eliminated by DVWA, which limits a further improvement of picking efficiency. In order to solve this problem, a compressible virtual window algorithm （CVWA） is presented. It not only inherits the merit of DVWA but also compresses the length of virtual containers without congestion of order accumulation by advancing the beginning time of order picking and reasonably coordinating the pace of order accumulation. The simulation result proves that the picking efficiency of automated sorting system is greatly improved by CVWA.展开更多
Current deep neural networks(DNN)used for seismic phase picking are becoming more complex,which consumes much computing time without significant accuracy improvement.In this study,we introduce a cascaded classificatio...Current deep neural networks(DNN)used for seismic phase picking are becoming more complex,which consumes much computing time without significant accuracy improvement.In this study,we introduce a cascaded classification and regression framework for seismic phase picking,named as the classification and regression phase net(CRPN),which contains two convolutional neural network(CNN)models with different complexity to meet the requirements of accuracy and efficiency.The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0,respectively.The second stage of CRPN is used for high precision classification and regression.The regression is designed to reduce the time difference between the probability maximum and the real arrival time.After being trained using 500,000 P and S phases,the CRPN can process 400 hours’seismic data per second,whose sampling rate is 1 Hz and 25 Hz for the two stages,respectively,on a Nvidia K2200 GPU,and pick 93%P and 89%S phases with the error being reduced by 0.1s after regression correction.展开更多
基金National Natural Science Foundation of China(No.50175064)
文摘Compared to fixed virtual window algorithm （FVWA）, the dynamic virtual window algorithm （DVWA） determines the length of each virtual container according to the sizes of goods of each order, which saves space of virtual containers and improves the picking efficiency. However, the interval of consecutive goods caused by dispensers on conveyor can not be eliminated by DVWA, which limits a further improvement of picking efficiency. In order to solve this problem, a compressible virtual window algorithm （CVWA） is presented. It not only inherits the merit of DVWA but also compresses the length of virtual containers without congestion of order accumulation by advancing the beginning time of order picking and reasonably coordinating the pace of order accumulation. The simulation result proves that the picking efficiency of automated sorting system is greatly improved by CVWA.
基金This work is financially supported by the National Key R&D Program of China(No.2018YFC1504200)National Natural Science Foundation of China(No.41661164035)+1 种基金the LU JIAXI International Team Program from the KC Wong Education Foundation and CAS(No.GJTD-2018-12)We thank two anonymous reviewers for their valuable comments and suggestions,which substantially improve the article.
文摘Current deep neural networks(DNN)used for seismic phase picking are becoming more complex,which consumes much computing time without significant accuracy improvement.In this study,we introduce a cascaded classification and regression framework for seismic phase picking,named as the classification and regression phase net(CRPN),which contains two convolutional neural network(CNN)models with different complexity to meet the requirements of accuracy and efficiency.The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0,respectively.The second stage of CRPN is used for high precision classification and regression.The regression is designed to reduce the time difference between the probability maximum and the real arrival time.After being trained using 500,000 P and S phases,the CRPN can process 400 hours’seismic data per second,whose sampling rate is 1 Hz and 25 Hz for the two stages,respectively,on a Nvidia K2200 GPU,and pick 93%P and 89%S phases with the error being reduced by 0.1s after regression correction.
