When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturin...When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.展开更多
With the nature of the high wind and sand in western China,the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm.To address this issue,this study aimed ...With the nature of the high wind and sand in western China,the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm.To address this issue,this study aimed to develop an algorithm for accurately detecting and recognizing wolfberries.YOLOv8,an algorithm promoted by Ultralytics,supports image classification,object detection,and instance segmentation tasks.To enhance the performance of the original YOLOv8 model,a novel YOLOv8 algorithm incorporating FasterNet,RepBiFPN,and Lightweight Asymmetric Dual-Head was proposed.Firstly,thousands of Chinese wolfberry images were collected from the Ningxia Academy of Agriculture and Forestry Science,China,and random noises were added to simulate the wind and sand conditions typical of spring.Secondly,leveraging the advantages of YOLOv8n,such as its high speed and accuracy,this research innovatively integrated the FasterNet block into the C2f module of YOLOv8 to improve the effective handling of data uncertainty and noise.Additionally,an innovative RepViT+BiFPN,a new detective head,and a Lightweight Asymmetric Dual-Head were introduced to improve the training efficiency of the YOLOv8 network.Finally,to evaluate the effectiveness of improved YOLOv8 for the recognition of wolfberry,the dataset of wolfberry images was divided into a training set,a validation set,and a testing set to assess the performances of different models.Experiment results demonstrate that the YOLOv8-FasterNet+LADH+RepBiFPN model outperforms other models in terms of mAP@0.50-0.95,achieving a 4.5%improvement on the validation set compared to the original YOLOv8n.This research addresses the high-speed and accurate recognition of the Chinese wolfberry under strong winds and sand noise through algorithmic improvements and integration,which can facilitate the automation and intelligence of Chinese wolfberry harvesting and contribute to the advancement of agricultural mechanization.展开更多
基金Supported by Jiangsu Provincial Agriculture Science and Technology Innovation Fund(Grant No.CX(23)3036)National Natural Science Foundation of China(Grant No.52375479)+1 种基金Jiangsu Provincal Graduate Research and Practical Innovation Program(Grant No.KYCX24_0825)Changzhou Municipal Sci&Tech Program(Grant No.CM20223014).
文摘When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.
基金supported by the National Natural Science Foundation of China(Grant No.32201681)the Ningxia Hui Autonomous Region Science and Technology Program(Grant 2021BEF02001)+2 种基金the Fruit,Vegetable,and Tea Harvesting Machinery Innovation Project of the Chinese Academy of Agricultural Sciencesthe Jiangsu Agriculture Science and Technology Innovation Fund(Grant JASTIF,CX(23)3036)the Changzhou Sci&Tech Program(Grant CJ20241131).
文摘With the nature of the high wind and sand in western China,the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm.To address this issue,this study aimed to develop an algorithm for accurately detecting and recognizing wolfberries.YOLOv8,an algorithm promoted by Ultralytics,supports image classification,object detection,and instance segmentation tasks.To enhance the performance of the original YOLOv8 model,a novel YOLOv8 algorithm incorporating FasterNet,RepBiFPN,and Lightweight Asymmetric Dual-Head was proposed.Firstly,thousands of Chinese wolfberry images were collected from the Ningxia Academy of Agriculture and Forestry Science,China,and random noises were added to simulate the wind and sand conditions typical of spring.Secondly,leveraging the advantages of YOLOv8n,such as its high speed and accuracy,this research innovatively integrated the FasterNet block into the C2f module of YOLOv8 to improve the effective handling of data uncertainty and noise.Additionally,an innovative RepViT+BiFPN,a new detective head,and a Lightweight Asymmetric Dual-Head were introduced to improve the training efficiency of the YOLOv8 network.Finally,to evaluate the effectiveness of improved YOLOv8 for the recognition of wolfberry,the dataset of wolfberry images was divided into a training set,a validation set,and a testing set to assess the performances of different models.Experiment results demonstrate that the YOLOv8-FasterNet+LADH+RepBiFPN model outperforms other models in terms of mAP@0.50-0.95,achieving a 4.5%improvement on the validation set compared to the original YOLOv8n.This research addresses the high-speed and accurate recognition of the Chinese wolfberry under strong winds and sand noise through algorithmic improvements and integration,which can facilitate the automation and intelligence of Chinese wolfberry harvesting and contribute to the advancement of agricultural mechanization.
