In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production,a detection model of surface defects based on machine vision,CSC-YOLO,is proposed.The model uses...In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production,a detection model of surface defects based on machine vision,CSC-YOLO,is proposed.The model uses YOLOv4-tiny as the benchmark network.First,K-means clustering is introduced into the benchmark network to obtain anchor frames that match the self-built dataset.Second,a cross-region fusion module is introduced in the backbone network to solve the difficult target recognition problem by fusing contextual semantic information.Third,the spatial pyramid pooling-efficient channel attention network(SPP-E)module is introduced in the path aggregation network(PANet)to enhance the extraction of features.Fourth,to prevent the loss of channel information,a lightweight attention mechanism is introduced to improve the performance of the network.Finally,the performance of the model is improved by adding adjustment factors to correct the loss function for the dimensional characteristics of the surface defects.CSC-YOLO was tested on the self-built dataset of surface defects in copper strip,and the experimental results showed that the mAP of the model can reach 93.58%,which is a 3.37% improvement compared with the benchmark network,and FPS,although decreasing compared with the benchmark network,reached 104.CSC-YOLO takes into account the real-time requirements of copper strip production.The comparison experiments with Faster RCNN,SSD300,YOLOv3,YOLOv4,Resnet50-YOLOv4,YOLOv5s,YOLOv7,and other algorithms show that the algorithm obtains a faster computation speed while maintaining a higher detection accuracy.展开更多
In specific condition, when wrapping angle of cold rolling strip covering surface of shape detecting roll dynamically changes, online radial compression of the shape detecting roll is changed too, and it seriously aff...In specific condition, when wrapping angle of cold rolling strip covering surface of shape detecting roll dynamically changes, online radial compression of the shape detecting roll is changed too, and it seriously affects online shape detecting precision of cold strip. Based on the latest intelligent shape meter developed by Yanshan University, using PSO-BP neural network and actual working condition datum, the cold strip online dynamic wrapping angle compensation model is established, and successfully applied in 1250 mm 6-high cold mill, remarkable results are achieved. The error between calculated values and measured values of total tensions is within 3 %展开更多
A novel electrochemical immunoassay for cardiac troponin Ⅰ （cTnI） combining the concepts of the dual monoclonal antibody ＂sandwich＂ principle, the silver enhancement on the nano-gold particle, and the SBA-15 meso...A novel electrochemical immunoassay for cardiac troponin Ⅰ （cTnI） combining the concepts of the dual monoclonal antibody ＂sandwich＂ principle, the silver enhancement on the nano-gold particle, and the SBA-15 mesoporous modified carbon paste electrode （SBA-MCPE） is described. Four main steps were carried out to obtain the analytical signal, i.e., electrode preparation, immunoreaction, silver enhancement, and anodic stripping voltammetric detection. A linear relationship between the anodic stripping peak current and concentration of cTnI from 0.5 to 5.0 ng/mL and a limit of detection of 0.2 ng/mL of cTnI were obtained.展开更多
基金the Key Project of Basic Research of Yunnan Province(No.202101AS070016)。
文摘In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production,a detection model of surface defects based on machine vision,CSC-YOLO,is proposed.The model uses YOLOv4-tiny as the benchmark network.First,K-means clustering is introduced into the benchmark network to obtain anchor frames that match the self-built dataset.Second,a cross-region fusion module is introduced in the backbone network to solve the difficult target recognition problem by fusing contextual semantic information.Third,the spatial pyramid pooling-efficient channel attention network(SPP-E)module is introduced in the path aggregation network(PANet)to enhance the extraction of features.Fourth,to prevent the loss of channel information,a lightweight attention mechanism is introduced to improve the performance of the network.Finally,the performance of the model is improved by adding adjustment factors to correct the loss function for the dimensional characteristics of the surface defects.CSC-YOLO was tested on the self-built dataset of surface defects in copper strip,and the experimental results showed that the mAP of the model can reach 93.58%,which is a 3.37% improvement compared with the benchmark network,and FPS,although decreasing compared with the benchmark network,reached 104.CSC-YOLO takes into account the real-time requirements of copper strip production.The comparison experiments with Faster RCNN,SSD300,YOLOv3,YOLOv4,Resnet50-YOLOv4,YOLOv5s,YOLOv7,and other algorithms show that the algorithm obtains a faster computation speed while maintaining a higher detection accuracy.
基金Item Sponsored by National Science and Technology Support Plan of China(2007BAF02B10)Natural Science Foundation of Hebei Province of China(E2011203004)
文摘In specific condition, when wrapping angle of cold rolling strip covering surface of shape detecting roll dynamically changes, online radial compression of the shape detecting roll is changed too, and it seriously affects online shape detecting precision of cold strip. Based on the latest intelligent shape meter developed by Yanshan University, using PSO-BP neural network and actual working condition datum, the cold strip online dynamic wrapping angle compensation model is established, and successfully applied in 1250 mm 6-high cold mill, remarkable results are achieved. The error between calculated values and measured values of total tensions is within 3 %
文摘A novel electrochemical immunoassay for cardiac troponin Ⅰ （cTnI） combining the concepts of the dual monoclonal antibody ＂sandwich＂ principle, the silver enhancement on the nano-gold particle, and the SBA-15 mesoporous modified carbon paste electrode （SBA-MCPE） is described. Four main steps were carried out to obtain the analytical signal, i.e., electrode preparation, immunoreaction, silver enhancement, and anodic stripping voltammetric detection. A linear relationship between the anodic stripping peak current and concentration of cTnI from 0.5 to 5.0 ng/mL and a limit of detection of 0.2 ng/mL of cTnI were obtained.
