This study aims to eliminate the subjectivity and inconsistency inherent in the traditional International Association of Drilling Contractors(IADC)bit wear rating process,which heavily depends on the experience of dri...This study aims to eliminate the subjectivity and inconsistency inherent in the traditional International Association of Drilling Contractors(IADC)bit wear rating process,which heavily depends on the experience of drilling engineers and often leads to unreliable results.Leveraging advancements in computer vision and deep learning algorithms,this research proposes an automated detection and classification method for polycrystalline diamond compact(PDC)bit damage.YOLOv10 was employed to locate the PDC bit cutters,followed by two SqueezeNet models to perform wear rating and wear type classifications.A comprehensive dataset was created based on the IADC dull bit evaluation standards.Additionally,this study discusses the necessity of data augmentation and finds that certain methods,such as cropping,splicing,and mixing,may reduce the accuracy of cutter detection.The experimental results demonstrate that the proposed method significantly enhances the accuracy of bit damage detection and classification while also providing substantial improvements in processing speed and computational efficiency,offering a valuable tool for optimizing drilling operations and reducing costs.展开更多
Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect ...Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.展开更多
基金support of the CNPC International Collaborative Research Project(No.2022DQ0410)。
文摘This study aims to eliminate the subjectivity and inconsistency inherent in the traditional International Association of Drilling Contractors(IADC)bit wear rating process,which heavily depends on the experience of drilling engineers and often leads to unreliable results.Leveraging advancements in computer vision and deep learning algorithms,this research proposes an automated detection and classification method for polycrystalline diamond compact(PDC)bit damage.YOLOv10 was employed to locate the PDC bit cutters,followed by two SqueezeNet models to perform wear rating and wear type classifications.A comprehensive dataset was created based on the IADC dull bit evaluation standards.Additionally,this study discusses the necessity of data augmentation and finds that certain methods,such as cropping,splicing,and mixing,may reduce the accuracy of cutter detection.The experimental results demonstrate that the proposed method significantly enhances the accuracy of bit damage detection and classification while also providing substantial improvements in processing speed and computational efficiency,offering a valuable tool for optimizing drilling operations and reducing costs.
文摘Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.
