Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating mo...Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control processes.Given the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system integrity.To address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in PCBs.Specifically,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 Nano.Through rigorous experimentation and evaluation of our dataset which was acquired from Peking University’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing process.Our results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB faults.The model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of 0.98.These findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.展开更多
目的分析脊髓小脑共济失调8型(Spinocerebellar ataxia type 8,SCA8)患者的基因检测结果,总结有效治疗方法。方法对1例拟诊SCA8患者的基因检测结果和治疗方法作回顾性分析。结果18岁男性患者,因“步态不稳12年”就诊,临床表现为行走不...目的分析脊髓小脑共济失调8型(Spinocerebellar ataxia type 8,SCA8)患者的基因检测结果,总结有效治疗方法。方法对1例拟诊SCA8患者的基因检测结果和治疗方法作回顾性分析。结果18岁男性患者,因“步态不稳12年”就诊,临床表现为行走不稳、剪刀步态、双下肢肌张力增高。抽取患者及其父母的外周静脉血,行遗传性共济失调Panel基因检测、全外显子测序以及Sanger测序验证,基因检测结果显示:患者的ATXN8/ATXN8OS基因CAG重复分别是18次和68次,其母亲ATXN8/ATXN8OS基因CAG重复分别是24次和67次,患者父亲基因检测结果未见异常。结合患者临床症状、生物信息学软件预测结果,明确诊断为SCA8。为改善患者双下肢痉挛症状,实施选择性脊神经背根切断术(SDR)及脊神经根粘连松解术治疗,并进行术后康复锻炼。术后6个月,患者剪刀步态明显改善,行走基本平稳,MAS评分、VAS评分、坐位平衡及立位平衡均有改善。结论ATXN8/ATXN8OS基因串联重复区的CAG异常重复扩增可导致SCA8,临床表现为痉挛性步态不稳,结合基因检测结果可明确诊断,SDR可有效改善SCA8患者的痉挛性步态。展开更多
文摘Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control processes.Given the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system integrity.To address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in PCBs.Specifically,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 Nano.Through rigorous experimentation and evaluation of our dataset which was acquired from Peking University’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing process.Our results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB faults.The model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of 0.98.These findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.
文摘目的分析脊髓小脑共济失调8型(Spinocerebellar ataxia type 8,SCA8)患者的基因检测结果,总结有效治疗方法。方法对1例拟诊SCA8患者的基因检测结果和治疗方法作回顾性分析。结果18岁男性患者,因“步态不稳12年”就诊,临床表现为行走不稳、剪刀步态、双下肢肌张力增高。抽取患者及其父母的外周静脉血,行遗传性共济失调Panel基因检测、全外显子测序以及Sanger测序验证,基因检测结果显示:患者的ATXN8/ATXN8OS基因CAG重复分别是18次和68次,其母亲ATXN8/ATXN8OS基因CAG重复分别是24次和67次,患者父亲基因检测结果未见异常。结合患者临床症状、生物信息学软件预测结果,明确诊断为SCA8。为改善患者双下肢痉挛症状,实施选择性脊神经背根切断术(SDR)及脊神经根粘连松解术治疗,并进行术后康复锻炼。术后6个月,患者剪刀步态明显改善,行走基本平稳,MAS评分、VAS评分、坐位平衡及立位平衡均有改善。结论ATXN8/ATXN8OS基因串联重复区的CAG异常重复扩增可导致SCA8,临床表现为痉挛性步态不稳,结合基因检测结果可明确诊断,SDR可有效改善SCA8患者的痉挛性步态。
