To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and tr...To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and transfer learning-based method for printing defect detection was proposed in this study.This method enabled defect detection in printed surface without the need for extensive labeled defect.The ResNet101-SSTU model was used in this study.On the public dataset of printing defect images,the ResNet101-SSTU model not only achieves comparable performance and speed to mainstream supervised learning detection models but also successfully addresses some of the detection challenges encountered in supervised learning.The proposed ResNet101-SSTU model effectively eliminates the need for extensive defect samples and labeled data in training,providing an efficient solution for quality inspection in the printing industry.展开更多
苏州河河口水闸(以下简称苏闸)运行至今出现了诸多病险问题,已严重影响安全运行。因苏闸工程的特殊性,难以使用常规的风险识别方法。为了保障水闸的安全运行,基于故障树风险分析方法,按照功能边界将苏闸划分为5个子系统;确定了各子系统...苏州河河口水闸(以下简称苏闸)运行至今出现了诸多病险问题,已严重影响安全运行。因苏闸工程的特殊性,难以使用常规的风险识别方法。为了保障水闸的安全运行,基于故障树风险分析方法,按照功能边界将苏闸划分为5个子系统;确定了各子系统存在的26项故障模式,建立了苏闸故障树(Fault Tree Analysis),通过下行法确定一阶、二阶、三阶、五阶最小割集,识别苏闸的风险源为“地基不均匀沉降”“底轴不均匀变形”以及“监测系统缺陷”。研究成果不仅为管理部门提供了水闸风险管理的建议,而且对同类工程的设计、维护、监测、管理具有重要的参考价值。展开更多
文摘To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and transfer learning-based method for printing defect detection was proposed in this study.This method enabled defect detection in printed surface without the need for extensive labeled defect.The ResNet101-SSTU model was used in this study.On the public dataset of printing defect images,the ResNet101-SSTU model not only achieves comparable performance and speed to mainstream supervised learning detection models but also successfully addresses some of the detection challenges encountered in supervised learning.The proposed ResNet101-SSTU model effectively eliminates the need for extensive defect samples and labeled data in training,providing an efficient solution for quality inspection in the printing industry.
文摘苏州河河口水闸(以下简称苏闸)运行至今出现了诸多病险问题,已严重影响安全运行。因苏闸工程的特殊性,难以使用常规的风险识别方法。为了保障水闸的安全运行,基于故障树风险分析方法,按照功能边界将苏闸划分为5个子系统;确定了各子系统存在的26项故障模式,建立了苏闸故障树(Fault Tree Analysis),通过下行法确定一阶、二阶、三阶、五阶最小割集,识别苏闸的风险源为“地基不均匀沉降”“底轴不均匀变形”以及“监测系统缺陷”。研究成果不仅为管理部门提供了水闸风险管理的建议,而且对同类工程的设计、维护、监测、管理具有重要的参考价值。
