目的为了提高纸质医疗设备质控检测原始记录表手写数据的电子化录入效率,替代传统手工录入方式,实现手写检测数据的批量化自动录入。方法基于Python语言,开发一套基于深度学习光学字符识别(Optical Character Recognition,OCR)的医疗设...目的为了提高纸质医疗设备质控检测原始记录表手写数据的电子化录入效率,替代传统手工录入方式,实现手写检测数据的批量化自动录入。方法基于Python语言,开发一套基于深度学习光学字符识别(Optical Character Recognition,OCR)的医疗设备质控检测原始数据记录表智能识别系统。深度学习OCR技术采用百度智能云OCR云服务,实现批量识别质控检测记录表电子图片,获取结构化的检测数据识别结果,并将识别结果以电子表格的形式导出。结果该系统已实现8种常用医疗设备质控检测原始记录表的智能化识别,经实验测试,8种质控检测记录表平均识别耗时为5.45 s,平均识别正确率为95.94%。系统应用后,医疗设备质控检测原始记录表手写数据电子化录入用时显著低于传统手工录入方式,且差异有统计学意义(P<0.001)。结论该系统识别速度快,识别正确率高,实现了医疗设备质控检测原始记录表批量化、智能化、电子化自动录入,节省了大量人力,提高了质控检测数据整理效率,为质控检测数据的深度分析打下坚实基础。展开更多
FePt granular films were prepared by direct current facing-target magnetron sputtering system onto glass substrates and subsequently in-situ annealed in vacuum. Vibrating sample magnetometer, X-ray diffraction and sca...FePt granular films were prepared by direct current facing-target magnetron sputtering system onto glass substrates and subsequently in-situ annealed in vacuum. Vibrating sample magnetometer, X-ray diffraction and scanning probe microscope were applied to study the magnetic properties, microstructures, morphologies and domain structures of the samples. （FePt）27Ti73 bilayer films were fabricated at various conditions to investigate the effect of Ti on FePt grains. The results show that without Ti matrix layer, FePt films deposited onto the glass substrates are fcc disordered; with addition of Ti matrix layer, FePt/Ti films form a ternary （FePt）27Ti73 alloy possessing fcc and L10 （111） mixed texture. FePt/（FePt）27Ti73 films with perfectly ordered L10（111） structure and unique magnetic properties can be obtained at Ti thickness of 35nm and substrate temperature of 250℃. The maximum coercivity is more than 240kA/m and the squareness ratio is more than 0.9. The obtained results suggest that the granular FePt/（FePt）27Ti73 films can be applicable to ultrahigh-density magnetic recording media.展开更多
文摘目的为了提高纸质医疗设备质控检测原始记录表手写数据的电子化录入效率,替代传统手工录入方式,实现手写检测数据的批量化自动录入。方法基于Python语言,开发一套基于深度学习光学字符识别(Optical Character Recognition,OCR)的医疗设备质控检测原始数据记录表智能识别系统。深度学习OCR技术采用百度智能云OCR云服务,实现批量识别质控检测记录表电子图片,获取结构化的检测数据识别结果,并将识别结果以电子表格的形式导出。结果该系统已实现8种常用医疗设备质控检测原始记录表的智能化识别,经实验测试,8种质控检测记录表平均识别耗时为5.45 s,平均识别正确率为95.94%。系统应用后,医疗设备质控检测原始记录表手写数据电子化录入用时显著低于传统手工录入方式,且差异有统计学意义(P<0.001)。结论该系统识别速度快,识别正确率高,实现了医疗设备质控检测原始记录表批量化、智能化、电子化自动录入,节省了大量人力,提高了质控检测数据整理效率,为质控检测数据的深度分析打下坚实基础。
基金Project(10274018) supported by the National Natural Science Foundation of China project(Z200102) supported the KeyFoundation of Hebei Normal University project(2002116) supported the Foundation Education Department of of Hebei Provin
文摘FePt granular films were prepared by direct current facing-target magnetron sputtering system onto glass substrates and subsequently in-situ annealed in vacuum. Vibrating sample magnetometer, X-ray diffraction and scanning probe microscope were applied to study the magnetic properties, microstructures, morphologies and domain structures of the samples. （FePt）27Ti73 bilayer films were fabricated at various conditions to investigate the effect of Ti on FePt grains. The results show that without Ti matrix layer, FePt films deposited onto the glass substrates are fcc disordered; with addition of Ti matrix layer, FePt/Ti films form a ternary （FePt）27Ti73 alloy possessing fcc and L10 （111） mixed texture. FePt/（FePt）27Ti73 films with perfectly ordered L10（111） structure and unique magnetic properties can be obtained at Ti thickness of 35nm and substrate temperature of 250℃. The maximum coercivity is more than 240kA/m and the squareness ratio is more than 0.9. The obtained results suggest that the granular FePt/（FePt）27Ti73 films can be applicable to ultrahigh-density magnetic recording media.
