Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military e...Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.展开更多
目的评价并比较序贯性脏器衰竭评分(sequential organ failure assessment,SOFA)、急性生理学与慢性健康状况评分(acute physiology and chronic health evaluation,APACHE)Ⅱ、简明急性生理学评分(simplified acute physiology score,S...目的评价并比较序贯性脏器衰竭评分(sequential organ failure assessment,SOFA)、急性生理学与慢性健康状况评分(acute physiology and chronic health evaluation,APACHE)Ⅱ、简明急性生理学评分(simplified acute physiology score,SAPS)Ⅱ和Liano评分4种危重病评分系统及RIFLE标准对急性肾损伤(acute kidney injury,AKI)患者的预后评估价值。方法本研究为前瞻性、单中心研究,收集2008年12月到2009年11月复旦大学附属华山医院各种病因引起的AKI患者。AKI的诊断标准为RIFLE的肌酐标准,除外肾后性、肾小球性、肾血管性和间质性肾炎等引起的急性损伤。研究的主要终点是28d死亡率。比较存活组和死亡组的RIFLE分级、SOFA、APACHEⅡ、SAPSⅡ和Liano评分,并进行各种评分系统对死亡的ROC曲线分析,同时将4种评分方法根据RIFLE分级进行分层分析。结果共入选194例符合入选标准的AKI患者。存活组和死亡组的RIFLE分级、AKI病因、是否需要透析差异无统计学意义(P>0.05)。死亡组的机械通气比例、SOFA、APACHEⅡ、SAPSⅡ和Liano评分显著高于存活组(P<0.001)。SOFA、APACHEⅡ、SAPSⅡ和Liano评分预测死亡的受试者工作特性(ROC)曲线下面积分别为0.900、0.885、0.888、0.875(均P<0.001),而RIFLE的ROC曲线下面积为0.566(P>0.05)。按AKI的RIFLE级别进行分层分析时发现,4个评分方法在衰竭组(Fc)ROC曲线下面积最大,其中又以Liano评分最高。结论 RIFLE分级对AKI患者的预后无明显的判断价值,而危重病评分包括SOFA、APACHEⅡ、SAPSⅡ和Liano评分对AKI的预后具有良好的预测价值。展开更多
基金supported by the Future Strategy and Technology Research Institute(RN:23-AI-04)of Korea Military Academythe Hwarang-Dae Research Institute(RN:2023B1015)of Korea Military Academy,and Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A1A01040308).
文摘Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.
文摘目的评价并比较序贯性脏器衰竭评分(sequential organ failure assessment,SOFA)、急性生理学与慢性健康状况评分(acute physiology and chronic health evaluation,APACHE)Ⅱ、简明急性生理学评分(simplified acute physiology score,SAPS)Ⅱ和Liano评分4种危重病评分系统及RIFLE标准对急性肾损伤(acute kidney injury,AKI)患者的预后评估价值。方法本研究为前瞻性、单中心研究,收集2008年12月到2009年11月复旦大学附属华山医院各种病因引起的AKI患者。AKI的诊断标准为RIFLE的肌酐标准,除外肾后性、肾小球性、肾血管性和间质性肾炎等引起的急性损伤。研究的主要终点是28d死亡率。比较存活组和死亡组的RIFLE分级、SOFA、APACHEⅡ、SAPSⅡ和Liano评分,并进行各种评分系统对死亡的ROC曲线分析,同时将4种评分方法根据RIFLE分级进行分层分析。结果共入选194例符合入选标准的AKI患者。存活组和死亡组的RIFLE分级、AKI病因、是否需要透析差异无统计学意义(P>0.05)。死亡组的机械通气比例、SOFA、APACHEⅡ、SAPSⅡ和Liano评分显著高于存活组(P<0.001)。SOFA、APACHEⅡ、SAPSⅡ和Liano评分预测死亡的受试者工作特性(ROC)曲线下面积分别为0.900、0.885、0.888、0.875(均P<0.001),而RIFLE的ROC曲线下面积为0.566(P>0.05)。按AKI的RIFLE级别进行分层分析时发现,4个评分方法在衰竭组(Fc)ROC曲线下面积最大,其中又以Liano评分最高。结论 RIFLE分级对AKI患者的预后无明显的判断价值,而危重病评分包括SOFA、APACHEⅡ、SAPSⅡ和Liano评分对AKI的预后具有良好的预测价值。
