In positive-ion fast atom bombardment (FAB) mass spectrometry, when mono- and di- saccharides are mixed with an appropriate amount of NH4Cl, a highly abundan peak [M+NH4]+appers in FAB mass spectra . From the adduct ...In positive-ion fast atom bombardment (FAB) mass spectrometry, when mono- and di- saccharides are mixed with an appropriate amount of NH4Cl, a highly abundan peak [M+NH4]+appers in FAB mass spectra . From the adduct ion [M+NH4]+, the molecular weights of mono- and di- saccharides can be determined definitively展开更多
森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多...森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多视角可见光图像森林火灾高点检测数据集FFHPV(forest fire of high point view),旨在增强模型对多视角火点知识的学习能力;其次,引入全维动态卷积,构建空间金字塔池化层(OD-SPP),以此提升模型针对多视角数据的火点特征提取能力;最后,引入具有动态非单调聚焦机制的边界框定位损失函数Wise-IoU(wise intersection over union),降低低质量数据对模型精度的影响,提高小目标火点的检测能力.实验结果表明:所提出的FFD-YOLO方法相较于YOLOv7,精度提高3.9%,召回率提高3.7%,均值平均精度提高4.0%,F1分数提高0.038;同时,在与YOLOv5、YOLOv8、DDQ(dense distinct query)、DINO(detection transformer with improved denoising anchor boxes)、Faster R-CNN、Sparse R-CNN、Mask R-CNN、FCOS和YOLOX的对比实验中,FFD-YOLO具有最高的精度75.3%、召回率73.8%、均值平均精度77.6%和F1分数0.745,验证了该方法的可行性与有效性.展开更多
针对果园复杂环境下苹果检测模型大小与精度难以兼顾的问题,文章基于YOLOv8n(You Only Look Once Version 8n,YOLOv8n)提出YOLOv8n-Mob(You Only Look Once Version 8n-Mobile-NetV3,YOLOv8n-Mob)模型。该模型以移动网络版本3(Mobile Ne...针对果园复杂环境下苹果检测模型大小与精度难以兼顾的问题,文章基于YOLOv8n(You Only Look Once Version 8n,YOLOv8n)提出YOLOv8n-Mob(You Only Look Once Version 8n-Mobile-NetV3,YOLOv8n-Mob)模型。该模型以移动网络版本3(Mobile Network Version 3,MobileNetV3)为轻量化主干,结合分层通道注意力机制(Squeeze-and-Excitation,SE)/卷积块注意力模块(Convolutional Block Attention Module,CBAM),有效降低模型的计算复杂度;在颈部网络优化路径聚合网络—特征金字塔网络(Path Aggregation Network-Feature Pyramid Network,PAN-FPN),检测头中引入圆形感知交并比(Intersection over Union,IoU)损失函数,通过多模块协同优化提升模型检测精度。经试验,该模型参数量为0.9 MB、浮点运算次数(Floating Point Operations,FLOPs)为2.6 G、平均精度均值50(mean Average Precision 50,mAP50)为78.6%、帧速率(Frames Per Second,FPS)为625。对比试验与消融试验结果均表明,YOLOv8n-Mob模型在保持高检测精度的同时,显著降低了参数量与计算量,更适配果园复杂场景的部署需求。展开更多
Ultrasound plays an important role not only in preoperative diagnosis but also in intraoperative guidance for liver surgery.Intraoperative ultrasound(IOUS)has become an indispensable tool for modern liver surgeons,esp...Ultrasound plays an important role not only in preoperative diagnosis but also in intraoperative guidance for liver surgery.Intraoperative ultrasound(IOUS)has become an indispensable tool for modern liver surgeons,especially for minimally invasive surgeries,partially substituting for the surgeon’s hands.In fundamental mode,Doppler mode,contrast enhancement,elastography,and real-time virtual sonography,IOUS can provide additional real-time information regarding the intrahepatic anatomy,tumor site and characteristics,macrovascular invasion,resection margin,transection plane,perfusion and outflow of the remnant liver,and local ablation efficacy for both open and minimally invasive liver resections.Identification and localization of intrahepatic lesions and surrounding structures are crucial for performing liver resection,preserving the adjacent vital vascular and bile ducts,and sparing the functional liver parenchyma.Intraoperative ultrasound can provide critical information for intraoperative decision-making and navigation.Therefore,all liver surgeons must master IOUS techniques,and IOUS should be included in the training of modern liver surgeons.Further investigation of the potential benefits and advances in these techniques will increase the use of IOUS in modern liver surgeries worldwide.This study comprehensively reviews the current use of IOUS in modern liver surgeries.展开更多
为满足多数工业场景下钢板表面缺陷检测的需求,针对钢板表面缺陷检测准确率低及小目标缺陷检测率低等问题,文中提出了一种基于改进YOLOv5(You Only Look Once version 5)的钢板表面缺陷检测算法。在YOLOv5的基础上将CBAM(Convolution Bl...为满足多数工业场景下钢板表面缺陷检测的需求,针对钢板表面缺陷检测准确率低及小目标缺陷检测率低等问题,文中提出了一种基于改进YOLOv5(You Only Look Once version 5)的钢板表面缺陷检测算法。在YOLOv5的基础上将CBAM(Convolution Block Attention Module)注意力模块嵌入到主干网络中,提高网络检测精度。加入上下文增强模块,提高了算法对小目标的检测性能。使用NWD(Normalized Wasserstein Distance)度量标准代替原YOLOv5中的IoU(Intersection over Union)度量,提高了网络对裂纹缺陷的识别精确度。实验结果表明,钢板表面缺陷检测算法对裂纹、夹杂、斑块、麻点、压入氧化铁皮、划痕6类缺陷的平均检测精度达到了88.9%,每秒帧数达到110.4 frame·s-1,其中小目标裂纹准确率达到75%。展开更多
文摘In positive-ion fast atom bombardment (FAB) mass spectrometry, when mono- and di- saccharides are mixed with an appropriate amount of NH4Cl, a highly abundan peak [M+NH4]+appers in FAB mass spectra . From the adduct ion [M+NH4]+, the molecular weights of mono- and di- saccharides can be determined definitively
文摘森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多视角可见光图像森林火灾高点检测数据集FFHPV(forest fire of high point view),旨在增强模型对多视角火点知识的学习能力;其次,引入全维动态卷积,构建空间金字塔池化层(OD-SPP),以此提升模型针对多视角数据的火点特征提取能力;最后,引入具有动态非单调聚焦机制的边界框定位损失函数Wise-IoU(wise intersection over union),降低低质量数据对模型精度的影响,提高小目标火点的检测能力.实验结果表明:所提出的FFD-YOLO方法相较于YOLOv7,精度提高3.9%,召回率提高3.7%,均值平均精度提高4.0%,F1分数提高0.038;同时,在与YOLOv5、YOLOv8、DDQ(dense distinct query)、DINO(detection transformer with improved denoising anchor boxes)、Faster R-CNN、Sparse R-CNN、Mask R-CNN、FCOS和YOLOX的对比实验中,FFD-YOLO具有最高的精度75.3%、召回率73.8%、均值平均精度77.6%和F1分数0.745,验证了该方法的可行性与有效性.
文摘针对果园复杂环境下苹果检测模型大小与精度难以兼顾的问题,文章基于YOLOv8n(You Only Look Once Version 8n,YOLOv8n)提出YOLOv8n-Mob(You Only Look Once Version 8n-Mobile-NetV3,YOLOv8n-Mob)模型。该模型以移动网络版本3(Mobile Network Version 3,MobileNetV3)为轻量化主干,结合分层通道注意力机制(Squeeze-and-Excitation,SE)/卷积块注意力模块(Convolutional Block Attention Module,CBAM),有效降低模型的计算复杂度;在颈部网络优化路径聚合网络—特征金字塔网络(Path Aggregation Network-Feature Pyramid Network,PAN-FPN),检测头中引入圆形感知交并比(Intersection over Union,IoU)损失函数,通过多模块协同优化提升模型检测精度。经试验,该模型参数量为0.9 MB、浮点运算次数(Floating Point Operations,FLOPs)为2.6 G、平均精度均值50(mean Average Precision 50,mAP50)为78.6%、帧速率(Frames Per Second,FPS)为625。对比试验与消融试验结果均表明,YOLOv8n-Mob模型在保持高检测精度的同时,显著降低了参数量与计算量,更适配果园复杂场景的部署需求。
基金Supported by a grant from Japan China Sasakawa Medical Fellowship。
文摘Ultrasound plays an important role not only in preoperative diagnosis but also in intraoperative guidance for liver surgery.Intraoperative ultrasound(IOUS)has become an indispensable tool for modern liver surgeons,especially for minimally invasive surgeries,partially substituting for the surgeon’s hands.In fundamental mode,Doppler mode,contrast enhancement,elastography,and real-time virtual sonography,IOUS can provide additional real-time information regarding the intrahepatic anatomy,tumor site and characteristics,macrovascular invasion,resection margin,transection plane,perfusion and outflow of the remnant liver,and local ablation efficacy for both open and minimally invasive liver resections.Identification and localization of intrahepatic lesions and surrounding structures are crucial for performing liver resection,preserving the adjacent vital vascular and bile ducts,and sparing the functional liver parenchyma.Intraoperative ultrasound can provide critical information for intraoperative decision-making and navigation.Therefore,all liver surgeons must master IOUS techniques,and IOUS should be included in the training of modern liver surgeons.Further investigation of the potential benefits and advances in these techniques will increase the use of IOUS in modern liver surgeries worldwide.This study comprehensively reviews the current use of IOUS in modern liver surgeries.
文摘为满足多数工业场景下钢板表面缺陷检测的需求,针对钢板表面缺陷检测准确率低及小目标缺陷检测率低等问题,文中提出了一种基于改进YOLOv5(You Only Look Once version 5)的钢板表面缺陷检测算法。在YOLOv5的基础上将CBAM(Convolution Block Attention Module)注意力模块嵌入到主干网络中,提高网络检测精度。加入上下文增强模块,提高了算法对小目标的检测性能。使用NWD(Normalized Wasserstein Distance)度量标准代替原YOLOv5中的IoU(Intersection over Union)度量,提高了网络对裂纹缺陷的识别精确度。实验结果表明,钢板表面缺陷检测算法对裂纹、夹杂、斑块、麻点、压入氧化铁皮、划痕6类缺陷的平均检测精度达到了88.9%,每秒帧数达到110.4 frame·s-1,其中小目标裂纹准确率达到75%。
