结合异常检测的X射线安检图像识别方法

针对Faster R-CNN模型对X射线安检图像中危险品检测准确率低、误检率高的问题,提出了一种前置预分类头部的X射线安检图像检测网络(pre-classification Faster R-CNN,PC-Faster R-CNN)。该模型在骨干网络之后,区域建议网络(Region Proposal Networks, RPN)之前新增一个预分类模块先对X射线安检图像进行异常检测,提高模型对正常图像的识别能力;同时引入兴趣区域对齐(RoIAlign)模块,减小兴趣区域池化层(RoIPooling)引起的量化误差,进而提升Faster R-CNN的检测性能。新模型将浅层卷积层的低级边缘特征输入到预分类模块,使其学习正常图像的高级语义特征,从而改善整个模型的识别性能。实验结果表明,与原始的Faster R-CNN相比,本文模型对危险品的检测精度提升了9.03%,误检率降低了24.03%;同时预分类头部使得模型较大地提高了检测效率,比原始的Faster R-CNN提升了44.54%。     

基于改进Faster R-CNN的路面灌封裂缝检测方法

路面灌封裂缝对路面使用寿命的影响较为突出,为了解决目前灌封裂缝检测技术匮乏的问题,文中提出了一种基于改进Faster R-CNN的路面灌封裂缝检测方法。首先,建立灌封裂缝图像集,对采集到的图像进行增广处理,构建路面灌封裂缝标注样本数据集,并将图像集按6∶2∶2的比例划分为训练集、验证集和测试集;接着,采用Faster R-CNN模型对灌封裂缝进行检测,针对Faster R-CNN检测灌封裂缝存在漏检、定位效果不够理想的问题,文中分别将VGG16、ZFNet和Resnet50网络的特征提取层与Faster R-CNN模型进行结合,结果表明,VGG16和Faster R-CNN结合的模型检测精度最高,达到0. 9031;然后,通过增加灌封裂缝候选框宽高比的方法继续改进模型,检测精度达到0. 907 3,且原先被漏检的目标能被检测出来;最后,对改进Faster R-CNN与YOLOv2模型的检测精度及定位效果进行对比,结果表明,文中提出的改进Faster RCNN能够明显提高对灌封裂缝的检测准确率和定位精度。     

基于改进Faster R-CNN的无人机视频车辆自动检测

为了从广域视角准确提取道路交通信息,提出了一种用于无人机视频车辆自动识别的改进Faster R-CNN模型.该模型以基于ZF网络的Faster R-CNN为原型,优化调整学习策略、训练图像尺寸、学习率等模型参数,调整RPN网络卷积核并引入SoftNMS算法,增加1～3个特征提取卷积层和激活层.基于无人机交通视频构建了训练图像集,对现有Faster R-CNN模型及改进模型进行训练和测试.结果显示,与采用Step学习策略的模型相比,采用学习策略Inv的模型车辆识别平均准确率提高了0.4%～9.4%.引入SoftNMS算法的模型比引入前的模型平均准确率提高了0.1%～7.9%.提出的改进模型平均准确率为94.6%,较基于ZF的Faster R-CNN模型、基于VGGM的Faster R-CNN模型和基于VGG16的Faster R-CNN模型分别提高了13.1%、13.1%和4.1%,且训练时间减少约3%,对多种场景的视频车辆检测具有较好的适用性.     

基于改进Faster R-CNN算法的车辆检测

基于区域的网络Faster R-CNN算法在图像的目标检测领域取得了巨大突破.相比较于传统的目标检测方法R-CNN和Fast R-CNN,Faster R-CNN提出了一种候选区域网络(RPN)有效的提升了检测候选框的生成速度.本文通过分析提出的RPN网络的实现方法,引用了K-Means++聚类算法,来对训练数据集中的目标框大小进行聚类分析,替代原本算法中的9个区域框.通过实验对比分析,通过改进的方法提高了车辆检测的精度.     

基于改进的Faster R-CNN的息肉目标检测和分类方法

为了解决结肠镜下腺瘤性息肉和增生性息肉不易分型的问题，提出一种基于改进的Faster R-CNN的目标检测及息肉分类模型.在数据预处理阶段，对原有的2 426张息肉图像(1 582张腺瘤性息肉图像，844张增生性息肉图像)通过2种方式进行图像增强，并且通过改进的特征提取、边界框回归以及非极大值抑制的网络，用602张图像(386张腺瘤性图像，216张增生性息肉图像)进行测试.通过实验证明，在交并比(IoU)取0.5时，获得了86.8%的平均精度均值，相较于改进之前提升了2.3%.实验结果验证了该模型的潜在临床应用价值.     

基于深度学习算法的航拍巡检图像的绝缘子识别

针对绝缘子检测目标在航拍图像中尺寸变化剧烈的问题,提出一种改进Faster R-CNN的绝缘子检测算法.首先将FPN特征金字塔结构网络与Faster R-CNN算法进行结合,将不同尺度下的特征进行融合;然后,改进最大池化层,提升检测框的坐标精度;针对遮挡现象,采用Soft-NMS算法规避不同目标检测框因重叠而被误删的情况.经过对绝缘子航拍数据集的检测验证,对比原Faster R-CNN网络,本改进网络结构提高了平均准确率(MAP),且可以更有效地识别图像中更小比例的绝缘子目标.     

基于改进Faster R-CNN的行人检测算法

针对行人检测中复杂环境,提出一种改进Faster R-CNN的行人检测算法,使用深度卷积网络从图片中提取适合检测目标的特征。基于Faster R-CNN算法,以Soft-NMS算法代替传统NMS算法,加强Faster R-CNN算法对重叠区域的识别能力。同时,算法通过"Hot Anchors"代替均匀采样的锚点避免大量额外计算,提高检测效率。最后,将21分类问题的Faster R-CNN框架,修改成适用于行人检测的2分类检测框架。实验结果表明:改进Faster R-CNN的行人检测算法在VOC 2007行人数据集,检测效率和准确率分别提升33%、2.6%。     

基于深度网络模型的牛脸检测算法比较

针对传统检测方法在牛脸检测应用方面存在的检测设备易损、检测结果不理想等问题,根据大数据、多差异性原则,使用手机和相机在某奶牛养殖场采集奶牛数据,构建了一个超过10 000张不同条件下(如遮挡、模糊、光照变化等)的奶牛数据集.在此基础上使用目前有代表性的基于深度网络模型的目标检测方法(如SSD,Faster R-CNN和R-FCN等)对该数据集进行试验对比分析.结果表明:Faster R-CNN模型综合检测精度最高,可达0.990,但其检测速度相对较慢,为11 F·s~(-1);SSD模型的检测速度最快,为47 F·s~(-1),但其检测精度与Faster R-CNN相比略低,约为0.945.     

航拍图像中绝缘子串的识别与分割方法研究

为实现航拍图像中绝缘子串的识别与分割,以Faster R-CNN和全卷积神经网络(fully convolutional networks,FCN)为框架构建了深度学习算法,并以算法为核心搭建了绝缘子串图像检测平台.算法首先使用Faster R-CNN模型,结合Resnet-101深度残差卷积神经网络,对所有感兴趣区域进行分类,再对Bounding Box回归和坐标进行修正,用于绝缘子串的识别,接着以FCN网络为基础,通过微调使原网络适应新的绝缘子串数据集,实现了在复杂背景下绝缘子串的语义分割.实验结果表明,文中给出的方法与已有方法相比,不仅能够在不同光照条件、不同拍摄角度以及复杂背景干扰下实现绝缘子串的识别与分割,且处理时间短、精度高和鲁棒性强.     

基于改进Faster R-CNN+ZF模型的铁路桥梁裂缝分类方法

针对传统图像处理算法不能对存在过饱和像素和随机高强度噪声影响的铁路桥梁裂缝图像有效分类的问题,设计了一种基于改进Faster R-CNN+ZF模型的铁路桥梁裂缝自动分类方法.首先将原始图像集进行数据增强后,参照Pascal Voc数据集格式自制训练所需数据集;然后在Faster R-CNN网络模型前添加一层Prewitt算子锐化卷积层提升模型特征提取能力;最后重置ZF模型中相关卷积核的尺度、优化模型超参数和学习率,使模型的鲁棒性和高实时性得到有效保障.该方法在实际采集的49 124幅铁路桥梁裂缝图像数据集中进行测试.结果表明:新的算法能实现所有铁路桥梁裂缝类型的分类,有效裂缝识别率达93.7%以上,明显优于投影法和支持向量机法,具有很强的工程应用价值.     

Markedness and its Interpretation in Language Use

Language markedness is a common phenomenon in languages, and is reflected from hearing, vision and sense, i.e. the variation in the three aspects such as phonology, morphology and semantics. This paper focuses on the interpretation of markedness in language use following the three perspectives, i.e. pragmatic interpretation, psychological interpretation and cognitive interpretation, with an aim to define the function of markedness.     

Lower Paleozoic oil relationships within Williston Basin, Canada

The Williston Basin is a significant petroleum province, containing oil production zones that include the Middle Cambrian to Lower Ordovician, Upper Ordovician, Middle Devonian, Upper Devonian and Mississippian and within the Jurassic and Cretaceous. The oils of the Williston Basin exhibit a wide range of geochemical characteristics defined as "oil families", although the geochemical signature of the Cambrian Deadwood Formation and Lower Ordovician Winnipeg reservoired oils does not match any "oil family". Despite their close stratigraphic proximity, it is evident that the oils of the Lower Palaeozoic within the Williston Basin are distinct. This suggests the presence of a new "oil family" within the Williston Basin. Diagnostic geochemical signatures occur in the gasoline range chromatograms, within saturate fraction gas chromatograms and biomarker fingerprints. However, some of the established criteria and cross-plots that are currently used to segregate oils into distinct genetic families within the basin do not always meet with success, particularly when applied to the Lower Palaeozoic oils of the Deadwood and Winnipeg Formation.     

An Analysis of Catherine＇s Character——Wuthering Heights

Wuthering Heights, Emily Bronte＇s only novel, was published in December of 1847 under the pseudonym Ellis Bell. The book did not gain immediate success, but it is now thought one of the finest novels in the English language. Catherine is the key character of this masterpiece, because everybody and everything center on her though she had a short life. We can understand this masterpiece better if we know Catherine well.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

Features of Women's language

Language is a means of verbal communication. People use language to communicate with each other. In the society, no two speakers are exactly alike in the way of speaking. Some differences are due to age, gender, statue and personality. Above all, gender is one of the obvious reasons. The writer of this paper tries to describe the features of women＇s language from these perspectives： pronunciation, intonation, diction, subjects, grammar and discourse. From the discussion of the features of women＇s language, more attention should be paid to language use in social context. What＇s more, the linguistic phenomena in a speaking community can be understood more thoroughly.     

Design and analysis of generic LBS application developing platform

Location based services is promising due to its novel working style and contents.A software platform is proposed to provide application programs of typical location based services and support new applications developing efficiently. The analysis shows that this scheme is easy implemented, low cost and adapt to all kinds of mobile nework system.     

橡胶颗粒沥青混合料低温抗裂性试验研究

以AC-13级配为基础,将橡胶颗粒代替部分集料掺入混合料中,以低温弯曲试验为评价方法对不同橡胶颗粒掺量下沥青混合料的低温抗裂性进行研究,并引入应变能密度值对混合料的低温抗裂性进行综合评价.试验结果表明:橡胶颗粒沥青混合料试件的破坏微应变均超过2 300,满足冬寒区的技术指标;无论是否掺加橡胶颗粒,随着温度的下降,沥青混合料破坏时的最大弯拉强度增大,弯拉应变降低,劲度模量增大;弯曲应变能密度在胶粒掺量为1%左右时具有较大的弯曲应变能密度值,此时橡胶颗粒沥青混合料具有较好的低温抗裂性.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

低渗透非均质砂岩油藏启动压力梯度研究

理论推导与室内实验相结合,建立了低渗透非均质砂岩油藏启动压力梯度确定方法。首先借助油藏流场与电场相似的原理,推导了非均质砂岩油藏启动压力梯度计算公式。其次基于稳定流实验方法,建立了非均质砂岩油藏启动压力梯度测试方法。结果表明:低渗透非均质砂岩油藏的启动压力梯度确定遵循两个等效原则。平面非均质油藏的启动压力梯度等于各级渗透率段的启动压力梯度关于长度的加权平均;纵向非均质油藏的启动压力梯度等于各渗透率层的启动压力梯度关于渗透率与渗流面积乘积的加权平均。研究成果可用于有效指导低渗透非均质砂岩油藏的合理井距确定,促进该类油藏的高效开发。     

Quantitative trait loci （QTLs） for quality traits related to protein and starch in wheat

Quality traits in wheat （Triticum aestirum L.） were studied by quantitative trait locus （QTL） analysis in a recombinant inbred line （RIL） population, a set of 131 lines derived from Chuan 35050 × Shannong 483 cross （ChSh）. Grains from RILs were assayed for 21 quality traits related to protein and starch. A total of 35 putative QTLs for 19 traits with a single QTL explaining 7.99-40.52% of phenotypic variations were detected on 10 chromosomes, 1D, 2A, 2D, 3B, 3D, 5A, 6A, 6B, 6D, and 7B. The additive effects of 30 QTLs were positive, contributed by Chuan 35050, the remaining 5 QTLs were negative with the additive effect contributed by Shannong 483. For protein traits, 15 QTLs were obtained and most of them were located on chromosomes 1 D, 3B and 6D, while 20 QTLs for starch traits were detected and most of them were located on chromosomes 3D, 6B and 7B. Only 7 QTLs for protein and starch traits were co-located in three regions on chromosomes 1D, 2A and 2D. These protein and starch trait QTLs showed a distinct distribution pattern in certain regions and chromosomes. Twenty-two QTLs were clustered in 6 regions of 5 chromosomes. Two QTL clusters for protein traits were located on chromosomes 1D and 3B, respectively, three clusters for starch traits on chromosomes 3D, 6B and 7B, and one cluster including protein and starch traits on chromosome 1D.