Critical area computation for real defects and arbitrary conductor shapes

In current critical area models, it is generally assumed the defect outlines are circular and the conductors to be rectangle or the merger of rectangles. However, real defects and conductors associated with optimal layout design exhibit a great variety of shapes. Based on mathematical morphology, a new critical area model is presented, which can be used to estimate the critical area of short circuit, open circuit and pinhole. Based on the new model, the efficient validity check algorithms are explored to extract critical areas of short circuit, open circuit and pinhole from layouts. The results of experiment on an approximate layout of 4 × 4 shifts register show that the new model predicts the critical areas accurately. These results suggest that the proposed model and algorithm could provide new approaches for yield prediction.

A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

Dynamic Characteristic Mechanism of Atrial Septal Defect Using Real-Time Three-Dimensional Echocardiography and Evaluation of Right Ventricular Functions

The dynamic characteristics of the area of the atrial septal defect（ASD） were evaluated using the technique of real-time three-dimensional echocardiography（RT 3DE）, the potential factors responsible for the dynamic characteristics of the area of ASD were observed, and the overall and local volume and functions of the patients with ASD were measured. RT 3DE was performed on the 27 normal controls and 28 patients with ASD. Based on the three-dimensional data workstations, the area of ASD was measured at P wave vertex, R wave vertex, T wave starting point, and T wave terminal point and in the T-P section. The right atrial volume in the same time phase of the cardiac cycle and the motion displacement distance of the tricuspid annulus in the corresponding period were measured. The measured value of the area of ASD was analyzed. The changes in the right atrial volume and the motion displacement distance of the tricuspid annulus in the normal control group and the ASD group were compared. The right ventricular ejection fractions in the normal control group and the ASD group were compared using the RT 3DE long-axis eight-plane（LA 8-plane） method. Real-time three-dimensional volume imaging was performed in the normal control group and ASD group（n=30）. The right ventricular inflow tract, outflow tract, cardiac apex muscular trabecula dilatation, end-systolic volume, overall dilatation, end-systolic volume, and appropriate local and overall ejection fractions in both two groups were measured with the four-dimensional right ventricular quantitative analysis method（4D RVQ） and compared. The overall right ventricular volume and the ejection fraction measured by the LA 8-plane method and 4D RVQ were subjected to a related analysis. Dynamic changes occurred to the area of ASD in the cardiac cycle. The rules for dynamic changes in the area of ASD and the rules for changes in the right atrial volume in the cardiac cycle were consistent. The maximum value of the changes in the right atrial volume occurred in the end-systolic period when the peak of the curve appeared. The minimum value of the changes occurred in the end-systolic period and was located at the lowest point of the volume variation curve. The area variation curve for ASD and the motion variation curve for the tricuspid annulus in the cardiac cycle were the same. The displacement of the tricuspid annulus exhibited directionality. The measured values of the area of ASD at P wave vertex, R wave vertex, T wave starting point, T wave terminal point and in the T-P section were properly correlated with the right atrial volume（P〈0.001）. The area of ASD and the motion displacement distance of the tricuspid annulus were negatively correlated（P〈0.05）. The right atrial volumes in the ASD group in the cardiac cycle in various time phases increased significantly as compared with those in the normal control group（P=0.0001）. The motion displacement distance of the tricuspid annulus decreased significantly in the ASD group as compared with that in the normal control group（P=0.043）. The right ventricular ejection fraction in the ASD group was lower than that in the normal control group（P=0.032）. The ejection fraction of the cardiac apex trabecula of the ASD patients was significantly lower than the ejection fractions of the right ventricular outflow tract and inflow tract and overall ejection fraction. The difference was statistically significant（P=0.005）. The right ventricular local and overall dilatation and end-systolic volumes in the ASD group increased significantly as compared with those in the normal control group（P=0.031）. The a RVEF and the overall ejection fraction decreased in the ASD group as compared with those in the normal control group（P=0.0005）. The dynamic changes in the area of ASD and the motion curves for the right atrial volume and tricuspid annulus have the same dynamic characteristics. RT 3DE can be used to accurately evaluate the local and overall volume and functions of the right ventricle. The local and overall volume loads of the right ventricle in the ASD patients increase significantly as compared with those of the normal people. The right ventricular cardiac apex and the overall systolic function decrease.

Evaluation of Atrial Septal Defect Using Real-time Three-dimensional Echocardiography:Comparison with Surgical Findings

The present study evaluated the application of three dimensional echocardigraphy （3DE） in the diagnosis of atrial septal defect （ASD） and the measurement of its size by 3DE and compared the size with surgical findings. Two-dimensional and real-time three dimensional echocardiography （RT3DE） was performed in 26 patients with atrial septal defect, and the echocardiographic data were compared with the surgical findings. Significant correlation was found between defect diameter by RT3DE and that measured during surgery （r=0.77, P〈0.001）. The defect area changed significantly during cardiac cycle. Percentage change in defect size during cardiac cycle ranged from 6%-70%. Our study showed that the size and morphology of atrial septal defect obtained with RT3DE correlate well with surgical findings. Therefore, RT3DE is a feasible and accurate non-invasive imaging tool for assessment of atrial septal size and dynamic changes.

基于多尺度卷积注意力机制的输电线路防振锤缺陷检测

作为输电线路中的重要金具部件,防振锤的缺陷将对输电线路构成严重威胁。针对由于防振锤缺陷样本数量稀少、背景复杂、区域形状尺寸不一造成的防振锤缺陷识别能力不足的问题,提出一种基于多尺度卷积注意力机制的防振锤缺陷检测方法。首先,通过统计不同缺陷的防振锤尺寸,设计适应不同类别的多尺度卷积注意力机制,使网络重点关注图像中的防振锤区域;其次,引入结构重参数化方法,以将网络中的多分支结构无损失地转换为单分支结构,在提高网络检测性能的同时维持检测速度在较高水平;最后,以渐进式特征金字塔网络结构(AFPN)为基础,融合更多的浅层网络,提高了网络检测防振锤小目标的能力。实际收集的防振锤缺陷数据集实验结果表明,设计的检测方法可显著提升防振锤缺陷检测的性能,检测精度mAP0.5达到了91.9%,在TITAN XP平台下检测速度达60.88帧/s,可为输电线路防振锤智能化巡检提供参考。

Yield Modeling of Rectangular Defect Outline

In integrated circuits, the defects associated with photolithography are assumed to be in the shape of circular discs in order to perform the estimation of yield and fault analysis. However,real defects exhibit a great variety of shapes. In this paper,a novel yield model is presented and the critical area model of short circuit is correspondingly provided. In comparison with the circular model corrently available, the new model takes the similarity shape to an original defect, the two-dimensional distributional characteristic of defects, the feature of a layout routing and the character of yield estimation into account. As for the aspect of prediction of yield, the experimental results show that the new model may predict the yield caused by real defects more accurately than the circular model does. It is significant that the yield is accurately estimated and improved using the proposed model.

miRNA表达谱在产前诊断胎儿先天性心脏病中的研究

目的:探究微小RNA(microRNA,miRNA)表达谱在产前诊断胎儿先天性心脏病(congenital heart disease,CHD)中的应用。方法:收集2021年1月—2022年12月于天津市中心妇产科医院就诊的30例超声确诊为CHD的孕妇(病例组)和同期10例要求行羊水穿刺的孕妇(对照组),用Illumina测序平台对2组孕妇的羊水上清进行全转录组测序,2组孕妇的全部miRNA进行归一化,分析差异表达的miRNA。从差异表达的miRNA中挑选P<0.05和|log2 FC|>3(差异倍数,Fold Change,FC)的miRNA再在羊水和外周血中进行实时荧光定量聚合酶链反应(real time fluorescence quantitative polymerase chain reaction,RT-qPCR)验证,比较羊水中miRNA测序与RT-qPCR的差异倍数,挑选外周血与羊水表达调控方向一致的miRNA。结果:共发现138个差异表达miRNA,其中85个上调,53个下调。进一步挑选出了15个差异表达的miRNA,羊水中miRNA测序与RT-qPCR结果比较相一致。外周血与羊水中表达调控方向一致的miRNA有2个,分别为miR-222-3p和miR-189-5p,这2个miRNA在病例组母血中表达量较对照组显著上升(均P<0.05)。结论:母血中miRNA作为新的血清学标志物可以初步应用于筛查胎儿CHD。

基于迁移学习的嵌入式实时系统缺陷研究

为了减少环境变化对软件缺陷评估的影响,提出了一种基于迁移学习的嵌入式实时控制系统软件缺陷评估方法。首先,选择缺陷软件测量指标;在此基础上,利用特征聚类技术将相关的索引特征划分为同一个聚类;然后,根据两项间特征的分布相似性,找到相关特征,去除分布差异较大的特征;最后,从源项目中选择高质量的特征,构建训练数据集,通过权重调整,从源项目中选择更好的评价数据,实现对软件缺陷的准确评价。映射结果目标明确,设计方法的评价结果的错误率在8.7%以内,具有良好的评价效果。

实时三维经食管超声心动图在房间隔缺损封堵术中应用

目的探讨实时三维经食管超声心动图(RT-3D-TEE)在经皮房间隔缺损(ASD)封堵术中的应用价值。方法对38例ASD患者,分别行经二维经胸超声心动图(2D-TTE)及RT-3D-TEE检查,评估ASD形态,测量ASD最大径、各残端长度,RT-3D-TEE同时测量房间隔缺损面积,分析缺损最大径、面积与封堵器大小的相关性。根据测量结果选择封堵器大小,术中应用RT-3D-TEE监测引导封堵器的输送和释放。术后即刻评估封堵疗效并于术后3个月、6个月复查。结果下腔静脉侧残缘、主动脉侧残缘参数差异无统计学意义(P>0.05);RT-3D-TEE检测的上腔静脉残缘长度大于2D-TTE(P<0.05);2D-TTE、RT-3D-TEE测量的ASD最大径、RT-3D-TEE的面积测值与封堵器大小存在相关性(r=0.891,0.926,0.976,P<0.05),其中RT-3D-TEE测量面积与封堵器大小相关性最高。其中36例患者在RT-3D-TEE准确引导监测下,成功实施经皮房间隔缺损封堵术,术后3个月、6个月随访疗效佳,未见并发症。结论RT-3D-TEE在ASD封堵术前直观清晰显示ASD大小、形态及各残缘情况,为封堵器的选择提供可靠的参考,同时能安全有效引导封堵术,术后随访疗效佳,具有重要的临床应用价值。

基于改进YOLOv5s的轻量化布匹瑕疵检测算法

针对纺织生产中布匹瑕疵检测高精度、实时性的需求,提出了一种基于改进YOLOv5s的轻量化布匹瑕疵检测算法(GhostNet-CBAM-Partial convolution-YOLOv5s,GCP-YOLOv5s)。该算法首先引入GhostNet中的GhostConv模块,对原主干网络进行优化重构,大幅减少网络参数;其次,在主干特征提取网络中加入CBAM(Convolutional block attention module)注意力机制,增加网络的特征提取能力;最后,设计了基于Partial convolution的改进C3模块(C3-Partial convolution,C3-P),在降低模型参数量的同时提高特征融合能力。在自建布匹瑕疵数据集上进行了对比测试,结果表明:与基准模型YOLOv5s相比,GCP-YOLOv5s的参数量降低了41.6%,计算量降低了43.1%,检测速度提高了12 FPS,检测精度提升了1.7%。GCP-YOLOv5s算法在保证模型轻量化的同时具有较高的检测精度,可以满足布匹瑕疵检测的高精度和实时性要求。

HFW焊管超声探伤焊缝实时跟踪技术

HFW焊管在焊接成型过程中,可能会出现夹杂、裂纹和未熔合等缺陷。对焊缝进行自动超声波探伤可检测出这些缺陷,从而有效地控制HFW焊管的焊缝质量。但是对于长度为12~18 m的HFW焊管产品,由于钢管在轴向存在弯曲,使HFW焊管在超声探伤时会出现焊缝的偏转现象。为了使HFW焊管在自动超声探伤时探头保持在焊缝上,在研究焊缝实时跟踪技术的基础上,开发了适用于长度18 m以下HFW焊管的超声探伤焊缝实时跟踪系统。当探伤速度为1 m/s时,该系统能够及时发现焊缝偏转,并立即对探头架位置进行调整,使探头持续位于焊缝上,从而可使HFW焊管焊缝的所有部位被检测,有效地控制了HFW焊管焊缝的质量。

多切面多角度二维显像结合CDFI血流参数动态调节在胎儿单纯性室间隔缺损中的诊断价值及漏诊原因分析

目的:探讨多切面多角度二维显像结合彩色多普勒血流显影(CDFI)血流参数动态调节在胎儿单纯性室间隔缺损(isolated ventricular septal defect,i-VSD)的诊断价值,并分析其漏诊原因。方法:选取2019年5月至2023年6月于我院收治的5000例孕妇进行检查,将疑似i-VSD的143例孕妇作为研究对象,均行多切面多角度二维显像及CDFI检查,并依据产后结果分为不符组(31例)和符合组(112例)。采用单因素分析两组患者临床资料,采用多元Logistic回归模型分析多切面多角度二维显像结合CDFI血流参数诊断胎儿i-VSD漏诊的影响因素,采用ROC曲线、校准曲线评价模型的诊断效能。结果:143例胎儿疑似i-VSD,产后确诊准确度为85.31%,灵敏度为88.79%,特异度为70.37%,阳性预测值为92.79%,阴性预测值为59.38%,一致性检验结果显示多切面多角度实时二维显像结合CDFI血流参数检查与产后检查结果一致性相对较好;符合组与不符组在年龄、体质量、孕早期服药史、腹壁瘢痕、家族心脏病史、产检次数、羊水情况、医生检查经验、临床分型、缺损口直径方面差异显著(P<0.05);多元Logistic分层回归分析结果表明,年龄、体质量、羊水情况、腹壁瘢痕、临床分型、缺损口直径是多切面多角度二维显像结合CDFI血流参数诊断胎儿i-VSD漏诊的独立危险因素(P<0.05),并基于此构建了预测模型并绘制ROC曲线,结果显示该预测模型AUC为0.885(95%CI:0.810~0.903),灵敏度为87.2%,特异度为79.8%,表明该预测模型区分度较高,校准曲线表明该预测模型具有良好的准确度。结论:多切面多角度二维显像结合CDFI血流参数能提高i-VSD诊断符合率,而年龄、体质量、羊水情况、腹壁瘢痕、临床分型、缺损口直径均能影响其检查结果,因此对于存在上述特征的孕妇需增加检查次数,以降低胎儿i-VSD漏诊的发生风险。

Clinical Value of Stereoscopic Three-dimensional Echocardiography in Assessment of Atrial Septal Defects: Feasibility and Efficiency

Stereoscopic three-dimensional echocardiography（S-3DE） is a novel displaying technol-ogy based on real-time 3-dimensional echocardiography （RT-3DE）. Our study was to evaluate the feasibility and efficiency of S-3DE in the diagnosis of atrial septal defect （ASD） and its use in the guidance for transcatheter ASD occlusion. Twelve patients with secundum ASD underwent RT-3DE examination and 9 of the 12 were subjected to transcatheter closure of ASD. Stereoscopic vision was generated with a high-performance volume renderer with red-green stereoscopic glasses. S-3DE was compared with standard RT-3D display for the assessment of the shape, size, and the surrounding tis-sues of ASD and for the guidance of ASD occlusion. The appearance rate of coronary sinus and the mean formation time of the IVC, SVC were compared. Our results showed that S-3DE could measure the diameter of ASD accurately and there was no significant difference in the measurements between S-3DE and standard 3D display （2.89±0.73 cm vs 2.85±0.72 cm, P〉0.05; r=0.96, P〈0.05）. The appearance of coronary sinus for S-3DE was higher as compared with the standard 3D display （93.3% vs 100%）. The mean time of the IVC, SVC for S-3DE monitor was slightly shorter than that of the standard 3D display （11.0±3.8 s vs 10.3±3.6 s, P〉0.05）. The mean completion time of interven-tional procedure was shortened with S-3DE display as compared with standard 3D display （17.3±3.1 min vs 23.0±3.9 min, P〈0.05）. Stereoscopic three-dimensional echocardiography could improve the visualization of three-dimensional echocardiography, facilitate the identification of the adjacent structures, decrease the time required for interventional manipulation. It may be a feasible, safe, and efficient tool for guiding transcatheter septal occlusion or the surgical interventions.

基于YOLOv5的砂纸表面缺陷检测方法研究

针对目前工业生产过程中存在砂纸表面缺陷人工质量检测精度低和检测效率低问题,提出一种基于YOLOv5网络模型融合CA注意力机制的砂纸表面缺陷自动检测方法。首先对砂纸生产过程中的砂纸表面图像进行采样,将收集到的砂纸表面缺陷图像分成脱砂、堆砂、划痕和褶皱4种缺陷类型来制作砂纸表面缺陷数据集;其次将YOLOv5主干网络中的C3模块与CA注意力机制结合,改进为CAC3模块;最后将改进前后的网络模型在自建砂纸表面缺陷数据集上进行训练和验证。实验结果表明:得到改进后的YOLOv5+CAC3网络模型,其P、R、mAP@0.5、mAP@0.5:0.95和S的数值分别为96.2%,92.9%,95.8%,65.0%,16.8 ms,相比于改进前的YOLOv5网络模型分别提高了1.1%、2.2%、0.6%、1.7%、4.5 ms。该方法在砂纸表面缺陷检测中精度高、速度快、检测稳定,符合砂纸生产过程中砂纸表面缺陷检测的要求。

HFW焊管内毛刺刮除状态实时监控技术

为了解决HFW焊管生产过程中内毛刺刮除不彻底、打刀、刮偏等不符合产品质量要求的问题,对产生内毛刺相关缺陷的原因进行了分析研究,设计了内毛刺实时监控系统。该系统可检测焊缝两侧±25 mm范围内的内毛刺刮除状态,通过监控界面可以直观地看到内毛刺的实时刮除状态,根据状态及时进行动态调整或停机调整。该内毛刺实时监控系统应用于生产线,内毛刺相关缺陷得到很好控制,HFW钢管质量与成材率都得到提升。

基于稀疏字典优选的织物疵点检测方法

针对稀疏字典算法检测速度慢,无法满足实时检测需求的问题,提出了一种基于稀疏字典优化的疵点检测算法。首先采用一定尺寸的窗口对正常样本滑动取块进行学习得到字典库;然后对字典库进行分组优选,其策略是依据样本被近似的程度,按顺序分组挑选最优字典组;之后检测时选用字典组对织物图像求解系数并进行重构,得到重构图像及相应的残差图像,最后对残差图像进行疵点区域的判定。实验结果表明,此方法检测准确率平均可达96.22%,检出率高于无约束字典学习方法,图像大小为512像素×512像素时平均每张用时208 ms,为稀疏字典方法的0.26%,可达到在保证检测精度的同时仍具有实时性的效果。

基于分类算法的高实时性织物疵点检测

为了进一步提高织物疵点的检测效率,提出一种基于机器学习的高实时性织物疵点检测算法。首先分析了织物疵点检测算法流程,通过学习和分类2个阶段对疵点进行检测;然后利用高斯函数对织物纹理图像进行建模,并提取4类纹理特征刻画织物纹理并突出纹理中包含的疵点,采用分类算法获得分类器完成疵点判定;最后通过对比实例验证文章所提算法的可行性。研究结果表明:本文所提算法的平均准确率为97%,比传统算法高10%;采用分类算法可以节约训练时间,在织物幅宽相近的情况下,检测速度可达41 m/min,可以满足工业环境中在线实时检测要求。

基于改进YOLOv5木板材表面缺陷的定量识别

【目的】为解决人工及传统数字图像处理方法对木板材表面缺陷识别效果差、效率低等问题,提高木材利用率。以深度学习模型为基础,构建木板材表面缺陷检测系统,旨在拓展深度学习模型在木板材缺陷检测领域的应用。【方法】基于“Wood Defect Database”公开数据集中的839张木板材缺陷图像,使用Imgaug数据增强库对数据集进行扩充;通过在主干特征网络部分引入SE注意力机制,使用focus、FPN+PAN结构构建YOLOv5木板材表面缺陷目标检测框架,进而采用迁移学习思想改进训练方式,将训练过程分为两个阶段(冻结阶段和解冻阶段)。然后将构建的模型与当前主流深度学习目标检测模型进行对比,最后利用混淆矩阵、Loss值变化曲线、模型大小、检测时间以及均值平均精确率等指标评价模型。【结果】提出了一种基于YOLOv5模型对木板材表面缺陷中活节、死节、裂缝、孔洞的检测方法。模型对死节、活节、裂缝、孔洞识别结果的均值平均精确率分别约为98.66%、99.06%、98.10%和96.53%,并与当前主流检测模型进行比较,改进的模型具有更好的精确率、召回率和均值平均精确率,分别为97.48%、96.53%和98.22%。模型单幅图像平均检测时间为10.3 ms,最大检测耗时20.5 ms,检测效果与泛化特性较好,模型所占内存仅13.7 MB,易于移植。【结论】实验表明改进的YOLOv5模型可用于检测木板材表面主要缺陷。且模型对木板材表面缺陷的识别效果优于其他5种主流检测模型。在维持原有检测精度的基础上,提高了小目标缺陷的识别能力,减少了木板材缺陷漏检的情况,实现了在复杂场景下的快速检测。

多源信息融合的设备热缺陷智能实时检测方法

针对现有算法对多类变电站设备热缺陷诊断效率不高,难以满足边缘端实时检测需求等问题,提出了一种基于多源信息融合的设备热缺陷智能实时检测方法。首先利用粒子群优化的SIFT描述子实现多源图像的配准。其次提出改进的YOLOv4算法实现设备检测,将特征提取网络CspDarket53替换为轻量级网络GhostNet,并将特征融合模块的普通卷积层替换为深度可分离卷积,使模型轻量化;将三尺度检测扩充为四尺度,加强对遮挡目标的检测;在特征融合部分嵌入ASFF模块,提升设备检测精度。最后完成设备的热缺陷诊断以及缺陷等级判定。实验结果表明,文中方法达到93.56%的mAP值,推理速度达到35FPS,可用于变电站设备热缺陷的实时监测。

基于改进Itti显著模型的织物疵点实时检测

为克服人工疵点检测存在精度差、效率低、易疲劳等问题,研发了基于改进Itti显著模型的织物疵点实时检测系统。首先设计了专用的织物传动和退绕系统,实现对布卷的精确传递,采用不同光源和多台工业相机拍摄实现织物的实时采集;然后通过基于改进的Itti显著性模型对图像进行快速检测,利用下采样构建图像金字塔,并对金字塔图像进行中央周边差操作,获得织物亮度特征;接着对各尺度金字塔图像进行不同方向Gabor滤波边缘检测获得织物方向特征,归一化亮度与方向特征获得织物疵点显著图;最后通过自定义阈值对显著图进行分割。实验结果表明:本文系统能有效检测出白坯布、牛仔布含有的油污、断经、破洞、纬缩等常见织物疵点,疵点检测正检率为93%,实时检测速度最高达48 m/min,能满足实时检测需求。