High-precision automatic measurement of two-dimensional geometric features based on machine vision

To realize high-precision automatic measurement of two-dimensional geometric features on parts, a cooperative measurement system based on machine vision is constructed. Its hardware structure, functional composition and working principle are introduced. The mapping relationship between the feature image coordinates and the measuring space coordinates is established. The method of measuring path planning of small field of view （FOV） images is proposed. With the cooperation of the panoramic image of the object to be measured, the small FOV images with high object plane resolution are acquired automatically. Then, the auxiliary measuring characteristics are constructed and the parameters of the features to be measured are automatically extracted. Experimental results show that the absolute value of relative error is less than 0. 03% when applying the cooperative measurement system to gauge the hole distance of 100 mm nominal size. When the object plane resolving power of the small FOV images is 16 times that of the large FOV image, the measurement accuracy of small FOV images is improved by 14 times compared with the large FOV image. It is suitable for high-precision automatic measurement of two-dimensional complex geometric features distributed on large scale parts.

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

Evaluation of echo features of ultrasonic flaws and its intelligent pattern recognition

In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted from each echo samples. A method which is based on the xtatislical hypothesis testing and used for feature evaluation and optimum subset selection was explored. Thus, the dimensionality reduction of feature space was brought out, and simultaneously the amount of calculation was decreased. An intelligent pattern classifier with B-P type neural network was constructed which was characterized by high speed and accuracy for learning. Using a half of total samples as training set and others as testing set, the learning efficiency and the classification ability of network model were studied. The results of experiment showed that the learning rate of different training samples was about 100%. The results of recognition was satisfactory when the optimum feature subset was taken as the sample's feature vectors. The average recognition rate of three type flaws was about 87.6%, and the best recognition rate amounted to 97%.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

Surface Topography of Fine-grained ZrO_2 Ceramic by Two-dimensional Ultrasonic Vibration Grinding

The surface quality of fine-grained ZrO2 engineering ceramic were researched using 270# diamond wheel both with and without work-piece two-dimension ultrasonic vibration grinding（WTDUVG）. By AFM images, the surface topography and the micro structure of the two-dimensional ultrasonic vibration grinding ceramics were especially analyzed. The experimental results indicate that the surface roughness is related to grinding vibration mode and the material removal mechanism. Surface quality of WTDUVG is superior to that of conventional grinding, and it is easy for two-dimensional ultrasonic vibration grinding that material removal mechanism is ductile mode grinding.

Ultrasonic Measurement of Two-Dimensional Liquid Velocity Profile Using Two-Element Transducer

The flow field or multidimensional velocity distribution of the coolant in fuel rod bundles of the reactor core in pressurized water reactors (PWRs) is an important parameter that is revealed through experimental investigations. This paper presents the two-dimensional (2D) velocity profile measurement using a two-element ultrasonic transducer with both elements acting as a transceiver. The size of the transducer is minimized for compactness, leading to a narrow sound field appropriate for applications in fuel rod bundle flow. Furthermore, the transducer’s sound pressure is evaluated via simulations and experimental measurements. In order to confirm the ability of the ultrasonic velocity profiler (UVP) with a two-element transducer, the experimental measurement is conducted in turbulent horizontal pipe flow. The 2D velocity vector profile is obtained, and then the measurement in swirling flow is conducted. The 2D velocity profile in an axial and radial plane is obtained utilizing the UVP measurement. Lastly, the ability of the UVP to derive the 2D velocity profile in the narrow area of the rod bundles is demonstrated.

Improving Ultrasonic Testing by Using Machine Learning Framework Based on Model Interpretation Strategy

Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying damage.Extracting signifcant features from UT data is essential for efcient defect characterization.Moreover,the hidden physics behind ML is unexplained,reducing the generalization capability and versatility of ML methods in UT.In this paper,a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efciency of UT.Firstly,multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space.Subsequently,a feature selection method based on model interpretable strategy(FS-MIS)is innovatively developed by integrating Shapley additive explanation(SHAP),flter method,embedded method and wrapper method.The most efective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively.The proposed framework is validated by identifying and locating side-drilled holes(SDHs)with 0.5λcentral distance and different depths.An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments.The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival(ToAs)of the scattered waves emitted by adjacent SDHs.The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67%with an average error of 0.25%,signifcantly improving the time resolution of UT signals.On this basis,the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets.The imaging resolution is enhanced to 0.5λby implementing the total focusing method(TFM).The relative errors of hole depths and central distance are no more than 0.51%and 3.57%,respectively.Finally,the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques.

FEATURE EXTRACTION OF BONES AND SKIN BASED ON ULTRASONIC SCANNING

In the prosthetic socket design, aimed at the high cost and radiation deficiency caused by CT scanning which is a routine technique to obtain the cross-sectional image of the residual limb, a new ultrasonic scanning method is developed to acquire the bones and skin contours of the residual limb. Using a pig fore-leg as the scanning object, an overlapping algorithm is designed to reconstruct the 2D cross-sectional image, the contours of the bone and skin are extracted using edge detection algorithm and the 3D model of the pig fore-leg is reconstructed by using reverse engineering technology. The results of checking the accuracy of the image by scanning a cylinder work pieces show that the extracted contours of the cylinder are quite close to the standard circumference. So it is feasible to get the contours of bones and skin by ultrasonic scanning. The ultrasonic scanning system featuring no radiation and low cost is a kind of new means of cross section scanning for medical images.

Ultrasonic TOFD testing for aluminum alloy weld of thick plate

The ultrasonic time of flight diffraction (TOFD) testing method for aluminum alloy weld of thick plate was introduced, and the basic defect image features of crack in shape at different positions A, B, C were discussed. The TOFD testing for weld joints was carried out. The results show that the TOFD method has a good measurement accuracy and a good ability of finding the defect of crack in shape.

Experimental Investigation of Two-Dimensional Velocity on the 90°Double Bend Pipe Flow Using Ultrasound Technique

An experimental investigation was performed to investigate two-dimensional axial velocity field at downstream of the 90°double bend pipe with and without inlet swirling condition. The main objectives are to find separation region and observe the influence of inlet swirling flow on the velocity fluctuation using ultrasound technique. The experiments were carried out in the pipe at Reynolds number Re = 1 × 104. In case of inlet swirling flow condition, a rotary swirler was used as swirling generator, and the swirl number was setup S = 1. The ultrasonic measurements were taken at four downstream locations of the second bend pipe. Phased Array Ultrasonic Velocity Profiler (Phased Array UVP) technique was applied to obtain the two-dimensional velocity of the fluid and the axial and tangential velocity fluctuation. It was found that the secondary reverse flow became smaller at the downstream from the bend when the inlet condition on the first bend was swirling flow. In addition, inlet swirling condition influenced mainly on the tangential velocity fluctuation, and its maximum turbulence intensity was 40%.

Development of an Ultrasonic Nomogram for Preoperative Prediction of Castleman Disease Pathological Type

An ultrasonic nomogram was developed for preoperative prediction of Castleman disease(CD)pathological type(hyaline vascular(HV)or plasma cell(PC)variant)to improve the understanding and diagnostic accuracy of ultrasound for this disease.Fifty cases of CD confirmed by pathology were gathered from January 2012 to October 2018 from three hospitals.A grayscale ultrasound image of each patient was collected and processed.First,the region of interest of each gray ultrasound image was manually segmented using a process that was guided and calibrated by radiologists who have been engaged in imaging diagnosis for more than 5 years.In addition,the clinical characteristics and other ultrasonic features extracted from the color Doppler and spectral Doppler ultrasound images were also selected.Second,the chi-square test was used to select and reduce features.Third,a naïve Bayesian model was used as a classifier.Last,clinical cases with gray ultrasound image datasets from the hospital were used to test the performance of our proposed method.Among these patients,31 patients(18 patients with HV and 13 patients with PC)were used to build a training set for the predictive model and 19(11 patients with HV and 8 patients with PC)were used for the test set.From the set,584 high-throughput and quantitative image features,such as mass shape size,intensity,texture characteristics,and wavelet characteristics,were extracted,and then 152 images features were selected.Comparing the radiomics classification results with the pathological results,the accuracy rate,sensitivity,and specificity were 84.2%,90.1%,and 87.5%,respectively.The experimental results show that radiomics was valuable for the differentiation of CD pathological type.

老年乳腺癌超声影像特征与分子分型的相关性研究

目的 分析老年乳腺癌不同分子分型的超声影像特征。方法 回顾性分析行乳腺超声检查并经手术病理证实的117例老年乳腺癌病人的临床资料,根据免疫组化结果,确定肿瘤分子分型。观察并比较不同分子分型老年乳腺癌的病理特点、组织学分级和超声影像特征。结果 117例病人中,Luminal A型70例,Luminal B型14例,HER-2阳性12例,三阴型21例,不同分子分型病人的病理类型均以浸润性导管癌人数最多。不同分子分型浸润性导管癌病人的组织学分级比较,差异有统计学意义(χ^(2)=19.700,P<0.001)。根据老年乳腺癌的超声检查结果,肿瘤的形态、边缘、内部钙化、后方特征及腋窝异常淋巴结情况在不同分子分型间差异均有统计学意义(P<0.05);肿瘤的最大径线、纵横比、内部回声及血流情况在不同分子分型间差异无统计学意义(P>0.05)。结论 老年乳腺癌的分子分型与超声影像表现存在一定的相关性,通过观察其影像结果可以辅助临床医生判断老年乳腺癌的分子分型。

基于ELM的超声多特征融合螺栓应力测量方法

针对传统超声波螺栓应力测量中存在的非线性和不适定性问题,提出一种基于极限学习机(ELM)的超声多特征融合螺栓应力测量方法。首先基于声弹性理论和散射理论,根据超声回波信号提取声时差及瑞利散射范围内多晶体材料中纵波的衰减系数等超声波特征参数。然后通过向量降维选择声时差、衰减系数和有效受力长度作为模型输入特征向量,建立了基于ELM的超声多特征融合螺栓应力测量模型。搭建螺栓轴向应力超声波测量实验平台,对不同材料和规格的螺栓进行螺栓应力的测量,并对比了使用传统的超声测量方法的测量结果,验证了传统超声检测方法的局限性。对比了ELM与其他机器学习方法包括BP、支持向量回归(SVR)的测量结果和精度。结果表明,提出的方法有效克服了传统超声测量方法的不足,能实现不同材料不同规格的螺栓应力测量,并且测量精度更高(平均相对误差为3.86%),泛化能力更好。

胎儿左无名静脉异常超声诊断线索及临床意义

目的探讨胎儿左无名静脉异常的超声诊断线索及临床意义。方法回顾性分析2012年1月至2023年4月于广东省妇幼保健院超声诊断胎儿左无名静脉异常的类型,总结超声诊断线索。结果①10例胎儿左无名静脉异常(left brachio-cephalic vein,LBCV)中3例(30%)主动脉弓下LBCV,2例(20%)食管后LBCV,1例(10%)胸腺内LBCV,4例(40%)左无名静脉增宽。②3例主动脉弓下LBCV中1例伴巨细胞病毒感染,引产;2例食管后LBCV中1例合并复杂畸形,引产;1例胸腺内LBCV未合并其他结构异常;4例无名静脉增宽中2例合并血管畸形,2例合并轻微异常。③8例胎儿出生,情况良好。结论不同类型异常胎儿左无名静脉具有特征性的超声征象,产前区分无名静脉异常类型具有重要的临床意义。

乳腺癌超声图像特征与P53、PIK3CA蛋白表达状态相关性的研究

目的通过观测乳腺癌的超声图像特征,分析其与P53、PIK3CA蛋白表达的相关性。方法回顾性分析2022年1月—2023年10月本院经病理证实为乳腺癌的101例女性患者的临床资料,在获取病理前均行超声检查。二维超声用于观察乳腺癌大小、形态、边界、纵横比、后方回声以及有无微钙化,彩色多普勒超声用于观察病灶内部以及周边的血流情况。采用SP免疫组化法检测P53、PIK3CA蛋白的表达情况,分析其与超声图像特征的相关性。结果101例乳腺癌患者的临床病理资料显示,P53、PIK3CA的蛋白表达情况与乳腺癌病理亚型均具有相关性(χ^(2)=9.259、P=0.026,χ^(2)=7.927,P=0.048)。在超声图像特征中,血流强度、病灶后方回声衰减与P53蛋白表达具有相关性;病灶最长直径、病灶形态与PIK3CA蛋白表达具有相关性。P53蛋白表达阳性患者病灶血流信号丰富组(χ^(2)=8.191,P=0.004)和存在后方回声衰减组(χ^(2)=11.279、P<0.001)均高于阴性者,PIK3CA蛋白表达阳性患者声像图中的最长直径小于阴性者(t=2.132,P=0.036),而PIK3CA蛋白表达阳性患者病灶形态不规则比例(χ^(2)=4.551、P<0.033)高于阴性者。多因素Logistic回归表明,富血供是P53蛋白表达的影响因素,OR值为2.35[95%CI(1.11,5.74)];病灶形态不规则是PIK3CA蛋白表达的影响因素,OR值为4.58[95%CI(1.35,18.48)]。结论乳腺癌超声图像特征与P53、PIK3CA蛋白表达具有相关性,可以为临床诊疗乳腺癌提供重要的价值。

基于激光超声的玉米虫蚀粒检测研究

虫蚀影响玉米的外观质量和营养价值,研究采用了一种基于激光超声的玉米虫蚀粒检测方法对玉米虫蚀粒进行检测。首先,利用脉冲激光照射完善粒和虫蚀粒玉米的表面产生激光超声信号。然后,提取了超声信号时域峰值因子和脉冲因子,频域重心频率和均方频率,Hilbert域高频能量作为特征参数。最后,将这5个特征分别作为BP神经网络和粒子群优化支持向量机(PSO-SVM)算法的输入对虫蚀粒和完善粒进行了分类识别。实验结果表明,PSO-SVM算法建立的分类模型对玉米完善粒和虫蚀粒的分类识别更准确,训练集和测试集准确率分别为99.72%和98.33%,所采用的方法是可行的。

基于峰值比拟合匹配的时差式气体超声波流量计

时差式气体超声波流量计的回波信号在气体中传播时能量衰减严重,且存在着不同程度的幅值晃动问题。因此如何在幅值晃动的气体回波信号中确定特征点,以准确测量回波信号渡越时间及其顺、逆流渡越时间差,是当前时差式气体超声波流量计信号处理方法的主要研究热点。该文基于超声回波信号的数学模型,研究构建了一种基于峰值比拟合匹配的信号处理方法,利用拟合指标来筛选符合匹配条件的波形。并且通过实际数据验证了该方法的有效性和优越性。

超声波辅助低共熔溶剂提取对咖啡蛋白结构和功能特性的影响

为探索咖啡蛋白在食品加工中的应用,以脱脂生咖啡粉为原料,探究不同超声波功率(0、200、400、600、800 W)与低共熔溶剂法、碱法协同作用对咖啡蛋白组成成分(聚丙烯酰胺凝胶电泳,SDS-PAGE)、结构特性(粒径分布、傅里叶红外光谱、圆二色光谱、荧光光谱和扫描电镜)和功能特性(溶解度、吸水性、吸油性和乳化特性)的影响。结果表明:由低共熔溶剂提取的咖啡蛋白(DES)平均粒径(12.30μm)显著小于由碱法提取的咖啡蛋白(AEP)平均粒径(113.67μm);DES在酸性条件下溶解度更高,AEP在碱性条件下溶解度更高;DES比AEP有更好的吸水性和吸油性。经超声预处理后,咖啡蛋白电泳图谱、傅里叶红外光谱、圆二色光谱和荧光光谱结果分析表明,不同超声波功率预处理未引起蛋白质分子量分布的重大变化,但是对蛋白质的二级结构和三级结构产生影响。表面微观结构表明,DES表面结构较疏松多孔,AEP表面紧密平坦,超声预处理后AEP微观结构变得疏松多孔,纹理变得分散,并呈现出更多的不规则碎片;DES表面发生略微变化,在400 W和600 W处理15 min时表现出更多小颗粒多孔无序结构,微观结构变化可影响蛋白质的化学和物理性质及其功能特性。在超声功率为400 W时,DES粒径显著减小,粒径参数D[3,2]和D[4,3]分别从10.50、30.43μm减小到7.89、11.27μm;溶解度显著提高,在酸性、碱性条件下溶解度分别由47%、71%上升到75%、81%。此外,超声预处理可提高咖啡蛋白的吸水性和乳化活性,这些均为蛋白质在食品应用中的重要功能特性。研究表明采用超声波技术对咖啡蛋白进行改性有效果,研究结果可为咖啡蛋白在食品加工中的应用提供理论依据。

基于SHAP可解释性的焊缝缺陷类型超声识别XGBoost模型

针对焊缝缺陷机器学习超声识别过程中存在特征冗余、可解释性差等问题,提出了一种基于SHAP可解释性的焊缝缺陷超声识别XGBoost(极限梯度提升)模型。在碳钢焊缝试样上加工4类典型缺陷,采用横波斜入射法采集超声反射回波信号,分别提取16个时域特征、16个频域特征以及3个信息熵特征。计算SHAP值并选择其前8个高贡献特征构建特征子集,利用交叉验证和网格搜索优化XGBoost模型进行缺陷识别。试验结果表明,4种缺陷识别的平均准确率为96.7%;其中,横通孔的识别效果最佳,精确率、召回率和F_(1-score)均达到100%,三角槽次之,方形槽略差,矩形槽的识别结果最差,其精确率、召回率和F_(1-score)均为93.3%。最后,讨论了高贡献特征与缺陷类别之间的相关性,并对特征贡献差异及其原因进行了分析。

天然气超声流量计健康状态评价方法初探

目的天然气计量用超声流量计性能诊断主要采用设定参数阈值的诊断方法,为降低误报、漏报等情况,建立了基于生成对抗神经网络(generated adjoint neural network,GAN)和高维非线性无监督学习的超声流量计健康状态系统诊断方法。方法采用GAN对原始数据进行学习、生成和扩充,保障超声流量计健康状态诊断建模的数据基础,提取超声流量计在运行过程中的健康状态参数并对其进行时序分析,采用高维非线性无监督聚类学习方法,结合超声流量计失效模式分析,对超声流量计设备进行在线的健康状态诊断。结果结合超声流量计工作原理和现场实际采集数据,对生成的故障数据集进行了验证。结论能够准确识别超声流量计当前状态,显著解决传统阈值法误报率、漏报率高的问题,为超声流量计健康诊断的统一管理与开发给予支撑。