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.

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.

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.

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.

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.

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.

Research on Privacy Disclosure Detection Method in Social Networks Based on Multi-Dimensional Deep Learning

In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users,this paper takes microblog as the research object to study the detection of privacy disclosure in social networks.First,we perform fast privacy leak detection on the currently published text based on the fastText model.In the case that the text to be published contains certain private information,we fully consider the aggregation effect of the private information leaked by different channels,and establish a convolution neural network model based on multi-dimensional features(MF-CNN)to detect privacy disclosure comprehensively and accurately.The experimental results show that the proposed method has a higher accuracy of privacy disclosure detection and can meet the real-time requirements of detection.

Multi-dimensional and Multi-threshold Airframe Damage Region Division Method Based on Correlation Optimization

In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.

Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor

Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.

Enhanced Answer Selection in CQA Using Multi-Dimensional Features Combination

Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects,and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multidimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks:(a) classifying answers as good, bad, or potentially associated with a question, and(b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.

Event-based Two-stage Non-intrusive Load Monitoring Method Involving Multi-dimensional Features

This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.

Feature Extraction of Sectorial Scan Image of Thick-Walled Electron Beam Welding Seam Based on Principal Component Analysis

A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in defect recognition. Seven features were extracted from the image and represented 87. 3% information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore,crack,lack of fusion and lack of penetration are 93%,90.7%,94.7% and 89.3%,respectively,which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data.

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。

SPOC教学模式联合CBL教学法在超声诊断学教学中的应用研究

目的研究和分析在超声诊断学教学中应用小规模限制性在线课程(small private online course,SPOC)教学模式联合案例教学法(case-based learning,CBL)的教学效果。方法将200名于2020年1—10月在齐齐哈尔医学院附属第二医院进行超声影像实习的学生纳入至对照组,将189名于2021年1月—2021年10月在齐齐哈尔医学院附属第二医院进行超声影像实习的学生纳入至观察组,对照组学生所用教学方法为传统教学模式,观察组学生所用教学方法为SPOC教学模式联合CBL教学法,调查和对比2组教学效果,对比2组学生基础知识、实践技能、临床思维成绩以及2组学生教学评价。结果观察组的教学效果优于对照组,差异有统计学意义(P<0.001)。观察组理论知识成绩为(87.47±2.41)分、实践技能成绩为(85.56±2.15)分、临床思维成绩为(88.63±5.82)分,均高于对照组的(82.52±2.57)分、(82.01±1.74)分和(81.64±5.87)分,差异有统计学意义(P<0.001)。观察组学生完整描述超声图特征及部分描述超声图特征占比高于对照组学生,差异均有统计学意义(P<0.05)。结论将SPOC教学模式联合CBL教学法应用于超声诊断学教学过程中能够有效激发学生的学习兴趣和学习积极性,可丰富其理论知识、强化其操作技能并有助于培养和增强学生的临床思维能力,能够使教学效果得到提高,可准确描述超声图特征,对于提升教学质量和教学水平有重要意义。

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。