Effective separation of coal gasification fine slag: Role of classification and ultrasonication in enhancing flotation

Effective separation of residual carbon and ash is the basis for the resource utilization of coal gasification fine slag(CGFS).The conventional flotation process of CGFS has the bottlenecks of low carbon recovery and high collector dosage.In order to address these issues,CGFS sample taken from Shaanxi,China was used as the study object in this paper.A new process of size classification-fine grain ultrasonic pretreatment flotation(SC-FGUF)was proposed and its separation effect was compared with that of wholegrain flotation(WGF)as well as size classification-fine grain flotation(SC-FGF).The mechanism of its enhanced separation effect was revealed through flotation kinetic fitting,flotation flow foam layer stability,particle size composition,surface morphology,pore structure,and surface chemical property analysis.The results showed that compared with WGF,pre-classification could reduce the collector dosage by 84.09%and the combination of pre-classification and ultrasonic pretreatment could increase the combustible recovery by 17.29%and up to 93.46%.The SC-FGUF process allows the ineffective adsorption of coarse residual carbon to collector during flotation stage to be reduced by pre-classification,and the tightly embedded state of fine CGFS particles is disrupted and surface oxidizing functional group occupancy was reduced by ultrasonic pretreatment,thus carbon and ash is easier to be separated in the flotation process.In addition,some of the residual carbon particles were broken down to smaller sizes in the ultrasonic pretreatment,which led to an increase in the stability of flotation flow foam layer and a decrease in the probability of detachment of residual carbon particles from the bubbles.Therefore,SCFGUF could increase the residual carbon recovery and reduce the flotation collector dosage,which is an innovative method for carbon-ash separation of CGFS with good application prospect.

Ultrasonic signal classification based on ambiguity plane feature

Ambiguity function （AF） is proposed to represent ultrasonic signal to resolve the preprocessing problem of different center frequencies and different arriving times among ultrasonic signals for feature extraction, as well as offer time-frequency features for signal classification. Moreover, Karhunen-Loeve （K-L） transform is considered to extract signal features from ambiguity plane, and then the features are presented to probabilistic neural network （PNN） for signal classification. Experimental results show that ambiguity function eliminates the difference of center frequency and arriving time existing in ultrasonic signals, and ambiguity plane features extracted by K-L transform describe the signal of different classes effectively in a reduced dimensional space. Classification result suggests that the ambiguity plane features obtain better performance than the features extracted by wavelet transform （WT）.

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.

Ultrasonic vocalizations in mice: relevance for ethologic and neurodevelopmental disorders studies

Mice use ultrasonic vocalizations(USVs)to communicate each other and to convey their emotional state.USVs have been greatly characterized in specific life phases and contexts,such as mother isolation-induced USVs for pups or female-induced USVs for male mice during courtship.USVs can be acquired by means of specific tools and later analyzed on the base of both quantitative and qualitative parameters.Indeed,different ultrasonic call categories exist and have already been defined.The understanding of different calls meaning is still missing,and it will represent an essential step forward in the field of USVs.They have long been studied in the ethological context,but recently they emerged as a precious instrument to study pathologies characterized by deficits in communication,in particular neurodevelopmental disorders(NDDs),such as autism spectrum disorders.This review covers the topics of USVs characteristics in mice,contexts for USVs emission and factors that modulate their expression.A particular focus will be devoted to mouse USVs in the context of NDDs.Indeed,several NDDs murine models exist and an intense study of USVs is currently in progress,with the aim of both performing an early diagnosis and to find a pharmacological/behavioral intervention to improve patients’quality of life.

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.

ACR TI-RADS分类联合实时剪切波弹性成像对甲状腺微小结节的诊断价值

目的探讨美国放射学会甲状腺影像报告和数据系统(ACR TI-RADS)分类联合实时剪切波弹性成像(RT-SWE)对甲状腺微小结节的诊断价值。方法对201个甲状腺微小结节行RT-SWE,以病理结果为金标准,绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC),得出RT-SWE诊断甲状腺微小结节良恶性的最佳阈值。比较ACR TI-RADS分类、RT-SWE以及两者联合诊断甲状腺微小结节的灵敏度、特异度、准确度,分析RTSWE联合ACR TI-RADS对不同大小的甲状腺微小结节临床诊断价值。结果RT-SWE选取Emax=27.6 kPa为最佳阈值,ACR TI-RADS联合RT-SWE诊断甲状腺微小结节良恶性的灵敏度、特异度、准确度分别为91.91%、89.23%、91.04%,其AUC为0.92,明显大于ACR TI-RADS分类(0.80)、RT-SWE(0.84)单独诊断,差异均有统计学意义(Z分别=2.55、2.29,P均<0.05)。两者联合应用对0.5~1.0 cm组甲状腺微小结节诊断的灵敏度、准确度明显高于≤0.5 cm组,差异均有统计学意义(χ2分别=4.73、3.97,P均<0.05)。结论ACR TI-RADS分类联合RT-SWE可进一步提高甲状腺微小结节的诊断水平。

Classification of flaw by adaptive filtering deconvolution of ultrasonic backscattering echo

A convolution model of flaw scattering echoes and an adaptive filtering deconvolution method are presented. The effect of the method is analyzed by simulating a given system. By deconvolution, the influence of the transducer on echoes is reduced greatly and the flaw features stand out more clearly in the deconvolved echoes than in flaw echoes themselves. flaw echo signals of 18 flaw samples are processed by adaptive filtering deconvolution. As a result, flaws are classified successfully

超声参数与瘢痕妊娠分型的关联性及诊断价值研究

目的 探讨超声参数与瘢痕妊娠分型的关联性及诊断价值。方法 选取2021年10月~2022年12月河北省衡水市第二人民医院73例CSP患者设为研究组,另选取同期正常妊娠女性73例设为对照组。两组受检者均接受TVS检查,统计研究组与对照组、研究组不同CSP分型患者超声检查参数[瘢痕肌层厚度(CSM)、子宫下段前壁血流阻力指数(RI)、最小矢状肌厚度],分析超声检查参数与CSP分型的关联性,并比较各参数对CSP的单独及联合诊断效能。结果 研究组CSM、RI、最小矢状肌厚度低于对照组(P<0.05)。不同CSP患者CSM、RI、最小矢状肌厚度间存在显著差异(P<0.05);多重比较,Ⅱ型CSP患者CSM、RI、最小矢状肌厚度低于Ⅰ型CSP患者,Ⅲ型CSP患者CSM、RI、最小矢状肌厚度低于Ⅱ型CSP患者(P<0.05)。CSM、RI、最小矢状肌厚度与CSP疾病分型存在显著负相关关系(P<0.05)。CSM、RI及最小矢状肌厚度联合诊断CSP的AUC为0.719%,大于CSM、RI及最小矢状肌厚度单独诊断的0.706、0.660、0.625。结论 CSP经超声检查可见明显异常,不同CSP分型患者各检查参数存在显著差异,两者具有紧密相关性,临床可通过超声检查对疾病进行诊断评估。

火炮摇架焊缝缺陷智能分类

针对火炮摇架结构复杂,焊缝内部缺陷检测效果不理想,选择对非平面物体和复杂结构体均适用的超声相控阵检测技术对摇架焊缝缺陷进行检测。将得到的超声相控阵图谱与ResNeXt网络模型相结合,实现焊缝缺陷的智能分类。将SK卷积单元引入ResNeXt网络模型,对摇架焊缝缺陷进行定性分析。改进后的网络模型比原ResNeXt网络的分类准确率提升5.5%,最终达到98.2%。

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。

马达加斯加鳞片石墨精矿提纯

这是一篇矿物加工工程领域的论文。实验以马达加斯加某地鳞片石墨浮选精矿为原料进行氢氟酸法提纯。提纯原料品位为94.96%,通过单因素条件实验的探究,确立了盐酸与氢氟酸组成的混酸与石墨液固比为3.5 mL/g,HF体积分数40%,60℃水浴反应8 h可将石墨品位提纯至99.98%的提纯工艺。针对细粒级石墨提纯效果差的问题,提纯前对石墨进行筛分分级。对细粒级石墨进行超声波震荡预处理,超声波震荡60 min后进行酸浸提纯。提纯后细粒级石墨固定碳含量≥99.92%。

随机森林算法在超声缺陷识别中的应用研究

超声波检测是一种常见的钢材缺陷检测方法,通过机器学习算法建立分类模型能够实现有效的缺陷识别。神经网络是目前最常采用的一种算法,但存在模型结构复杂且需要大量训练数据的问题。对此,提出一种基于随机森林的超声缺陷识别方法,能够实现对缺陷类型的智能、准确识别,以解决模型结构复杂和训练数据需求大的问题。首先对方体试件中的不同形状、尺寸和深度的缺陷进行超声检测实验,基于实验数据利用随机森林算法建立超声缺陷识别模型;进而对模型的缺陷识别效果进行分析,并与支持向量机、K-近邻分类算法、AdaBoosting算法和卷积神经网络比对分析缺陷识别效果;然后利用验证试件进行缺陷识别验证实验,以进一步验证所建立缺陷识别模型的有效性。结果表明,所提缺陷识别方法相比其他算法具有最高的准确率,验证实验中缺陷分类准确率达到94.6%。

基于ResNet与分离注意力机制的肺部超声图像分类系统设计

目的为解决传统的深度学习模型在处理具有多样性图像质量和微妙病变区域差异的肺部超声图像方面性能不佳的问题,设计一种基于残差网络(Residual Network,ResNet)与分离注意力机制的肺部超声图像分类系统。方法采用ResNet152作为基础模型,结合分离注意力机制,通过对肺部超声图像进行预处理、数据增强和标准化处理,以提高模型的特征提取和分类能力。模型首先通过ResNet152进行深度特征提取,随后在各层引入分离注意力机制,增强模型对重要图像特征的关注,从而提高分类性能。结果实验结果表明,优化后模型与原始模型相比,分类准确度在A线、B线、胸腔积液和肺实变上分别提升了0.51%、0.95%、14.17%和6.29%。通过消融实验,当同时使用Mish函数和分离注意力机制时,混合模型达到了97.92%的准确度。结论本文提出的融合ResNet与分离注意力机制的肺部超声图像分类系统模型可为临床超声诊断提供较高的参考价值。

常规超声及UE联合DBT对乳腺肿块良恶性诊断鉴别的准确性分析

目的 分析常规超声、超声弹性成像(UE)与数字乳腺断层合成(DBT)联合应用对乳腺肿块的定性诊断价值。方法 选择2022年1月至2023年12月152例乳腺肿块女性患者,年龄22~65岁,平均年龄44.12岁;身体质量指数18~26 kg/m^(2),平均身体质量指数22.45 kg/m^(2);肿块位置:左侧100例,右侧52例;肿块直径0.5~4.7cm,平均直径2.33 cm。均行常规超声、UE和DBT检查。以手术病理诊断为金标准,对比常规超声、UE和DBT单独使用与联合应用的定性诊断价值;对比良恶性乳腺肿块患者常规超声参数、UE参数和DBT参数。结果 手术病理诊断显示,152例均为单发性结节患者,共有152个肿块,84个为恶性,68个为良性。常规超声、UE和DBT单独使用对乳腺肿块良恶性的诊断效能对比,差异无统计学意义(P> 0.05);联合诊断灵敏度更高(95.24%vs 71.43%、76.19%、73.81%),对比单一诊断差异有统计学意义(P <0.05)。恶性乳腺肿块弹性应变率、肿块直径、直径变化率、肿块面积、面积比、边缘清晰占比低于良性(2.92%±0.54%vs 6.06%±1.72%、13.56 mm±3.32 mm vs 19.78 mm±3.72 mm、0.22%±0.05%vs 0.39%±0.12%、8.68 cm2±1.72 cm2vs 16.54 cm2±3.18 cm^(2)、1.21%±0.24%vs 1.92%±0.36%、4.76%vs 73.53%),UE评分、血流信号等级、边缘模糊、遮蔽、毛刺、肿块结构扭曲、血液运行增加和血管穿入占比明显高于良性,组间差异有显著统计学意义(P <0.01)。结论常规超声、UE与DBT联合应用,可准确诊断乳腺肿块良恶性,可将影像学征象作为评估依据,有应用价值。

细粒煤分级溢流颗粒粒度在线检测研究

对细粒煤分选中分级溢流颗粒粒度进行实时在线检测,进而调控分级参数,可减少溢流中粗颗粒含量,提高总精煤回收率。现有研究对溢流颗粒粒度的检测上限普遍在180μm左右,矿浆体积浓度上限为10%,无法满足粒度较粗、粒级较宽且体积浓度较高的细粒煤分级旋流器溢流颗粒粒度检测要求。为提高煤颗粒粒度和矿浆体积浓度检测上限,开发了一套超声波在线颗粒粒度检测系统。基于超声波声衰减模型,构建了适用于煤颗粒粒度为44.5~600μm、矿浆体积浓度为0~40%的细粒煤分级现场工况的煤颗粒粒度检测模型。采用粒子群优化算法优化的BP神经网络建立了煤颗粒粒度分布预测模型,实现对细粒煤分级旋流器溢流矿浆粒度分布预测。基于煤颗粒粒度检测模型的模拟结果表明,超声波衰减值随煤颗粒粒度增大而先减小后增大,随超声波频率和矿浆体积浓度增大而增大。分别使用超声波在线颗粒粒度检测系统和煤颗粒粒度分布预测模型对某矿水力分级旋流器溢流颗粒粒度(实际值为150.0,215.0,315.0μm)分布进行检测,结果表明检测系统测量值相对误差为10.87%,9.81%,8.48%,预测模型的预测值相对误差为9.27%,6.05%,6.92%,均实现了细粒煤分级溢流颗粒粒度的准确检测。

配网超声波带电检测技术及缺陷分类识别方法

在对配网进行带电检测的环节中,常规的方法对于开关柜等复杂设备的处理效果不佳,因此,提出配网超声波带电检测技术及缺陷分类识别方法。首先,计算超声波带电检测的均值,进一步构造音频中循环放电处的特征向量,分析音频数据的重复性特征,完成配网缺陷基础信息数据的处理,提取数据特征。然后,在此基础上,建立缺陷分类数据库,构建仿真模型,调整训练规则,消除二次回路缺陷信号中的噪声。最后,通过最优超平面即可实现缺陷分类识别。结果表明,该方法的精确度为98.7%,在各样本上的表现都较为稳定,可以提升配网超声波技术的检测效果。

高压组合电器铁心类设备超声波和振动信号的分类和识别

本文介绍流域内水电厂高压组合电器的超声波和振动现象,提出通过分类和识别对高压组合电器铁心类设备超声波和振动信号进行研究,结合实际检测案例,为高压组合电器带电检测提供一些参考素材。

超声弹性成像联合量化分级系统对糖尿病前期合并甲状腺结节良恶性鉴别诊断的价值

目的:研究超声弹性成像(UE)联合量化分级系统对糖尿病前期合并良恶性甲状腺结节(TN)患者的综合诊断价值及胰岛素抵抗程度与UE和量化分级之间的关系。方法:选取2015年3月至2017年3月于我院就诊的糖尿病前期合并TN患者190例。所有患者均依次进行UE以及超声量化分级检查,并与手术病理诊断结果对比分析,分别计算UE、超声量化分级以及两者综合诊断的灵敏度、特异度以及准确度,并分析胰岛素抵抗程度与UE和量化分级之间的关系。结果:良性结节UE评分为1分的人数占比为50.91%(56/110),高于恶性结节的0.00%(0/80),而评分为3分的人数占比为9.09%(10/110),低于恶性结节的63.75%(51/80),均具有统计学差异(均P<0.05)。良性结节超声量化分级系统分析结果为Ⅱ级的人数占比高于恶性结节,而Ⅲ-Ⅳ级人数占比低于恶性结节组,均具有统计学差异(均P<0.05)。综合法诊断良恶性TN的灵敏度、特异度以及准确度分别为94.55%、97.50%、95.79%,均高于UE的81.82%、88.75%、84.74%以及超声量化分级的74.55%、77.50%、75.79%,均具有统计学差异(均P<0.05);良性甲状腺结节,胰岛素抵抗指数与UE之间均存在正相关关系(r_s=0.871,P<0.05),胰岛素抵抗指数与量化分级之间存在正相关关系(r_s=0.883,P<0.05);恶性甲状腺结节,胰岛素抵抗指数与UE之间均存在正相关关系(r_s=0.899,P<0.05),胰岛素抵抗指数与量化分级之间存在正相关关系(r_s=0.909,P<0.05)。结论:UE联合量化分级系统诊断良恶性TN的灵敏度、特异度以及准确度均较高,胰岛素抵抗和与甲状腺结节的发病是明显相关的,随着胰岛素抵抗程度的增加,甲状腺结节的UE分级及量化分级也随之增高,恶性结节的可能性也越大。

慢性心力衰竭患者中医证型与超声心动图的相关性研究

[目的]探讨慢性心力衰竭(CHF)患者中医证型与超声心动图的相关性。[方法]选择符合标准的CHF患者62例,进行严格的中医辨证,分为气阴虚和气阳虚两组,分别进行主证计分和证候积分,采用改良二维超声心动图(2DE)改良Simpson法进行超声心动图检查,测量反映心脏结构指标、收缩功能指标和舒张功能指标,进一步分析比较上述指标在气阴虚与气阳虚两大证型组间的差异,并进行相关分析。[结果]1)反映心脏结构指标左室收缩末内径(LVESD)、左室舒张末内径(LVEDD)、舒张末期左室后壁厚度(LVPW)气阳虚组>气阴虚组,气阴虚、气阳虚证候积分与LVPW呈显著正相关,且LVPW与证候积分的相关系数(r=0.141)大于心功能分级(r=0.016)。2)反映收缩功能指标左室射血分数(LVEF)气阴虚组明显高于气阳虚组,每搏量(SV)、心输出量(CO)、心脏指数(CI)及左室短轴分数(LVFS)均呈气阴虚组>气阳虚组趋势,而左室收缩末容积(LVESV)则呈气阳虚组>气阴虚组趋势;气阴虚、气阳虚证候积分与SV、CO、CI、LVEF呈显著负相关,LVEF与证候积分的相关系数(r=-0.514)大于心功能分级(r=-0.151)。3)反映舒张功能指标舒张末容积(EDV)、舒张早期血流峰值/舒张晚期血流峰值(E/A)值气阳虚组>气阴虚组;气阴虚、气阳虚证候积分与EDV呈显著负相关,与E/A值呈正相关,EDV与证候积分的相关系数(r=-0.229)大于心功能分级(r=-0.027)。[结论]心脏超声指标与CHF中医证型存在一定相关性,证候积分可能是反映CHF患者LVPW、LVEF及EDV的敏感指标之一,证候积分越高者其LVPW越大、LVEF越低、EDV越小,心功能越差,舒张功能受损亦越严重。可为慢性心力衰竭气阴虚、气阳虚微观证辨证提供一定参考。

功率超声技术的分类研究及应用

介绍了功率超声及其特殊的力学和声学效应,概述了功率超声应用领域中研究比较广泛的几种功率超声技术应用,主要包括功率超声清洗、功率超声铸态处理、功率超声乳化、功率超声淬火、功率超声加工、功率超声焊接、功率超声焊后处理及在激光焊接和熔覆中引入超声振动等。按照所利用的超声波效应将功率超声的相关应用分为声学效应应用、力学效应应用及力-声联合效应应用三大类,并分析了各功率超声应用的基本原理、研究现状及适用对象。