QUANTITATIVE STUDIES ON THE DNA CONTENT AND MORPHOMETRIC FEATURES OF LIVER CELL DYSPLASIA

The DNA content and morphometric features of hepatocellular carcinoma (HCC) and liver cell dysplasia (LCD), including nuclear area, nuclear perimeter, nuclear maximum diameter and nuclear circle diameter, were quantitatively determined by means of image analysis technology. The results showed that in comparison with normal hepatocytes, LCD had a markedly increased DNA content and nuclear morphometric parameters, but the values were lower than those for HCC. LCD showed a slight increase in nuclear atypia represented by the nuclear irregular index, which was also less than HCC. The findings indicate that LCD may be a precaneerous lesion of HCC, to the cells in an abnormal proliferative state.

A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy

In the spectral analysis of laser-induced breakdown spectroscopy,abundant characteristic spectral lines and severe interference information exist simultaneously in the original spectral data.Here,a feature selection method called recursive feature elimination based on ridge regression(Ridge-RFE)for the original spectral data is recommended to make full use of the valid information of spectra.In the Ridge-RFE method,the absolute value of the ridge regression coefficient was used as a criterion to screen spectral characteristic,the feature with the absolute value of minimum weight in the input subset features was removed by recursive feature elimination(RFE),and the selected features were used as inputs of the partial least squares regression(PLS)model.The Ridge-RFE method based PLS model was used to measure the Fe,Si,Mg,Cu,Zn and Mn for 51 aluminum alloy samples,and the results showed that the root mean square error of prediction decreased greatly compared to the PLS model with full spectrum as input.The overall results demonstrate that the Ridge-RFE method is more efficient to extract the redundant features,make PLS model for better quantitative analysis results and improve model generalization ability.

A bearing fault feature extraction method based on cepstrum pre-whitening and a quantitative law of symplectic geometry mode decomposition

In order to extract the fault feature of the bearing effectively and prevent the impact components caused by bearing damage being interfered with by discrete frequency components and background noise,a method of fault feature extraction based on cepstrum pre-whitening(CPW)and a quantitative law of symplectic geometry mode decomposition(SGMD)is proposed.First,CPW is performed on the original signal to enhance the impact feature of bearing fault and remove the periodic frequency components from complex vibration signals.The pre-whitening signal contains only background noise and non-stationary shock caused by damage.Secondly,a quantitative law that the number of effective eigenvalues of the Hamilton matrix is twice the number of frequency components in the signal during SGMD is found,and the quantitative law is verified by simulation and theoretical derivation.Finally,the trajectory matrix of the pre-whitening signal is constructed and SGMD is performed.According to the quantitative law,the corresponding feature vector is selected to reconstruct the signal.The Hilbert envelope spectrum analysis is performed to extract fault features.Simulation analysis and application examples prove that the proposed method can clearly extract the fault feature of bearings.

Quantitative Damage Detection for Planetary Gear Sets Based on Physical Models

Planetary gear set is the critical component in helicopter transmission train, and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection. In order to resolve this problem, an approach based on physical models is presented to detect damage quantitatively in planetary gear set. A particular emphasis is put on a feature generation and selection method, which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system. In this feature generation procedure, the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case. Then, a feature based on the spectrum of simulation signals of the dynamical models is generated. Aiming at selecting the best feature suitable for quantitative damage detection, a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing. A feature named SR, which had better performance in tracking damage, is proposed to detect damage in planetary gear set. Meanwhile, the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above, and then the test vibration signal is picked up and used for damage detection. With the results of several experiments for quantitative damage detection, the feasibility and the effect of this approach are verified. The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system.

Relevance of EGFR gene mutation with pathological features and prognosis in patients with non-small-cell lung carcinoma

Objective:To study the relevance of EGFR gene mutation with pathological features and prognosis in patients with non-small-cell lung carcinoma.Methods:A total of 297 patients from July 2009 to May 2013 were chosen as objects.EGFR gene mutation were detected with fluorescence quantitative PCR.Relevance of EGFR gene mutation with clinical and pathological features was analyzed,and the prognosis of EGFR- mutant-patients and that of EGFR- wide type-patients was compared.Results:In 297 patients.136(45.79%) showed EGFR gene mutation.EGFR gene mutation had no significant relevance with age.gender,smoking history,family history of cancer and clinical stage(P>0.05);there was significant relevance between EGFR gene mutation and blood type,pathologic types,differentiation and diameter of cancer(P<0.05).The difference between prognosis of EGFR- mutant-patients and that of EGFR- wide type-patients was statistical significance(P<0.05).Conclusions:EGFR gene mutation has significant relevance with pathological features,the prognosis of EGFRmutant-paticnts is better than that of EGFR- wide type-patients.

Texture features analysis on micro-structure of paste backfill based on image analysis technology

The strength of cement-based materials,such as mortar,concrete and cement paste backfill(CPB),depends on its microstructures(e.g.pore structure and arrangement of particles and skeleton).Numerous studies on the relationship between strength and pore structure(e.g.,pore size and its distribution)were performed,but the micro-morphology characteristics have been rarely concerned.Texture describing the surface properties of the sample is a global feature,which is an effective way to quantify the micro-morphological properties.In statistical analysis,GLCM features and Tamura texture are the most representative methods for characterizing the texture features.The mechanical strength and section image of the backfill sample prepared from three different solid concentrations of paste were obtained by uniaxial compressive strength test and scanning electron microscope,respectively.The texture features of different SEM images were calculated based on image analysis technology,and then the correlation between these parameters and the strength was analyzed.It was proved that the method is effective in the quantitative analysis on the micro-morphology characteristics of CPB.There is a significant correlation between the texture features and the unconfined compressive strength,and the prediction of strength is feasible using texture parameters of the CPB microstructure.

Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks.However,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples.As a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm.Based on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities.Furthermore,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming time.In the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed.All experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.

金属磁记忆检测的关键技术研究现状与展望

金属磁记忆检测技术是一种绿色无污染、可检出早期应力集中、隐性损伤以及宏观缺陷的全寿命无损检测技术,经过20多年发展,已取得众多研究成果并应用于压力容器、石油管道、铁路桥梁、能源电力、航空航天等重要领域。针对金属磁记忆检测技术近年来的热点与难点问题,从理论基础到工程应用中的关键技术出发,归纳总结了磁记忆检测技术的机理、信号降噪与特征提取,以及机器学习在磁记忆检测评价中的应用等方面的研究现状,并对磁记忆检测技术亟待解决的关键难点问题与未来发展方向进行了探讨。

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

高血压性颅内出血患者预后不良的定量脑电图特征

目的:探讨高血压性颅内出血患者预后不良的定量脑电图特征。方法:前瞻性连续收集2017年1月至2023年6月在开滦总医院神经内科重症加强治疗病房诊治的高血压性颅内出血患者69例为研究对象。根据出院30d后的改良Rankin评分(mRS评分)随访结果将患者分为两组:预后良好组(mRS≤2分)、预后不良组(mRS3~6分)。收集患者一般资料、qEEG监测指标〔左、右侧前额区(Fp1、Fp2)、额区(F3、F4)、中央区(C3、C4)、顶区(P3、P4)、枕区(O1、O2)、前颞区(F7、F8)、中颞区(T3、T4)、后颞区(T5、T6)的α、θ、δ、β脑波功率值。结果:高血压性颅内出血预后不良组年龄大于预后良好组,P<0.05。高血压性颅内出血预后不良组10-20导联中Fp2、F4、C4、P4、F7-8、T4导联δ脑波功率值高于预后良好组,P<0.05。预后不良组10-20导联中Fp1-2、F3、C3、P4、O1、F7、T3导联θ脑波功率值高于预后良好组,P<0.05。预后不良组10-20导联中Fp1-2、F3-4、C3-4、P3-4、O1、F7-8、T4、全脑平均慢波指数功率值高于预后良好组,P<0.05。结论:高血压性颅内出血预后不良患者,定量电脑图δ、θ、全脑平均慢波指数功率值呈明显增高改变。

基于孪生神经网络的楔形环连接结构预紧状态辨识

楔形环连接结构由于其连接简单可靠、同时兼具节省空间及减重的优势,常被应用于鱼雷、航天飞行器等武器装备。针对楔形环连接结构预紧状态辨识方面存在的机理模型复杂、样本量小且类别不平衡的问题,提出了一种基于孪生神经网络模型的预紧状态辨识方法。为提高模型训练效率和效果,首先利用时频处理技术进行孪生神经网络模型特征增强,基于增强特征建立了3层孪生神经网络分类模型,实现楔形环预紧状态宏观分类。同时,为指导楔形环精密装配,通过特征可视化技术,深入分析了孪生神经网络训练过程特征聚类效果,并基于二维特征建立了预紧状态定量表征模型,引入目标状态聚类中心与接受域参量,用于实现楔形环连接结构预紧状态定量评估。通过试验验证了所提方法的有效性,该方法可为楔形环连接结构定量辨识提供新的技术途径和思路,具有一定工程应用价值。

中文期刊论文层级量化差异研究——以图书情报领域为例

[目的/意义]探索图书情报领域中文期刊论文量化特征层级差异,为论文评价研究提供参考。[方法/过程]以图书情报领域为例,获取CNKI和人大复印报刊资料官网数据,划分为复印报刊资料、CSSCI和其他期刊三大层级,从被引次数、基金资助、论文主题三个方面分析量化特征差异。[结果/结论]三大层级在载文量、基金资助等方面有所进步,第三层级进步最为显著,但愈发难以获得第一层级的青睐;第一层级转载对论文被引有极大推动作用,转载论文选题区分度大,有一定的主题倾向;第一层级对第二、三层级论文的主题引导分别以研究主题和研究方法为主。层级差异依然显著,但不应忽略第三层级论文的价值,第三层级论文在一些量化指标上进步明显,把关论文创新性是未来提高第三层级论文质量的重点。

电子笔迹多特征色彩量化图的CNN注意力机制融合识别

为了提取电子笔迹的有效特征实现身份识别任务,提出将电子笔迹的各类特征进行色彩量化,形成笔痕点阵y-x形态特征图、坐标值及压力值的x/y/p-t时序变化特征图、运笔8方向特征图、运笔速度特征图及压力变化速度特征图;然后构建5层的卷积神经网络(CNN),并提出一种双向注意力机制捕获跨通道的特征交互信息,增加特征的聚合程度;最后,将电子笔迹的多特征图作为CNN网络的输入数据,并在不同的网络层进行融合识别。实验结果表明,所提出的方法显著加快网络收敛速度,并提高认证准确率和鲁棒性,12个人的识别正确率可达95%以上,50个人的识别正确率可达92%以上。电子笔迹多类特征的可视化图可用于辅助进行笔迹鉴定,融合识别方法应用于身份认证可规避个人隐私泄露的风险。

乳腺浸润性癌中CD147和PHH3的表达与临床病理特征的关系

目的观察浸润性乳腺癌中CD147蛋白的定量表达和PHH3阳性细胞数目,分析二者与多个临床病理特征的关系及相关性。方法选取80例乳腺原发浸润性癌手术切除标本为研究对象,29例正常乳腺组织作为对照组,采用免疫组织化学SP法检测CD147和PHH3的表达,用IPP6.0图像分析软件对CD147蛋白表达进行定量测试,计数PHH3阳性个数和有丝分裂指数,分析浸润性乳腺癌中CD147和PHH3的表达与临床病理特征的关系,用Spearman法分析二者之间的相关性。结果CD147主要定位于乳腺癌细胞胞膜或膜浆,PHH3主要定位于浸润性乳腺癌有丝分裂的细胞核内,二者在浸润性乳腺癌组织中的定量表达和阳性数目均显著高于正常乳腺组织(P=0.000)。80例浸润性乳腺癌中,CD147蛋白定量表达在肿瘤直径较大、WHO分级较高、淋巴结有转移、TNMⅢ-Ⅳ期、雌激素受体(estrogen receptor,ER)阴性、Her-2基因无扩增阳性强度较高(P均<0.05);PHH3阳性数目和有丝分裂指数在肿瘤直径较大、WHO分级较高、淋巴结有转移状况及TNM分期Ⅲ-Ⅳ期(P_(均)<0.05)。CD147蛋白表达越高,PHH3阳性数目相应越多,有丝分裂指数越高,Spearman分析显示CD147分别与PHH3阳性数目和有丝分裂指数呈显著正相关(r=0.950,r=0.706,P=0.000)。结论浸润性乳腺癌中CD147和PHH3高表达,与肿瘤的发生发展有关,二者呈正相关,检测乳腺癌中CD147蛋白定量表达、PHH3阳性数目和有丝分裂指数有助于侵袭和转移的判断,为乳腺癌临床病理诊断提供了参考价值。

新能源电力系统频率时空分布特性分析

随着新能源和电力电子装备在电力系统中的占比快速提升,系统的频率动态特性发生重大变化。为了研究新能源机组并网对新型电力系统频率时空分布特性的影响:首先,建立了新能源电力系统的频率动态响应解析模型,并分析了频率时空分布特性的影响因素及新能源并网对该特性的影响机理;其次,讨论了研究频率时空分布特性的必要性及描述该特性的量化指标;最后,基于仿真结果分析了不同电网形态下的频率时空分布特性,并总结了新能源机组并网对该特性的影响规律。

基于波长注意力的多特征融合卷积神经网络的近红外光谱定量方法

深度学习技术越来越多地应用在近红外光谱的定量分析中,由于近红外光谱数据存在光谱数据量少、数据质量不足等问题,将传统卷积神经网络应用在光谱的定量分析中会出现过拟合,为提升卷积神经网络提取光谱信息的能力,增强网络的泛化性,提出了基于波长注意力的多特征融合卷积神经网络模型(MWA-CNN),对芒果近红外光谱进行干物质含量定量分析。MWA-CNN在传统卷积神经网络的基础上加入了注意力机制以及多特征融合机制,网络可以在训练过程中学习到不同光谱特征以及不同波段的权重信息,从而提取到高质量的光谱信息,缓解传统卷积神经网络中的过拟合问题,提升回归分析的精度。研究中采用11691个芒果样本的近红外光谱数据,采用随机法将80%的样本作为训练集,20%的样本作为测试集,通过测试集均方根误差(RMSEP)、训练集均方根误差(RMSEC)、决定系数(R2)、平均绝对误差(MAE)进行模型评价。先对光谱数据进行标准化预处理,然后通过与偏最小二乘回归(PLS)、极限学习机回归(ELM)、支持向量机回归(SVR)和传统的卷积神经网络(CNN)四种传统模型在原始光谱条件下的预测结果进行对比。预测结果表明MWA-CNN网络在五种方法中表现最佳,MWA-CNN在测试集中的RMSE为0.6699,传统的CNN效果仅次于MWA-CNN,RMSE为0.7408,且MWA-CNN的过拟合程度相较传统CNN下降明显,MWA-CNN中测试集相较于训练集的RMSE增加了15.69%,而CNN中测试集相较于训练集的RMSE增加了151.45%。通过对光谱加入不同信噪比的噪声,再对加噪之后的光谱分别用五种模型进行预测,实验结果表明,在多种信噪比条件下,MWA-CNN模型均能取得五种模型中最优的效果,从实验结果表明,MWA-CNN在近红外光谱定量回归中具有较高的预测精度和泛化能力,同时具有一定的抗噪能力。

高帧率超声造影定性特征联合定量参数鉴别前列腺良恶性疾病的应用价值

目的:探讨高帧率超声造影(high frame rate contrast-enhanced ultrasound,H-CEUS)定性特征联合定量参数对前列腺良恶性疾病的鉴别诊断价值。方法:选取2022年02月至2023年01月在我院就诊疑似前列腺癌(prostate cancer,PCa)并进行前列腺穿刺活检的患者60例(共67个病灶),根据病理结果分为良性组和恶性组,穿刺前行经直肠常规超声及H-CEUS,记录前列腺基本情况、造影定性特征并绘制时间强度曲线获得定量分析参数,比较两组间差异;以病理结果为“金标准”绘制受试者工作特征(receiver operating characteristic,ROC)曲线,应用Z检验比较H-CEUS定性特征、定量参数单独及联合应用对于前列腺病变良恶性的诊断效能。结果:与良性组相比,恶性组H-CEUS定性特征为供血动脉形态不规则(1/33 vs 11/34)及走形异常(3/33 vs 20/34)、快进(9/33 vs 29/34)、高增强(4/33 vs 25/34)、造影剂分布不均匀(9/33 vs 13/34)的比例较大,差异具有统计学意义(χ2=30.41、18.37、22.96、25.72、8.06,P<0.001、<0.001、<0.001、<0.001、=0.005);定量参数PCa较良性组造影到达时间早[(16.93±3.69)s vs(21.54±3.86)s],峰值强度[(48.8±5.58)dB vs(45.77±4.42)dB]、强度差[4.87(0.87,8.03)vs-0.44(-2.22,2.35)]及强度比[(1.15±0.24)vs(1.01±0.97)]的值较良性大,差异具有统计学意义(t/U=4.24、-2.324、151、-2.535,P<0.001、=0.025、=0.004、=0.015)。ROC曲线示H-CEUS定性及定量联合应用的AUC=0.938,截断值为0.44时诊断效能最佳,约登指数、敏感度、特异度、准确度、阳性预测值及阴性预测值为0.750、89.29%、85.71%、87.75%、89.3%、85.7%。根据净重新分类指数NRI值,联合应用对定性特征及定量参数均为正改善(P<0.05)。结论:H-CEUS应用于前列腺有助于观察造影灌注细节、分析成像特征,对于前列腺良恶性疾病具有较好的鉴别诊断能力,将造影灌注定性特征与定量参数结合的诊断效能优于单独应用。

“言够”与“言多”——论“够”的量性特征及其副词规约化用法

本文基于构式互动来考察“够”在用变和演变中的量性特征及其副词的规约化用法。“够”可与名词、动词、形容词组配形成构式群。随着主观性增强,“够”由表客观的足量向抽象的程度量虚化,词性上由判断动词向程度副词演变。其语法化等级呈现出非均质性,其中“够+A”时语法化等级最高。功能上“够”实现了客观的定量评估向主观的模糊定性转变。

改进Canny算子的小管径弯头漏磁缺陷图像量化方法

弯头作为管道的重要组成部件,受流体冲刷侵蚀等形成的缺陷对其安全运行构成威胁。漏磁检测是管道缺陷检测的有效技术,缺陷的精确量化具有重要意义。为了研究小管径弯头不同位置缺陷图像规律,并提高缺陷量化精度,提出一种改进Canny算子的小管径弯头漏磁缺陷图像量化方法。通过建立小管径弯头漏磁内检测仿真模型,分析了弯头不同位置处金属损失缺陷的图像规律。采用形态滤波和OTSU优化Canny算子,结合图像处理方法构建了缺陷图像量化模型,并对弯头不同位置缺陷深度量化做修正处理。实验结果表明,弯头不同位置缺陷在图像上呈现出明显的差异性,量化模型对缺陷长度和宽度的量化具有较高的精度,误差均小于2 mm。缺陷深度的量化误差较大,修正后深度量化的精度为86.34%,满足金属损失缺陷量化精度需求。该方法可实现缺陷漏磁图像量化处理,对管道漏磁缺陷量化具有一定意义。

基于递归量化分析的CFRP超声检测缺陷识别方法

为了解决碳纤维增强复合材料(CFRP)超声检测缺陷识别不准确、不可靠的问题,提出自适应变分模态分解(AVMD)与递归量化分析技术(RQAT)特征提取和卷积神经网络(CNN)识别方法.实验预埋6种模拟缺陷,使用超声相控阵检测后,每种缺陷取500个A扫描波形信号数据,利用蝠鲼智能优化算法优化出变分模态分解(VMD)所需的K、Alpha,使用优化参数的VMD得到本征模态函数(IMF)分量,筛选高频噪声部分,对剩余IMF分量使用递归量化分析技术.每个信号得到72个特征值,将特征值组成特征向量,输入CNN进行识别,训练集识别正确率为99.94%,验证集识别正确率为98.09%,测试集识别正确率为98.27%.结果表明,AVMD与RQAT、CNN的结合解决了CFRP超声检测中缺陷的识别分类问题.