Multidimensional defects tailoring local electron and Mg^(2+) diffusion channels for boosting magnesium storage performance of WO_(3)/MoO_(2)

Defect engineering presents great promise in addressing lower specific capacity,sluggish diffusion kinetics and poor cycling life issues in energy storage devices.Herein,multidimensional(0D/2D/3D) structural defects are constructed in WO_(3)/MoO_(2) simultaneously via competing for and sharing with O atoms during simple hydrothermal process.OD and 2D defects tailor local electron,activating more sites and generating built-in electric fields to yield ion reservoir,meanwhile,3D defect owning lower anisotropic property tailors Mg^(2+) diffusion channels to fully exploit Mg^(2+) adsorbed sites induced by OD and 2D defects,enhance the kinetics and maintain structural stability.Benefitted from synergistic effect of 0D/2D/3D structural defects,the designed WO_(3)/MoO_(2) shows the higher specific capacity(112.8 mA h g^(-1) at 50 mA g^(-1) with average attenuation rate per cycle of 0.068%),superior rate capability and excellent cycling stability(specific capacity retention of 80% after 1500 cycles at 1000 mA g^(-1)).This strategy provides design ideas of introducing multidimensional structural defects for tailoring local electron and microstructure to improve energy storage property.

An Improved Coupled Dynamic Modelling for Exploring Gearbox Vibrations Considering Local Defects

Gearbox is a key part in machinery,in which gear,shaft and bearing operate together to transmit motion and power.The wide usage and high failure rate of gearbox make it attract much attention on its health monitoring and fault diagnosis.Dynamic modelling can study the mechanism under different faults and provide theoretical foundation for fault detection.However,current commonly used gear dynamic model usually neglects the influence of bearing and shaft,resulting in incomplete understanding of gearbox fault diagnosis especially under the effect of local defects on gear and shaft.To address this problem,an improved gear-shaft-bearing-housing dynamic model is proposed to reveal the vibration mechanism and responses considering shaft whirling and gear local defects.Firstly,an eighteen degree-of-freedom gearbox dynamic model is proposed,taking into account the interaction among gear,bearing and shaft.Secondly,the dynamic model is iteratively solved.Then,vibration responses are expounded and analysed considering gear spalling and shaft crack.Numerical results show that the gear mesh frequency and its harmonics have higher amplitude through the spectrum.Vibration RMS and the shaft rotating frequency increase with the spalling size and shaft crack angle in general.An experiment is designed to verify the rationality of the proposed gearbox model.Lastly,comprehensive analysis under different spalling size and shaft crack angle are analysed.Results show that when spalling size and crack angle are larger,RMS and the amplitude of shaft rotating frequency will not increase linearly.The dynamic model can accurately simulate the vibration of gear transmission system,which is helpful for gearbox fault diagnosis.

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

The Problem of Photovoltaic(PV)defects detection and classification has been well studied.Several techniques exist in identifying the defects and localizing them in PV panels that use various features,but suffer to achieve higher performance.An efficient Real-Time Multi Variant Deep learning Model(RMVDM)is presented in this article to handle this issue.The method considers different defects like a spotlight,crack,dust,and micro-cracks to detect the defects as well as loca-lizes the defects.The image data set given has been preprocessed by applying the Region-Based Histogram Approximation(RHA)algorithm.The preprocessed images are applied with Gray Scale Quantization Algorithm(GSQA)to extract the features.Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons.Each class neuron has been designed to measure Defect Class Support(DCS).At the test phase,the input image has been applied with different operations,and the features extracted passed through the model trained.The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image.Further,the method uses the Higher-Order Texture Localization(HOTL)technique in localizing the defect.The pro-posed model produces efficient results with around 97%in defect detection and localization with higher accuracy and less time complexity.

Effects of local thickness defects on the buckling of micro-beam

A buckling model of Timoshenko micro-beam with local thickness defects is established based on a modified gradient elasticity.By introducing the local thickness defects function of the micro-beam,the variable coefficient differential equations of the buckling problem are obtained with the variational principle.Combining the eigensolution series of the complete micro-beam with the Galerkin method,we obtain the critical load and buckling modes of the micro-beam with defects.The results show that the depth and location of the defect are the main factors affecting the critical load,and the combined effect of boundary conditions and defects can significantly change the buckling mode of the micro-beam.The effect of defect location on buckling is related to the axial gradient of the rotation angle,and defects should be avoided at the maximum axial gradient of the rotation angle.The model and method are also applicable to the static deformation and vibration of the micro-beam.

CONSTITUTIVE EQUATION WITH ORDERED PARAMETERS FOR VISCOELASTIC MATERIAL CONTAINING DEFECTS

Based on the experimental results of local temperature field formed in the evolution period of defects, the defect field is defined by this internal temperature field. The evolutionary processes of statistically correlative meso-scopic defects are analysed with the growth rate and nucleation rate of the meso-defect, and it is deemed that the dynamic failure process of the viscoelastic material with defects can be quantitatively described by the normal method of the procedure of heat wave transmission. The defect field is regarded as a complex system of the union of the real sets and null sets, its main characteristics depending on the stratum hypothesis about the activities of the subsystems of the highest stratum described by ordered parameters. The fluctuation of ordered parameters is demonstrated by means of the projection operator method. The constitutive equation with ordered parameters for the meso-defect evolutionary state of viscoelastic material with defects is deduced from the nonlinear rheological dynamic approach, and its solution is obtained.

C-H complex defects and their influence in ZnO single crystal

Infrared absorption local vibration mode（LVM） spectroscopy is used to study hydrogen related defects in n-type ZnO single crystal grown by a closed chemical vapor transport（CVT） method under Zn-rich growth conditions, in which carbon is used as a transport agent. Two C-H complex related absorption peaks at 2850 cm-1and 2919 cm-1 are detected in the sample. The formation of the C-H complex implies an effect of carbon donor passivation and formation suppression of H donor in ZnO. The influence of the complex defects on the electrical property of the CVT-ZnO is discussed based on Hall measurement results and residual impurity analysis.

The effects of point defects on transmission coefficients in two-dimensional piezoelectric phononic crystals

Based on finite-difference time-domain(FDTD) method,the wave propagation and localization in two-dimensional defect-containing piezoelectric phononic crystals are investigated when the mechanical-electrical coupling is taken into account.The characteristics of localized defect modes are studied,and the effects of the number and direction of defects on the defect modes and transmission coefficients are discussed.Numerical results of defect modes and transmission coefficients are presented for BaTiO3/polymer piezocomposite,and from which we can see that the number and direction of defects have pronounced effects on the defect modes and transmission coefficients.The results also show the existence of elastic wave localization in piezoelectric phononic crystals containing defects.

Convergence Properties of Local Defect Correction Algorithm for the Boundary Element Method

Sometimes boundary value problems have isolated regions where the solution changes rapidly.Therefore,when solving numerically,one needs a fine grid to capture the high activity.The fine grid can be implemented as a composite coarse-fine grid or as a global fine grid.One cheaper way of obtaining the composite grid solution is the use of the local defect correction technique.The technique is an algorithm that combines a global coarse grid solution and a local fine grid solution in an iterative way to estimate the solution on the corresponding composite grid.The algorithm is relatively new and its convergence properties have not been studied for the boundary element method.In this paper the objective is to determine convergence properties of the algorithm for the boundary element method.First,we formulate the algorithm as a fixed point iterative scheme,which has also not been done before for the boundary element method,and then study the properties of the iteration matrix.Results show that we can always expect convergence.Therefore,the algorithm opens up a real alternative for application in the boundary element method for problems with localised regions of high activity.

Electronic transport of bilayer graphene with asymmetry line defects

In this paper,we study the quantum properties of a bilayer graphene with（asymmetry） line defects.The localized states are found around the line defects.Thus,the line defects on one certain layer of the bilayer graphene can lead to an electric transport channel.By adding a bias potential along the direction of the line defects,we calculate the electric conductivity of bilayer graphene with line defects using the Landauer-Biittiker theory,and show that the channel affects the electric conductivity remarkably by comparing the results with those in a perfect bilayer graphene.This one-dimensional line electric channel has the potential to be applied in nanotechnology engineering.

Local random flaps for cervical circumferential defect or tracheoesophageal fistula reconstruction after failed gastric pull-up: Two case reports

BACKGROUND Total pharyngo-laryngo-esophagectomy with a reconstruction of gastric pull-up is the most common treatment method for patients with multiple primary upper digestive tract carcinomas,such as hypopharyngeal carcinoma with thoracic esophageal carcinoma.However,neck circumferential defect and tracheoesophageal fistula after gastric necrosis are still challenging problems for surgeons and patients.CASE SUMMARY This case report presents 2 patients who underwent reconstructive surgeries using 4 local random flaps with a split thickness skin graft in the first case,and 6 local random flaps in the second case to close the circumferential defect and tracheoesophageal fistula after failed gastric pull-up.Both patients achieved good swallowing function and could take solid diet without dysphagia postoperatively.CONCLUSION For selected patients,local random flaps(with a split thickness skin graft)can be a simple and reliable solution for reconstructing tracheoesophageal fistula or cervical circumferential defect after gastric necrosis,especially when the necrosis extends below the thoracic inlet.

Tire Defect Detection Using Local and Global Features

In this paper, we present a tire defect detection algorithm based on sparse representation. The dictionary learned from reference images can efficiently represent the test image. As the representation coefficients of normal images have a specific distribution, the local feature can be estimate by comparing representation coefficient distribution. Meanwhile, a coding length is used to measure the global features of representation coefficients. The tire defect is located by both these local and global features. Experimental results demonstrate that the proposed method can accurately detect and locate the tire defects.

A Methodology for Identifying Defects in Wire Rope Based on Permanent Magnet Excitation

A wire rope defects detection method based on permanent magnet excitation is proposed.A detection system,mainly composed of permanent magnet excitation,distance detection,multi-sensor magnetic flux leakage signal acquisition and data analysis device,is set up.According to the different characteristics of the multi-sensor magnetic flux leakage signal,the localized fault(LF)and loss of metallic cross-sectional area(LMA)signal is separated,and then the two defects can be detected.The experiments show that the method can effectively detect the two defects when they appear simultaneously on the wire rope.

Deep_FGDL:一种基于深度神经网络的细粒度缺陷定位方法

传统静态缺陷定位技术仅能实现函数级或语句级的粗粒度定位,且过度依赖于缺陷报告等标注信息,限制了其在实际应用中的有效性。针对以上问题,本文提出一种基于深度神经网络(Deep Neural Networks,DNN)技术挖掘代码抽象语法树(Abstract Syntax Tree,AST)细粒度特征信息的缺陷定位方法Deep_FGDL。该方法利用正确代码片段构建模板库突破缺乏标注数据的限制。首先,通过代码语义相似度分析方法匹配正确模板进行初步定位。其次,提出了怀疑度公式,结合代码缺陷检测模型对结果进行加权操作,得到最为可能的细粒度缺陷定位。最后,为增强模型有效性,对于无法匹配合适模板的情况,引入了基于缺陷模式的缺陷定位方法。选用SARD数据集,将本文方法与几种静态缺陷定位方法进行实验对比,实验结果表明,该方法定位准确性在Top-5排名上提升15.0%、精确度提升19.1%。

基于深度智能视觉的表面缺陷检测研究进展

基于深度智能视觉的表面缺陷检测研究在制造业中起着越发重要的作用,本文阐述深度智能视觉的表面缺陷检测在现代工业质检中的重要性,对现有研究进展进行梳理总结。深度智能视觉以机器视觉和深度学习为技术基础,为不同工业场景提供高精高效的表面缺陷检测算法。本文从检测细粒度的角度将表面缺陷检测分为表面缺陷分类、定位、分割检测3个部分,并分别对分类、定位、分割方法进行系统综述,梳理现有表面缺陷检测研究的问题和思路。分类检测针对数据和缺陷图形特征问题进行研究,因其基础性和易拓展性于不同工业场景的应用呈现分散发展;定位检测以模型框架、矩形框检测和标注成本为主要问题,表现出追求轻量化和特征融合机制的研究趋势;分割检测更关注图像细节特征。通过研究分类、定位、分割的多任务模型框架以探索分类、分割检测之间的互补性。最后总结目前表面缺陷检测研究存在的问题,并对发展趋势进行展望。

基于反射系数与核函数构建的新型电缆缺陷诊断方法

为解决现有的电缆局部缺陷检测方法中缺陷定位效果差、缺陷类型判断困难等问题,提出一种基于反射系数谱(RCS)的新型电缆缺陷诊断方法。首先,利用电缆线路的分布参数模型,建立了含局部缺陷电缆线路的RCS数学模型,根据RCS数学模型,确定合理的核函数,设计出基于RCS的新型缺陷诊断函数,该缺陷诊断函数的极值峰可用于定位电缆线路中的缺陷,并确定缺陷的类型、严重程度等特征;然后,对不同类型与严重程度的电缆线路缺陷进行建模仿真诊断,测试该方法的可行性与准确性;最后,在同轴电缆与10 kV电力电缆上设计了局部缺陷,并用该方法对真实电缆缺陷进行诊断分析。仿真与实测结果表明,该方法能精准地定位电缆线路中的缺陷,并且可以准确地判断出缺陷的类型与严重程度,说明了该方法的实用性,且具有较高的实际工程价值。

基于指针神经网络的细粒度缺陷定位

软件缺陷定位是指找出与软件失效相关的程序元素.当前的缺陷定位技术仅能产生函数级或语句级的定位结果.这种粗粒度的定位结果会影响人工调试程序和软件缺陷自动修复的效率和效果.专注于细粒度地识别导致软件缺陷的具体代码令牌,为代码令牌建立抽象语法树路径,提出基于指针神经网络的细粒度缺陷定位模型来预测出具体的缺陷代码令牌和修复该令牌的具体操作行为.开源项目中的大量缺陷补丁数据集包含大量可供训练的数据,且基于抽象语法树构建的路径可以有效捕获程序结构信息.实验结果表明所训练出的模型能够准确预测缺陷代码令牌并显著优于基于统计的与基于机器学习的基线方法.另外,为了验证细粒度的缺陷定位结果可以贡献于缺陷自动修复,基于细粒度的缺陷定位结果设计两种程序修复流程,即代码补全工具去预测正确令牌的方法和启发式规则寻找合适代码修复元素的方法,结果表明两种方法都能有效解决软件缺陷自动修复中的过拟合问题.

基于特征提取和图像分类的螺旋网疵点自动检测

为了解决当前螺旋网人工疵点检测效率低、误检率高等问题,提出了一种基于分类思想的螺旋网疵点检测方法。对螺旋网图像提取多模式多尺度的LBP特征,充分表征螺旋网图像的信息,通过构建支持向量机(Support vector machine,SVM)分类器实现螺旋网疵点自动检测。结果表明:对于螺旋网疵点图像的局部二值模式(Local binary pattern,LBP)特征,采样半径为2,采样点个数为8时的均匀模式LBP的分类准确率优于其他模式和尺度的LBP,达到了100%,检测速度为0.48 s/张。通过对比不同的特征提取方法和分类器,验证了该文方法对于螺旋网疵点自动检测的适用性,可以实现纺织企业中螺旋网的自动化检测。

融合注意力机制的弱监督纸板表面缺陷检测

目的 针对目前表面缺陷检测方法因缺少实例级标签,使深度神经网络在工业检测上的应用受到限制的问题。本文面向实际的纸板表面缺陷检测任务,提出弱监督学习下融合卷积和注意力机制的神经网络算法。方法 该网络通过将通道注意力模块和梯度类激活映射模块相结合,进一步提高类激活图的精细度,实现纸板表面缺陷的精确定位;同时通过倒残缺结构和上采样层的组合操作,进一步细化浅层特征提升网络的特征提取能力,加快网络收敛速度。结果 通过在公开的纸板缺陷数据集上进行实验,本文提出的算法在使用图像级标签训练的情况下,分类正确率与定位正确率分别达到99.0%和92.2%,验证了该算法的有效性。结论 避免了实例级标签数量较少和过于主观的缺点,为基于机器人的缺陷纸板剔除奠定了基础。

含人工缺陷的2195-T8铝锂合金在30%HNO_(3)中的腐蚀行为

为研究2195-T8铝锂合金在酸性介质中的腐蚀性能,通过扫描电子显微镜(SEM)、扫描透射电子显微镜(STEM)等微观表征手段,分析2195-T8铝锂合金在30%HNO_(3)中的腐蚀形貌。提出一种块分割和边缘检测相结合的图像处理方法,从统计的角度对2195-T8铝锂合金在30%HNO_(3)中的腐蚀规律进行分析。结果表明:合金在浸泡不同时长后出现了典型的点蚀和晶间腐蚀形貌;人工缺陷深度会加剧腐蚀的程度;在腐蚀的初期阶段,蚀坑的数量快速增长,蚀坑面积主要集中在0~20μm^(2)。而在腐蚀的中后期,蚀坑的数量及面积变化不大,只有少量的蚀坑可以继续扩展形成面积大于50μm^(2)的蚀坑。

基于Transformer与局部特征融合的轨道紧固件缺陷检测方法

为解决传统人工巡检轨道交通线路存在的效率低和有安全隐患等问题,提出一种基于Transformer与局部特征融合的轨道紧固件缺陷检测方法。构建轨道紧固件缺陷检测模型,将Transformer与局部特征模块融合,整合局部信息,进而提取轨道紧固件缺陷特征;同时,采用数据增强的方法对轨道紧固件缺陷样本进行数据扩增,扩充数据集,验证所建模型的检测效果。实验结果表明,相较于传统方法,文章提出的方法在识别轨道紧固件缺失和损坏两类缺陷方面的精度和平均准确率均有所提升,在不同的轨道线路实验环境下也表现出良好的检测效果。