Experimental investigation of far-field human cough airflows from healthy and influenza-infected subjects

Seasonal influenza epidemics have been responsible for causing increased economic expenditures and many deaths worldwide. Evidence exists to support the claim that the virus can be spread through the air, but the relative significance of airborne transmission has not been well defined. Particle image velocimetry (PIV) and hot-wire anemometry (HWA) measurements were conducted at 1 m away from the mouth of human subjects to develop a model for cough flow behavior at greater distances from the mouth than were studied previously. Biological aerosol sampling was conducted to assess the risk of exposure to airborne viruses. Throughout the investigation, 77 experiments were conducted from 58 different subjects. From these subjects, 21 presented with influenza-like illness. Of these, 12 subjects had laboratory-confirmed respiratory infections. A model was developed for the cough centerline velocity magnitude time history. The experimental results were also used to validate computational fluid dynamics (CFD) models. The peak velocity observed at the cough jet center, averaged across all trials, was 1.2 m/s, and an average jet spread angle of θ = 24° was measured, similar to that of a steady free jet. No differences were observed in the velocity or turbulence characteristics between coughs from sick, convalescent, or healthy participants.     

Sparse non-convex Lp regularization for cone-beam X-ray luminescence computed tomography

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. However, the XLCT imaging suffers from a severe ill-posed problem. In this work, a sparse nonconvex L_p (0?p?L₁ regularization. Further, an iteratively reweighted split augmented lagrangian shrinkage algorithm (IRW_SALSA-L_p) was proposed to efficiently solve the non-convex L_p (0?p?p-values (1/16, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8) in both 3D digital mouse experiments and in vivo experiments. The results demonstrate that the proposed non-convex methods outperform L₂ and L₁ regularization in accurately recovering sparse targets in CB-XLCT. And among all the non-convex p-values, our L_p(1/4?p?相似文献   

Detection of fresh palm oil adulteration with recycled cooking oil using fatty acid composition and FTIR spectral analysis

There is a growing concern over the food safety issue related to increased incidence of cooking oil adulteration with recycled cooking oil (RCO). The objective of this study was to detect fresh palm olein (FPO) adulteration with RCO using fatty acid composition (FAC) and Fourier-transform infrared spectroscopy (FTIR) spectral analyses combined with chemometrics. RCO prepared in the laboratory was mixed with FPO in the proportion ranged from 1% to 50% (v/v) to obtain the adulterated oil samples (AO). FACs for FPO, RCO, and AO were determined using gas chromatography equipped with a flame ionization detector (GC-FID). The compositions of most fatty acids in RCO lied within the normal ranges of Codex standard, except for C8:0, C10:0, C11:0, C15:0, trans C18:1, and polyunsaturated fatty acids (PUFAs), C20:5. PUFAs showed a consistent decreasing trend with increasing magnitude of change with respect to increasing adulteration level and thus might be a good indicator for detecting FPO adulteration with RCO. The evaluation parameters (coefficient of determination, root mean standard error) of the FTIR-partial least square (PLS) model of palm oil adulteration with recycled oil are R² = 0.995 and 3.25, respectively. For FTIR spectral analysis, the distinct variations in spectral regions and aberrations in characteristic bands between FPO and RCO were observed. The optimized PLS calibration model developed from normal spectral of the combined region at 3602–3398, 3016–2642, and 1845–650 cm^?1 overpredict the adulteration level. On the other hand, the discriminant analysis classification model was able to classify the FPO and AO into two distinct groups. Improvement of the principles of combined techniques in authenticating AO from fresh oil is beneficial as a guideline to detect adulteration in cooking oil.     

Corrosion behaviour and mechanism of 6082 aluminium alloy in NaCl and Na2SO4 etchants

The corrosion behaviour of 6082 aluminium alloy was studied by measuring the electrochemical impedance spectra and electrode polarization curves. After the electrochemical tests, a microstructural analysis of the samples was conducted by using optical microscopy and electron scanning microscopy techniques to determine the corrosion mechanism. The results show that the Nyquist plot of the electrochemical impedance data in the NaCl solution consists of high- and low-frequency capacitive impedance loops. When ions are added to the NaCl etchant, the Nyquist plots of the electrochemical impedance data are composed of two different curves: a high-frequency capacitive impedance loop and a low-frequency inductive impedance loop. The corrosion current density increases with increasing concentration, and as a result, the corrosion resistance of the aluminium alloy decreases. The microstructures of 6082 aluminium alloy consist of Mg₂Si secondary particles in a large α-Al matrix. Pitting corrosion initially occurs at the boundary between the matrix and secondary particles because the electrode potentials of the matrix and secondary particles are different. Then, corrosion paths develop along the network-like grain boundaries, and finally, massive network-like corrosion occurs throughout the entire alloy.     

基于光谱分类模型的保险杠物证无损研究

采用中红外光谱结合化学计量学的方法对车用保险杠碎片进行鉴别，分别对52个车用保险杠碎片样本的全波段光谱数据、指纹区光谱数据和主成分分析降维后的光谱数据建立Fisher判别分析和K近邻算法2种分类模型，并对分类结果进行比较。结果表明，主成分分析提取特征变量后构建的分类模型，分类的准确率更高，对聚丙烯（PP）、PP/滑石粉、PP/滑石粉/碳酸钙（CaCO₃）3种类型的样本分类准确率达到92.3 %，对PP/滑石粉类型中的10种品牌样本分类准确率达到88.9 %，分类结果理想；在构建的2种分类模型中，Fisher判别分析模型的分类率远高于K近邻算法模型，分析认为K近邻算法模型受到样本不均衡的影响；中红外光谱结合化学计量学可以实现对车用保险杠碎片的准确区分，且满足快速、无损的检验要求。     

基于彩色图像处理的浮选尾煤灰分软测量研究

浮选尾煤灰分是浮选产品的一个重要指标。针对选煤厂浮选尾煤灰分多采用离线检测而无法实现在线准确测量，以及当前浮选软测量多采用单一的灰度图像从而导致软测量模型精度及适应性较差的问题，提出了一种基于彩色图像处理的浮选尾煤软测量方法，建立了基于最小二乘支持向量机(LSSVM)的浮选尾煤灰分软测量模型。模型以不同颜色空间的彩色特征、灰度均值以及浓度特征为输入变量，以尾煤灰分作为输出变量，采用粒子群优化算法对LSSVM模型参数进行优化。结果表明：所建立的尾煤灰分软测量模型可以较好地实现浮选尾矿灰分的在线预测，引入浮选尾矿图像的彩色特征可以提高尾煤图像分析的精度，预测精度达96.89%。研究成果在柳湾选煤厂现场应用，并取得了较好的尾矿灰分测量效果。     

自适应天牛须优化与K均值聚类的图像分割算法研究

在机器识别中,图像分割是重要的一个步骤,传统分割手段存在一定缺陷。针对传统K均值聚类分割的初始聚类中心敏感的缺陷进行了优化,利用自适应天牛须优化算法,避免了这一问题。通过实验结果表明,该算法(ABASK)对图像进行分割,既可以保证图像轮廓的分割,同时还可以更多地保留图像细节。     

煤矿地面轨道运输矿车自动避障技术研究

介绍了煤矿地面轨道运输矿车自动避障技术。首先介绍了基于雷达的矿车前方障碍物检测技术及其相关算法,然后详细介绍了基于机器视觉的矿车前方障碍物检测系统和常用的图像处理方法以及相关的算法,最后介绍了雷达和机器视觉融合的障碍物检测技术在煤矿地面轨道运输矿车自动避障过程中的应用。     

Mechanical and fracture investigation of magnesia refractories with acoustic emission-based method

In this study, the fracture behaviour of magnesia, magnesia chrome and magnesia spinel (MgAl₂O₄ and FeAl₂O₄) refractories under wedge splitting test are qualitatively and quantitatively investigated with the acoustic emission (AE) and digital image correlation (DIC). First of all, the concepts of characteristic widths are proposed for estimating the brittleness of refractory materials according to the shape of load-displacement curve and validated by their good correlation with the characteristic length. Besides, the AE data are analyzed with AE parameter-based approaches and offer new insight into the fracture behaviour of refractory materials, including the classification of the cracking events in grains and in matrix, the distinction between the tensile mode and shear mode damage, and the visualization of the fracture process zone development. It confirms that the pre-existing micro-crack networks in refractories are favourable for the brittleness reduction, which enhance their nonlinear fracture behaviour and thermal shock resistance.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。