全视场在线图像可视铁谱磨粒显微成像特性分析

为了评估全视场在线图像可视铁谱磨粒显微成像特性,提出了一种反射光显微成像模型。首先,基于朗伯余弦与小角度散射理论建立了全视场(OLVF)的反射光辐照度模型,实现了磨粒显微成像清晰度定量评价。然后,仿真计算磨粒显微成像的反差透视比,确定了最优光学倍率和适用于全视场OLVF探测的油液衰减系数范围,明确了光学参数对磨粒显微成像质量的影响规律。结果显示:光学倍率为2.0×且油液衰减系数≤2.0条件下,磨粒沉积于物方视场的主光轴附近,全视场OLVF可获得最佳磨粒显微成像清晰度。最后,开展了磨粒显微成像实验测试,结果表明:全视场OLVF能够从油液衰减系数小于2.0的在用液压油和齿轮油中获取反射光谱片图像,并提取磨粒视觉信息进行磨损在线监测。

In situ hyperspectral data analysis for pigment content estimation of rice leaves

Analyses of the correlation between hyperspectral reflectance and pigment content including chlorophyll a, chlorophyll b and carotenoid of leaves in different sites of rice were reported in this paper. The hyperspectral reflectance of late rice during the whole growing season was measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm. The chlorophyll a, chlorophyll b and carotenoid contents in rice leaves in rice fields to which different levels of nitrogen were applied were measured. The chlorophyll a content of upper leaves was well correlated with the spectral variables. However, the correlation between both chlorophyll b and caroteniod and the spectral variables was far from that of chlorophyll a. The potential of hyperspectral reflectance measurement for estimating chlorophyll a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. This study showed that the most suitable estimated model of chlorophyll a of upper leaves was obtained by using some hyperspectral variables such as SD r, SD b and their integration.

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

To further develop the methods to remotely sense the biochemical content of plant canopies,we report the results of an experiment to estimate the concentrations of three biochemical variables of corn,i.e.,nitrogen(N),crude fat(EE) and crude fiber(CF) concentrations,by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed,and a set of estimation models were established using curve-fitting analyses. Coefficient of determination(R2),root mean square error(RMSE) and relative error of prediction(REP) of estimation models were calculated for the model quality evaluations,and the possible opti-mum estimation models of three biochemical variables were proposed,with R2 being 0.891,0.698 and 0.480 for the estimation models of N,EE and CF concentrations,respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation,and that the first derivative reflectances at 759 nm,1954 nm and 2370 nm were most suitable to develop the estimation models of N,EE and CF concentrations,respectively. In addition,the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained,especially for nitrogen(r=0.948).

气泡强干扰小波调制的全视场在线可视铁谱磨屑图像分割算法

为解决全视场在线可视铁谱(Online visual ferrograph,OLVF)工程应用存在的离焦气泡干扰状态下反射光谱片磨屑图像分割问题,构建了基于双曲正切函数的低频与高频非线性阈值函数,以此为基础,提出了气泡强干扰小波调制的磨屑图像分割算法,抑制了反射光谱片中的气泡阴影,消除了背景噪声,增强了磨屑图像锐度和对比度,实现了全视场OLVF反射光谱片磨屑图像的有效分割。对比不同算法处理的磨屑图像分割效果,结果显示:本算法通过调制气泡阴影和背景噪声能够更准确达到磨屑图像完整分割。最后,开展了齿轮磨损在线监测实验,提取了磨屑浓度指标反映齿轮全生命期的磨损变化,结果表明:本算法克服了全视场OLVF采样过程中气泡阴影对反射光谱片磨屑图像分割的影响,可准确的探测出齿面异常磨损失效故障,进一步验证了本算法应用于全视场OLVF油液磨屑监测之有效性。