单板层积材动态弹性模量的无损检测

利用振动无损检测方法,研究了在不同边界条件下杨木单板层积材的动态弹性模量,并且与常规静弹性模量进行比较,探索适合单板层积材弹性模量无损检测的方法。结果表明:对于不同的边界条件,单板层积材有不同的振型和固有频率,四边简支振动法与四边自由振动法测得的动态弹性模量平均值比较接近,回归分析表明二者之间具有密切的相关性;四边简支振动法比四边自由振动法操作简单,更容易实现;采用四边简支振动法、四边自由振动法测得的杨木单板层积材的动态弹性模量和国家标准的破损检测法测得的静态弹性模量在0.01水平下具有密切的相关性,静态弹性模量可以通过线性回归方程用动态弹性模量来表征。     

基于可见近红外光谱结合不同光谱选择方法检测生姜含水率研究

快速检测生姜含水率对生姜的存储加工和国际贸易非常重要。本文应用可见近红外光谱快速检测生姜含水率,采集330个生姜的可见近红外光谱(光谱范围350~1800nm),然后用烘干法测定生姜的含水率,把330个生姜样本按照含水率的大小以2:1的比例分成校正组和预测组。应用专业知识法、偏最小二乘法和遗传算法三种光谱选择方法建立生姜含水率的预测模型,其模型的精度比应用全光谱(包含1451个光谱变量)所建立的模型精度高。通过比较,应用遗传算法所得预测模型的效果最好,选定的光谱数和因子数分别是300和6,预测组的相关系数、均方根误差和分别是0.9900和4.4440。     

基于DSP的禽蛋蛋壳无损检测系统

设计了一种基于DSP的蛋壳无损检测硬件系统,该硬件系统具有控制敲蛋装置、声音信号采集与处理、破损识别和与PC机通信等功能。实践应用证明该系统可实现蛋壳无损检测工作快速、机动、可靠和高效的要求。     

基于荧光高光谱图像的柑桔糖度无损检测

采用632nm的连续波激光作为激发光,应用激光诱导荧光高光谱成像技术对柑桔的糖度值进行无损测量。先将该激光照射到南丰蜜桔和脐橙样品上,后用高光谱图像采集系统收集诱导出的荧光散射图像;接下来对荧光散射图像进行分析,选取100×50像素的荧光区域作为感兴趣区域(ROIs);再提取感兴趣区域在波长700～1000nm的光谱值作为荧光高光谱图像数据;最后用线性回归方法建立荧光高光谱图像数据预测果实糖度值的预测模型。结果表明,该模型预测柑桔糖度值的相关系数分别为南丰蜜桔的R=0.970,脐橙的R=0.960。因此可以看出,应用激光诱导荧光高光谱成像对柑桔糖度值进行无损检测是一种很有效的方法。     

A statistical algorithm for comparing mode shapes of vibration testing before and after damage in timbers

Instances of local damage in timber such as knots, decay, and cracks can be translated into a reduction of service life due to mechanical and environmental loadings. In wood construction, it is very important to evaluate the weakest location and to detect damage at the earliest possible stage to avoid future catastrophic failure. In this study, modal testing was used on wood beams to generate the first two mode shapes. A novel statistical algorithm was proposed to extract a damage indicator by computing mode shapes of vibration testing before and after damage in timbers. The different damage severities, damage locations, and damage counts were simulated by removing mass from intact beams to verify the algorithm. The results showed that the proposed statistical algorithm is effective and suitable for the designed damage scenarios. It is reliable for the detection and location of local damage of different severities, location, and number. The peak values of the damage indicators computed from the first two mode shapes were sensitive to different damage severities and locations. They were also reliable for the detection of multiple cases of damage.     

基于电阻层析成像技术和ResNet的萝卜根系表型无损检测方法研究

【目的】针对现有根系表型检测方法存在价格昂贵、需要专人操作以及无法对根系表型进行原位无损检测等问题，提出一种基于电阻层析成像技术(Electrical resistance tomography,ERT)和深度残差神经网络(Deep residual network,ResNet)的萝卜根系表型无损检测方法。【方法】首先，利用COMSOL软件对萝卜-琼脂场域不同情况的ERT正问题进行仿真分析，并获得大量边界电压数据；然后，基于ResNet对萝卜-琼脂场域的内部电导率分布与边界电压之间的非线性映射关系建立模型，对萝卜-琼脂场域进行图像重建；最后，基于ERT研制一套萝卜根系表型检测装置，并进行试验验证。【结果】基于ERT和ResNet的萝卜根系表型检测方法能够实现萝卜根系表型的可持续无损检测，试验装置操作简单、成本低，图像重建相对误差小于5%。【结论】基于ERT的萝卜根系表型检测方法可以实现对萝卜根系表型的无损检测；结合ResNet算法，成像精度较高。该方法可有效应用于萝卜根系表型的检测。     

Postharvest noninvasive assessment of fresh chestnut (Castanea spp.) internal decay using computer tomography images

X-ray computed tomography (CT) is an effective noninvasive tool to visualize fresh agricultural commodities’ internal components and quality attributes, including those of chestnuts (Castanea spp). There is no procedure to automatically, effectively and efficiently classify fresh commodities from a continuous inline flow through a CT system. If the information obtained by CT scanning of fresh agricultural commodities is to be used in an industrial application (e.g. inline sorting), automated interpretation of CT images is essential. For this purpose, an image analysis method (algorithm) for the automatic classification of CT images obtained from 2848 fresh chestnuts (cv. ‘Colossal’ and ‘Chinese seedlings’), during the harvesting years from 2009 to 2012, was developed and tested. Classification accuracy was evaluated by comparing the classes obtained from six CT images per chestnut to their internal quality assessment. An experienced human rater performed internal quality assessment by visually and invasively rating fresh chestnut internal decay severity (quality) into 5-, 3- and 2-classes.After CT image preprocessing, cropping and segmentation, 1194 grayscale intensity and textural features were extracted from six resultant CT images per sample. Relevant features were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. 86, 155 and 126 features were effective in designing a quadratic discriminant classifier with a 4-fold cross-validation with a performance accuracy of 85.9%, 91.2% and 96.1% for 5, 3 and 2 classes, respectively. This method is accurate and objective in determining fresh chestnut internal quality, and the methodology is applicable to automatic noninvasive inline CT sorting system development.     

Studies on classification models to discriminate ‘Braeburn’ apples affected by internal browning using the optical properties measured by time-resolved reflectance spectroscopy

This work aimed at studying the feasibility of time-resolved reflectance spectroscopy (TRS) to nondestructively detect internal browning (IB) in ‘Braeburn’ apples through the development of classification models based on absorption (μ_a) and scattering (μ_s′) properties of the pulp.This research was carried out in two seasons: in 2009, apples were measured by TRS at 670 nm and in the 740–1040 nm spectral range on four equidistant points around the equator, whereas in 2010 apples were measured by TRS at 670 nm and at 780 nm on eight equidistant points.The values of the absorption coefficients measured in the 670–940 nm range increased with IB development. On the contrary, μ_s′780 was higher in healthy fruit than in IB ones. The μ_a780 also significantly increased with IB severity, showing high values when IB affected the pulp tissues compared to the core ones. Also μ_a670 changed with IB development, but it was not able to clearly discriminate healthy fruit from IB ones because its value was also affected by the chlorophyll content of the pulp.The absorption and scattering coefficients were used as explanatory variables in the linear discriminant analysis in order to classify each apple tissue as healthy or IB; then the models obtained were used for fruit classification. The best classification performance was obtained in 2010 using μ_a780 and μ_s′780 and considering the IB position within the fruit: 90% of healthy fruit and 71% of IB fruit were correctly classified. By using all the μ_a measured in the 670–1040 nm range plus the μ_s′780, IB fruit classification was slightly better while healthy fruit classification was worse. The better result of 2010 was due to the increased number of TRS measurement points that allowed better exploration of the fruit tissues. However, the asymmetric nature of this disorder makes detection difficult, especially when the disorder is localized in the inner part of the fruit (core) or when it occurs in spots. A different TRS set-up (position and distance of fibers, time resolution) should be studied in order to reach the deeper tissue within the fruit in order to improve browning detection.