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测量不确定度是评定测量结果可靠性的指标,不确定度是表征被测量值分散性的参数,是检测结果的一部分。不确定度理论虽已建立,但在各领域的应用还有待深入。本文主要研究测量不确定在纺织品检测中目光评级项目的应用,主要分析了目光评级项目A类、B类不确定的来源和分类,给出了目光评级不确定度的评定方法。 相似文献
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色牢度是指纺织品颜色对它在加工和应用过程中所受各种作用的抵抗力,它是印染织物的一项重要品质指标,色牢度评定准确与否,直接关系到对产品品质的最终评定。目光评级是度量色牢度的方法之一,评级除了要操作正确,技术熟练外,还要避免那些影响评级的其它因素。现就这一问题谈几点认识和看法。 2.1光源的选定 织物的颜色与光源有关。由于各种光源(日光、白炽灯、日光灯)的光谱功率分布不同,同一织物在不同光源下观察,其结果是不相同的。为了保证每次评级重演性好,我们采用北空天然光照或模拟昼光——CIE 相似文献
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贴衬织物在试验中已经发生变化,用其作为评定沾色牢度的参比样会影响评级结果。本文指出应该用经过空白试验的贴衬作参比样的方法来评定沾色牢度。 相似文献
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正日前,《广西乡村旅游质量等级评定管理暂行办法》正式实施。该办法规定,评级后复核达不到标准的乡村旅游区和农家乐,将会受到降级或取消评级处理。目前,已有9家乡村旅游区和农家乐被摘星或降级。广西乡村旅游质量等级评定包括星级乡村旅游区、星级农家乐质量等级评定,从高到低分为五星级、四星级、三星级、二星级、一星级共5个等级。该办法原则上只对依法注册登记的独立管 相似文献
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通过实验室内部检验人员目光比对的方式找出色牢度评级中容易产生色差评定偏松或偏紧的色系,并针对目光调整提出相关建议,探讨了影响评级的其他相关因素。 相似文献
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为了开发营养丰富的多谷物面包预拌粉,本研究以杂粮粉为主要原料,添加谷朊粉构建面包网络结构,通过评价混合粉黏度特性,面团发酵流变特性和面包加工品质,研究多谷物粉含量对面团加工特性和面包品质的影响。结果表明:随着多谷物粉含量的增加,多谷物预拌粉的糊化黏度呈显著性上升,糊化温度呈显著性下降,面团的最大发酵高度和最终发酵高度显著降低(p<0.0.5)。当多谷物粉添加量从50%增加到80%时,面包的比容从5.10 mL/g降低到1.83 mL/g,体积降低,弹性下降,硬度和耐咀性增加。多谷物粉含量、糊化特性、部分发酵流变特性与面包质构特性、比容和颜色L*、a*之间均呈显著或极显著相关。当多谷物粉添加量在50%~60%之间,面包品质良好;添加量≥65%时,所制成的面包品质较差。 相似文献
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Design of an Optimum Computer Vision‐Based Automatic Abalone (Haliotis discus hannai) Grading Algorithm
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An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R2 value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R2 value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half‐oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross‐validation indicate root mean square and worst‐case prediction errors of are 2.8 and ±8 g, respectively. 相似文献
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为实现茶叶品质在线评价及自动分级,摒除茶叶品质人工感官审评存在的缺陷,本文研发一套基于计算机视觉技术的茶叶品质在线评价及自动分级系统。采用Open CV和Visual C++开发茶叶品质在线评价软件,结合监督正交局部保持投影方法(supervised orthogonal locality preserving projections,SOLPP)对图像特征变量进行降维处理,对比随机森林(random forest,RF)、反向传播神经网络(back-propagation artificial neural network,BP-ANN)和相关向量机(relevance vector machine,RVM)茶叶品质在线评价模型,得出随机森林算法所建模型性能最好。系统自动完成茶样图像采集、原始图像预处理、特征提取、基于所建模型对待检茶样进行等级评价。控制系统根据评价结果,驱动分级及收集装置将检测茶样输送到相应等级槽中。经测试,研发系统对市售婺源仙芝绿茶、碧螺春绿茶的分级准确率达到93.00%以上。本系统结构简单,运行稳定,能将待检茶样准确送入到相应等级槽中,满足茶叶等级在线评价要求。 相似文献
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BackgroundOptical techniques, including computer vision, spectral imaging, near-infrared technology and other emerging imaging and spectroscopy techniques, have been rapidly developing and widely applied in fruit and vegetable grading systems for nondestructive quality inspecting and grading over the past decades. However, automatic detection of quality and grading is still difficult due to some still existing challenges, which are the key of blocking their commercialization in robotic fruit and vegetable grading systems. The challenges include the following aspects: the influence of physical and biological variability, whole surface detection, discrimination between defects and stems/calyxes, unobvious defect detection, robustness of the features and algorithms, as well as rapid optical detection system development. These challenges can reduce the fruit or vegetable quality inspection accuracy, thus greatly reducing automatic level of the quality inspecting and grading machines.Scope and approachAs agricultural engineers with about eight years of technical experience in fruit grading systems, we believe the ultimate goal of each scientific research should seek its task in serving the engineering. So, we have made many attempts to solve the challenges and increase the automation of the grading machines.Key findings and conclusionsThe review gives a detailed summary about the challenges and solutions of optical-based nondestructive quality inspection for fruit or vegetable grading systems from the perspective of engineering. Particular attention has been paid to the techniques that can improve the automation degree of the grading robot in this review. The advantages and disadvantages of the solutions are compared and discussed. Additionally, the remaining engineering challenges and future trends are also discussed. 相似文献
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设计了基于机器视觉技术相配套的红枣全表面信息无损分拣系统,整机由红枣自动喂料机构、红枣排序机构、输送及调整机构、分级执行机构、检测及控制部分组成。该系统利用镜面反射原理设计的红枣表面信息采集机构,能同时采集红枣正面、两侧面和底部图像,完成红枣全表面信息的获取,解决了以往分拣系统无法在高速运输状态下在线呈现红枣全表面信息的难点。基于视觉的无损分级软件将实时处理采集到的图像,分析与获取红枣的果形大小、表面质量、纹理等表面信息,并采用信息融合技术进行判别分级。试验结果表明,本系统分级速度快,分级准确率达到90%以上,可较好满足红枣自动化快速检测分级生产要求。 相似文献
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目的:实现烟叶分级流程烟梗部位的智能抓取,防止智能烟叶分级系统中机械手在抓取烟叶时对叶面造成损伤,减少烟叶智能分级设备生产中的人为操作,解决烟叶分级系统中的单片烟叶识别分类问题与对应等级单片烟叶存放问题。方法:提出一种基于改进YOLOv3的卷积神经网络烟梗自动识别定位模型。该模型在原有的YOLOv3的基础模型上改变单元模块结构引入注意力机制模块,优化模型参数,使用Swish激活函数,实现了对烟叶图像全部信息进行目标定位识别,构建烟梗目标检测模型。结果:改进后的YOLOv3模型的loss能更快的收敛,其mAP由90.46%提升为97.48%,准确率由95.33%提升为97.35%,回归率由84.65%提升为95.65%,为后续烟叶自动化分类打下基础。结论:与YOLOv3、Faster-rcnn、YOLOv4、Efficientdet算法作对比分析表明试验提出的算法更加轻量化,识别效果更好,能减少对烟梗试验平台的硬件配置要求,提高烟叶分类系统的经济效益,为烟叶分级系统中烟叶上料与分仓提供准确的位置信息。 相似文献