基于比累对切透镜的联合菲涅耳变换相关器特性研究

相关器的特性变化规律对其实际应用中参数的设定具有重要的指导意义。针对基于比累对切透镜的联合菲涅耳变换相关器,通过数值仿真详细分析了比累对切透镜缝宽变化对相关器输出特性和空变特性的影响。结果表明,比累对切透镜缝宽能一定程度地控制联合菲涅耳变换功率分布的有效面积、相关峰的尖锐度及其空变特性的敏感度。实验采用纯相位空间光调制器编码比累对切透镜相位掩模替代实体透镜的方式搭建了相关器,实验结果与仿真结果一致性好,进一步验证了上述结论的正确性。     

生物氧化预处理中进气量实时预测数学模型研究

精准预测生物氧化预处理中的进气量对提高黄金提取率和节能降耗具有重要意义。以气体管流连续性方程和运动方程为控制方程，采用Preissmann隐格式法作为差分方法。同时，根据集合卡尔曼滤波(Ensemble Kalman filter，EnKF)算法原理，构造进气量、压强的状态空间模型。结果表明，基于气体管流控制方程建立的进气量模型预测结果与实际进气量观测值具有较好的一致性；与传统静态预测方法相比，EnKF同化方法引入实时观测值和模型参数的更新，有效提高了进气量的预测精度，其平均绝对误差、平均相对误差和均方根误差有明显的降低。可见，基于气体管流控制方程建立的预测模型结合EnKF同化方法是提高生物氧化槽进气量预测精度的有效手段。     

数字化羊绒测色法及其在长度测量中的应用

针对光电法测试纤维长度等性质时,存在纤维的透光及反光信号与颜色高度相关的问题,采用数字化方法表征羊绒的颜色类别并精准地测量羊绒的长度。将彩色光电耦合器作为测色传感器,搭建了更适合纤维材料的测色装置;通过颜色模型在3种色空间的转换,得到3类颜色的羊绒在三维色空间的分布;基于系列实验,创建了有色羊绒的光学参数即无穷厚时的表观反射率表观反射率与测色装置计算的亮度指标之间的换算模型,并用实验证明采用测得的表观反射率可计算得到准确的须丛线密度曲线,从而可用光电法精准测量有色羊绒的长度。结果表明,该测色方法的测样面积大,代表性好,成本低,同时可用于其他纤维和纺织品的颜色测量。     

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。     

亮色系建筑涂料色浆的研制及应用

阐述了自制亮色系色浆的必要性。介绍了自制亮色系色浆的颜料筛选,配方调整及其在建筑涂料中应用后对面漆性能的影响。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Testing the performance of CIECAM02 from 100 to 3500 cd/m2

Color appearance models were developed to characterize the color attributes of stimuli under different viewing conditions based on data collected through magnitude estimation or color matching experiments. Although human beings experience very high light levels under daylight and the reproduction of colors under daylight is important in the color and imaging industries, the existing color appearance models were developed based on the data that were collected under the conditions with luminance levels below 700 cd/m² due to the lack of facilities to produce stable illumination at high light levels. A recent study investigating color preference of an artwork under a wide range of light levels from 20 to 15 000 lx suggested that CIECAM02 cannot accurately characterize the color appearance under extremely high light levels. This study was designed to directly test the performance of CIECAM02 from 100 to 3500 cd/m². Human observers performed color match for four hues under a series pairs of adapting conditions with a haploscopic viewing condition. It was found that CIECAM02 had the best performance in characterizing the hue angles but the worse performance in characterizing the brightness with a maximum underprediction around 200% across a wide range of luminance. This was mainly due to the fact that CIECAM02 was developed based on the data collected under relatively low adapting luminance levels. The color appearance model that was proposed to use the adapting luminance levels in characterizing the cone compression in the postadaptation process was found to have a much better performance in characterizing the brightness.     

Multiemotional product color design using gray theory and nondominated sorting genetic algorithm-III

When users select products, they consider the emotional experience resulting from the color of the product. However, the emotional demands of users for product color are multidimensional and diverse. It is very important yet difficult to accurately grasp multiemotional image requirements and effectively convert them into design elements. Therefore, multiemotional product color design (MEPCD) has become a very important and challenging research topic. In this article, a novel MEPCD system using gray theory (GT) and nondominated sorting genetic algorithm-III (NSGA-III) is proposed to effectively solve the MEPCD problem. First, the image perception spaces of users, which exist in different emotional dimensions, were collected using factor analysis and the semantic differential technique. Then, GT was used to establish a multidimensional emotional product color image evaluation model. Finally, NSGA-III was used to optimize and design a multiemotional color scheme for a product. Furthermore, according to actual conditions, an MEPCD system was established based on the proposed method. The design case study shows that the method and design system proposed in this article have a certain range of applicability and can effectively improve the practicality of MEPCD.     

Optimization of quinacridone magenta,Cu-phthalocyanine cyan,and arylide yellow ink films formulated for maximum color gamut

Investigation of ink formulation options with the purpose to obtain color-gamut-optimal set of Cyan Magenta and Yellow CMY inks is reported. Implementation of the thickness dependent Kubelka-Munk model on multiple ink layers having different and well-defined thicknesses, provides characteristic absorption and scattering (K, S ) spectra of the ink ingredients. These data enable accurate computation of the reflectance spectrum and thus the L*a*b* color coordinates for any given ink thickness or substrate. Pigment materials investigated are quinacridone as magenta, copper-phthalocyanine as cyan, and arylide yellow. Scaling the peak of the absorption band to the number of molecules per unit area for the specific pigments studied in this article provides the molar extinction coefficients, 1.21 × 10⁴ , 4.7 × 10⁴ , and 3.3 × 10⁴ cm²/millimole respectively, regardless of the different ink formulations used, in accord with Avogadro's principle. Having a set of three pairs of K, S spectra is used to compute the color gamut of any CMY color combination in the L*a*b* space as a function of ink layer thickness and formulation. Using an iterative algorithm, a color-gamut-optimal set of CMY inks is obtained.