基于多混沌系统的彩色图像加密方法

为了提高彩色图像加密的安全性和加密性能,设计了一种基于多混沌系统的彩色图像加密方法.将一个彩色图像分解为R、G、B三个灰度图像,使用MD5算法动态生成加密算法的初始值,然后使用三种不同的基于混沌的加密结构对三个图像进行加密.对R图像使用Feistel结构加密,其中Feistel结构的S一盒由Logistic混沌序列和Hyperhenon混沌序列组合产生;对G图像使用由Lorenz系统产生加密序列对图像进行代替和置换操作的加密结构;对B图像使用由分段线性混沌映射产生加密序列,然后加密图像的加密结构,再把加密后的图像结合起来生成加密后的图像.理论分析和实验结果表明,该加密方法能够较为有效地保证彩色图像加密的安全性.     

UCC日产万吨熟料水泥生产线的工艺设计及调试

UOC10 000 t/d熟料水泥生产线以"性能可靠、技术先进、经济实用、节能降耗"为工艺设计的总体原则,其特点为:窑头废气处理使用袋收尘器;窑尾废气降温没有使用增湿塔;窑尾废气处理使用旁路放风系统;燃料使用天然气和重油。采取措施解决了调试中部分车间扬尘严重,生料磨达不到设计产量,窑尾烟室有结皮现象等问题。调试后,该生产线实现了一次性点火成功;一次性投料成功;一次性考核通过。     

一类离散混沌系统的传输受限同步问题

研究了一类离散混沌系统信息受限条件下的同步问题.将具有离散形式的混沌驱动系统和具有控制输入的响应系统通过一个有限容量信道的通讯网络进行连接.在这种情况下,设计了合适的编解码方法使得传输误差在一定的条件下达到渐近稳定.同时,在线性矩阵不等式条件下,获得了同步误差方程关于传输误差是输入状态稳定,从而实现了混沌系统在信道容量有限条件下的完全同步.最后,通过Fold混沌系统验证了本文方法的有效性.     

局部颜色特征与工艺参数融合的铜浮选入矿类型识别

针对铜浮选过程中矿源变化频繁而造成的浮选工况难以识别、生产过程不稳定等问题,在分析铜浮选流程特点的基础上,提出了一种局部颜色特征与关键工艺参数融合的铜浮选入矿类型识别方法.首先采用多元图像处理方法提取泡沫图像局部颜色特征,然后采用混合滤波、时间配准算法将其与关键工艺参数进行数据融合,最后用融合后的混合特征进行模糊聚类,得到隶属度矩阵和聚类中心.工业数据仿真结果验证了本文所提方法的有效性.     

基于弹性网格混合特征的脱机手写维文识别

手写体维文识别技术的关键在于特征提取方法的选择。为探求一种可靠性高、可分性强的脱机手写维文特征提取方法,在分析现有方法的基础上,结合维吾尔文字词自身的特点,提出一种在局部特征上基于弹性网格区域笔划密度、方向分解特征,在全局特征上提取交叉点、环、弧形笔划、附加笔划、外围轮廓特征的混合特征提取方法。通过在IFN/ENIT标准数据库及自采样数据集的聚类分析实验,识别正确率分别达到85%、84.3%。结果表明,方法综合统计特征和结构特征提取的优点,具有较强的抗扰能力,可分性优于GSC法。     

基于特征融合的调制识别增强与迁移演化

针对调制识别中单一图像的特征信息不足,区分度不够高,识别范围受限的问题。本文提出了一种基于时频图和星座图特征融合的调制识别特征增强方法,利用深度学习神经网络提取信号图像的特征,构建特征空间,通过多维特征融合,挖掘和整合不同特征的优势,增强模型算法的鲁棒性。此外运用了模型迁移的方法,仅需对分类器进行训练,大幅节约了训练时间和资源,具有很强的实时性和实用性。仿真结果显示,在0db左右的条件下,相比于单一特征图像,采用特征融合增强的方法能将信号的平均识别率提高约25%,通过模型迁移,省去了卷积神经网络的训练,所需的训练时间约为迁移前的9.6%,消耗内存约为迁移前的7.3%,同时模型的识别率损失控制在了5%以内。     

单螺杆混沌挤出过程周期性行为数值模拟

建立了混沌单螺杆挤出机计量段物理和数学模型。定义了更合理的边界条件,采用Carreau本构方程,采用有限体积方法对聚合物熔体在螺槽内流动的周期性效应进行了数值模拟分析。得到了不同位置的速度分布细节以及流体示踪粒子的行为轨迹。发现螺槽内速度分布表现出周期性和对称性的变化,揭示了同宿点及椭圆点存在及位置不变性,为进一步分析流体质点在相空间的动力学行为奠定了基础。     

3D fingerprint reconstruction system using feature correspondences and prior estimated finger model

The paper studies a 3D fingerprint reconstruction technique based on multi-view touchless fingerprint images. This technique offers a solution for 3D fingerprint image generation and application when only multi-view 2D images are available. However, the difficulties and stresses of 3D fingerprint reconstruction are the establishment of feature correspondences based on 2D touchless fingerprint images and the estimation of the finger shape model. In this paper, several popular used features, such as scale invariant feature transformation (SIFT) feature, ridge feature and minutiae, are employed for correspondences establishment. To extract these fingerprint features accurately, an improved fingerprint enhancement method has been proposed by polishing orientation and ridge frequency maps according to the characteristics of 2D touchless fingerprint images. Therefore, correspondences can be established by adopting hierarchical fingerprint matching approaches. Through an analysis of 440 3D point cloud finger data (220 fingers, 2 pictures each) collected by a 3D scanning technique, i.e., the structured light illumination (SLI) method, the finger shape model is estimated. It is found that the binary quadratic function is more suitable for the finger shape model than the other mixed model tested in this paper. In our experiments, the reconstruction accuracy is illustrated by constructing a cylinder. Furthermore, results obtained from different fingerprint feature correspondences are analyzed and compared to show which features are more suitable for 3D fingerprint images generation.     

A global energy optimization framework for 2.1D sketch extraction from monocular images

The 2.1D sketch is a layered image representation, which assigns a partial depth ordering of over-segmented regions in a monocular image. This paper presents a global optimization framework for inferring the 2.1D sketch from a monocular image. Our method only uses over-segmented image regions (i.e., superpixels) as input, without any information of objects in the image, since (1) segmenting objects in images is a difficult problem on its own and (2) the objective of our proposed method is to be generic as an initial module useful for downstream high-level vision tasks. This paper formulates the inference of the 2.1D sketch using a global energy optimization framework. The proposed energy function consists of two components: (1) one is defined based on the local partial ordering relations (i.e., figure-ground) between two adjacent over-segmented regions, which captures the marginal information of the global partial depth ordering and (2) the other is defined based on the same depth layer relations among all the over-segmented regions, which groups regions of the same object to account for the over-segmentation issues. A hybrid evolution algorithm is utilized to minimize the global energy function efficiently. In experiments, we evaluated our method on a test data set containing 100 diverse real images from Berkeley segmentation data set (BSDS500) with the annotated ground truth. Experimental results show that our method can infer the 2.1D sketch with high accuracy.     

GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.