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Web的重点建设项目管理系统

为提高重点建设项目的管理水平和工作效率,在详细分析重点建设项目的业务流程和用户需求的基础上,提出基于Web的重点建设项目信息管理系统。该系统采用典型的三层体系架构,具有较强的可扩展性和灵活性。通过该系统对重点建设项目信息科学管理,可以有效克服传统管理方式的弊端,极大地提高重点建设项目信息管理工作的效率。     

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。     

重力坝深层抗滑稳定设计初探

针对某水库重力坝坝基地层倾角平缓(8°~15°),基岩为薄层状结构,坝基深层抗滑设计是工程关键技术,在利用抗剪断强度理论进行坝基抗滑稳定计算的基础上,通过分析比较常见的坝基基础处理措施,最终确定重力坝坝基采用坝趾处设置混凝土深齿墙措施,可同时提高坝基浅层及深层抗滑稳定安全系数。     

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.     

试谈高可用性计算机实训室的配置与管理

基于计算机实训教学的需求,重点探讨高可用性计算机实训室的搭建要求、配置特点与管理策略。     

中国古代陶瓷雕塑的美学特征

中华民族是个古老的、有着悠久历史和灿烂文明的伟大民族,远在八千年前,中国的文明在世界就已初露曙光。漫步在漫长的历史长河边,流连于丰富的博物馆展厅里,对我国悠久的历史和灿烂的文明作一次美的巡礼该是使人感到无比的欣慰和自豪的了。美学特征乃艺术的属性,总结它不是为了停留于前人的成果上,而是为了新的创造和新的开拓。