以家园文化为基础探索构建幸福企业

马钢的生存发展与职工的生存发展息息相关、浑然一体。只有企业得到发展壮大,每一名员工才能得到更好的发展,也只有员工的积极参与和努力奋斗,才能有企业的日益强大。从现实而言,为应对钢铁"去产能"压力,增强国有企业活力,就必须贯彻落实党的十八大精神,落实供给侧改革措施,踏踏实实地加强和完善国有企业党的工作和群团工作,在工作中不断创新和改革。动员和带领全体员工,激发创新活力,就是要使"人民对美好生活的向往"的奋斗目标具体化,就是建设"美丽安徽"、"幸福社区"和"幸福企业"。立足岗位开展"奋力求生存、携手保家园"活动。     

基于解析解的四阶PDE曲面裁剪方法

该文提出了一种可以广泛应用于四阶PDE曲面的裁剪方法。利用PDE的参数域内的曲线在曲面上投影,得到所求裁剪曲面的边界曲线,然后通过边界曲线的导矢与曲面在边界曲线处的法向量得到边界曲线处的跨界导矢,最后以求得的裁剪曲面的边界曲线以及裁剪曲面在边界曲线处的跨界导矢为PDE曲面的边界条件,用四阶的PDE曲面方程求得PDE裁剪曲面。     

市场机制下建筑节能相关利益主体博弈研究

充分发挥市场机制对节能产品的调控作用是市场经济的基本内容。通过对建筑节能市场发展中存在的相关问题研究,发现我国建筑节能市场的外部性和信息不对称性是产生问题的根源。全面分析建筑节能市场中政府、开发商、消费者以及其他服务组织等利益主体对市场培育的核心作用,通过构建政府与开发商、开发商与消费者之间的动态博弈模型,研究相关利益主体进一步完善建筑节能市场应采取的对策。     

非平稳时序循环平稳趋势提取与机械故障诊断

在简单介绍非平稳时序模型周期趋势项提取方法的基础上，提出了机械故障诊断和分析方法，该方法利用旋转机械出现故障以后所呈现的循环平稳特性，使用循环平稳分析方法剔除非平稳时间序列模型中的趋势项，然后建立平稳时序模型，用该模型进行机械故障诊断分析。并运用仿真和实例对方法的正确性进行了验证。     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

基于Word文档的数据隐藏方法

在分析当前数据隐藏与恢复相关技术研究基础上,参照微软官方最新公布的复合文档存储规范,从利用载体Word文档现有空间中未真正使用部分和适度扩展新空间两条思路出发,设计一种利用Word文档作载体隐藏秘密信息的解决方案,包括在文字、各种嵌入式对象及其相应控制结构数据中隐藏信息。实践表明,隐藏数据后的载体文档可以被正常拷贝和使用电子邮件传送,根据需要隐藏的信息随机选择使用该方案,被隐藏信息更不容易被侦测出来,被二次编辑后被隐藏信息一般不会被破坏,鲁棒性不错,具有一定的实用价值。     

网电空间战与能力建设研究

随着信息化步伐的加快,网电空间正在不断加深对人类社会的影响。网电空间的重要作用已经得到世界各国的广泛认同。网电空间战将成为未来战争的重要部分。本文分析了网电空间的定义和重要性,介绍了网电空间战的威力,研究了美国网电空间战能力建设中的重要举措,指出我国加强网电空间战能力建设的必要性。     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

Mining e-Learning domain concept map from academic articles

Recent researches have demonstrated the importance of concept map and its versatile applications especially in e-Learning. For example, while designing adaptive learning materials, designers need to refer to the concept map of a subject domain. Moreover, concept maps can show the whole picture and core knowledge about a subject domain. Research from literature also suggests that graphical representation of domain knowledge can reduce the problems of information overload and learning disorientation for learners. However, construction of concept maps typically relied upon domain experts in the past; it is a time consuming and high cost task. Concept maps creation for emerging new domains such as e-Learning is even more challenging due to its ongoing development nature. The aim of this paper is to construct e-Learning domain concept maps from academic articles. We adopt some relevant journal articles and conference papers in e-Learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-Learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to the e-Leaning field, to study related issues, for teachers to design adaptive learning materials, and for learners to understand the whole picture of e-Learning domain knowledge.     

一种多分类器集成的手写体汉字识别方法

多分类器集成是手写体汉字识别领域的新方向。本文提出的多分类器集成方法通过改进的欧氏距离分类器将待识别汉字分类到某个粗分结果集中,然后根据粗分结果集选择1-N(one-against-rest)的SVM分类器对待识别汉字进行细分,最后用贝叶斯集成两级分类器。实验对国标一级汉字中的1034个手写汉字进行识别,证明了方案的有效性。