基于IOWA算子的组合预测方法

有序加权平均(OWA)算子是近年来发展的在多属性决策中用于集结各决策者的偏好信息或方案优选的方法。通过引进诱导有序加权平均(IOWA)算子，提出了以误差平方和为准则新的组合预测模型，给出了IOWA权系数的确定的数学规划方法。该模型能提高组合预测精度，最后的实例分析表明了模型的有效性。     

Two direct Tustin discretization methods for fractional-order differentiator/integrator

This paper deals with fractional calculus and its approximate discretization. Two direct discretization methods useful in control and digital filtering are presented for discretizing the fractional-order differentiator or integrator. Detailed mathematical formulae and tables are given. An illustrative example is presented to show the practically usefulness of the two proposed discretization schemes. Comparative remarks between the two methods are also given.     

一类改进的自适应蚁群算法

从系统学的角度分析了蚁群算法的分布武计算、自组织和正反馈等的系统特征．给出了改进的蚁群算法详细分析和分类,通过TSP问题应用TSP-Ei151在MATLAB7．6中进行了仿真实验．实验结果证明改进的算法在迭代次数、牧敛性和全局性上优于基本蚁群算法．     

基于MADM方法的个性化推荐研究

个性化推荐服务可以帮助用户便捷高效地获取所需的商品,本文通过将语言标度引入到MADM方法中的模糊语言量词引导OWA算子方法中,利用Web用户反馈的不完全信息来分析具体信息用户偏好,向具体用户推荐其可能感兴趣的商品来提高用户的忠诚度。     

一类四重迭代差分不等式

建立了一类新的差分不等式,利用差分定义、求和技巧、差分方程比较原理,给出了不等式中未知函数的估计,推广了已有的结果。     

锥度量空间中两类混合单调算子的公共不动点定理

在锥度量空间中,由一类混合单调算子的不动点定理扩展到两类混合单调算子的公共不动点定理.同时证明了增算子的不动点定理.     

一种基于边缘信息的车牌字符分割算法

提出了一种新型的基于边缘信息的车牌字符分割方法,首先对采集的彩色车牌进行灰度化,在去除噪声后,对车牌区域利用SUSAN算子进行边缘检测.通过垂直投影分割,确定字符区域的不同宽度,利用字符宽度的分类与区域前后文宽度的变化可降低误分割的概率.实验结果表明,采用该算法对车牌字符进行分割时,可显著提高分割的正确性.     

一类乘积形式的差分不等式

建立了一类新的差分不等式,利用差分定义、求和技巧、差分方程比较原理,给出了不等式中未知函数的估计,推广了已有的结果.     

A prospective,randomized crossover study comparing direct inspection by light microscopy versus projected images for teaching of hematopathology to medical students

Instruction in hematopathology at Mayo Medical School has evolved from instructor‐guided direct inspection under the light microscope (laboratory method), to photomicrographs of glass slides with classroom projection (projection method). These methods have not been compared directly to date. Forty‐one second‐year medical students participated in this pilot study, a prospective, randomized, crossover study measuring educational performance during a hematology pathophysiology course. The students were randomized to one of two groups. All students received the same didactic lectures in the classroom and subsequent case‐based review of peripheral blood smears using either laboratory or projection methods, on day one with a crossover to the other method on day two. Pre‐ and post‐test examinations centered on morphology recognition measured educational performance on each day, followed by a questionnaire identifying the student's favored method. There was no significant difference in the pre‐test and post‐test scores between the two teaching methods (rank‐sum P = 0.43). Students overwhelmingly preferred the projection method and perceived it as superior (76%), although post‐test scores were not significantly different. Student's recommended method was split with 50% favoring the projection method, 43% favoring a combined approach, and 23% noting logistical challenges to the laboratory. In this study, the laboratory and projection method were equivalent in terms of educational performance for hematopathology among medicals students. A classroom‐based approach such as the projection method is favored, given the large class sizes in undergraduate medical education, as well as the ergonomic challenges and additional resources required for large group instruction in a laboratory setting. Anat Sci Educ 7: 130–134. © 2013 American Association of Anatomists.     

基于蚁群算法的图像分割方法改进研究

图像分割是一种对不同特征的像素进行聚类的过程,过程中涉及像素的梯度、灰度及邻域特征。由于蚁群算法的离散性、并行性、全局优化性和稳定性等特点,基于蚁群算法提出一种有效的图像分割改进算法。首先通过蚁群改进算法的模糊聚类能力可以分别计算出像素与目标、背景、噪声点、边界点的隶属关系;然后对于蚁群算法循环次数多、计算量较大的问题,设置启发式引导函数和聚类中心,解决传统聚类中运行效率低、初始化敏感度高等缺点;最后引入梯度算子,对目标与背景灰度值相似图像进行分割,对结果进行了进一步的优化。实验表明,应用该改进算法得到的图像分割结果具有较高的准确度和效率。