Kerr介质中级联三能级Jaynes-Cummings模型原子跃迁转移

在假设输入光场为单模相干场,原子制备在最高激发态的条件下,运用全量子论方案研究了Kerr介质中一个级联三能级原子布居随时间演化特点。结果表明:当单光子失谐量较大时,改变输入光场强度可以实现原子跃迁转移。文中对该现象给出了一种解释。     

光电倍增管的结构与性能研究

光电倍增管以其优越的性能,成为高技术测量设备中的首选光传感器。以常规光电倍增管以及两种新型光电倍增管（微通道板光电倍增管和位置灵敏光电倍增管）为例介绍它们各自的结构和工作原理,并在此基础上对其特性进行分析和讨论,展示三代光电倍增管在结构、性能方面的超越。对光电倍增管的发展前景作出了展望。     

基于Arena的乌江梯级枢纽区域货运组织仿真模拟优化

针对贵州省乌江梯级枢纽区域复杂的货运系统,为弥补理论优化模型不能反映堵船、升船机服务效率、港口及升船机工作强度的局限性,结合arena平台进行货运组织的仿真模拟和方案优化,得到广义费用相对较低且资源利用均衡的货运组织方案,经实例证明,这种结合理论方法和仿真技术的优化方法是切实可行的。     

Automated vision inspection of rail surface cracks: A double-layer data-driven framework

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection.     

基于聚类的朴素贝叶斯分类无监督学习方法

为实现朴素贝叶斯分类模型的无监督学习,提出一种基于数据挖掘理论中聚类算法思想的学习方法。该方法首先定义不同类型单维状态分量的差异度量方法和混合型多维向量的联合差异度量方法,通过分析样本数据中向量之间的差异性进行聚类统计,得到研究对象的分类类别,然后对各单维状态分量分别聚类得到特征核值,进而确定不同类别各分量对应单维特征核值空间的概率隶属度。仿真实验结果表明,该方法能有效进行朴素贝叶斯分类学习。     

多分类器融合在水中目标识别中的应用

为了提高对水中目标的识别能力,研究使用DS证据理论对多分类器进行融合,论述以往确定BPA方法的优缺点,并在此基础上提出一种新的利用分类器性能和输出信任度来确定BPA的多分类器融合方法,实验证明这种方法在对水中目标进行识别问题中的优越性,为进一步研究分类器融合在目标识别中的应用提供参考.     

沥青路面破损图像分割方法研究

提出了一种减少沥青路面破损图像识别计算量的图像分割方法。将路面图像等分为64×64像素的子块图像,并用灰度方差值描述子块图像特征。同时,设计了基于BP人工神经网络的子块图像模式分类器,利用子块图像模式分类结果所组成的矩阵作为路面破损图像分割结果。实验表明,该方法可较好地分割路面破损图像。     

A Multiple Model Approach to Modeling Based on Fuzzy Support Vector Machines

A new multiple models (MM) approach was proposed to model complex industrial process by using Fuzzy Support Vector Machines(F_SVMs). By applying the proposed approach to a pH neutralization titration experiment, F_SVMs MM not only provides satisfactory approximation and generalization property, but also achieves superior performance to USOCPN multiple modeling method and single modeling method based on standard SVMs.     

基于粗糙集和贝叶斯分类器的病毒程序检测

在病毒程序检测中将粗糙集与贝叶斯分类器相结合．该方法在粗糙集属性约简的基础上，综合考虑了条件属性和决策属性的依赖性以及条件属性间的依赖性对约简的影响．通过基于依赖性的属性约简，减少对属性变量间独立性的限制，发挥贝叶斯分类器的鲁棒性潜能，优化贝叶斯分类器的特性．实验结果表明，检测率达到97．88％，正确率为97．16％，明显高于传统的基于特征和RIPPER的方法，也高于多贝叶斯方法；虚警率为5．19％，也比上述所有方法均有所降低．     

基于串行分类器的车牌字符识别

在特征提取的基础土,对采用Bayes分类器和支持向量机的车牌字符识别方案进行比较,提出在Bayes分类器的基础上,再利用支持向量机对Bayes分类器不易区分的车牌字符进行识别的二级串行分类器融合的改进方案,在一定程度上既克服了Bayes分类器对车牌字符识别率低的问题,又解决了支持向量机识别速度慢的问题,可满足车牌字符识别实时性和高识别率的要求。