基于梯度提升决策树的车轮轮缘厚度磨耗预测

轮对在列车走行过程中起着导向、承受以及传递载荷的作用，其踏面及轮缘磨耗对地铁列车运行安全性和钢轨的寿命都将产生重要影响。根据地铁列车车轮磨耗机理，分析车轮尺寸数据特点，针对轮缘厚度这一型面参数，基于梯度提升决策树算法构建轮缘厚度磨耗预测模型。在该模型的基础上，任意选取某轮对数据进行验证分析，结果表明：基于梯度提升决策树的轮对磨耗预测模型具有较好的预测精度，可预测出1~6个月的轮缘厚度变化趋势范围，预测时间范围较长，可为地铁维保部门对轮对的维修方式由状态修转为预防修提供指导性建议。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

门架式堆取料机在铜冶炼中的生产实践

针对铜冶炼工艺的特点,从门架式堆取料机对前后工序衔接、结构、原理出发,介绍了其在熔炼炉系统和吹炼炉系统中的应用情况。经过试生产期的生产实践证明,门架式堆取料机具有场地利用率高、取料量大,自动化程度高和安全环保等优点。     

基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

地统计克立格插值法在工程土方计算中的应用

土方计算是工程中经常碰到的问题。尝试利用地统计学的克立格插值法来计算土方，将其应用于余姚市泗门镇土地整理工程中进行实测试验。介绍了其基本的原理和公式，并与利用数字高程模型计算得到的土方工程量进行了比较，发现地统计插值法可较精确地确定设计标高和土方量，证明该方法的可行性和优越性。     

Revised Estimates of Dimension and Exercise Variance Components in Assessment Center Postexercise Dimension Ratings.

The authors reanalyzed assessment center (AC) multitrait-multimethod (MTMM) matrices containing correlations among postexercise dimension ratings (PEDRs) reported by F. Lievens and J. M. Conway (2001). Unlike F. Lievens and J. M. Conway, who used a correlated dimension-correlated uniqueness model, we used a different set of confirmatory-factor-analysis-based models (1-dimension-correlated Exercise and 1-dimension-correlated uniqueness models) to estimate dimension and exercise variance components in AC PEDRs. Results of reanalyses suggest that, consistent with previous narrative reviews, exercise variance components dominate over dimension variance components after all. Implications for AC construct validity and possible redirections of research on the validity of ACs are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

应用改进BP网络对潼关高程的预测

作者在分析了影响汛前和汛后潼关高程变化值的因素的基础上，利用改进BP网络模型对非特征汛期和汛期潼关高程的变化值进行了计算。计算中不采用潼关的绝对高程，而用高程的变化量作为网络的较出物正量，从而减少计算误差，同时又能客观地考虑各影响因子的作用。改进后的BP模型学习时间短，精度满足要求。用该模型计算的潼关高程变化趋势与实际水库调度运行中的高程基本吻合。