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。     

Methodology for optimization of pouring variables using bottom pour for low masses of SS304 (<300 kg) in investment casting process (case study)

Bottom pour ladles with stopper rod systems are commonly used in the metal casting industry. However, stopper rod bottom-pouring systems have not yet been developed for the lower thermal masses of alloys typically used in the investment casting industry. Large thermal masses used with bottom pour systems are typically limited for ladles larger than 700 kg and to certain alloys with higher fluidity and longer solidification time like cast iron, aluminum alloys etc. In this study, bottom pour ladle designs and low thermal mass refractory systems have been developed and evaluated in production investment foundry trials with 300 kg pouring ladle. The ladles system and pouring practices used will be described along with the results from the pouring trials for SS304 that represents typical alloys used in Investment casting industries. Optimization of the variables used in an experimentation using Genetic algorithm is also explained.     

Physics‐based spectral sharpening through filter‐chart calibration

The spectral overlap of color‐sampling filters increases errors when using a diagonal matrix transform, for color correction and reduces color distinction. Spectral sharpening is a transformation of colors that was introduced to reduce color‐constancy errors when the colors are collected through spectrally overlapping filters. The earlier color‐constancy methods improved color precision when the illuminant color is changed, but they overlooked the color distinction. In this article, we introduce a new spectral sharpening technique that has a good compromise of color precision and distinction, based on real physical constraints. The spectral overlap is measured through observing a gray reference chart with a set of real and spectrally disjoint filters selected by the user. The new sharpening method enables to sharpen colors obtained by a sensor without knowing the camera response functions. Experiments with real images showed that the colors sharpened by the new method have good levels of color precision and distinction as well. The color‐constancy performance is compared with the data‐based sharpening method in terms of both precision and distinction. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 564–576, 2015     

高精度多环片自动装配控制技术

为了实现对微小环片零件的自动化装配,搭建了自动装配系统.通过4根直线导向轴与4个直线轴承来提高系统的导向精度和刚度.采用直线导轨进行各装配作业模块之间的切换,保证了微小环片零件的自动装配与取出.在环片的装配方向上,螺旋升降机和光栅尺实现环片的位置精度控制.在Lab VIEW编程环境中,采用分层软件架构和模块化控制思想,避免了不必要的数据循环检测与丢失,能够达到环片组件的装配精度要求.控制系统分为系统初始化模块、参数设置模块、装配模块和取出模块,自动装配系统通过各个模块间的相互交流配合完成装配任务.采用本文中自动装配系统装配环片的实验结果表明,环片零件装配的最大位置误差为26μm,垂直度误差为17μm,平均装配时间为75 s/片,可满足环片组件所需的精度要求.     

测试(试井)成果影响因素浅析

随着测试技术的发展、测试质量及资料解释水平的提高，测试（试井）成果的可靠程度越来越受到重视。影响解释成果的环节和因素很多，从客观和主观两方面分析其主要因素，找出症结所在，从而完善现场环节，完善资料解释过程，提高测试技术及解释水平。