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.     

服役后20号钢在低温下的力学性能研究

分析了20号无缝钢管的室温和低温力学性能,结合显微组织观察、晶粒度统计和断口分析,发现低温低应力工况下服役后20号无缝钢管的力学性能完全符合冷库用液氨管道的使用要求。     

共享交通的时空轨迹检索与群体发现

为解决共享交通下的共乘用户群体发现效率低、准确率不高问题，依据R-树原理建立GeoOD-Tree索引，并在此基础上提出以最大化共乘率为目标的群体发现策略。首先，对原始时空轨迹数据进行特征提取与标定处理，挖掘有效出行起讫点（OD）轨迹；其次，针对用户起讫点轨迹的特征，建立GeoOD-Tree索引进行有效的存储管理；最后，给出以最大化共乘行程为目标的群体发现模型，并运用K最近邻（KNN）查询对搜索空间剪枝压缩，提高群体发现效率。采用西安市近12000辆出租车营运轨迹数据，选取动态时间规整（DTW）等典型算法与所提算法在查询效率与准确率上进行性能对比分析。与DTW算法相比，所提算法的准确率提高了10.12%，查询效率提高了约15倍。实验结果表明提出的群体发现策略能有效提高共乘用户群体发现的准确率和效率，可有效提升共乘出行方式的出行率。     

Metal-polyphenol-network coated CaCO3 as pH-responsive nanocarriers to enable effective intratumoral penetration and reversal of multidrug resistance for augmented cancer treatments

Construction of multifunctional stimuli-responsive nanotherapeutics enabling improved intratumoral penetration of therapeutics and reversal of multiple-drug resistance (MDR) is potent to achieve effective cancer treatment. Herein, we report a general method to synthesize pH-dissociable calcium carbonate (CaCO₃) hollow nanoparticles with amorphous CaCO₃ as the template, gallic acid (GA) as the organic ligand, and ferrous ions as the metallic center via a one-pot coordination reaction. The obtained GA–Fe@CaCO₃ exhibits high loading efficiencies to both oxidized cisplatin prodrug and doxorubicin, yielding drug loaded GA–Fe@CaCO₃ nanotherapeutics featured in pH-responsive size shrinkage, drug release, and Fenton catalytic activity. Compared to nonresponsive GA–Fe@silica nanoparticles prepared with silica nanoparticles as the template, such GA–Fe@CaCO₃ confers significantly improved intratumoral penetration capacity. Moreover, both types of drug-loaded GA–Fe@CaCO₃ nanotherapeutics exhibit synergistic therapeutic efficacies to corresponding MDR cancer cells because of the GA–Fe mediated intracellular oxidative stress amplification that could reduce the efflux of engulfed drugs by impairing the mitochondrial-mediated production of adenosine triphosphate (ATP). As a result, it is found that the doxorubicin loaded GA–Fe@CaCO₃ exhibits superior therapeutic effect towards doxorubicin-resistant 4T1 breast tumors via combined chemodynamic and chemo-therapies. This work highlights the preparation of pH-dissociable CaCO₃-based nanotherapeutics to enable effective tumor penetration for enhanced treatment of drug-resistant tumors.

     

化工过程分析与合成课程教学模式改革与实践

基于工程教育专业认证的核心要求和学生素质教育的目标,化工过程分析与合成课程进行了教学模式改革与相应教学内容的建设,通过多层次的教学环节设计,探索以学生为中心的课程教学方法和评价模式,引导学生从整体上把握化工过程,培养学生把握问题本质及分析和解决问题的综合能力,使学生掌握过程分析与合成的方法论。     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.