One-Dimensional Deep Convolutional Neural Network for Mineral Classification from Raman Spectroscopy

Neural Processing Letters - Raman spectroscopy is often used for the composition determination and rapid classification of materials because it can reflect the molecular information of materials....     

Fractional order battery modelling methodologies for electric vehicle applications: Recent advances and perspectives

Science China Technological Sciences - Accurate modelling of lithium ion batteries is crucial for battery management in electric vehicles. Recent studies have revealed the fractional order nature...     

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.     

快速切换(SMED)在空调制造厂的研究与应用

在目前市场需求呈现规模扩张和品类个性化的冲击下,制造业面对的竞争压力愈发明显.且随着新时代自动化、信息化、大数据智能化等先进制造特征的兴起,更加促使制造业向转型升级不断进军.以空调制造业的控制器版块为例,从制约此单位效率的SMT车间切换浪费为切入点,在SMED快速换模的理念基础上,通过使用IE分析工具对切换流程进行研究,提炼出一套多模组固定料站切换模型,同时结合自动化、信息化等智能制造技术引进,完成SMT车间人力投入减少35人,日均产出点数提升18.29％,设备运行率提升15.46％,综合成本降低110.6万元/年的效益.成功推广应用至电子物料拣选版块,并开发信息化看板,夯实应用效益,再次证实"多模组固定料站"快速切换模型是典型小、多、高生产模式,具有可行性的解决方案.     

An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications

Scientometrics - Altmetrics indices are increasingly applied to measure scholarly influence in recent years because they can reflect the influence of research outputs more timely comparing with...     

Densification mechanism during reactive hot pressing of B4C-ZrO2 mixtures

The densification behaviors of pure B₄C and B₄C-ZrO₂ mixtures were compared during hot pressing. The results showed that in-situ formed ZrB₂ effectively enhanced the densification process of B₄C-ZrO₂ mixtures, more significantly during the intermediate stage. Within the relative density ranging from 0.75 to 0.90, the B₄C-15?wt%ZrO₂ mixture (B15Z) achieved the maximum densification rate as twice much as that of pure B₄C. The stress exponent n>3 indicated plastic deformation was the dominant densification mechanism of B15Z. The viscosities of plastic flow were evaluated using Murray-Rodger-William equation and the viscosity of B15Z was only a quarter of that in pure B₄C. The sintering activation energy was calculated to be 305.9?kJ/mol for pure B₄C and 197?kJ/mol for B15Z, respectively. It was proposed that the lower viscosity of plastic flow and activation energy accelerated the sliding and propagating motions of plastic flow, by which underlain the enhanced densification behaviors of B₄C-ZrO₂ mixtures.     

酸碱理论及其在有机反应中的应用研究

酸碱反应无论是在无机化学还是有机化学中,都占有极高的地位,文章以综述的形式讨论酸碱理论及其在一些重要的有机反应中应用研究。