新型钢丝圈清洁器的使用效果

为了解决钢丝圈挂花问题,阐述了LH001新型钢丝圈清洁器的特点和应用效果;通过试验说明使用新型钢丝圈清洁器断头率低,基本无挂花,纺纱效果好。指出:新型钢丝圈清洁器是普通钢丝圈清洁器的更新换代产品,值得推广。     

前混合磨料射流锚杆拆除装置设计

介绍锚杆应用及常用的拆锚方法,针对现有前混合磨料射流退锚装置存在锚杆拆卸时间长、工人作业环境差、装置部件锈蚀维护性差等问题,提出了一种前混合磨料射流退锚改进装置,并进行现场试验,结果表明,所改进设计装置具有退锚时间短、对工人伤害小、操作简便、部件维护周期长和无需专门配备泵站等特点,对进一步提高锚杆拆除效率具有重要参考价值。     

燃煤锅炉除尘专用滤料的开发

介绍机械制造厂燃煤锅炉的烟尘特点,分析滤料失效的原因,提出一套针对该工况的滤料解决方案。介绍针对复杂工况条件所选用的纤维种类以及复合面层原料成分配比的确定,最终选用针刺工艺加工并对该新产品的基本性能进行了测试分析。     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

Oxide-Scale Evolution on a New Ni–Fe-Based Superalloy at High Temperature

Oxidation of Metals - The isothermal oxidation behavior and oxide-scale evolution on a newly developed Ni–Fe-based superalloy were investigated. Three oxidation stages were generally...