Development of Knowledge Integration Model for E-Maintenance

Abstract: In recent years, e-maintenance provides the opportunity for a new maintenance generation. Knowledge is one of core e-maintenance resources. To improve the efficiency of knowledge management and so as to improve the quality of e-maintenance work, a knowledge integration model for e-maintenance was proposed. The proposed model was made up of the relationships among role knowledge, task knowledge and equipment knowledge, to achieve the integration of maintenance business and knowledge resources. This study involved the following tasks: (1) developed the key components of the proposed model, including e-maintenance federation ontology, emaintenance knowledge space and knowledge integration network; (2) designed the construction procedures for the proposed model; (3) presented an example to illustrate the application of the proposed model. Results of this study can improve the level of knowledge management for e-maintenance

Design and Implementation WiMAX Transceiver on Multicore Platform

Before we design the WiMAX Base Station (BS), three questions we should answered. The first is what kind of system parameters will be selected? The second is which platform will be used for the BS design. And the third one is which algorithm will be selected for some modules that are not defined in 16e standard, especially for the receiver modules, such as synchronization, channel estimation, and STC (Space Time Coding) decoder, etc. When all the above questions obtain proper answers, we can start the BS design and implementation on specific platform to achieve aimed system performance. In this chapter, we will focus on the PHY (physical Layer) design of WiMAX BS

Fracture failure analysis and bias tearing strength criterion for PVDF coated bi-axial warp knitted fabrics

This paper concerns the fracture failure and bias tearing strength criterion for a PVDF coated bi-axial warp knitted fabrics (BWKFs) widely used in air supported membrane structures (ASMSs). Central slit tearing tests were carefully conducted on bias specimens with seven off-axis angles, and the corresponding tearing properties, including failure behaviors and tearing strength criterion were discussed. Results show that coated bi-axial warp knitted fabrics are typical direction-depended materials, and their tearing characteristics vary greatly with the bias angles. Typical tearing stress-displacement curves of bias samples could exhibit four characteristic regions: a co-deformation region, a shear deformation region, a plateau region, and a post peak region. No matter what the orientation of the initial slit or the yarn is, the propagation is always parallel to the secondary yarns. For specimens with different bias angles, some obvious differences in tearing behaviors are observed in terms of maximum displacement, damage mode, curve slope, and number of stress peaks, and these differences could be attributed to the material orthotropy and different failure mechanism of constituent materials. Unlike results of tensile strength for most of woven fabrics, for the studied BWKF composite, there is a W-shaped relationship between tearing strength and bias angle, with a local strength peak at 45o angle. The new tearing strength criterion proposed in the prior research is validated due to the strong agreements between the calculated and experimental results for the BWKF

Robust prior-based single image super resolution under multiple Gaussian degradations

Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance. These priori hypotheses often fail and lead to reconstruction error in real scenario. In this paper, we propose an end-to-end CNN model RPSRMD to handle SR problem in multiple Gaussian degradations by extracting and using as side information a shared image prior that is consistent in different Gaussian degradations. The shared image prior is generated by an AED network RPGen with a rationally designed loss function that contains two parts: consistency loss and validity loss. These losses supervise the training of AED to guarantee that the image priors of one image with different Gaussian blurs to be very similar. Afterwards we carefully designed a SR network, which is termed as PResNet (Prior based Residual Network) in this paper, to efficiently use the image priors and generate high quality and robust SR images when unknown Gaussian blur is presented. When we applied variant Gaussian blurs to the low resolution images, the experiments prove that our proposed RPSRMD, which includes RPGen and PResNet as two core components, is superior to many state-of-the-art SR methods that were designed and trained to handle multi-degradation