132 research outputs found
Properties of oriented carbon fiber/polyamide 12 composite parts fabricated by fused deposition modeling
No full textThis paper reports the thermal and mechanical properties of carbon fiber (CF) reinforced polyamide 12 (PA12) composites for fused deposition modeling (FDM) process. The printable filaments of carbon fiber/PA12 composites with different mass fraction were fabricated and applied in FDM. The results indicate that the tensile strength and flexural strength of 10 wt% CF/PA12 composites are enhanced by 102.2% and 251.1% respectively. The laser-flash diffusivity analysis measurements exhibit remarkable improvements on thermal conductivity (lambda) of carbon fiber/PA12 composites. Moreover, the carbon fiber/PA12 composites mechanical properties are greatly improved. Our work presents a kind of anisotropic high performance composite for FDM. (C) 2017 Elsevier Ltd. All rights reserved
Notice of Retraction: The Complex Effect of the Financial Crisis on U.S. S&P 500 Stock Market
No full textMulti-View and Multi-Type Feature Fusion Rotor Biofouling Recognition Method for Tidal Stream Turbine
No full textPower generation is affected and structural instability may occur when biofouling attaches to the rotor of tidal stream turbines (TSTs). Image signals are used to identify biofouling for biofouling recognition, thus achieving on-demand maintenance, optimizing power generation efficiency, and minimizing maintenance costs. However, image signals are sensitive to background interferences, and underwater targets blend with the water background, making it difficult to extract target features. Changes in water turbidity can affect the effectiveness of image signal biofouling recognition, which can lead to reduced recognition accuracy. In order to solve these problems, a multi-view and multi-type feature fusion (MVTFF) method is proposed to recognize rotor biofouling on TSTs for applications in TST operation and maintenance. (1) Key boundary and semantic information are captured to solve the problem of background feature interference by comparing and fusing the extracted multi-view features. (2) The local geometric description and dependency are obtained by integrating contour features into multi-view features to address the issue of the target mixing with water. The mIoU, mPA, Precision, and Recall of the experimental results show that the method achieves superior recognition performance on TST datasets with different turbidity levels
catena-Poly[[[(1,10-phenanthroline-κ2N,N′)praseodymium(III)]-di-μ-4-hydroxybenzoato-κ4O1:O1′-μ-nitrato-κ3O,O′:O] bis(1,10-phenanthroline)]
No full textThe title complex, [Pr(C7H5O3)2(NO3)(C12H8N2)]·2C12H8N2, has a polymeric chain structure, with two uncoordinated 1,10-phenanthroline molecules in the lattice. The PrIII centre has a monocapped square-antiprismatic coordination geometry, comprised of two N atoms from one chelating 1,10-phenanthroline ligand, four carboxylate O atoms from four 4-hydroxybenzoate anions and three O atoms from two nitrate anions. The 4-hydroxybenzoate and nitrate anions function as μ2-bridging ligands and link the PrIII ions into a one-dimensional chain structure along the c axis. Intermolecular O—H...N hydrogen bonds are observed between the 4-hydroxybenzoate anions and the uncoordinated 1,10-phenanthroline molecules
Analysis of influence factors of corporate technology innovation based on interpretative structural model
No full textReduction of void content of vacuum‐assisted resin transfer molded composites by infusion pressure control
No full textVideo Fire Detection Algorithm using Multi-Feature Fusion
No full textAt present, the moving target detection and flame characteristics extraction almost become the most important parts in majority of video fire detection systems. Through the above two-part study, a new fire features detection method is presented in precise moving target area. That is, using the improved background difference method and flame features (such as the color and uniformity, Wavelet energy, stroboscopic and contour features) to detect fire. Experiments show that this method can improve theaccuracy and anti-interference ability of fire detection. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.334
Video Fire Detection Algorithm using Multi-Feature Fusion
No full textAt present, the moving target detection and flame characteristics extraction almost become the most important parts in majority of video fire detection systems. Through the above two-part study, a new fire features detection method is presented in precise moving target area. That is, using the improved background difference method and flame features (such as the color and uniformity, Wavelet energy, stroboscopic and contour features) to detect fire. Experiments show that this method can improve theaccuracy and anti-interference ability of fire detection. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.334
Flexible Gabor-Based Superpixel-Level Unsupervised LDA for Hyperspectral Image Classification
No full textHyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG-SuULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial-spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called SuULDA, is skillfully introduced to reduce the extracted large amount of FG features. The SuULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG-SuULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG-SuULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility.No Full Tex
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