A color-separation algorithm was proposed to predict the length of each color fiber in mixed-wool fiber assemblies based on a red, green, and blue transmission image. In this work, mixed-wool fiber assemblies consisted of different color wool fibers and a digital color image was obtained by a scanner. The relative thickness of the fiber assemblies was measured based on the Beer-Lambert theory. The color-separation formula was constructed to calculate the quantity of each color fiber at every point of the mixed-wool top to achieve the relative linear density curve and the average length. A series of systematic experiments demonstrated high consistency with the reference relative linear density curve and average length and confirmed the validity of the color-separation formula. This algorithm could be used for quality detection and control of mixed-wool tops. It could be also extended to uniformity detection of other mixed-color fiber assemblies.
In wireless sensor networks, the reliability of communication can be greatly improved by applying low-density parity-check (LDPC) codes. Algorithms based on progressive-edge-growth (PEG) pattern and quasi-cyclic (QC) pattern are the mainstream approaches to constructing LDPC codes with good performance. However, these algorithms are not guaranteed to remove all short cycles to achieve the desired girth, and their excellent inputs are difficult to obtain. Herein, we propose an algorithm, which must be able to construct LDPC codes with the girth desired. In addition, the optimal input to the proposed algorithm is easy to find. Theoretical and experimental evidence of this study shows that the LDPC codes we construct have better decoding performance and less power consumption than the PEG-based and QC-based codes.
In order to study the influence of steel fiber on the damage characteristics of concrete under uniaxial tension, based on the improved G-P algorithm, the acoustic emission event rate correlation dimension (hereinafter referred to as correlation dimension) curves of steel fiber reinforced concrete under uniaxial tension failure before and after peak stress were obtained, and the evolution characteristics of the correlation dimension of concrete with different steel fiber contents analyzed. According to the correlation dimension, the R value, the F value and the variance (σ2) were proposed to measure the effect of steel fiber on the micro-failure characteristics of concrete. The results show that before peak stress, the correlation dimension first increases and then decreases with an increase in the stress level; the mean correlation dimension increases with an increase in steel fiber; the addition of steel fiber reduces the R value by 36.13 % to 65.04 % compared with plain concrete, but has little effect on the F value. After peak stress, with a decrease in the stress level, the correlation dimension decreases with some fluctuation; and the variance of correlation dimension decreases with the increase of steel fiber; whereas the order degree of the system microstructure, measured by the correlation dimension, increases with an increase in steel fiber. These results can be used for structural health monitoring and the non-destructive testing of steel fiber reinforced concrete, which have an important value for enriching the early warning of fracture failure in steel fiber reinforced concrete based on acoustic emission technology.
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