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Fabric density measurement plays a key role in the analysis of fabric structural parameters. Existing automatic measurement methods lack varieties of adaptability and present poor performance in practical application. In order to solve these problems, we use convolutional neural networks (CNNs) to locate warps and wefts for woven fabric density measurement. First, we use a portable wireless device to capture high-resolution fabric images and set up a new dataset with labeled yarns location. Based on this dataset, we propose an effective multi-scale convolutional neural network (MSnet) architecture to locate warps and wefts. Then, by using Hough transform and image projection of predicted yarns location, the fabric density is measured accurately. The experimental results emphasize that the proposed method has reached high accuracy under various kinds of patterns and densities of the fabrics and is superior to the state-of-theart methods in terms of its accuracy and robustness. Promisingly, the proposed method can provide novel ideas for more fabric structural parameter analyses. INDEX TERMS Warps and wefts locating, density measurement, fabric structural parameters, multi-scale convolutional neural networks.

Weaving enterprises are faced with problems of small batches and many varieties, which leads to difficulties in manual scheduling during the production process, resulting in more delays in delivery. Therefore, an automatic scheduling method for the weaving process is proposed in this paper. Firstly, a weaving production scheduling model is established based on the conditions and requirements during actual production. By introducing flexible model constraints, the applicability of the model has been greatly expanded. Then, an improved ant colony algorithm is proposed to solve the model. To address the problem of the traditional ant colony algorithm that the optimizing process usually traps into local optimum, the proposed algorithm adopts an iterative threshold and the maximum and minimum ant colony system. In addition, the initial path pheromone distribution is formed according to the urgency of the order to balance each objective. Finally, the simulation experiments confirm that the proposed method achieves superior performance compared with manual scheduling and other automatic methods. The proposed method shows a certain guiding significance for weaving scheduling in practice.

Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV) was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV) regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV) denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.

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