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Under the widely used testing methods for fabric bending behavior, only one result for one direction can be obtained by using one fabric which is low efficient. To obtain fabric bending anisotropy, it is necessary to conduct a great many testing experiments. The purpose of this paper is to investigate a novel, efficient and visual method that can measure fabric bending anisotropy.

Fabrics are first cut into special shapes with eight strips including four directions, 0°(warp direction), 90°(weft direction), 45 and 135°(true bias), then are put onto the self-designed instrument. After that a camera is used to take picture from the right above the prism. New parameters, projection area, projection length, projection length, falling height and falling index (S, L, H and I in short) are obtained. Furthermore, standard deviation of them (SDS, SDL, SDH and SDI in short) are extracted for the characterization of bending anisotropy.

Results show that the new method has good feasibility and S, L and I can be used to express fabric bending property well. Of all the four new parameters, SDL has the highest correlation with SD of bending length, SDS the second and SDH the third. That is, SDL can characterize bending anisotropy best. Taken convenience of data acquisition and correlation into consideration, bending length L is the best parameter. Average L and SDL in four directions can be combined to express the comprehensive bending behavior of fabrics.

The new method can measure and characterize both the fabric bending property and bending anisotropy. Besides its high efficiency, it can display fabric bending or bending anisotropy visually and directly.

The current bending test method can only test the bending performance of fabric in one direction at a time. It is not possible to directly observe the bending morphology of fabrics in different directions, and it is necessary to cut samples and repeat the test several times, which takes more time. For this situation, a multidirectional visualization of the fabric bending test method is proposed, using which multiple results can be obtained at one time and the fabric bending can be visualized.

About 17 fabrics are tested using a self-designed device. The fabrics are cut into special triangles and multiple sets of results in three directions are obtained at once using the device. The experimental specimens are photographed from the above and the transverse elongation length, bending projection area and circumference are extracted after image processing.

The results show that the correlation coefficients of transverse elongation, bending projected area and circumference are good with the bending length measured by the cantilever method. In which, all three indicators are positively correlated with the bending length. This indicates the good feasibility of the new method.

This method can get the bending index of fabrics in three directions, with five samples in each direction at one time. Meanwhile, it can also visualize the flexural differences between different fabrics and directions of the same fabric. It can provide more efficient testing means for the textile testing field, and the testing efficiency is 15 times of the existing method, which has better theoretical significance and practical values.

Nowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.

Firstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.

Results show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.

Compared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.

Currently, the Tang tomb mural cultural relic resources are presented in a multi-source and heterogeneous manner, with a lack of effective organization and sharing between resources. Therefore, this study aims to propose a multidimensional knowledge discovery solution for Tang tomb mural cultural relic resources.

Taking the Tang tomb murals collected by the Shaanxi History Museum as an example, based on clarifying the relevant concepts of Tang tomb mural resources and considering both dynamic and static dimensions, a top-down approach was adopted to first construct an ontology model of Tang tomb mural type cultural relics resources. Then, the actual case data was imported into the Neo4J graph database according to the defined pattern hierarchy to complete the static organization of knowledge, and presented in a multimodal form in knowledge reasoning and retrieval. In addition, geographic information system (GIS) technology is used to dynamically display the spatiotemporal distribution of Tang tomb mural resources, and the distribution trend is analysed from a digital humanistic perspective.

The multi-dimensional knowledge discovery of Tang tomb mural cultural relics resources can help establish the correlation and spatiotemporal relationship between resources, providing support for semantic retrieval and navigation, knowledge discovery and visualization and so on.

This study takes the murals in the collection of the Shaanxi History Museum as an example, revealing potential knowledge associations in a static and intelligent way, achieving knowledge discovery and management of Tang tomb murals, and dynamically presents the spatial distribution of Tang tomb murals through GIS technology, meeting the knowledge presentation needs of different users and opening up new ideas for the study of Tang tomb murals.