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The degree of pilling and fuzzing in textile fabrics is a crucial indicator of textile product quality. Current evaluation methods predominantly rely on subjective judgments, leading to issues such as rating errors and inefficiency. To achieve objective assessment of pilling and fuzzing grades, this study proposes a Hybrid Feature-Based Machine Vision Method for Objective Evaluation of Textile Pilling and Fuzzing. The method incorporates a Hybrid Feature-based Depthwise Separable Attention Network for Objective Evaluation of Textile Pilling and Fuzzing (HDAN-PF), which effectively extracts and fuses frequency and Space domain features. A Channel Attention mechanism enhances the model’s ability to capture subtle features, while Depthwise Separable Convolutions reduce computational complexity, improving evaluation speed while maintaining high accuracy.The model size is approximately 327.37 MB with a total parameter count of 135,115,512. Experimental results demonstrate that the proposed method achieves a classification accuracy of 96.26% on diverse fabric images, showcasing robust generalization and practical utility.By leveraging this machine vision approach, the proposed method offers a transformative solution for achieving objective, consistent, and efficient assessments of pilling and fuzzing grades, advancing textile quality evaluation practices.

Fabric tearing performance testing experiment is an important part of evaluating fabric durability. The aim of this paper is to solve the problem of real-time prediction of fabric tearing performance testing by effectively extracting key features from experimental data and constructing a prediction model applicable to the process of fabric tearing performance testing. In this study, the trend prediction model for the experimental process of fabric tear performance testing (BLTT-FT) based on the \"bidirectional long- and short-term attention mechanism\" is adopted. A prediction model combining the improved Bi-directional Long Short-Term Memory (BiLSTM) structure, Transformer encoding layer, and Temporal Convolutional Network (TCN) layer is proposed. While considering sequence information globally, the model captures the bidirectional dependence of time series, reduces model complexity through the TCN layer, and finally optimizes prediction accuracy via the fully connected layer and activation function, thus achieving multi-step prediction. Analysis of variance (ANOVA) indicates that, across multiple datasets constructed from fabrics with different elasticity grades, the model shows extremely significant differences (p < 0.001) in the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) at each prediction step. Furthermore, it maintains a low error level even in the long-range prediction scope: the average RMSE of multi-step prediction is 0.0881, the average MAE of multi-step prediction is 0.0609, the average MAPE of multi-step prediction is as low as 3.06%, and the average coefficient of determination (R2) of multi-step prediction is as high as 0.9572. The ablation experiments confirm that multi-modular hierarchical modeling effectively solves the problem of detail accuracy of single-step prediction and long-range dependence of multi-step prediction. The results show that the proposed model performs well in real-time trend prediction results for different data sets constructed from fabrics with different elasticity grades. By predicting the dynamics of the experimental process of fabric tearing performance testing in real time, this study has exploratory value in improving the experimental efficiency and optimizing the experimental process.

The tear strength of textiles is a crucial characteristic of product quality. However, during the laboratory testing of this indicator, factors such as equipment operation, human intervention, and test environment can significantly influence the results. Currently, there is a lack of traceable records for the influencing factors during the testing process, and effective classification of testing activities is not achieved. Therefore, this study proposes a state-awareness and classification approach for fabric tear performance testing based on multi-source data. A systematic design is employed for fabric tear performance testing activities, which can real-time monitor electrical parameters, operational environment, and operator behavior. The data are collected, preprocessed, and a Decision Tree Support Vector Machine (DTSVM) is utilized for classifying various working states, and introducing ten-fold cross-validation to enhance the performance of the classifier, forming a comprehensive awareness of the testing activities. Experimental results demonstrate that the system effectively perceives fabric tear performance testing processes, exhibiting high accuracy in the classification of different fabric testing states, surpassing 98.73%. The widespread application of this system contributes to continuous improvement in the workflow and traceability of fabric tear performance testing processes.

The capability parameters of third-party testing institutions not only serve as a critical reflection of their technical and quality management capabilities but also form the key basis for categorizing their testing abilities. However, current Chinese phrase-based descriptions of these capability parameters are influenced by diverse expression styles and varying internal standards, making it difficult to establish consistent criteria for classifying testing capabilities. This inconsistency presents notable difficulties for clients and regulatory bodies. Therefore, leveraging clustering techniques to uncover the intrinsic relationships and latent information between testing capabilities and their corresponding parameters is one of the crucial approaches to achieving scientific and reasonable classification of testing capabilities. Traditional text feature extraction methods suffer from several limitations, including sparse features, high-dimensional features, and lack of semantic information. These shortcomings complicate the classification and analysis of testing capability descriptions. To address this issue, this study focuses on the “products and testing objects” within the capability parameters of Chinese testing institutions as the research subject and proposes a joint model based on BERT and semi-supervised K-Means clustering. This model employs BERT to extract textual features from Chinese descriptive phrases and combines them with a small number of labeled samples for semi-supervised K-Means clustering analysis. The clustering results are then used to train a multi-output Softmax classifier, thereby enabling the classification of testing capabilities for third-party institutions. Experimental results demonstrate that the proposed method outperforms traditional methods such as TF-IDF and one-hot encoding when applied to the Chinese description datasets of testing institutions. Specifically, it exhibits advantages in reducing the dimensionality of textual features and enhancing clustering performance. When the proportion of labeled samples accounts for 10% of the total sample size, the method achieves optimal clustering results, with an average classifier accuracy of 89.8%.

Intelligent Internet of Vehicles is the hot spot of current traffic industry, Video information as a very important part of multidimensional perception, is widely used in vehicle-vehicle interconnection, vehicle-road interconnection and vehicle-management platform interconnection. Because there are obvious differences in the efficiency level between different methods and devices in specific operation, relevant video exchange testing and verification become necessary. This paper presents a test platform and method for video information exchange in Intelligent Internet of Vehicles.Through this platform, a combination of subjective evaluation of video streams and objective testing of transmission network is used to test the availability of the test platform for video exchange on the Internet of Vehicles.

This article through the design which has the function of transcoding, distribution and authentication exchange unit, achieve a face made car video information exchange method. REST method is used to realize the interface call of video information exchange, which includes control flow, media flow and structured data flow of video content analysis results. It can be used in video exchange scenarios of vehicle and vehicle, vehicle and infrastructure, vehicle and remote management platform.

Hydraulic structures such as dams and reservoirs pose significant construction challenges in karst areas due to severe and costly leakage issues. In this study, we apply a numerical model to test the hypothesis that karst aquifers in water divide areas may contain intrinsically low-karstified rock-blocks (LKB), which form due to the specific evolution of unconfined aquifer with recharge distributed to the water table. We develop, test, and apply a model of flow, transport, and dissolution in a 2D fracture network with a fluctuating water table. The model's structure and boundary conditions are based on the conceptualization of the Luojiaao (China) interfluve aquifer. First, we simulate the evolution of an unconfined network, representing the interfluve, up to a stage resembling the present conditions at Luojiaao. We then analyse leakage through the evolved aquifer from a reservoir at different water levels and simulate further aquifer evolution under reservoir conditions. Our results demonstrate the formation of the LKB and highlight its role in mitigating leakage.

In order to understand the dynamic interaction process among species, enzymes, and metabolites during the fermentation process of Baobaoqu, which is a representative Daqu starter for Chinese baijiu, the intimate connection between the progression of microbial communities and the diversities and activities of enzymes was examined by metagenomics, metatranscriptomics and metaproteomics. It was found that while 5211 species of microorganisms were detected by metagenomics, only 1774 active species were detected by metatranscriptomics, which indicated that only a small proportion (34.04%) were active. The metabolic routes associated with the breakdown of substrates and synthesis of metabolites were redesigned, and the special functional microorganisms for lactate, pyrazines and phenylethyl alcohol production were isolated. It was found that the progression of the microbial community was highly coupled with the components of enzymes and flavor substrates, precisely corresponding to the three stages of the Baobaoqu fermentation process, and were regulated by multiple physical factors. During the Baobaoqu-making process of the fermentation, microorganisms with different functions work together to complete metabolism in different stages. These findings will aid us in gaining a deeper and clearer understanding of the “species–enzyme–metabolite” system within the Daqu starter culture, thus offering valuable perspectives for developing artificial synthetic communities and the production of high-quality Baobaoqu.