This paper introduces an interactive calibration system for color vision assessment utilizing augmented reality (AR) glasses, and investigates its potential relevance as a perceptual calibration stage for human-robot systems. The system re-imagines the traditional Farnsworth-Munsell 100 Hue (FM 100 Hue) test-a benchmark in the textile industry for evaluating the color discrimination of quality control personnel-as an interactive AR-based task, enabling portable color discrimination assessment under ordinary ambient lighting conditions. We quantitatively evaluate the correlation between this AR-implemented assessment and the standard physical test through a comparative user study. Results demonstrate a high degree of diagnostic consistency, indicating that the AR platform can reproduce key characteristics of the conventional assessment while reducing part of the variability associated with display and ambient conditions. Beyond clinical assessment, this work positions AR glasses as a bidirectional perceptual interface, enabling individualized modeling of human color perception for robotic systems. The proposed method facilitates robust, color-aware perception, decision-making, and task execution in human-centered robotics, with potential applications in automated textile quality assurance and assistive robotics. This calibration framework is a promising step toward achieving seamless and adaptive visual perception alignment between humans and humanoid or other robotic platforms.
Natural fiber fabrics are increasingly used in sustainable fashion apparel because of their renewability and biodegradability, but hemp- and linen-containing fabrics often suffer from excessive stiffness, poor drape, and dimensional instability after laundering. This study developed an integrated framework for process optimization and apparel-oriented evaluation of natural-fiber fabrics. Three woven fabrics, namely organic cotton, hemp/organic cotton, and linen/organic cotton, were selected. An enzyme-assisted finishing process for hemp/organic cotton fabric was optimized using a Box-Behnken design with cellulase concentration, treatment temperature, and treatment time as variables. Flexural rigidity, tensile strength retention, air permeability, and residual shrinkage were used as response indicators. The optimum condition was 1.18% owf cellulase, 54 °C, and 42 min. Under this condition, flexural rigidity decreased by 28.6%, tensile strength retention remained 93.6%, air permeability improved, and residual shrinkage was reduced to 2.5%. Comparative evaluation showed that the optimized hemp/cotton fabric achieved the highest overall apparel suitability index among the tested materials. However, the improvement in softness and dimensional behavior was accompanied by a moderate loss in tensile strength retention, indicating a practical compromise rather than simultaneous optimization of all properties.
The mechanical properties of mine roof strata affect the safety of fully mechanized mining face. The roof is easy to collapse, under the action of cutting vibration and gravity. In ultra-thin coal seams with a thickness of less than 0.8 meters and relatively high coal hardness, the cutting of coal mining machines will cause serious interference to the weak roof, thereby reducing coal quality and endangering production safety. To address this issue, this study established a cutting dynamics model of the shearer drum for ultra-thin coal seams under soft roof conditions using the discrete element method in EDEM. Based on the single factor method and using the top plate damage rate as the evaluation index, the influence of drum structure and operating parameters on soft top plate damage was systematically studied. The results show that the drum diameter is 650 mm, the hub diameter is 325 mm, the traction speed is 2 m/min, and the cutting depth is 650 mm under the single factor condition, and the shearer drum in ultra-thin coal seam has achieved the best cutting effect respectively.
Traditional deep learning-based image classification methods rely on large-scale labeled data. In practical scenarios such as medical imaging and agricultural monitoring, obtaining labeled data requires substantial manpower and time, becoming a bottleneck for deployment. Similarly, in the textile industry, the automated inspection of fabric defects and high-precision fiber texture identification often face similar challenges due to the scarcity of highquality annotated data. Self-supervised learning offers a solution. This study evaluates the effectiveness of contrastive learning-based selfsupervised feature representation in image classification, focusing on feature extraction from unlabeled data. An efficient contrastive learning framework was constructed and evaluated on CIFAR-10, ImageNet-1K, and CUB-200-2011. Based on the ResNet architecture, combined with the InfoNCE loss and data augmentation, a two-stage training strategy of self-supervised pre-training followed by supervised fine-tuning was adopted. Model performance was assessed using classification accuracy and F1 score. Results show that the proposed method outperforms traditional supervised learning, especially in low-label regimes and as a robust initialization strategy across datasets. The model demonstrates strong generalization in low-sample settings and adaptability to different data distributions. This study clarifies the role of contrastive learning in feature representation for image classification and provides support for applying self-supervised learning in domains with limited annotations, such as medical image analysis and agricultural monitoring. It also offers a transferable framework for related computer vision tasks.
To address national total medal count prediction and performance evaluation, within the context of increasing techno logical competition in sports equipment, a Bivariate-Hurdle-Tobit composite model was constructed. This model is suitable for predicting zero-inflated, count-type, and highly correlated dependent variables. The model recognizes that a nation's athletic success is increasingly underpinned by its industrial and technological prowess, including advancements in textile engineering for. Through factor analysis and correlation tests, six significant regression factors were identified: total athlete count, host status, participation rate in dominant events. The model underwent comprehensive evaluation via rolling cross-validation and five metrics, demonstrating low prediction error and high accuracy with AUC values reaching 0.98 and 0.96 respectively, and Pseudo R2 consistently exceeding 75%. Predictions indicate the United States, China, and the United Kingdom will occupy the top three positions on the medal tally. The study reveals a significant positive impact of the "host nation effect" on national performance. For instance, the U.S. as the next host nation is projected to increase its gold medal share by 0.75%. Additionally, countries like Andorra, Benin, and Belize are predicted to have a 95% probability of winning their first-ever medals. Analyzing the influence of great coaches, the study quantifies the "great coach effect" using Chow tests and Difference-in-Differences models. Findings indicate this effect has limited impact in non-host nations but exhibits significant synergistic effects in host countries. For additional insights into the Olympic medal standings, the model incorporates gender ratio analysis. Results show male athlete participation rates have a significant negative impact on medal distribution, suggesting an increase in female athlete representation is advisable.
