KEYWORDS: Image segmentation, Data modeling, Semantics, Transformers, Deep learning, Education and training, Image enhancement, Cultural heritage, Convolution
Moss can cause significant damages to stone artifacts. In order to effectively protect stone artifacts, it is crucial to accurately obtain information about the moss growth on the surface of the stone artifacts. In response to the laborious and inefficient process of manually detecting moss coverage on stone artifacts, this paper introduces an enhanced semantic segmentation model that integrates Swin Transformer and convolutional neural networks to accurately detect the moss coverage and growth rate. Through a comparison with current state-of-the-art models, our model outperforms them by the average intersection over union on the LoveDA and moss datasets, reaching a result of 94.79%. on the moss dataset. This indicates that our model can accurately segment rocks and moss, and thus enabling the effective calculation of moss coverage and growth rate on stone artifacts and providing vital support for their preventive protection.
In this work, we introduced a polymer-based fiber Bragg grating sensor for carbon dioxide (CO2). The device integrates a polymer coating on the fiber Bragg grating sensor as a CO2-sensitive region, and then a hydrophobic zeolite is coated on the surface to isolate water vapor interference. To eliminate the effect of temperature on the sensor detection performance, an uncoated fiber Bragg grating was introduced as a temperature compensation unit. Then, a CO2 detection system was constructed to simulate the carbon sequestration environment to calibrate the CO2 concentration for sensors and to analyze the sensor performance under different environmental conditions. The experimental analysis shows that the fiber Bragg grating CO2 sensor not only has high CO2 sensitivity, but also has excellent reversibility and stability in high temperature and high humidity environments.
To obtain in situ real-time information on temperature and micro-strain changes in the body of stone cultural relics during chemical corrosion damage, a fiber Bragg grating (FBG) detection system was prepared. A theoretical model of the sensor to monitor the temperature and micro-strain was established. The temperature and micro-strain changes of the sandstone samples under deionized water with alkaline solutions and temperatures were examined online in-situ using the FBG measurement system. The results show that the material conversion exothermic chemical reaction between sandstone and NaOH solution and the reaction with deionization are dissolution reactions.
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