Abstract: This invention pertains to the field of refrigeration materials, unveiling a novel ceramic designed for daytime passive radiative cooling endowed with a self-cleaning capability, along with its preparation method. The architectural design of this cooling ceramic incorporates a base layer composed of a porous composite ceramic, characterized by its intricate structure of closed pores arranged according to a varied grading of sizes, potentially enhanced with an additional protective layer. The method of preparation involves integrating a polymer solution with metal oxides and micron-sized hollow glass microspheres to craft a malleable slurry. This slurry is then shaped within a mold, compacted under pressure to form a preliminary structure, which is subsequently dried and sintered, culminating in the production of a porous composite ceramic specifically engineered for radiative cooling.

Abstract: Provided is a method and system for predicting a hydration reaction degree of cement based on a cycle generative adversarial network (CycleGAN). The method includes the following steps: S1, acquiring a micro-structure image of a cement paste test specimen; S2, establishing a micro-pore structure image dataset; S3, establishing a cement micro-hydration prediction model based on a CycleGAN; and S4, completing prediction based on a final cement micro-hydration prediction model. A deep learning algorithm is applied to micro-hydration prediction of cement. A complex theoretical formula is replaced with a data driven mode. Dependence on ideal hypotheses is reduced, and the accuracy of prediction on micro-hydration of cement is thus improved.

Abstract: A method for segmentation of underground drainage pipeline defects based on full convolutional neural network includes steps of: collecting a data set of the underground drainage pipeline defects; processing the data set of the underground drainage pipeline defects; optimizing with a semantic segmentation algorithm; adjusting model hyperparameters; training a model; verifying the model; and testing the model. The method adopts a deep learning algorithm, optimizes the FCN full convolutional neural network, develops a semantic segmentation method suitable for complex and similar defect characteristics of underground drainage pipelines, and adopts real underground drainage pipeline defect detection big data, thereby realizing pixel-level segmentation of the underground drainage pipeline defects and providing better robustness and generality. The detection accuracy and efficiency of the underground drainage pipeline defects are effectively improved.