Abstract: Provided is a new compound capable of effectively inhibiting ATX. The compound is represented by formula I, or the compound is a tautomer, a stereoisomer, a hydrate, a solvate, a salt, or a prodrug of the compound represented by formula I. In formula (I), R1 and R2 are independently selected from —H or —CH3, provided that: R1 and R2 are not —H at the same time or are not —CH3 at the same time.

Abstract: A method of constructing a water supply pipeline leakage identification model based on deep learning and an application thereof are provided, which relate to the technical field of nondestructive detection and positioning of municipal water supply pipeline leakage. By extracting Mel-frequency cepstral coefficient of leakage sound signals as a basis for distinguishing pipeline leakage, a neural memory network is introduced to learn leakage features and establish a leakage feature neural network model, and the features of a signal of a point to be detected are input into the model to determine whether the point leaks, so as to realize accurate positioning of the pipeline leakage position.

Abstract: The present disclosure belongs to field of nondestructive testing and positioning of urban water supply pipe leakage in municipal engineering and discloses a method for detecting leakage of water supply pipe based on ground-penetrating radar three-dimensional image attribute analysis including: acquiring ground-penetrating radar original image data of water supply pipe by longitudinal scanning; de-noising and filtering acquired original image data; fitting processed image data into three-dimensional data body by interpolation, extracting multiple planar or stereo image attributes and displaying image attributes by longitudinal, transverse, horizontal, irregular profiles and iso-surface; and accurately identifying and positioning pipe leakage positions and scale by multi-attribute comprehensive analysis.

Abstract: The present disclosure belongs to field of nondestructive testing and positioning of urban water supply pipe leakage in municipal engineering and discloses a method for detecting leakage of water supply pipe based on ground-penetrating radar three-dimensional image attribute analysis including: acquiring ground-penetrating radar original image data of water supply pipe by longitudinal scanning; de-noising and filtering acquired original image data; fitting processed image data into three-dimensional data body by interpolation, extracting multiple planar or stereo image attributes and displaying image attributes by longitudinal, transverse, horizontal, irregular profiles and iso-surface; and accurately identifying and positioning pipe leakage positions and scale by multi-attribute comprehensive analysis.

Abstract: A method and apparatus for identifying a concrete crack includes: obtaining a crack video, and manually annotating a video image frame by using a label; predicting a future frame and label for the annotated frame by using a spatial displacement convolutional block, propagating the future frame and label, to obtain a synthetic sample, and preprocessing the synthetic sample, to form a crack database; modifying input and output ports of data of a deep learning model for video semantic image segmentation and a parameter, to enable the deep learning model to accept video input, and establishing a concrete crack detection model based on the video output; using a convolutional layer in a trained deep learning model as an initial weight of the concrete crack detection model for migration; inputting the crack database into a migrated concrete crack detection model, and training the concrete crack detection model for crack data.

Abstract: A method and apparatus for identifying a concrete crack includes: obtaining a crack video, and manually annotating a video image frame by using a label; predicting a future frame and label for the annotated frame by using a spatial displacement convolutional block, propagating the future frame and label, to obtain a synthetic sample, and preprocessing the synthetic sample, to form a crack database; modifying input and output ports of data of a deep learning model for video semantic image segmentation and a parameter, to enable the deep learning model to accept video input, and establishing a concrete crack detection model based on the video output; using a convolutional layer in a trained deep learning model as an initial weight of the concrete crack detection model for migration; inputting the crack database into a migrated concrete crack detection model, and training the concrete crack detection model for crack data.