Abstract: The provided are a device and method for measuring a bonding strength between a contaminated rock surface and a solidified material. The device includes a fixing and measuring assembly and four rock slabs enclosing a rectangular prism, where outer surfaces of the four rock slabs are fitted to two transverse support plates and two longitudinal support plates, respectively; the two longitudinal support plates are arranged between the two transverse support plates; upper and lower ends of the rock slabs are provided with an upper cover and a bottom plate, respectively; the upper cover and the bottom plate are detachably fixed to the two transverse support plates, respectively; the two transverse support plates are detachably fixed to each other through a second screw; a hydraulic device is provided among the four posts; and a movable end of the hydraulic device is provided with a supporting airbag.

Abstract: A wind power generation quantile prediction method based on machine mental model and self-attention includes: using human cognitive decision-making mechanism for reference to construct the machine mental model as the basic framework of WQPMMSA, and then the seasonal power generation rules and intraday power generation trend are encoded into WQPMMSA as the input information of the prediction method, using the self-attention layer to replace the recurrent neural network in the original machine mental model, and establishing the statistical relationship between the seasonal power generation rules and the intraday power generation trend effectively, reducing the long-range forgetting of the original machine mental model-convert the continuous rank probability score in the integral form into a summation form, and using it as a loss function to train WQPMMSA, so that WQPMMSA approaches the optimal quantile prediction result with the highest efficiency.

Abstract: Disclosed are a multimodal weakly-supervised three-dimensional (3D) object detection method and system, and a device. The method includes: shooting multiple two-dimensional (2D) red, green and blue (RGB) images with a camera, acquiring ground points by a vehicle LiDAR sensor and generating a 3D frustum based on 2D box labels on each of the 2D RGB images; filtering ground points in the 3D frustum and selecting a region with most 3D points; generating a 3D pseudo-labeling bounding box of an object according to the region with the most 3D points; training a multimodal superpixel dual-branch network with the 3D pseudo-labeling bounding boxes as labels and the 2D RGB image and the 3D point cloud as inputs; and inputting a 2D RGB image of a current frame and a 3D point cloud of a current scenario to a trained multimodal superpixel dual-branch network to generate an overall 3D point cloud.

Abstract: A three-dimensional data augmentation method includes that an original two-dimensional image and truth value annotation data matching the original two-dimensional image are acquired, that the original two-dimensional image and two-dimensional truth value annotation data are transformed according to a target transformation element to obtain a transformed two-dimensional image and transformed two-dimensional truth value annotation data, that an original intrinsic matrix is transformed according to the target transformation element to obtain a transformed intrinsic matrix, that a two-dimensional projection is performed on three-dimensional truth value annotation data according to the transformed intrinsic matrix to obtain projected truth value annotation data, and that three-dimensional augmentation image data is generated according to the transformed two-dimensional image, the transformed two-dimensional truth value annotation data, and the projected truth value annotation data.