6results about How to "Achieve learning" patented technology

The application discloses a high-precision map dynamic detection and updating method adopting a general image recovery strategy, belongs to the technical field of high-precision map dynamic detection and updating methods, and through a pre-training process, a degraded image is input into a network, features are extracted and enhanced layer by layer by adopting a feature generation module and a feature selection module, image recovery is realized by combining multi-dimensional feature fusion and a cross attention mechanism, the detail restoration and global consistency of the degraded image are improved, according to a BEV representation generated by the pre-training model in step S1, in combination with the historical state of the high-precision map, a dynamic detection module is adopted to identify elements that are newly added, deleted or unchanged in the map, and a difference map is output, according to the difference map generated in step S2, a map updating module is adopted to perform geometric adjustment and semantic correction on the marked added and deleted areas, an accurate and coherent local high-precision map is generated, and through a model evaluation framework, the performance of dynamic detection and map updating is comprehensively evaluated.

The application relates to the technical field of energy storage, and discloses a hybrid energy storage system and method supporting mode switching, which comprises a data acquisition and communication module, an operation state analysis and mode decision module, a switching trajectory planning and cooperative control module, a safety monitoring and dynamic compensation module, a panoramic instruction integration and execution module, and a strategy self-learning and optimization module; target operation mode instructions are generated by inputting comprehensive operation state characteristic data of the system into a pre-trained mode research and judgment neural network model, combining a pre-set hysteresis comparison strategy and a minimum time interval constraint of mode switching, and realizing research and judgment on multiple operation modes such as power smoothing, peak clipping and valley filling, frequency modulation auxiliary services, so that frequent mode switching caused by state fluctuations is avoided; characteristic data, control sequences and safety evaluation data of the whole switching process are recorded to form a history library, and the optimization of the switching strategy is realized.

The invention relates to the technical field of wearable intelligent equipment, and further relates to a sound effect recommendation method and device, a computer readable storage medium and wearable intelligent equipment. The method comprises the following steps: determining a first vector according to acquired behavior feature data and environment feature data; determining a current scene and a first confidence coefficient according to the multi-source sensing data; determining a plurality of target sound effect templates according to the current scene, the first confidence and the scene priority matrix; and determining a target divergence according to the first vector, the current scene and the plurality of target sound effect templates, and determining a target sound effect according to the target divergence. According to the method, on the premise that scene adaptability and brand specifications are guaranteed, learning of user preferences can be achieved, so that the problem that the recommended sound effect is different from the expectation of the user is effectively avoided, meanwhile, mistaken recommendation caused by low-reliability preferences is restrained through a confidence coefficient mechanism, and sound effect selection better fitting individual habits can be provided for the user.

This invention relates to the field of defect detection technology, specifically to a method for improving the YOLOv8 network and its application in strip steel surface defect detection. Based on YOLOv8, this invention first proposes an improved coordinate attention mechanism, the three-channel coordinate attention mechanism TCCA. Addressing the overly simplistic offset mask generation method in DCNv2, which leads to insufficient deformation modeling capabilities, this invention proposes deeply embedding TCCA into DCNv2. Furthermore, this invention introduces a global attention mechanism to improve the model's ability to extract global features. The invention also introduces a BiFPN structure and dynamic serpentine convolution to enhance the model's multi-scale feature fusion capabilities. MDPioU replaces the original loss function of YOLOv8, solving the problem of loss of effectiveness due to identical aspect ratios in predicted bounding boxes, while increasing convergence speed and localization ability. Extensive comparative and ablation experiments on the NEU-DET dataset demonstrate that the improved algorithm of this invention achieves higher defect detection accuracy and faster speed.

The application discloses a three-dimensional point cloud few-shot classification method and system based on CLIP, which takes point cloud data and text description as original input, obtains multiple view images by projecting the point cloud at multiple angles, takes the multiple view images and the text description as the input of a pre-training model CLIP, obtains the corresponding text category of the multiple view images, and thus obtains the category of the original point cloud corresponding to the multiple view images. A learnable projection module is used to obtain several different optimal projection angles of the point cloud, and then a rotation matrix of the point cloud is obtained through the projection angle; secondly, a perspective projection method is used to obtain the projected multiple view two-dimensional images, and a ResNet network is used to extract the multiple view image features which can best reflect the object features, so that the classification is more simple and convenient; and the point cloud few-shot classification learning method has the advantages of simple training and strong universality.