3results about How to "Avoid learning" patented technology

This utility model relates to the field of desk technology, and discloses a student desk designed to prevent myopia, including a desk body; two sets of mounting bases installed on both sides of the desk body; support rods installed on the mounting bases; and a connecting rod between the two sets of support rods. The mounting bases are fixed to the side of the desk body with screws. This student desk, by using snap-fit ​​connectors to install the connecting rod between the two sets of support rods, forms a guardrail, effectively preventing students from studying in a posture of lying on the desk body or too close to it. This design guides students to maintain correct viewing distance and reading posture, reduces prolonged close-range eye use, effectively reduces the risk of myopia, improves the efficiency of the device, and meets practical application needs.

The application discloses a modal balance and semantic enhancement training method of a full-modal safety guardrail, which significantly reduces the missed detection and false rejection of cross-modal queries by improving the training data and designing the training target, and improves the safety and reliability of the full-modal guardrail. The application efficiently constructs modal de-biased data, constructs a multi-modal query quadruple based on semantic pairing of a text seed query, and enumerates combinations of modal subsets and safety semantic labels to construct risk variant query samples. The data construction method can efficiently expand the training data based on a small amount of text samples, so that the safety and unsafe states of various modal combinations are comprehensively covered, the model is prevented from learning modal shortcuts during the training process, the dependence of the guardrail on different modal safety semantics is rebalanced, fine-grained safety supervision signals are efficiently expanded, and the cross-modal safety semantic understanding of the guardrail model is improved.

This invention discloses an online prediction method for battery SOC based on an incremental correlation vector machine (RVM) strategy using a multi-kernel ensemble approach. The method includes the following steps: Step 1, data preprocessing; Step 2, training set sampling; Step 3, kernel function selection; Step 4, model training; Step 5, model validation; Step 6, adaptive kernel parameters; Step 7, RVM model ensemble; Step 8, model prediction; Step 9, incremental learning strategy; and Step 10, online incremental prediction. From a practical perspective, this invention addresses the complexities and diverse needs of various applications. Drawing on the ideas of incremental learning and ensemble learning, it generates highly differentiated RVM individual learning models containing multiple kernel functions through dual perturbation of training samples and kernel functions. Combined with a novel incremental ensemble strategy, it avoids the problem of model overlearning, improves the model's generalization ability and robustness, and expands its application scope.