6results about How to "Avoid learning" patented technology

The invention discloses a target object pose estimation method, system and device and a medium, and relates to the technical field of pose estimation, and the method comprises the steps of offline data set establishment and online real-time multi-target pose estimation. In the off-line process, continuous RGBD streams surrounding a single object are collected, the relative poses of front and back frames are calculated frame by frame, and meanwhile, the three-dimensional surface of a target object is reconstructed to obtain a key frame off-line data set; in the online process, a visible target set in a data stream is firstly detected, VOS segmentation is performed on each target to obtain real-time effective texture and point cloud, offline key frame images of to-be-detected targets are arranged to realize single matching of all feature points and find out a closest reference frame at the same time, and RANSAC is adopted to calculate relative poses of a current frame target and the reference frame; according to the method, an expensive data labeling process is avoided, rapid integrated deployment of detection of an object without a new target is realized, the matching speed is improved, and meanwhile, online real-time multi-target pose estimation can be realized.

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.

The embodiment of the invention aims to provide a two-layer load balancing method for forwarding a client request to corresponding processing equipment by calculating a hash value of a source MAC (Media Access Control), and a database firewall system using the method. Firstly, a hash value is calculated for a data source MAC (Media Access Control) from a client, then a hash result is used for carrying out remainder operation on a node number, so that a current client message is shunted to a firewall node with a corresponding serial number, two-layer load balancing under database firewall cluster deployment can be realized, and the reliability, availability and high-concurrency processing capability of a system are improved; meanwhile, for a firewall deployed in a bridge mode, a source MAC of a message of a database server is modified into a bridge port MAC of the firewall on a client side, so that a switch on the client side can be prevented from learning the MAC of the database server to a fixed port, and a scheduling algorithm is prevented from losing efficacy; moreover, the client side port of the firewall filters out the message of which the source MAC is the MAC of the port at the same side of other firewalls, and the server side port of the firewall filters out the message of which the source MAC is the client side MAC, so that the network loop problem can be avoided.

The application discloses a time series classification method based on frequency domain feature extraction, which first acquires time series information of different dimensions in a data set and applies standardization. Secondly, the standardized time series information is put into a time block layer for decomposition, and the decomposed data is input into a self-attention layer in a Transformer to extract time domain features. Then, Fourier decomposition is performed on the time domain features, and high-frequency components and low-frequency components on the frequency domain are retained simultaneously by selecting an operator, and then inverse Fourier transformation is performed to convert the time series data to the time domain dimension. Finally, according to the time domain feature and the frequency domain feature information, the time series data is classified by a classifier. Thus, the application solves the problem of lacking learning of frequency domain information in the classification process of time series data, and improves the accuracy of time series classification.