3 results about "Pattern perception" patented technology

The invention relates to the technical field of artificial intelligence and computer system security, in particular to a log anomaly detection method based on pattern awareness and a synthetic attention mechanism, which comprises the following steps: generating initial event embedding through log analysis and vectorization; constructing a parallel mode perception path and a sequence context path, respectively utilizing a dynamic graph attention network to extract macroscopic service mode structure features, and capturing a microscopic time sequence dependency relationship based on a Transform encoder; designing a bidirectionally guided synthetic attention mechanism, realizing mode-to-sequence focusing guidance and sequence-to-mode anomaly backtracking, and generating a synthetic anomaly representation fused with a context abstract and a mode deviation; and finally, outputting an abnormal score and interpretable alarm information through an MLP classifier. According to the technical scheme, collaborative modeling of the macroscopic mode and the microscopic details in the log data can be achieved, and the accuracy, robustness and interpretability of anomaly detection in a complex scene are remarkably improved.

The application discloses a soft measurement modeling method based on mode perception dynamic variational autoencoding regression, and comprises the following steps: (1) acquiring three-phase flow process data with multiple mode dynamic characteristics; (2) data division and pretreatment operation; (3) establishing a mode perception dynamic variational autoencoding regression model, and realizing the prediction of an online quality variable; and (4) predicting a three-phase flow process pressure variable and performing model performance evaluation. The encoder-decoder network with a convolution-deconvolution structure learns the hidden layer feature representation of dynamic data, the multiple mode characteristics of the data are mined in the hidden space by using a Gaussian mixture distribution, the mapping relationship between the latent variable and the key quality variable is effectively learned, in addition, the reconstruction of dynamic multiple mode data is constrained by using a Wasserstein distance, and finally the purpose of improving the accuracy of key quality inference is achieved.

The invention belongs to the technical field of computer vision related to virtual fitting, and particularly relates to a pluggable garment pattern control model construction method for a multi-architecture virtual fitting model, and the method comprises the steps: eliminating the contour leakage of original garments through a sample construction module, and generating a standardized mask and a dense posture for the constructed control model; utilizing a model perception layout generator to generate a body-clothing segmentation layout map according to the target model label, and extracting multi-scale model features; the features are matched and injected into the target virtual try-on model through a multi-scale model injector, and a try-on image conforming to the target model is driven to be generated. The garment pattern control model constructed by the method serves as a universal plug-in, accurate pattern control can be realized without changing the structure of the existing virtual fitting model, the problems of pattern fixing, control capability and model architecture binding and the like in the prior art are solved, and the customization level and user experience of virtual fitting are remarkably improved.