3 results about "Probability representation" patented technology

The application provides a cross-condition multivariate time series anomaly detection method based on phase-aware transfer diffusion, comprising: obtaining historical monitoring multivariate time series data, preprocessing and cutting to obtain a source domain training sample set, a target domain adaptation sample set and a to-be-detected sample set; performing time local standardization on each sample set, combining BallTree to construct a dynamic graph structure, performing spatial neighborhood weighted standardization, using a time convolution network and a graph attention network to extract spatio-temporal joint features; training a graph variational autoencoder and a phase encoder to obtain a phase probability representation; using a phase conditional diffusion model to train a source domain pre-training model, constructing a phased normal prototype library and a source domain prior threshold; performing cross-condition transfer adaptation through phase-aware statistical alignment, prototype-driven constraint and parameter efficient fine-tuning to obtain a final detection model and output a detection result. The method realizes stable cross-condition anomaly detection under a few sample conditions, and improves the accuracy and robustness of detection.

A probabilistic representation method for underwater explosion shock wave load based on Bayesian reasoning is disclosed, relating to the field of underwater explosion load calculation. The method is based on Bayesian probability models of underwater shock wave loads, and performing probability representations of underwater explosion shock wave loads. The method is used to effectively represent uncertainty of underwater explosion shock wave loads, and to provide random inputs for modeling load variability for the design of explosion-proof underwater structures.