6 results about "Conditional mutual information" patented technology

The application discloses a cross-user wearable activity recognition method based on group-specific concept-aware representation learning, which comprises the following steps: collecting multi-user sensor data and preprocessing; measuring the concept drift degree from the time sequence perspective and the semantic perspective respectively, fusing the multi-perspective measurement results to perform user clustering, and generating group-specific concept labels; constructing a perception model comprising an activity encoder, a user encoder and multiple classifiers, and performing supervised learning by minimizing the joint loss of activity, user and group-specific concept classification; introducing a conditional discriminator to construct a representation pair of joint distribution and marginal distribution, minimizing the conditional mutual information of activity representation and user representation under the group-specific concept through adversarial training, and obtaining a trained model; and inputting test data into the trained model for activity recognition. The application explicitly models the group-specific concept and decouples the activity and user features, eliminates the cross-user concept drift, and significantly improves the activity recognition generalization ability of the model on new users.

The application discloses a data governance effect evaluation method, and relates to the technical field of data governance. Firstly, the application collects evaluation index time series of a complete business cycle before and after the implementation of a governance action, and then, after discretization processing, the application screens the correlation edges between indexes based on mutual information and marks the correlation polarity, so as to construct an undirected dependence network before and after the governance. Then, the application uses conditional mutual information to complete the discrimination of the causal direction, and generates a corresponding causal graph. Finally, the application compares the edge changes of the causal graph, identifies the effect offset phenomenon between indexes, calculates the net effect value, and realizes threshold early warning. The application can accurately capture the index causal dependence structure changes caused by the governance, and improves the accuracy and reliability of the data governance effect evaluation.

The application provides a multi-level causal inference method and equipment for multi-omics heterogeneous data. By establishing a feature-level causal constraint based on conditional mutual information and designing a two-stage causal inference scheme of feature level and representation level, the multi-level causal relationship is systematically inferred from the multi-omics heterogeneous data. The essence of the multi-omics causal relationship is strictly captured by using the conditional mutual information. The framework establishes a strict mathematical basis, so that the causal inference is improved from experience to theory, and the risk of false causal discovery is greatly reduced. The two stages of feature level and representation level are mutually constrained and supplemented. The feature level constraint ensures the accuracy of the basic causal relationship, and the representation level inference captures complex multi-step causal chains. Compared with the single-level method, the multi-level design can capture more rich causal structures.

This invention discloses a method and device for early fault warning of wind turbines based on specific causal networks, relating to the field of data processing. The method includes: calculating the directed transfer entropy between any two variables in each time window using conditional mutual information based on the data matrix of each time window; constructing a specific causal network for each time window and calculating the variational causal characteristics of the current time window; calculating the dynamic network marker score of the causal variation potential of each node in the specific causal network of each time window and the comprehensive state index value of each time window based on the variational causal characteristics of each time window, and calculating the change in the comprehensive state index value between the current time window and the previous time window; and determining whether the current time window is a warning window based on the change in the comprehensive state index value. This invention solves the problem of difficulty in providing early warnings based on SCADA data.