3 results about "Explicit model" patented technology

The invention discloses a fan trigger type fault-tolerant control method fusing zotope and a fatigue load prediction model. The method comprises the following steps: firstly, constructing a quasi-linear parameter time-varying model of state-driven scheduling, setting a key dynamic parameter as a state-dependent function, and realizing smooth parameter scheduling and global closed-loop stability through a convex combination structure; secondly, designing a sliding mode interval observer based on zonotope set membership estimation, defining external disturbance and obtaining upper and lower bounds of a residual error, enhancing set estimation convergence in combination with a super-twisted sliding mode arrival law, and realizing efficient fault detection by checking whether a residual error interval contains zero or not; thirdly, designing a multi-cell expressed semi-explicit model predictive controller under the Q-LPV framework, constructing control input into a convex combination of vertex control input, and balancing real-time control performance and calculation processability by pre-calculating a generalized multi-parameter quadratic programming critical region and a control law; and meanwhile, a fatigue load prediction model is fused to realize multi-objective optimization of power tracking and load suppression.

The application provides a kind of test time self-adaptive method and device based on sparse supervision explicit modeling field variable, comprising: first, for the setting that test stage only contains untagged stream data, the exclusive field representation of each sample is explicitly constructed;Then, the dense and continuous multimodal features are extracted using a visual-language pre-training model, and the field parameter of each sample is parameterized as a learnable distribution variable;Then, a momentum-updated sparse field library is designed, which provides structured prior for the field variable through decoupling supervision mechanism;On this basis, the learned explicit field clues are injected into the downstream model, and efficient adaptation can be achieved by using only the basic entropy minimization criterion without relying on complex online optimization strategy;Finally, through the system experiment verification on multiple standard robustness benchmarks, by introducing explicit and structured field representation mechanism, the application reveals that the key of robust adaptation does not come from complex adaptation algorithm.

The invention discloses a hypergraph link prediction method based on structure perception and edge feature fusion, and the method comprises the steps: introducing edge information explicit modeling under a hypergraph frame, combining a self-adaptive sub-graph sampling mechanism, taking the difference of a local structure into consideration while modeling a high-order relation, and introducing a hypergraph modeling method with edge information as the center. According to the method, nodes in an original graph are converted into edges in a hypergraph, the edges of the original graph are converted into nodes in the hypergraph, by means of the natural high-order relation modeling capacity of a hypergraph convolutional network, the over-smoothing problem is relieved while the expression capacity of a model for a complex structure is effectively improved, and by dynamically adjusting the hop count, the over-smoothing performance of the model is improved. And the most relevant neighbor node is selected to construct a local context so as to improve the structural information capture capability of link prediction.