4 results about "Function representation" patented technology

The application relates to a seismic damage structure reinforcement scheme optimization method and equipment based on a digital twin model, and belongs to the technical field of computers. The method comprises the following steps: acquiring a three-dimensional geometric model of a seismic damage structure containing component semantic information, and crack characteristic data and measured dynamic response data associated with the three-dimensional geometric model; generating a digital twin basic model based on the three-dimensional geometric model of the seismic damage structure; determining a crack characteristic vector of an analysis object of the seismic damage structure according to the crack characteristic data, and constructing a seismic damage state digital twin model based on the digital twin basic model, a mapping function and the measured dynamic response data; the mapping function represents a mapping relationship from the crack characteristic vector to a material damage factor; the seismic damage state digital twin model is virtually reinforced by activating a zero stress of a reinforcement layer unit of a reinforcement scheme, and an optimized reinforcement scheme is determined according to reinforcement performance index constraints and reinforcement implementation optimization targets, so that the objectivity, rationality and adaptability of the reinforcement scheme determination are significantly improved.

A program according to one embodiment of the present disclosure causes a computer to execute: a first input procedure for inputting a first nonterminal node and a second nonterminal node on a zero-suppressed binary decision diagram; a second input procedure for inputting at least designation of a first function representing a condition to be satisfied by a set pair of a set included in a set group represented by the first nonterminal node and a set included in a set group represented by the second nonterminal node; and a calculation procedure for executing a set group calculation of the set group represented by the first nonterminal node and the set group represented by the second nonterminal node, on the basis of the first nonterminal node, the second nonterminal node, the first function, and a template of an algorithm for achieving the set group calculation.

The application relates to a trajectory prediction method and device, electronic equipment and a computer readable storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a semantic map corresponding to a prediction target and a historical observation trajectory sequence corresponding to the prediction target, the historical observation trajectory sequence representing the trajectory position of the prediction target at each historical moment; then, the historical observation trajectory sequence corresponding to the prediction target is represented by sequence coding to obtain sequence coding features; a function representation corresponding to the semantic map is generated based on the semantic map corresponding to the prediction target, the function representation corresponding to the semantic map representing the mapping relationship between semantics and trajectory positions; then, trajectory prediction is performed based on the sequence coding features and by using the function representation corresponding to the semantic map to obtain a future prediction trajectory sequence. The application relates to a trajectory prediction method and device, electronic equipment and a computer readable storage medium, which can improve the vehicle trajectory prediction accuracy.

A computer-implemented method in a model-based reinforcement learning (RL) system with logic states includes switching an agent between a first mode and a second mode, the first mode being a probabilistic planning mode and the second mode being an information gathering mode. In response to the agent being in the probabilistic planning mode, the agent computes a predictive state representation, given a history of observations and actions taken, and the agent scores action candidates based on planning with the predictive state representation so that actions with resolved plans with confidence to achieve a goal state are scored higher than actions without resolved plans. In response to the agent being in the information gathering mode, the agent scores action candidates based on a Q function of a value of expected information to be gathered from a given pair of state and action.