5 results about "Fuzzy object" patented technology

The application belongs to the technical field of simultaneous localization and mapping, and particularly relates to a SLAM algorithm based on feature reinforcement and motion judgment in a dynamic scene, which applies a feature reinforcement instance segmentation network FENET and comprises the following steps: step S1, collecting image information and realizing feature recovery of a dynamic fuzzy object through a fuzzy feature recovery module; step S2, guiding a model to focus on key features of an object based on a reinforced feature recognition mechanism, and recognizing potential dynamic objects; and step S3, jointly estimating the pose of a camera itself and judging the motion of an object to remove a dynamic object. The application can reconstruct and recover lost feature information from a fuzzy image, greatly improves the recognition accuracy of a system for a dynamic object, greatly improves the recognition accuracy of a dynamic object, and avoids misjudgment of static features.

The methods presented herein are used to de-duplicate object representations in a three-dimensional (3D) scene. One or more features may be extracted from an object in a three-dimensional (3D) scene to compute an individual object signature, which may be a representation of an appearance of the object. The object signatures may then be compared using one or more similarity metrics in order to determine whether the object is duplicated and / or nearly duplicated. For duplicated objects, the common representation may be used for each duplicated object rendered at a given location and orientation within the scene, thereby rendering objects within the scene using less memory and computing resources.

Approaches presented herein provide for deduplication of object representations in three-dimensional (3D) scenes. One or more features may be extracted from objects in a 3D scene to compute an individual object signature, which may be a representation of an appearance of the object. Object signatures may then be compared using one or more similarity metrics in order to determine whether or not objects are duplicates and / or near-duplicates. For duplicate objects, a common representation may be used for each duplicate object that may be rendered within the scene at a given location and orientation, thereby using less memory and compute resources to render objects within the scene.

The present invention discloses a nonlinear modeling method for a synchronous rotating coordinate system phase-locked loop based on the sector interval method, belonging to the technical field of nonlinear modeling of grid-connected converters. Based on the nonlinear model of the synchronous rotating coordinate system phase-locked loop, its operating point is converted to the coordinate origin, and the sine nonlinear link in the phase-locked loop control is selected as the fuzzy object; the initial sector interval is designed according to the selected fuzzy object; the phase-locked loop control state matrix is determined according to the designed sector interval; the linear matrix inequality of the phase-locked loop control system is written based on the state matrix, the common positive definite matrix P is solved, and the size of the sector interval of the phase-locked loop fuzzy system is adjusted by the presence or absence of the matrix P, that is, the maximum universe of discourse of the state variables in the fuzzy object is obtained; the membership function and fuzzy rules of the phase-locked loop control system are determined according to the maximum universe of discourse, and the equivalent model of the system is constructed by the membership function and fuzzy rules. The present invention can achieve accurate modeling with fewer fuzzy rules.

The present invention relates to data mining and intelligent information processing technology, and in particular, to a personalized recommendation method based on a language-value binary concept lattice that can resolve recommendation interpretation ambiguity and cold start issues and can handle different data types. The method is performed in the following steps: data acquisition and preprocessing, collecting data information in multiple data formats, and determining whether the type of fuzzy data information collected by the computer is uniform. If not, the data type is converted into a language-value binary tuple: language-value binary concepts for user sets and item sets are constructed; language-value binary evaluation data for product decision attribute sets is obtained from users; a cognitive system for a training data set is constructed; a sufficient knowledge base and a fuzzy object language knowledge pseudo-lattice for the training data set are constructed; a necessary knowledge base and a fuzzy object language knowledge pseudo-lattice for the training data set are constructed; rules are extracted, selection and judgment are performed, and recommendation results are obtained.