10 results about "Fuzzy object" patented technology

The invention provides a light-weight road crack semantic segmentation algorithm fusing dense connection. The algorithm is an improved segmentation algorithm based on a DeeplabV3 + network model. The structure of a network body is similar to that of a DeeplabV3 + network, a lightweight network is adopted as a network trunk, densely connected cavity space pyramid pooling layers are designed, the output of each layer serves as the input of each later layer, the feature information of cracks is overlapped layer by layer, and finally the crack features under multiple scales are gathered. The improved DeeplabV3 + network model can be used for more accurately segmenting a crack fuzzy object image, can improve the feature multiplexing efficiency while ensuring the overall segmentation precision, solves the problem of slow training time caused by the complex structure of the original DeeplabV3 + network, and effectively captures information blur and low segmentation accuracy caused by the complex environment.

The invention provides a video processing method and apparatus, a terminal device and a computer readable storage medium. The method comprises the steps of obtaining to-be-processed video information; determining a target video frame based on the ambiguity of the to-be-processed video information; and performing alignment fusion processing on the target video frame to obtain target video information. According to the video processing method provided by the invention, the video frame with relatively high ambiguity in the video is taken as the reference of the video frame with relatively poor ambiguity, and the blurred object in the video frame with relatively poor ambiguity is replaced, fused and the like through the same object in the video frame with relatively high ambiguity, so that the blurring in the current video frame is eliminated; and the clarity of the current video frame is improved.

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 invention belongs to a data mining and intelligent information processing technology, and particularly relates to a personalized recommendation method based on a language value two-tuple concept lattice. The personalized recommendation method can solve the problems of fuzzy recommendation interpretation and cold start and can process different data types. The method comprises the following steps: collecting and preprocessing data, collecting data information in a multi-type data form, and judging whether the types of fuzzy data information collected by a computer are unified or not. If not, converting the data type into a language value two-tuple; constructing a language value two-tuple concept of the user set and the article set; obtaining language value two-tuple evaluation data of the user on the commodity decision attribute set of the commodity set; constructing a cognitive system of the training data set; constructing a sufficient knowledge base and a fuzzy object language knowledge simulation lattice of the training data set; constructing a necessary knowledge base and a fuzzy object language knowledge simulation lattice of the training data set; and extracting rules, and performing selection judgment to obtain a recommendation result.

The present invention belongs to the field of temperature monitoring and relates to data analysis technology. The present invention is used to solve the problem in the prior art that the temperature monitoring system of electric power equipment cannot adopt different monitoring methods to perform temperature monitoring on components with different temperature performance characteristics. Specifically, the present invention is a temperature monitoring system of electric power equipment based on a platinum thermal resistor temperature sensor, comprising a temperature monitoring platform. The temperature monitoring platform is communicatively connected with a feature analysis module, a data processing module, a combined monitoring module and a storage module. The feature analysis module is used to analyze the fault temperature characteristics of the electric power equipment: when the electric power equipment fails, the monitored object is marked as a clear object or a fuzzy object through the analysis process; a platinum thermal resistor temperature sensor is added to the fuzzy object. The present invention can analyze the fault temperature characteristics of the electric power equipment, differentiate the temperature characteristics of the monitored object through an appearance coefficient, and add a platinum thermal resistor temperature sensor to the fuzzy object to improve the accuracy of the temperature monitoring result.

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.

The present invention discloses an equipment operation monitoring system and method based on intelligent sensors, belonging to the field of sensor technology. Construct a synchronous sub-monitoring sequence data cluster, the data cluster includes synchronous sub-sequence number information, intelligent sensor coding information and equipment operation parameter information; characterize the monitoring behavior curve of each intelligent sensor in the form of coordinate points, and the monitoring behavior curve records the synchronous sub-sequence number information and equipment operation parameter information; capture the jump point and jump value of the monitoring behavior curve, form a synchronous interval class, and generate a fuzzy object; form a synchronous perception fuzzy set, and analyze the membership of the fuzzy object based on the jump value; determine whether there is perception synergy between intelligent sensors, update the synchronous perception fuzzy set, analyze the perception synergy between intelligent sensors, and perform intelligent early warning; and then, based on the synchronous perception behavior sequence of the sensor, while performing fuzzy reasoning, it is possible to achieve early warning synergy between multiple sensors.