9 results about "Visibility graph" patented technology

The invention relates to the field of robot path planning, in particular to a multi-robot path planning method based on group control, which comprises the following steps: on the basis of a visibility graph, generating a communication undirected weighted graph, introducing a Laplacian matrix, and reflecting the connectivity of the graph according to a second small feature value of the Laplacian matrix; the arrival time of the robots is controlled by adjusting the side weights, space-time conflicts are avoided, the robot path planning sequence is determined according to the contribution value of the second small feature value, and the overall performance of multi-robot cooperation is optimized; according to the method, data in robot path planning are integrated through the Transform model, the comprehensiveness of environmental understanding is improved, spatial constraints of the path are optimized through a message passing mechanism of the graph neural network, the robot dynamically adapts to environmental changes, the path is generated through the generative adversarial network, diversity and high efficiency are achieved, local optimum is avoided, and the method is suitable for being popularized and applied. And dynamic obstacle avoidance and real-time path optimization are realized through cooperation of multiple models.

The invention discloses a rolling bearing fault diagnosis method, device and equipment and a storage medium, and belongs to the technical field of rolling bearings, and the method comprises the steps: generating a vibration signal sequence according to the vibration data of a rolling bearing; generating energy values of a plurality of local signal segments by using the vibration signal sequence; constructing a coarse-grained sequence by using the energy value of the local signal segment and the vibration sequence number sequence; converting the coarse-grained sequence into a sub-sequence set corresponding to each scale factor by using a preset scale factor set; establishing a weighted natural visibility graph for each sub-sequence in the sub-sequence set, and calculating a graph signal discrete entropy value of each sub-sequence based on the weighted natural visibility graphs; generating a vibration signal multi-scale discrete entropy spectrum feature by using the graph signal discrete entropy value of each sub-sequence; and inputting the vibration signal multi-scale discrete entropy spectrum features into the trained support vector machine model to obtain a fault diagnosis result. And high-precision and high-reliability rolling bearing fault diagnosis can be realized.

The invention discloses a city scene channel gain graph construction method based on mask constraint, which comprises the following steps: firstly, carrying out space division on a whole scene area, setting a minimum sample and a minimum verification / test lattice constraint, then generating a thick / thin wall binary mask based on a top view, carrying out pixel-level uniform sampling along a connecting line of any two points, and finally, carrying out pixel-level sampling on the pixel-level binary mask; on the basis, estimating a local scale by using KDTree, setting an adaptive radius, a maximum connecting edge distance, an out-degree interval and a soft radius bag bottom, constructing a visibility graph, defining a learning target in a residual domain by using FSPL as a baseline, obtaining stable and convergent path loss estimation, carrying out whole graph reasoning on grids, and fusing by using Gaussian / Hanning weight, so as to obtain a path loss estimation result; and then smooth convergence is carried out on results of multiple transmitters to obtain a continuous and consistent full-scene gain graph. According to the method, the high-resolution and high-reliability channel gain graph can be quickly generated in a complex city scene, and a theoretical basis and data support can be provided for network deployment and coverage analysis of a next-generation communication system.

The invention discloses an epileptic seizure detection system based on a double-branch space-time diagram neural network. The epileptic seizure detection system comprises an electroencephalogram signal collector, a signal preprocessor, a double-branch diagram constructor, a double-branch diagram neural network feature learning device, a feature fusion device and a classifier. Wherein the double-branch graph constructor comprises a time branch and a space branch; the time branch maps a down-sampled electroencephalogram sequence into a graph structure for each channel by adopting a horizontal visibility graph algorithm; the spatial branch constructs node features based on multi-scale visual graph network statistical features, and constructs edge weights based on dispersion indexes and cross-channel phase amplitude coupling. The double-branch graph neural network feature learner automatically learns time dynamic features and space correlation features from two types of graph structures, and aggregates multi-channel information through an attention mechanism. And the feature fusion device adopts an attention mechanism to carry out weighted fusion on the double-branch features. According to the invention, the accuracy of epilepsy detection is significantly improved by fusing spatial-temporal features and graph neural network automatic learning through a double-branch architecture.

The application relates to the technical field of ship path planning, and particularly discloses a ship path planning method and system for exploring potential water surface dangerous areas, which comprises the following steps: acquiring known dangerous areas and potential dangerous areas; generating observation points on the edges of each potential dangerous area to obtain a set of potential observation points; establishing a visibility graph, wherein the nodes of the visibility graph are the observation points, the edges are paths between two observation points which do not pass through the known dangerous areas and the potential dangerous areas, and the edge weight is the length of the path; solving a TSP problem with group constraints by using an improved ant colony algorithm on the visibility graph to obtain a shortest path covering all the potential dangerous areas, which is used as a ship planning path. The application realizes shortest path planning of a ship in a target sea area to bypass the known dangerous areas and traverse the edges of all the potential dangerous areas, and realizes safe and efficient shortest path planning.

To provide a method and a system for classifying a rolling bearing state based on a motif feature of a horizontal visual graph.SOLUTION: Converting the vibration signals of each type of rolling bearing into a horizontal visual graph, labeling nodes of the horizontal visual graph according to a time sequence to obtain n-point motifs, for the n-point motifs with m motif types, calculating an appearance frequency of each type of n-point motif, and obtaining a feature vector based on the appearance frequencies of all types of n-point motifs, extracting a feature element of a bearing vibration signal of a current fault type based on the feature vector, obtaining feature elements of bearing vibration signals of all known rolling bearing fault types, obtaining bearing vibration signals to be classified, and selecting a known rolling bearing fault type corresponding to a maximum value of similarities as a fault type of the bearing vibration signal to be classified.SELECTED DRAWING: Figure 1

The present invention discloses an apparatus for use by a pathfinding device, comprising at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: obtain a first set of nodes of a polygon indicating a first Permitted-Moving-Zone, PMZ, wherein, the first PMZ is not overlapping with any other PMZs; determine a group of Non-permitted-Moving-Zones, NMZs, that lies within the first PMZ or intersects with the first PMZ as its relevant NMZs; and determine a visibility graph for the first PMZ based on its relevant NMZs.

Provided is a method for optimizing searching of a previously viewed scene for a head-mounted display (HMD) device including receiving a plurality of views corresponding to a real-world scene, determining landmarks within each view, determining a co-visibility of the landmarks between different views, generating a co-visibility graph based on the co-visibility of the landmarks between the different views, the co-visibility graph including multiple levels, which include nodes that represent at least one view and edges between the nodes that represent the co-visibility of the landmarks between the at least one view, and detecting a pose of the HMD device based on the co-visibility graph of the real-world scene.

The invention discloses an occluded fruit detection method based on deep learning and multi-modal fusion, and aims to solve the problem that the complete contour and size of a fruit are difficult to recover due to occlusion. The method comprises the following steps: constructing a three-dimensional Gaussian representation with multispectral and view-angle-related transparency through short-time multi-view acquisition and registration; according to the method, a bladeless view and a visibility graph are generated through semantic hierarchical rendering, and in combination with micro-robust ellipsoid fitting and thin-shell constraint, Gaussian parameters are reversely optimized through re-projection joint loss, so that the technical effects of more accurate estimation of the modal mask and the three-dimensional ellipsoid, less repeated counting, faster convergence and more stable grabbing posture in a re-shielding and backlight scene are achieved.