39 results about "Visibility graph" patented technology

The present invention provides a comprehensive method to design an automatic media viewership measurement system, from the problem of sensor placement for an effective sampling of the viewership to the method of extrapolating spatially sampled viewership data. The system elements that affect the viewership—site, display, crowd, and audience—are identified first. The site-viewership analysis derives some of the crucial elements in determining an effective data sampling plan: visibility, occupancy, and viewership relevancy. The viewership sampling map is computed based on the visibility map, the occupancy map, and the viewership relevancy map; the viewership measurement sensors are placed so that the sensor coverage maximizes the viewership sampling map. The crowd-viewership analysis derives a model of the viewership in relation to the system parameters so that the viewership extrapolation can effectively adapt to the time-changing spatial distribution of the viewership; the step identifies crowd dynamics, and its invariant features as the crucial elements that extract the influence of the site, display, and the crowd to the temporal changes of viewership. The extrapolation map is formulated around these quantities, so that the site-wide viewership can be effectively estimated from the sampled viewership measurement.

A method and system that archive a three-dimensional site in a highly-compact manner such that real-time, three-dimensional exploration and interaction with the site with high-resolution graphics is enabled. During authoring, information is collected about a site, and processed into a walkmap comprising a number of maps. A visibility map indicates which ones of the many polygons that make up a site are potentially visible from a given region. A collision map establishes where a can navigate in the site. A ground map tracks the terrain for reproducing camera heights and viewing angles, and a trigger map causes scripts to be fired from locations in the site. During navigation, only the maps relevant to a user's current position are active, whereby rapid rendering of an appropriate image for the user's current perspective is possible in real-time, providing a first person, perspective tour of the site in a perceived three-dimensional environment.

The invention provides an unmanned vessel ant colony consumption optimal global path planning method based on an electronic nautical chart. The method comprises the steps of 1, processing the electronic nautical chart, wherein after the electronic nautical chart is converted into the form of a file capable of being processed, a feasible area and an obstacle area of a map are determined, a visibility graph method is utilized to find out a set of feasible paths among an initial point, a target point and the vertex of an obstacle, it is guaranteed that the paths do not pass through an unfeasiblearea, and the sailing safety of the unmanned surface vessel is guaranteed; 2, building a motion math model, wherein the motion model of an unmanned vessel is built, and thus the thrust and motion characteristics of the unmanned vessel are grasped; 3, obtaining unmanned vessel data, wherein when the unmanned vessel sails on the water surface, information data like the heading, the vehicle body position and vertical acceleration of the vehicle is obtained; 4, utilizing an ant colony algorithm to search for a track meeting requirements; 5, building an unmanned surface vessel energy consumption model under ocean current disturbance, wherein with the optimal energy consumption being the premise, the ant colony algorithm is improved to obtain the path satisfying safe sailing conditions and having the smallest energy consumption. By comparing the paths under two preconditions, the effectiveness of the designed algorithm is proved.

The invention relates to an epilepsy electroencephalogram signal identification method based on an optimal kernel time-frequency distribution visibility graph, which comprises: acquiring original electroencephalogram data, and calculating adaptive optimal kernel time-frequency distribution of a preprocessed electroencephalogram signal; carrying out classification of epilepsy states; for each obtained time energy sequence, carrying out visualization analysis, obtaining an energy time sequence visibility graph complex network, extracting to obtain feature indexes of the energy time sequence visibility graph complex network, extracting adaptive optimal kernel time-frequency distribution indexes, and classifying the epilepsy electroencephalogram (EEG) signal by combining a support vector machine. According to the epilepsy electroencephalogram signal identification method based on the optimal kernel time-frequency distribution visibility graph, which is disclosed by the invention, the energy time sequence visibility graph complex network is constructed by combining an adaptive optimal kernel time-frequency distribution principle and a visibility graph idea, the indexes of the complex network are extracted, and identification on the epilepsy electroencephalogram signal is implemented.

An edge routing system is described herein that uses a spatial decomposition to achieve faster routing, and more quickly generates a sparse visibility graph using a cone spanner. The system provides two approaches that can be used separately or in combination to achieve faster—and hence more scalable and more interactive—edge routing using approximate shortest paths. The first approach uses a spatial decomposition of the nodes in a graph, moving them slightly to obtain strictly disjoint convex hulls around groups of nodes, and then computing visibility graphs over these composite hulls rather than individual nodes. The second approach generates a sparse visibility-graph spanner to accelerate the process of producing the visibility graph. The system allows high quality obstacle avoiding edge routing for large diagrams in interactive diagramming applications where very fast refreshes of routing are used with many nodes moving at the same time.

The invention relates to an environment modeling method, in particular to an environment modeling method based on a recursive reduced visibility graph. The environment modeling method includes the steps: (1) recording the position of a robot and the reaching position of the robot; (2) connecting S-H-G-L-S to form a quadrangle; (3) traversing each current edge of the quadrangle; (4) judging whether the current edges are intersected with obstacles or not; (5) determining a middle point; (6) turning edges of a newly enclosed polygon S-A-H-B-G-L-C-S to the step 3 for traversing; (7) finishing traversing the current edges of the polygon. A polygonal area is finally obtained, all obstacles outside the rear can be simplified, and only the obstacles inside the area need to be considered by robot route planning. The number of visibility lines can be effectively decreased, and the executing efficiency of a subsequent robot route planning algorithm is improved.

The invention discloses a network visibility graph based passive indoor positioning method. The network visibility graph based passive indoor positioning method comprises the steps of building a channel state information data collection platform and building a network visibility graph by utilizing a common WLAN device; performing a positioning process, wherein the positioning process comprises twostages, including, an offline training stage and an online testing stage; analogizing a subcarrier sequence signal into a time sequence signal during the offline straining stage and building a complex network through a visibility graph and a horizontal visibility graph so as to extract network characteristics used for building a position fingerprint library; collecting data and performing position classification by utilizing multiple machine learning algorithms during the online testing stage. Meanwhile, amplitude and phase position are combined, and multiple pairs of antennas are synthesizedto further improve detection accuracy and stability. The network visibility graph based passive indoor positioning method can greatly reduce the actual operation cost, accordingly realizes passive location of personnel indoors efficiently, and can realize more than 95% classification accuracy under an optimal condition. The network visibility graph based passive indoor positioning method has certain application values in the fields such as smart home, Internet of Things and intrusion detection.

The invention discloses a multiple dimensioned dense matching method and system. The multiple dimensioned dense matching method includes the following steps: constructing image pyramids for multiple images, and calculating a relevant matching image set of each image, wherein each image pyramid has a zeroth to (n-1)th layer and each layer corresponds to a dimension; constructing a depth range of each image; calculating a depth map, a normal vector map and a visibility map of each layer sequentially from the (n-1)th layer of each image pyramid, converting to the next layer of each image pyramid,and calculating the depth map, the normal vector map and the visibility map of the present layer by taking the depth map, the normal vector map and the visibility map of the previous layer of each image pyramid as initial values of an algorithm of the present layer until to the zeroth layer of each image pyramid; and carrying out depth map fusion according to the depth map, the normal vector mapand the visibility map of the zeroth layer of each image pyramid to generate point clouds. The method and system of the invention use a depth fusion framework to carry out dense matching, consider depth information, normal vector information and visibility information, and combines constrains of optical consistency and geometric consistency, thereby improving the precision. Meanwhile, a multiple dimensioned strategy is used, and so the memory requirements are reduced and the efficiency is improved.

The invention discloses a gene expression time series data classification method based on a visibility graph algorithm. The method comprises the steps of (1) constructing a basic network, selecting adata strip according to preprocessed gene expression time series data, constructing a visibility graph and a connection graph by using the visibility graph algorithm, and determining a basic structureof a co-expression network, (2) extracting relevant traditional features according to the obtained basic network, (3) obtaining a feature vector of each gene node in the basic network by using second-order random walk and neural network model learning, and (4) integrating features of the basic network and using different strategies based on the obtained features of the basic network through a density clustering algorithm to complete the classification of gene expression time series data. The invention provides a method for realizing the gene expression time series data classification by usingvisibility graph foundation network construction, node feature vector extraction and the density clustering algorithm, and the method has good precision and practical performance.

The invention relates to a network visibility graph-based human body orientation detection method. According to the method, ordinary equipment is adopted to build an indoor wireless channel information data collection platform; positioning is divided into two phases, namely, an offline training phase and an online testing phase. According to the offline phase, the channel state information data ofa human body in different orientations are collected; the channel state information data are preprocessed; networking processing is performed on the data; network features are extracted; standardization processing is performed on the network features, and the processed network features are stored in a fingerprint database of different orientations; and an orientation-data fingerprint mapping relationship is built. According to the online phase, the data are processed in the same manner as the above mentioned, and a machine learning algorithm is used to perform classification detection on testdata; and in order to test the accuracy of classification, the amplitude and phase information of the channel state information data are comprehensively utilized to observe classification results under three cases. With the method of the invention adopted, the orientation detection of people in an indoor environment can be realized at a low cost. The method of the invention has a certain application value in the fields such as somatosensory games and the orientation identification of blind people.

A system determines a set of POIs that are visible from a location. The system stores a visibility map for each POI. A visibility map includes data about locations or regions from which the associated POI is visible. The data may include information about how visible the POI is from different locations. The system divides a region into road bins (e.g., geofences a route passes through). Each road bin is associated with a set of POIs that are visible from locations within the road bin. When the system receives a request from a user device for information about visible POIs, the system determines a road bin that corresponds to the location of the user device, identifies POIs associated with the road bin, and transmits information about the set of POIs to the user device.

The invention discloses an intrusion detection method for constructing a visibility graph network based on channel state information. The intrusion detection method is divided into two stages: offlinetraining stage and the online testing stage; at the offline training stage, an abnormal value of the CSI time sequence signal is removed through Pauta method, the high-frequency noise is eliminated through a low-pass filter, and finally the smooth filtering processing is performed by using the a sliding average filter, and then 30 sub-carriers are reduced to the one-dimension time sequence signalby using the PCA dimension-reduction method, and the time sequence signal is intercepted by selecting the window size, and the time sequence signal is constructed as the network by using the visibility graph network construction method, and the related network feature is extracted as the machine learning classification feature; at the online testing stage: the intrusion detection estimation classification is performed by using different machine learning algorithms, the amplitude and the phase information are combined at this time, and the accuracy and the stability of the detection are improved by synthesizing multiple pairs of antennas. The intrusion detection method disclosed by the invention is low in cost, free from detection dead zone, and the simpler and more feasible in deploymentrealization.

An edge routing system is described herein that uses a spatial decomposition to achieve faster routing, and more quickly generates a sparse visibility graph using a cone spanner. The system provides two approaches that can be used separately or in combination to achieve faster-and hence more scalable and more interactive-edge routing using approximate shortest paths. The first approach uses a spatial decomposition of the nodes in a graph, moving them slightly to obtain strictly disjoint convex hulls around groups of nodes, and then computing visibility graphs over these composite hulls rather than individual nodes. The second approach generates a sparse visibility-graph spanner to accelerate the process of producing the visibility graph. The system allows high quality obstacle avoiding edge routing for large diagrams in interactive diagramming applications where very fast refreshes of routing are used with many nodes moving at the same time.

The invention belongs to the technical field of related data processing in a building plane, and discloses a method and system for calculating a point-to-point shortest path in a plane in a building.The method comprises the steps: abstracting a plane space into a point Point, an edge Edge, a polygon Polygon and a graph Graph which can be operated by a computer; calculating a visibility graph Visibilities Graph of the initial graph Graph by using a visibility point algorithm; for a point to be calculated, adding an additional visible edge (Advanced Visible Edge) into the Visible Graph (Graph)of the visibility graph; and a Dijkstra algorithm is used to find the shortest path. In the shortest path calculation provided by the invention, the calculated shortest path is a real shortest path; compared with the prior art, the accuracy is greatly improved.

The invention relates to a robot path planning method based on combination of a visibility graph method and a greedy algorithm. The robot path planning method based on combination of the visibility graph method and the greedy algorithm comprises the steps of: (1), scanning a map environment involved by robot motion by utilizing a computer, identifying obstacles in the map environment, performing enveloping, and screening vertexes through a visual condition; (2), connecting a start point to a target point, wherein a connecting line can pass through multiple obstacles, vertexes on the obstacles,which are passed through, are put into a set S, and, vertexes on the obstacles, which are not passed through, are put into another set V; and (3), according to a principle of minimum angle, continuously updating a next point and current, obtaining the vertex of each section, connecting the vertexes of the various obstacles, and finally, obtaining a global optimal solution. By means of the methodin the invention, the calculation amount is greatly reduced; unnecessary obstacles and vertexes are deleted; the modelling composition complexity is greatly reduced; the calculation efficiency is increased; and possible collision of a robot with an obstacle in a motion process can be avoided.

A sleep stage classification method based on complex networks and deep learning and application thereof are provided. The sleep stage classification method comprises: acquiring characteristic indexesof a finite passage visibility graph complex network, to be specific, establishing the finite passage visibility graph complex network to extract node degrees, acquiring a node degree sequence according to the node degrees, and applying the node degree sequence as a characteristic index of the finite passage visibility graph complex network; establishing finite passage visibility graph complex networks having a finite passage visual range of 1 for sleep stage electroencephalography fragments; classifying the sleep stage electroencephalogram fragments into four classes according to awake stage,light sleep stage, deep sleep stage and rapid eye movement stage by means of 10-fold cross validation and a long short term memory model. The method of the invention is applicable to head-set intelligent wearable devices; by analyzing sleep electroencephalogram measured by the intelligent wearable devices, it is possible to understand the brain state of a user and provide necessary early warnings.

The invention relates to an image generation method and device, an electronic device and a storage medium. The method comprises the steps of obtaining an optical flow graph between an initial postureand a target posture and a visibility graph of the target posture according to first posture information corresponding to the initial posture of a first object in a to-be-processed image and second posture information corresponding to the target posture; and generating a first image according to the to-be-processed image, the optical flow graph, the visibility graph and the second attitude information. According to the image generation method disclosed by the embodiment of the invention, the visibility graph can be obtained according to the first posture information and the second posture information, the visibility of each part of the first object can be obtained, the visible part of the first object of the target posture can be displayed in the generated first image, the image distortioncan be improved, and artifacts are reduced.

A modulation signal classification method based on circular system limited crossing visibility graph networking comprises the following steps: S1, collecting I / Q modulation signals, processing the collected I / Q modulation signals, and converting the dual-channel I / Q modulation signals into four-channel signals; s2, respectively converting the four-channel signals into weighted directed network graphs by adopting a circle system limited crossing visual graph networking method; s3, performing feature extraction on the four weighted directed network graphs to obtain four feature vectors, and performing space expansion on the feature vectors to obtain a fusion feature vector of each I / Q modulation signal; s4, training the modulation signal classification model, wherein the classification precision is smaller than a preset threshold value, adjusting hyper-parameters in the circular system limited crossing visible graph networking method, repeating the steps S2 to S3 until the classificationprecision is larger than or equal to the preset threshold value, obtaining the trained modulation signal classification model, and finishing classification of the I / Q modulation signals through the trained classification model. According to the invention, the classification precision of the I / Q modulation signals can be improved.

The invention provides an autonomous detection method for a complex underwater environment of an underwater robot. The method comprises the following steps: obtaining scanning data of a current position of the underwater robot; performing scanning registration according to the scanning data, and combining each piece of new scanning data with the previous scanning data; performing data sampling according to a scanning registration result to obtain candidate viewpoints; calculating the views of the candidate viewpoints by adopting an octree algorithm; obtaining an optimal candidate viewpoint according to the visibility graph by adopting probability analysis; and calculating the barrier-free path of the underwater robot moving from the current position to the optimal candidate viewpoint by adopting an RRT algorithm. According to the invention, path planning can be realized without a prior model so as to autonomously detect a complex underwater environment, and the positioning accuracy ofthe underwater robot and the consistency of complex underwater environment representation can be improved.

Method of patterning nanowire-based transparent conductors are described. In particular, the methods are directed to partial etching that generates low-visibility or invisible patterns.

A deep learning model based on complex networks and its application in measurement signal analysis: set the principle of constructing complex networks for measurement signals through the method of visual graphs, and establish complex networks of visual graphs; use visual graphs for each channel measurement signal Methods The complex network was established, and for each network, the following indicators were extracted: node aggregation coefficient, node degree, node betweenness, node proximity centrality, and node eigenvector centrality; a deep learning model was established. The vertical oil-water two-phase flow experiment is carried out to obtain the measurement signal by using the circularly excited conductivity sensor; the ratio of the oil phase and the water phase is fixed, and the flow rate of the oil phase and the water phase is changed for the experiment. The present invention constructs a complex network for the measurement signal, uses a large number of indicators of the complex network to form samples, and uses a layer-by-layer optimization mechanism to combine supervised learning and unsupervised learning to obtain a deep learning model. It can be used to predict and classify measurement signals of unknown categories.

The invention relates to an epilepsy electroencephalogram signal identification method based on an optimal kernel time-frequency distribution visibility graph, which comprises: acquiring original electroencephalogram data, and calculating adaptive optimal kernel time-frequency distribution of a preprocessed electroencephalogram signal; carrying out classification of epilepsy states; for each obtained time energy sequence, carrying out visualization analysis, obtaining an energy time sequence visibility graph complex network, extracting to obtain feature indexes of the energy time sequence visibility graph complex network, extracting adaptive optimal kernel time-frequency distribution indexes, and classifying the epilepsy electroencephalogram (EEG) signal by combining a support vector machine. According to the epilepsy electroencephalogram signal identification method based on the optimal kernel time-frequency distribution visibility graph, which is disclosed by the invention, the energy time sequence visibility graph complex network is constructed by combining an adaptive optimal kernel time-frequency distribution principle and a visibility graph idea, the indexes of the complex network are extracted, and identification on the epilepsy electroencephalogram signal is implemented.

The invention relates to an environment modeling method, in particular to an environment modeling method based on a recursive reduced visibility graph. The environment modeling method includes the steps: (1) recording the position of a robot and the reaching position of the robot; (2) connecting S-H-G-L-S to form a quadrangle; (3) traversing each current edge of the quadrangle; (4) judging whether the current edges are intersected with obstacles or not; (5) determining a middle point; (6) turning edges of a newly enclosed polygon S-A-H-B-G-L-C-S to the step 3 for traversing; (7) finishing traversing the current edges of the polygon. A polygonal area is finally obtained, all obstacles outside the rear can be simplified, and only the obstacles inside the area need to be considered by robot route planning. The number of visibility lines can be effectively decreased, and the executing efficiency of a subsequent robot route planning algorithm is improved.

The invention belongs to the field of atrial fibrillation electrocardiosignal processing, and provides a method and system for identifying atrial fibrillation electrocardiosignals through a weighted multi-scale limited penetrable visibility graph. The method comprises the following steps: acquiring electrocardiosignals with a set length; extracting original features of the electrocardiosignals to obtain corresponding original feature vectors; converting the electrocardiosignals into a network form, and extracting corresponding network features so as to obtain corresponding network feature vectors, wherein the network features comprise newly-added local efficiency entropies; and fusing the original feature vectors and the network feature vectors and then inputting the fused vectors into a machine learning model so as to obtain a category identification result of the electrocardiosignals, wherein the categories of the electrocardiosignals comprise atrial fibrillation electrocardiosignals, normal electrocardiosignals, noise signals and other electrocardiosignals.

A method for obtaining the temporal and spatial distribution of the mass concentration of sand and dust particles in the near-surface layer, the method steps are as follows: 1) firstly select the location of each meteorological station to observe and obtain the meteorological visibility under the sand and dust weather conditions at the same time under the sand and dust weather conditions 2) Statistical meteorological visibility data from the observation data, according to the analysis and calculation of the relationship between visibility and dust particle concentration, the visibility observed by each meteorological station is converted into the mass concentration of sand and dust particles; 3) The mass concentration of sand and dust particles is Ordinate, visibility as abscissa, establish mass concentration-visibility relationship diagram, fit mass concentration-visibility relationship curve; 4) obtain mass concentration spatiotemporal distribution diagram according to mass concentration-visibility relationship curve. The invention solves the problem that the time-space distribution of particle concentration under the condition of dust weather cannot be observed so far in my country, and provides an effective observation method for the research of sandstorm forecast and early warning and sand prevention and control in my country.

The invention discloses an analysis method and application of EEG signals based on a complex network. The analysis method of the EEG signals based on the complex network comprises the following steps: constructing multi-scale level limited penetrable visibility graph complex networks; calculating characteristic indexes of each multi-scale level limited penetrable visibility graph complex network; combining a support vector machine to classify the EEG signals, namely using a leave-one-out cross-validation and support vector machine classifier to classify all two-dimensional index vectors, and using a ten-fold cross-validation and support vector machine classifier to classify all the two-dimensional index vectors. According to the invention, multi-scale ideas and level limited penetrable visibility graph theories are combined to construct an EEG multi-scale level limited penetrable visibility graph complex network so as to extract complex network indexes, and the support vector machine classifier in machine learning is combined to realize high-accuracy classification for different EEG signals. The analysis method and application of the EEG signals based on the complex network can be applied to smart head-mounted wearable equipment, and sleep EEG signals are measured through analyzing the smart wearable equipment to monitor the brain state of a user, furthermore, necessary early warning can be provided.

The invention provides a terrain visibility analysis-oriented visibility graph network construction method. The method includes the following steps: (1) scanning DEM grid data, analyzing whether null data exist, and if yes, performing interpolation calculation, and supplementing data; (2) selecting a DEM grid unit neighbor mode of a constructed graph network, mode types including a 4-grid unit neighbor mode, a 8-grid unit neighbor mode and 16-grid unit neighbor mode; (3) calculating attribute values of vertices in the visibility graph network: based on a visible range analysis algorithm, calculating the number of visible grid points of a grid unit corresponding to each vertex in the network or the area of a visible range; (4) according to the selected neighbor mode type, calculating the weights of sides in the visibility graph network; and (5) saving visibility graph network data. The terrain visibility analysis-oriented visibility graph network construction method provided by the invention can be completely applied to graph network-based optimization modeling and solving of different fields of terrain visibility analysis of large-scale mass data, and processing efficiency can be improved.