48 results about "Tree clustering" patented technology

The invention discloses an internet of things-based intelligent building monitoring managing system, which mainly realizes remote monitoring and managing on an intelligent building. The system comprises four parts of a building equipment managing system, a firefighting linkage alarm system, a positioning navigation system and a security system; the building equipment managing system realizes concentrated managing on building equipment based on a tree cluster network established by an intelligent sensor module; the firefighting linkage alarm system adopts an optimized A*algorithm to search for an optimal fire escape path; the positioning navigation system is achieved by a mobile navigation robot covering floors of the building, and if a user inputs a destination, the navigation robot directly inserts virtual scene information into a reality scene, and displays a direction mark; the security system is realized by a mobile patrolling robot based on a GPS / DR combined navigation technology, and an intruder is alarmed after being found, the current intelligentized managing requirements of the building are met and the system is suitable for a high-rise building such as a commercial building with a large scale.

According to one aspect of the invention, a method is provided in which a set of multiple mixture monophone models is created and trained to generate a set of multiple mixture context dependent models. A set of single mixture triphone models is created and trained to generate a set of context dependent models. Corresponding states of the triphone models are clustered to obtain a set of tied states based on a decision tree clustering process. Parameters of the context dependent models are estimated using a data dependent maximum a posteriori (MAP) adaptation method in which parameters of the tied states of the context dependent models are derived by adapting corresponding parameters of the context independent models using the training data associated with the respective tied states.

The invention provides a single-plant tree point cloud automatic extraction method based on vehicle-mounted laser scanning data, and the method mainly comprises two big steps: obtaining point clusters, called as a candidate tree clusters, from the vehicle-mounted laser scanning data based on the rough classification of cluster features, wherein the point clusters may comprise trees; extracting a single-plant tree point cloud from each candidate tree cluster based on two-stage voxel regional growth. The method combines the overall and local geometrical characteristics of trees, can automatically recognize the seeds of single-plant trees in a complex environment, respectively sets different growth rules for the growing of tree trunks and crowns, and achieves the extraction of the single-plant trees. Meanwhile, compared with other single-plant tree extraction algorithm, the method achieves greater improvement on the extraction integrity and accuracy of trees, the extraction precision of a single-plant tree point cloud and the extraction in the complex environment, and can be used for further information extraction of trees.

The invention discloses a complex network community mining method based on an improved genetic algorithm and belongs to the technical field of complex network community mining method research. The complex network community mining method based on the improved genetic algorithm uses the improved genetic algorithm based on clustering and double population thought fusion to mine communities in a complex network. The complex network community mining method based on the improved genetic algorithm uses a normalization common information similarity standard as the standard for measuring the similarity between individuals in the population and fuses the clustering and double population thought. The complex network community mining method based on the improved genetic algorithm includes that introducing the clustering thought, using a minimum spanning tree clustering method to classify the population, introducing the double population thought, and determining the main type and auxiliary type for the clustering. The main type maintains the population evolution direction to get close to the optimal solution of an objective function; the auxiliary type is mainly used for duly providing diversity for the main type so as to enable the main type to be capable of coming out to search the other solution space to realize the complex network community mining when the main type is located at the local optimum.

The invention discloses a method for automatically extracting point clouds of an electric tower from airborne LiDAR data. The electric tower is important contents in high-voltage line patrol. Based on the plane position and spatial geometric characteristics of the electric tower, a method for extracting the electric tower from point clouds of an airborne laser radar is proposed and comprises the steps of (1) performing electric tower point cloud coarse extraction on original point cloud data, obtained by the airborne laser radar, of a power transmission line according to a two-dimensional grid neighborhood clustering method; (2) preprocessing a coarse extraction result by adopting Kd-tree clustering and spatial grid region growth methods; (3) through a spatial geometric structure of the electric tower, extracting a main region of the electric tower, and determining a tower body edge line equation in combination with an RANSAC spatial linear fitting method; and (4) removing noisy points based on growth of a point cloud model of a main region of a line tower, and removing bottom noisy points of the electric tower by adopting a specific method in combination with different bottom structures of the electric tower, thereby finishing fine extraction of the point clouds of the electric tower. According to the method, fine classification of the point clouds of the electric tower is realized directly through structural characteristics of the point clouds of the electric tower; the problem of relatively poor quality of point cloud data of the electric tower can be solved to a certain extent; and the classification efficiency and precision are high.

The invention discloses a mobile Sink data collection method for a wireless sensor network based on a tree cluster. The method comprises the steps of establishing a distributed tree cluster; calculating node weights by taking surrounding energy and density information of nodes into comprehensive consideration; selecting suitable root nodes as resident points RPs of a mobile Sink through distributed information exchange of a local range; accessing all RPs by the mobile Sink for data collection in a stable data collection stage; providing a new conditional re-clustering method; setting data collection number of rounds; and selecting new RPs for alternation. According to the method, the hotspot problem can be effective mitigated; the network load can be balanced; the energy consumption of nodes can be reduced; and the network lifetime can be prolonged.

The invention relates to an energy-saving dormancy awakening method based on a ZigBee technology. The method comprises the following steps that: S1, a coordinator node establishes a tree cluster type network; and a terminal node and a routing node are added into the network established by a coordinator; S2, the network is graded; time synchronization of a network node and the previous stage is carried out stage by stage; and finally, the whole-network time synchronization with the coordinator is realized; S3, the terminal node and the routing node enter a working period; the terminal node performs acquisition and transmission of data; and the routing node forwards the data to the previous-stage node; S4, after receiving the data uploaded by the terminal node, the coordinator sends dormancy control information to the routing node; and the routing node forwards the dormancy control information to the terminal node; S5, the node receives a dormancy instruction, sets a dormancy time control parameter and enters a dormancy state; and S6, when the dormancy time is up, the node is synchronously awakened to add the network and then enters the working state. By means of the method, the energy consumption of the system can be reduced; the energy utilization rate can be increased; the use time of the system is prolonged; and simultaneously, the maintenance cost of the network can also be reduced.

The invention provides a suffix tree clustering method on basis of word segmentation and part-of-speech analysis. The method involves three parts, namely a document word segmenting module, a part-of-speech analyzing module and a suffix tree clustering module, accomplishes word segmentation, part-of-speech tagging, word weight calculation and essence extraction to a document, can realize dimension reduction process to an original document, reduces the complexity of the processing procedure of the suffix tree clustering method, and meanwhile, ensures the clustering accuracy.

Systems and methods for training a region clustering forest include receiving a set of medical training images for a population of patients. A set of image patches is extracted from each image in the set of medical training images. A plurality of region clustering trees are generated each minimizing a loss function based on respective randomly selected subsets of the set of image patches to train the region clustering forest. Each of the plurality of region clustering trees cluster image patches at a plurality of leaf nodes and the loss function measures a compactness of the cluster of image patches at each leaf node in each of the plurality of region clustering trees.

The invention discloses a tower inclination early warning method based on airborne liDAR point cloud data. The tower inclination early warning method based on the airborne liDAR point cloud data comprises the steps that the tower point cloud data scanned by an airborne lidar based on Kd-tree clustering and spatial grid growth is preprocessed to remove noise points; secondly, point clouds in a tower trunk area are extracted based on tower geometric features; then angular point coordinates of the point cloud data of each layer in the tower trunk area are calculated based on a coordinate system rotation method again; finally, center point coordinates of each layer are calculated by using the angular point coordinates and a space linear equation is fitted to obtain the inclination radian and inclination angle of a tower; and safety warning is given to the tower with the inclination angle exceeding a certain safety threshold value. The defect that traditional manual detection is not suitable for efficient detection in a complex environment is overcome, the inclination and inclination radian of the tower can be automatically obtained based on the airborne LiDAR tower point cloud data, high measurement efficiency and high precision are achieved, and early warning can be timely given to the tower with serious inclination to prevent more serious consequences in the later period.

The invention relates to a taxi wireless immediate calling system based on a ZigBee technique. The invention is aimed at providing a calling system with convenience, automatic treatment on taxi calling problems, low cost and convenient maintenance. The system comprises a user handheld miniature caller, a ZigBee router installed along a street, a vehicle-mounted terminal configured in a taxi and an information management center. The user handheld miniature caller, the ZigBee router, the vehicle-mounted terminal and the information management center are in wireless connection to form a ZigBee self-organization network which can position the taxi in a coverage scope and a user sending out calls by adopting a tree cluster topological structure, the taxi and the user can carry out communication to realize appointment, and then the system provides distance information of both parties having reached appointment to assist both parties to accomplish appointment behaviors. The system can ensure that a passenger and the taxi perform real-time information communication, and is convenient for passenger traveling and for recognition between the passenger and the reserved taxi, so as to effectively improve the operating efficiency of the taxi, relieve the pressure on urban traffic and reduce resource waste.

The invention relates to a clustering multi-hop routing method based on a maximum and minimum distance method. A maximum and minimum distance method is adopted to select a cluster center, re-clustering is carried out according to the cluster center, the selection of a cluster head is carried out according to a maximum weight principle, a new cluster is formed according to distance between a node and the cluster head, the insufficiency of random selection of a LEACH (Low Energy Adaptive Clustering Hierarchy) protocol cluster head is overcome, so that the energy consumption of a network is evenly consumed on each node. A link with a shortest communication path is generated between the cluster head and Sink by adopting a continuous Hopfield neural network, the link is optimized to form a multi-hop tree cluster type link which takes the Sink as a center, and energy consumption generated by long-distance communication caused by adopting a single-hop way is lowered. The deficiencies of the cluster head selection and a node single-hop mechanism in the LEACH protocol under the energy heterogeneous environment of a wireless sensor network can be eliminated, and aspects on network stable phase prolonging and energy balance are obviously improved than a LEACH algorithm.

The invention discloses a data collection method based on a tree cluster and a mobile element. The method comprises the following steps of network region division based on a region core distance: selecting a central point and a region center to divide a whole network into a plurality of regions; selecting a vehicle start position for collecting data in each region; selecting a data aggregation node SP in a rub-region and using as a root node as a single tree, computing a parent node set and a son node set of each node based on the shortest hop counts with the root node, and building a data collection tree of each aggregation node SP; meanwhile building an optimization function related to energy consumption, and computing a data generation rate and a link transmission rate of each node; using the aggregation node SP as a resident collection point when a data collection vehicle (DCV) goes round in the network, and re-selecting the aggregation node SP after a fixed period, and then generating the data collection tree. Therefore, according to the method provided by the invention, data transmission energy consumption and DCV movement consumption in the network are effectively reduced, data collection delay is reduced, a hotspot problem is relieved, and a network life cycle is prolonged.

The invention discloses a robot positioning and navigation method based on a bag-of-words tree cluster model. The method comprises the following steps: 1) inputting a long-time video stream containing a low dynamic indoor scene change; 2) performing off-line feature extraction on an image of the video stream; 3) clustering the obtained features through a k-means++ algorithm; 4) performing iterative clustering on the subspace obtained after clustering; 5) building a bag-of-words tree model for features after iterative clustering; 6) building a bag-of-words tree cluster model; 7) summarizing the motion law of a low dynamic object through a statistical principle; 8) analyzing and determining a bag-of-words tree model corresponding to the current moment based on the motion law according to the bag-of-words tree cluster model; and 9) using the determined bag-of-words tree model for loopback detection to realize robot positioning and navigation in a low dynamic environment. According to the invention, long-time self positioning and navigation of a robot can be realized in a low dynamic environment such as indoor space and the like at low cost under the condition of avoiding the use of expensive laser sensors.

The invention provides a wireless sensor network routing protocol based on improved tree clustering; in a wireless sensor network, a cluster head is elected out and clustered; a tree structure is constructed in the cluster; with the cluster head as a root node, each sensor node in the cluster directly communicates with the cluster head if being close to the cluster head; otherwise, other sensor node relatively close to the cluster head is selected to communicate with the cluster head; with the cluster head closest to an aggregation node as the root node, all cluster heads form a tree, and theroot node directly communicates with the movable aggregation node; after every sensor node is elected out to be the cluster head for one time, a new round of clustering is started for the whole network; and residual energy of the sensor nodes is considered when electing the cluster head. According to the routing protocol, the sensor nodes are prevented from premature death, and the service life ofthe wireless sensor network is prolonged.

The invention claims an image retrieval method based on tree cluster vector quantization. The method comprises the following steps: S1, preprocessing a picture and extracting vector characteristics ofthe picture; S2, clustering the vectors of the images; S3, dividing the data space by using recursive clustering; S4, stopping clustering according to the formulated rules; And S5, storing the path fingerprint of the picture and the tree model. According to the method, the high-dimensional feature vector of the image is extracted by using the feature extraction capability of the deep learning model based on the tree clustering vector quantization algorithm, the content and semantics of the image are fully expressed, and the search capability of the tree clustering algorithm is combined, so that the image retrieval accuracy and the retrieval speed are improved.

The invention relates to an infrared multi-target segmentation method based on minimum tree clustering. The method is characterized in that an image is preprocessed by the adoption of a background differencing method and morphological operation, and a binary image with a target area and a background area separated is obtained; uniform sampling and nearest neighbor classification are carried out on all pixel points in the target area in the binary image so as to initially classify the pixels of the target area into subclasses; the minimum tree is constructed with the subclasses as top points and with the space, obtained by calculation through an L1 norm and a Gaussian kernel function, between the subclasses as the side length; the obtained side length threshold is calculated, the long side of the minimum tree is cut, a final clustering result is obtained, isolated points and noise points are removed, and each residue class serves as a target; all target centroids are marked through crosses in an original infrared image, and all target special ranges are marked through rectangular boxes.

The invention discloses an author disambiguation device based on subject tree clustering. The author disambiguation device comprises the following steps: obtaining text data with authors; processing the text data to extract key information; extracting representative words according to the text data; sequentially matching the text data with each subject node of the subject tree based on the representative words and the key information of the text data, and calculating a matching degree; and selecting the subject node with the highest matching degree, connecting the subject node with the text data, and taking the author with the same name under the subject node as the author. According to the method, the subject tree is constructed, clustering calculation is carried out on the basis of the subject tree to eliminate the problem of text data classification errors under the condition of different persons with the same name, the accuracy of author recognition of the text data is improved, then the text retrieval quality is improved, and an effective auxiliary analysis means is provided for computer semantic analysis.

The invention discloses a clustering algorithm on the basis of minimum spanning trees and initial clustering centers. The clustering algorithm includes steps of S1, inputting to-be-clustered data setsD and category numbers K of the data sets D; S2, creating the minimum spanning trees T<D> of the data sets and computing geodesic distances between optional two nodes in each minimum spanning tree T<D>; S3, selecting the initial clustering centers; S4, creating the minimum spanning trees T of the initial clustering centers; S5, disconnecting K-1 edges of each minimum spanning tree T<D> of thecorresponding data set. The clustering algorithm has the advantages that minimum spanning tree clustering algorithms are combined with selection of the initial clustering centers, the to-be-deleted edges are constrained on paths between the initial clustering centers and can be accurately found by the aid of comprehensive values of the density and the distances of the upper edges for forming the paths, accordingly, the clustering accuracy can be further improved, and interference of noise in the data sets can be prevented.

The invention aims to provide an LiDAR point cloud data individual tree extraction method based on a spectral clustering algorithm, which specifically comprises the steps of normalizing height information of LiDAR point cloud data, and performing voxelization by using a mean shift clustering algorithm; constructing a similarity graph in the voxel space based on a Gaussian similarity function; calculating feature values and feature vectors of the similarity graph by using a method, and determining a segmentation individual tree number k by using a feature value interval; and taking the featurevectors corresponding to the first k minimum feature values as columns to construct a feature vector matrix, performing k-means clustering on normalized row elements of the feature vector matrix in afeature space, and mapping a segmentation result back to the LiDAR point cloud to obtain single-tree clustering, thereby realizing single-tree segmentation of the point cloud. The method provided by the invention not only can carry out effective individual tree segmentation on the sample plot scale, but also can provide a stable segmentation result for the regional scale, and has a very high practical value.

The invention relates to the technical field of image processing, and discloses a Marker point three-dimensional reconstruction method and device, equipment and a storage medium. The method comprisesthe following steps: acquiring shooting information in a plurality of cameras to form a two-dimensional coordinate point set; calculating a matching error rate between every two two-dimensional coordinate points in the two-dimensional coordinate point set; sequentially connecting the two-dimensional coordinate points in the two-dimensional coordinate point set to generate a matching tree diagram of the two-dimensional coordinate point set; screening out a tree-shaped single chain with the longest length in the matched tree diagram; on the basis of the tree-shaped single chain with the longestlength, matching branches with crossed two-dimensional coordinate points with the tree-shaped single chain with the longest length in the matching tree diagram, removing the two-dimensional coordinatepoints in the branches, and obtaining a maximum non-crossed tree-shaped group; and calculating a three-dimensional coordinate corresponding to the maximum non-crossed tree-shaped group. According tothe method, the maximum non-crossed tree clusters are extracted through epipolar error calculation, the matching degree of the 2D points is improved, and the accuracy of restoring the three-dimensional coordinates of the 2D points is improved.

The invention provides a minimum spanning tree clustering algorithm and system based on a density core. The algorithm comprises the following steps: constructing a KD tree; obtaining reverse neighborinformation and natural characteristic values of the data points by adopting a natural neighbor method, and counting the number of reverse neighbors of each data point; taking the data points of whichthe reverse neighbor number is not less than the natural characteristic value as core points, and forming a density core point set by the core points; establishing a minimum spanning tree according to the density core set to obtain a set of weights of each edge in the minimum spanning tree; calculating a trimming threshold value according to the set of the weights of the edges in the minimum spanning tree, and cutting off the edges connected with different clusters in the minimum spanning tree according to the trimming threshold value to obtain a minimum spanning sub-tree of each sub-cluster;generating a sub-tree clustering density core according to the obtained sub-cluster minimum; and distributing the non-density core points to the cluster of the density core closest to the non-densitycore points to complete clustering. According to the algorithm, the approximate shape and structure of the cluster can be well reserved, so that the algorithm can adapt to a data set with a complex shape.

The invention relates to a quantum key synchronization system and method based on hierarchical tree cluster units; the quantum key synchronization system based on hierarchical tree cluster units is composed of a plurality of tree cluster units, and a topology unit is divided into a top layer tree cluster unit, a two-layer tree cluster unit and an N-layer tree cluster unit according to network planning and logic hierarchy. Compared with the prior art, the method is based on the global tree cluster relation table recording the node membership, and the two nodes can obtain the shared key from thetop-level node with the closest membership, so that the complexity, communication and calculation pressure of each central node are reduced; therefore, the defects that in a large-scale network structure, the complexity of a single center node is too high, the communication calculation pressure is too large, and the system performance and stability are influenced are avoided; meanwhile, by adopting the topological structure of the multi-center and multi-layer tree cluster unit, the system and method can be applied to a scene of large-scale multi-party encryption communication, the limitationon the practicability of quantum communication is removed, the network topology can be flexibly expanded, and the deployment cost of quantum channels is reduced to a certain extent.

Systems and methods for training a region clustering forest include receiving a set of medical training images for a population of patients. A set of image patches is extracted from each image in the set of medical training images. A plurality of region clustering trees are generated each minimizing a loss function based on respective randomly selected subsets of the set of image patches to train the region clustering forest. Each of the plurality of region clustering trees cluster image patches at a plurality of leaf nodes and the loss function measures a compactness of the cluster of image patches at each leaf node in each of the plurality of region clustering trees.

The invention provides a double-frequency-band communication device for an indoor light sensor wireless network. The double-frequency-band communication device is a wireless network communication device of a tree cluster shape which is connected with gateway nodes and sub nodes in a cluster in an indoor light environment wireless network and can receive wireless data in two different frequency bands, so danger of breakdown of the whole system due to the same frequency interference and loss of important data during switching of the frequency bands can be avoided effectively, and the data processing capacity of the light sensor wireless network is improved. The double-frequency-band communication device comprises a wireless network data processing device and a wireless network data acquisition device, wherein the wireless network data processing device comprises a first main control module, a first wireless network communication module, a first frequency setting module, a universal serial bus (USB) interface module, a communication quality indication module and a double-core communication quality monitoring module; and the wireless network data acquisition device comprises a second main control module, a second wireless network communication module, a second frequency setting module, a data acquisition indication module and a power supply management module.

The invention discloses an analysis method for distinguishing citrus chachiensis hortorum from pericarpium citri reticulatae through HPLC finger print in combination with stoichiometry. The method comprises the following steps: performing pretreatment on citrus chachiensis hortorum samples and pericarpium citri reticulatae samples respectively, and performing gradient elution through a high performance liquid chromatograph, so as to obtain citrus chachiensis hortorum common mode finger print, analyzing finger print information, so as to obtain 17 common peaks of aurantiamarin taken as a reference peak, and identifying 3 common peaks through comparison of the sample and standard substance; preprocessing HP original data of different batches of citrus chachiensis hortorum samples and pericarpium citri reticulatae samples, and then performing similarity calculation, clustering analysis and principal component analysis. The method has the advantages that analysis results are displayed in modes of similarity data range, tree clustering analysis graphics and PCA score plots, a constructed citrus chachiensis hortorum quality control mode clearly reveals the regularity and difference of citrus chachiensis hortorum from pericarpium citri reticulatae, can visually distinguish varieties of citrus chachiensis hortorum from pericarpium citri reticulatae, relevant verification and analysis are not required, quantitative difference between the varieties is embodied, and the method can serve as an analysis method for distinguishing citrus chachiensis hortorum from pericarpium citri reticulatae.

The invention provides a data routing transmission control method of a wireless autonomous monitoring network of a wind farm. By adopting the data routing transmission control method of a wireless autonomous monitoring network of a wind farm provided by the invention, when a node performing data transmission and a destination node are positioned in different sub-tree clusters, as long as a neighbor node meeting the routing conditions and the energy storage requirements exists, cross-tree cluster routing transmission is directly performed without the necessity of forwarding through a root node (a network coordinator) again, thus the routing path is effectively shortened, the routing transmission delay is reduced, and the real-time property of data transmission of the wireless autonomous monitoring network of the wind farm is improved; and moreover, the method also performs judgment on the energy storage conditions of the neighbor node, the phenomenon that ongoing data routing transmission cannot be supported due to insufficient energy storage of the neighbor node during the cross-tree cluster routing transmission is avoided, and the data transmission safety of the wireless autonomous monitoring network of the wind farm is improved.

The invention provides a positioning base station selection method based on a minimum spanning tree clustering algorithm. The method comprises the following steps: firstly, acquiring positions of basestations and signal time of arrival (TOA) measured by the base stations; secondly, dividing the base stations into k initial clusters through the minimum spanning tree clustering algorithm, and taking a base station position mean value in each class as a data center of the class; after that, carrying out iteration, selecting a base station nearest to a clustering center in each class as a representative, judging whether the positioning precision of a mobile terminal meets the requirement or not, if yes, taking the k base stations as initial base station groups, otherwise, expanding k and clustering again; and finally, sorting the obtained base station groups according to the measured TOA values from small to large, and gradually reducing the base station with a large TOA value until the minimum number of base stations are output. According to the positioning base station selection method based on the minimum spanning tree clustering algorithm provided by the invention, the k base stations can be selected from all N base stations to participate in positioning, so as to achieve the approximately optimal positioning precision by using as few base stations as possible.

A quantum key synchronization system and method based on hierarchical tree cluster units, including a quantum key synchronization system based on hierarchical tree cluster units, consisting of several tree cluster units, and topological units are divided into top-level tree clusters according to network planning and logical levels Unit, two-level tree cluster unit, N-level tree cluster unit. Compared with the prior art, the present invention is based on a global tree cluster relationship table that records the affiliation of nodes, and two nodes can obtain the shared key from the top-level node with the closest affiliation, reducing the complexity, communication and computing pressure of each central node, Avoid the defects of high complexity of a single central node and excessive communication and calculation pressure in a large-scale network structure, which will affect the performance and stability of the system; The scenario of large-scale multi-party encrypted communication has lifted the restrictions on the practical application of quantum communication, and can also flexibly expand the network topology, reducing the deployment cost of quantum channels to a certain extent.