Patents
Literature
Patsnap Copilot is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Patsnap Copilot

65 results about "Tree modeling" patented technology

Binary prediction tree modeling with many predictors and its uses in clinical and genomic applications

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.
Owner:DUKE UNIV

Binary prediction tree modeling with many predictors and its uses in clinical and genomic applications

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.
Owner:DUKE UNIV

Tree three-dimensional reconstruction method based on unmanned aerial vehicle aerial photo sequence image

The invention discloses a tree three-dimensional reconstruction method based on unmanned aerial vehicle aerial photo sequence images. According to the tree three-dimensional reconstruction method, firstly, characteristic regions of a crown are extracted in a watershed partition method, then, the characteristic regions are matched by calculating region correlation coefficients in an RGB color space, matched characteristic dot pairs are extracted, depth information is calculated through the binocular stereo vision principle combined with imaging models of an aerial photo camera, and finally a three-dimensional model of a tree is constructed based on L system laws. Compared with the prior art, the tree three-dimensional reconstruction method has the advantages that grain, color and outline information of the crown are considered, and the characteristic spot set which can show the crown complicated structure is fully extracted; in addition, the depth information of the characteristic spot set is calculated according to complicated imaging models of the aerial photo camera with shake, transverse moving and the like of the camera in the aerial photo process of an unmanned aerial vehicle considered, a reasonable tree modeling approach is adopted, and therefore reasonable approximate three-dimensional models of the tree are constructed.
Owner:TIANJIN UNIV

Individual plant wood modeling method driven by domain ontology

The invention discloses an individual plant wood modeling method driven by domain ontology, belonging to the intersection filed of an ontology method and plant modeling. The individual plant wood modeling method is characterized by including the steps as follows: firstly obtaining the morphological structure characteristics, the habitat domain concept terms and the growth rhythm of a plant, and creating a plant domain concept ontology base by utilizing an ontology tool protege and a plant growth rhythm ontology base by utilizing a semantic network developing tool Jena; then creating a three-dimensional morphological structure model base of the plant in different growth stages by utilizing a parameterization individual-tree modeling method; and later obtaining the morphological characteristic parameters of the plant by utilizing ontology reasoning according to the morphological structure characteristics or the habitat, the growth stages and the phenological period description of the plant, and creating a vivid three-dimensional morphological structure model by utilizing the parameterization individual-tree modelling method. By utilizing the individual plant wood modeling method, a user with less botany knowledge can create a model confirming to the characteristics of botany, or a botanist creates a vivid three-dimensional model in a short time.
Owner:FUZHOU UNIV

Rapid individual tree modeling method by close shot ordinary digital camera

The invention discloses a rapid individual tree modeling method by a close shot ordinary digital camera and belongs to a real three-dimensional visualization expression method for forest resource information. The method is characterized in that the ordinary digital camera is used as a tool, an individual tree is photographed around, artificial marks are arranged around the tree to be tested to establish relative control, and then the space coordinate of a tree morphological structure is obtained through a self-checking analysis method; a structural geometric primitive function is used for modeling, and then a three-dimensional model of an individual tree morphological structure is obtained; and finally tree texture information obtained through photographing is combined with the individual tree three-dimensional morphological structure model by texture mapping to obtain an individual tree three-dimensional model with reality. According to the rapid individual tree modeling method by the close shot ordinary digital camera, the characteristics of portability and flexibility of the digital camera are adequately used, the operating mode is easy to learn, the cost of modeling is reduced, and the modeling period is shortened; and the highly reductive tree morphological structure model is combined with the real texture information, so that the precision and the sense of reality of the individual tree model are greatly improved.
Owner:BEIJING FORESTRY UNIVERSITY

Improved availability evaluation method for nuclear power design phase

An improved availability evaluation method for a nuclear power design phase comprises S1, screening out a system in which a fault may cause the reactor tripping and the turbine tripping of a unit; S2,performing FMEA analysis on the equipment in the system screened out in the step S1 to determine all single and dual failure modes that cause the reactor tripping and the turbine tripping of the unit; S3, taking an event that the system is unavailable and causes the unit to stop and reduce the power as a top event, and for each failure mode, determining the minimum cut set by fault tree modeling;S4, determining a failure rate and a mandatory unavailable time of each minimum cut set; S5, calculating an annual unplanned shutdown frequency of a nuclear power plant by using the failure rate of each minimum cut set and the annual number of times/annular time of the fault; and calculating the annual mandatory unavailable time of the nuclear power plant based on the failure rate and the mandatory unavailable time of each minimum cut set; and S6, determining the availability of the nuclear power plant based on the annual unplanned shutdown frequency and the annual mandatory unavailable time.The method can quantitatively predict the availability of the nuclear power plant.
Owner:SUZHOU NUCLEAR POWER RES INST +2

Multi-output gradient lifting tree modeling method for survival risk analysis

ActiveCN110119540AImprove accuracySolve the problem of insufficient explanationForecastingDesign optimisation/simulationRisk profilingSurvival analysis
The invention provides a multi-output gradient lifting tree modeling method for survival risk analysis, which comprises the following steps: firstly, constructing an expression of survival data for establishing a survival prediction model of financial, insurance, medical, traffic or industrial target industries under a model algorithm framework of an optimal gradient lifting tree (XGBoost); then defining and calculating a loss function corresponding to the survival data; then, defining and calculating a first step degree and a second step degree corresponding to the loss function; and finally,inputting the calculated loss function value and the first-order gradient value and the second-order gradient value of the loss function into an XGBoos model algorithm framework at the same time, andperforming automatic training to generate a survival prediction model of the target industry. The modeling method provided by the invention can better represent the relationship between the model covariable and the risk prediction value. The prediction performance and the generalization capability of the model are improved. The prediction performance and the risk distinguishing degree are better,and the application scene is wide.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Gradient boosting tree modeling method and device and terminal

PendingCN112052954ASpeed up circulationEffective Privacy ProtectionMachine learningData setAlgorithm
The embodiment of the invention provides a gradient boosting tree modeling method and device and a terminal, and the method comprises the steps: carrying out the intersection operation of a first sample data set with a label value and a plurality of second sample data sets according to an identification, and obtaining a first data intersection set with a label value and a plurality of second dataintersection sets; obtaining a target value of the first decision tree according to the label value, and encrypting the target value of the first decision tree to obtain an encrypted target value of the first decision tree; determining an optimal splitting point of the first decision tree according to the target value of the first decision tree, the first data intersection, the encryption target value of the first decision tree and the second data intersection; splitting the node at the position of the optimal splitting point of the first decision tree to obtain a second decision tree; after the first decision tree is subjected to iteration of a preset training round number, an Nth decision tree is generated, and N is larger than or equal to 2; and obtaining a gradient boosting tree modelaccording to the first decision tree to the Nth decision tree. Multi-party joint gradient boosting tree modeling is adopted, and respective private data cannot be leaked.
Owner:BEIJING BAIDU NETCOM SCI & TECH CO LTD

Multifunctional intelligent tree modeling equipment

The invention discloses multifunctional intelligent tree modeling equipment. The equipment comprises a baseboard, a supporting column is arranged at the center of the upper surface of the baseboard, the upper end of the supporting column is provided with a first cylinder telescopic rod, an L-shaped connecting plate is arranged at the telescopic end of the first cylinder telescopic rod, a first electric telescopic rod is connected with the upper surface of the fold of the L-shaped connecting plate through a hinging base, an electric saw is arranged at one end, far away from connection with the first cylinder telescopic rod, of the L-shaped connecting plate, the first cylinder telescopic rod is welded with a supporting base, one side of the supporting base is connected with a third cylinder telescopic rod through a bolt. Trimming on trees by the modeling machine in the advancing process is achieved through the arrangement of the first cylinder telescopic rod, the L-shaped connecting plate, the first electric telescopic rod and the electric saw, the L-shaped connecting plate can be driven to rotate through extension and retraction of the first electric telescopic rod to adjust the angle of the electric saw, and trees are trimmed through the electric saw so as to make tree trimming more convenient.
Owner:吴海锋
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products