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

3818 results about "Pruning" patented technology

Pruning is a horticultural and silvicultural practice involving the selective removal of certain parts of a plant, such as branches, buds, or roots. Reasons to prune plants include deadwood removal, shaping (by controlling or redirecting growth), improving or sustaining health, reducing risk from falling branches, preparing nursery specimens for transplanting, and both harvesting and increasing the yield or quality of flowers and fruits.

Methods and apparatus related to pruning for concatenative text-to-speech synthesis

The present invention provides, among other things, automatic identification of near-redundant units in a large TTS voice table, identifying which units are distinctive enough to keep and which units are sufficiently redundant to discard. According to an aspect of the invention, pruning is treated as a clustering problem in a suitable feature space. All instances of a given unit (e.g. word or characters expressed as Unicode strings) are mapped onto the feature space, and cluster units in that space using a suitable similarity measure. Since all units in a given cluster are, by construction, closely related from the point of view of the measure used, they are suitably redundant and can be replaced by a single instance. The disclosed method can detect near-redundancy in TTS units in a completely unsupervised manner, based on an original feature extraction and clustering strategy. Each unit can be processed in parallel, and the algorithm is totally scalable, with a pruning factor determinable by a user through the near-redundancy criterion. In an exemplary implementation, a matrix-style modal analysis via Singular Value Decomposition (SVD) is performed on the matrix of the observed instances for the given word unit, resulting in each row of the matrix associated with a feature vector, which can then be clustered using an appropriate closeness measure. Pruning results by mapping each instance to the centroid of its cluster.
Owner:APPLE INC

Five-degree of freedom green fence pruning machine

A five-degree of freedom green fence pruning machine comprises a pruning manipulator posture adjusting platform installed in front of a bearing cart, a rotation base which is installed on the pruning manipulator posture adjusting platform and is capable of revolving 360 degrees, an elevating mechanism which is fixedly installed on the rotation base, a telescoping mechanism which is fixed on an elevating rack of the elevating mechanism and is capable of ascending and descending along with the elevating rack, a pruning bit rotating device which is fixed at the front end of the telescoping mechanism and is capable of driving a pruning bit to rotate 360 degrees, a vertical lifting mechanism which is installed on the pruning bit rotating device, a pruning bit inclination-degree adjusting joint which is fixed at the lower end of the vertical lifting mechanism and is capable of ascending and descending along with the vertical lifting mechanism, and the pruning bit which is installed on an output shaft end of the pruning bit inclination-degree adjusting joint. By the pruning machine, the pruning of a plurality of mold modes of road green belts and garden sightseeing trees can be realized, a pruning process is completed automatically, the pruning machine is high in automation degree and simple in mechanical structure, the labor intensity of pruning green fences can be reduced, and work efficiency is improved.
Owner:GUANGXI UNIV

Planting method of Chinese trichosanthes

The invention relates to a planting method of Chinese trichosanthes, belonging to the technical field of plantation and comprising the steps of: variety breeding, seedling reproduction, transplanting, field management, disease and pest control, harvesting and processing, wherein the variety breeding step includes the sub-steps of the tests in different production places, amplification and verification tests, resistance test, field pest investigation and trichosanthes nematode control; the seedling reproduction step includes the sub-steps of seed reproduction and root reproduction; the transplanting step includes the sub-steps of land selection and preparation, ridging, reproduction and planting, planting method implementation and field management; the land selection and preparation step includes the sub-steps of detection on chemical characteristic of soil, basic fertilizer application and disinfection; the reproduction and planting step includes the sub-steps of seed bud selection, rood cutting and breeding, planting and shed putting up; the planting method implementation step includes the sub-steps of planning density control and staminiferous plant matching; the filed management step includes the sub-steps of intertillagement and weeding, top application, shed putting up, pruning and cold prevention, in particular to intertillagement and weeding, root airing, intercrop, earthing, vine supporting and racking, top application, pruning, flower and fruit protection, chemical control and foliage application; and the disease and pest control step includes the sub-steps of control of diseases and pest and control method implementation.
Owner:BOZHOU HUQIAO PHARMA

Method to reduce I/O for hierarchical data partitioning methods

A method and system for generating a decision-tree classifier from a training set of records, independent of the system memory size. The method includes the steps of: generating an attribute list for each attribute of the records, sorting the attribute lists for numeric attributes, and generating a decision tree by repeatedly partitioning the records using the attribute lists. For each node, split points are evaluated to determine the best split test for partitioning the records at the node. Preferably, a gini index and class histograms are used in determining the best splits. The gini index indicates how well a split point separates the records while the class histograms reflect the class distribution of the records at the node. Also, a hash table is built as the attribute list of the split attribute is divided among the child nodes, which is then used for splitting the remaining attribute lists of the node. The method reduces I/O read time by combining the read for partitioning the records at a node with the read required for determining the best split test for the child nodes. Further, it requires writes of the records only at one out of n levels of the decision tree where n>/=2. Finally, a novel data layout on disk minimizes disk seek time. The I/O optimizations work in a general environment for hierarchical data partitioning. They also work in a multi-processor environment. After the generation of the decision tree, any prior art pruning methods may be used for pruning the tree.
Owner:IBM CORP
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