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

328 results about "High-level programming language" patented technology

In computer science, a high-level programming language is a programming language with strong abstraction from the details of the computer. In contrast to low-level programming languages, it may use natural language elements, be easier to use, or may automate (or even hide entirely) significant areas of computing systems (e.g. memory management), making the process of developing a program simpler and more understandable than when using a lower-level language. The amount of abstraction provided defines how "high-level" a programming language is.

Computer implemented machine learning method and system including specifically defined introns

In a computer implemented learning and / or process control system, a computer model is constituted by the most currently fit entity in a population of computer program entities. The computer model defines fitness as a function of inputs and outputs. A computing unit accesses the model with a set of inputs, and determines a set of outputs for which the fitness is highest. This associates a sensory-motor (input-output) state with a fitness in a manner that might be termed "feeling".The learning and / or control system preferably utilizes a Compiling Genetic Programming System (CGPS) in which one or more machine code entities such as functions are created which represent solutions to a problem and are directly executable by a computer. The programs are created and altered by a program in a higher level language such as "C" which is not directly executable, but requires translation into executable machine code through compilation, interpretation, translation, etc. The entities are initially created as an integer array that can be altered by the program as data, and are executed by the program by recasting a pointer to the array as a function type. The entities are evaluated by executing them with training data as inputs, and calculating fitnesses based on a predetermined criterion. The entities are then altered based on their fitnesses using a genetic machine learning algorithm by recasting the pointer to the array as a data (e.g. integer) type. This process is iteratively repeated until an end criterion is reached.
Owner:FRANCONE FR D +2

Computer implemented machine learning method and system

One or more machine code entities such as functions are created which represent solutions to a problem and are directly executable by a computer. The programs are created and altered by a program in a higher level language such as "C" which is not directly executable, but requires translation into executable machine code through compilation, interpretation, translation, etc. The entities are initially created as an integer array that can be altered by the program as data, and are executed by the program by recasting a pointer to the array as a function type. The entities are evaluated by executing them with training data as inputs, and calculating fitnesses based on a predetermined criterion. The entities are then altered based on their fitnesses using a machine learning algorithm by recasting the pointer to the array as a data (e.g. integer) type. This process is iteratively repeated until an end criterion is reached. The entities evolve in such a manner as to improve their fitness, and one entity is ultimately produced which represents an optimal solution to the problem. Each entity includes a plurality of directly executable machine code instructions, a header, a footer, and a return instruction. The instructions include branch instructions which enable subroutines, leaf functions, external function calls, recursion, and loops. The system can be implemented on an integrated circuit chip, with the entities stored in high speed memory in a central processing unit.
Owner:NORDIN PETER +1

Underwater target three-dimensional reconstruction method based on line structured light

InactiveCN103971406AOvercome the disadvantage of excessive calculationThe effect of reducing detection accuracyOptical detection3D modellingUnderwaterReconstruction method
The invention discloses an underwater target three-dimensional reconstruction method based on line structured light. The method includes the following steps that a, an underwater two-dimensional image including depth information is acquired; b, enhancement processing is conducted on the underwater two-dimensional image; c, noise reduction processing is conducted on the two-dimensional image acquired in the step b; d, image edge detecting and image shape processing are carried out on the two-dimensional image acquired in the step c, and the center of each laser streak is extracted; e, the two-dimensional image acquired in the step d is calibrated; f, three-dimensional coordinates are acquired from the two-dimensional image acquired in the step e; g, the three-dimensional coordinates are reconstructed through computer high-level programming languages, namely visual underwater target three-dimensional information is obtained. By means of the underwater target three-dimensional reconstruction method based on line structured light, the influence on detecting precision because of suspended matter in water, light absorbing and scattering by seawater, uneven light fields and the like can be effectively lowered, wide-angle and long-distance underwater target three-dimensional information extraction is achieved, and underwater long-distance high-precision real-time target three-dimensional detection is realized.
Owner:QINGDAO UNIV

Structural finite-element parametric modeling method applicable to grating-configuration rudder surface

The invention discloses a structural finite-element parametric modeling method applicable to a grating-configuration rudder surface. According to the method, a mapping transformation method based on a finite element model and a parametric approach to achieving conversion from two-dimensional mesh parameterization division to three-dimensional appearance expansion are adopted. The method includes the steps of specifically conducting two-dimensional plane projection on the grating-configuration rudder surface, extracting characteristic parameters, and conducting two-dimensional parameterization division; building a mapping relation between a two-dimensional finite element mesh and a three-dimensional finite element mesh, designing a numbering rule for finite element mesh points, and achieving conversion from the two-dimensional mesh to the three-dimensional appearance expansion; by means of a high-level computer language program, implementing the structural finite-element parametric modeling procedures for the grating-configuration rudder surface. By using the method, in a conceptual design phase or a preliminary design phase, the structural modeling efficiency can be greatly increased, the costs of time and manpower are low, parameters of self-compiled programs can be adjusted quickly, analysis applicability is high, the obtained model is applicable to analytical calculation of structural vibration, structural dynamics and the like, and the method is applicable to grating-configuration rudder surfaces and grating-configuration airfoils.
Owner:BEIHANG UNIV

Computer implemented machine learning method and system

One or more machine code entities such as functions are created which represent solutions to a problem and are directly executable by a computer. The programs are created and altered by a program in a higher level language such as "C" which is not directly executable, but requires translation into executable machine code through compilation, interpretation, translation, etc. The entities are initially created as an integer array that can be altered by the program as data, and are executed by the program by recasting a pointer to the array as a function type. The entities are evaluated by executing them with training data as inputs, and calculating fitnesses based on a predetermined criterion. The entities are then altered based on their fitnesses using a machine learning algorithm by recasting the pointer to the array as a data (e.g. integer) type. This process is iteratively repeated until an end criterion is reached. The entities evolve in such a manner as to improve their fitness, and one entity is ultimately produced which represents an optimal solution to the problem. Each entity includes a plurality of directly executable machine code instructions, a header, a footer, and a return instruction. The alteration process is controlled such that only valid instructions are produced. The headers, footers and return instructions are protected from alteration. The system can be implemented on an integrated circuit chip, with the entities stored in high speed memory in a central processing unit.
Owner:NORDIN PETER
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