Patents by Inventor Peter VRANCX

Peter VRANCX has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • COMPUTER IMPLEMENTED METHOD FOR TRANSFORMING A PRE TRAINED NEURAL NETWORK AND A DEVICE THEREFOR
    Publication number: 20240046098
    Abstract: The present invention relates to a computer implemented method (30) for transforming a pre-trained neural network.
    Type: Application
    Filed: July 31, 2023
    Publication date: February 8, 2024
    Inventors: Nathan LAUBEUF, Debjyoti BHATTACHARJEE, Peter VRANCX
  • Methods, controllers and systems for the control of distribution systems using a neural network architecture
    Patent number: 11341396
    Abstract: A deep approximation neural network architecture which extrapolates data over unseen states for demand response applications in order to control distribution systems like product distribution systems of which energy distribution systems, e.g. heat or electrical power distribution, are one example. The method is a model-free control technique mainly in the form of Reinforcement Learning (RL) where a controller learns from interaction with the system to be controlled to control product distributions of which energy distribution systems, e.g. heat or electrical power distribution, are one example.
    Type: Grant
    Filed: December 26, 2016
    Date of Patent: May 24, 2022
    Assignee: VITO NV
    Inventors: Bert Claessens, Peter Vrancx
  • MACHINE LEARNING SYSTEM
    Publication number: 20200302322
    Abstract: There is described a machine learning system comprising a first subsystem and a second subsystem remote from the first subsystem. The first subsystem comprises an environment having multiple possible states and a decision making subsystem comprising one or more agents. Each agent is arranged to receive state information indicative of a current state of the environment and to generate an action signal dependent on the received state information and a policy associated with that agent, the action signal being operable to cause a change in a state of the environment. Each agent is further arranged to generate experience data dependent on the received state information and information conveyed by the action signal. The first subsystem includes a first network interface configured to send said experience data to the second subsystem and to receive policy data from the second subsystem.
    Type: Application
    Filed: October 4, 2018
    Publication date: September 24, 2020
    Applicant: PROWLER ,IO LIMITED
    Inventors: Aleksi TUKIAINEN, Dongho KIM, Thomas NICHOLSON, Marcin TOMCZAK, Jose Enrique MUNOZ DE COTE FLORES LUNA, Neil FERGUSON, Stefanos ELEFTHERIADIS, Juha SEPPA, David BEATTIE, Joel JENNINGS, James HENSMAN, Felix LEIBFRIED, Jordi GRAU-MOYA, Sebastian JOHN, Peter VRANCX, Haitham BOU AMMAR
  • METHODS, CONTROLLERS AND SYSTEMS FOR THE CONTROL OF DISTRIBUTION SYSTEMS USING A NEURAL NETWORK ARCHITECTURE
    Publication number: 20190019080
    Abstract: A deep approximation neural network architecture which extrapolates data over unseen states for demand response applications in order to control distribution systems like product distribution systems of which energy distribution systems, e.g. heat or electrical power distribution, are one example. The method is a model-free control technique mainly in the form of Reinforcement Learning (RL) where a controller learns from interaction with the system to be controlled to control product distributions of which energy distribution systems, e.g. heat or electrical power distribution, are one example.
    Type: Application
    Filed: December 26, 2016
    Publication date: January 17, 2019
    Applicant: VITO NV
    Inventors: Bert CLAESSENS, Peter VRANCX