Robert Boschgmbh

Carnegie Mellon University

Robert Bosch Gmbh

Max-Planck-Gesellschaft Zur F&#xf6;rderung Der Wissenschaften E. V.

Csaba Domokos

Jeremy Zico Kolter

Christian Simonis

Po-Wei Wang

Christoph Woll

Daria Stepanova

Priya L Donti

Evgeny Levinkov

Andreas Geiger

Bjoern Andres

A computer-implemented method is introduced for predicting a residual service life of vehicle batteries of a fleet of electric vehicles. In the method, parameters of the vehicle batteries are measured during the operation of the electric vehicles and transmitted to a server; a conditional probability is determined that the residual service life of a specific vehicle battery undershoots a predefined limit value at a point in time lying in the past; and the residual service life of vehicle batteries of the fleet is predicted as a function of the conditional probability.

Method for predicting a residual service life of vehicle batteries of a fleet of electric vehicles

A computer-implemented method for determining an error of a behavior of an electrical energy store in a technical device includes sensing an operating variable profile of at least one operating variable of the electrical energy store, determining at least one operating feature from the operating variable profile of the at least one operating variable of the electrical energy store, and evaluating a variational autoencoder with a supplied input variable vector, which includes an operating feature point from the at least one operating feature, in order to determine a degree of deviation from a distribution in a latent state space or a reconstruction error. The method further includes detecting the error of the behavior depending on (i) the degree of deviation or the reconstruction error, and (ii) a predetermined error threshold.

Method and apparatus for operating an early warning system for robustly detecting an error of an electrical energy store for a device by means of machine learning methods

A system for applying a neural network to an input instance. The neural network includes an optimization layer for determining values of one or more output neurons from values of one or more input neurons by a joint optimization parametrized by one or more parameters. An input instance is obtained. The values of the one or more input neurons to the optimization layer are obtained and input vectors for the one or more input neurons are determined therefrom. Output vectors for the one or more output neurons are computed from the determined input vectors by jointly optimizing at least the output vectors with respect to the input vectors to solve a semidefinite program defined by the one or more parameters. The values of the one or more output neurons are determined from the respective computed output vectors.

Neural network with a layer solving a semidefinite program

A system and computer implemented method for learning rules from a data base including entities and relations between the entities, wherein an entity is either a constant or a numerical value, and a relation between a constant and a numerical value is a numerical relation and a relation between two constants is a non-numerical relation. The method includes: deriving aggregate values from said numerical and/or non-numerical relations; deriving non-numerical relations from said aggregate values; adding said derived non-numerical relations to the data base; constructing differentiable operators, wherein a differentiable operator refers to a non-numerical or a derived non-numerical relation of the data base, and extracting rules from said differentiable operators.

Method and system for learning rules from a data base

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Simmozheim, Germany

Csaba Domokos