Abstract: A computer-based method for simulating a human-like decision in an environmental context, comprising: capturing environmental data with at least one sensor, realising a computer based method for realising a bi-directional compression of high dimensional data by compressing the data into a lower-dimensional map, if environmental data are captured during a learning phase of a computer-based model, evaluate the map of compressed data by determining the quality of the map by how well it separates data with different properties, the captured data corresponding to known pre-recorded data that have been pre-evaluated, if environmental data are captured after the learning phase, add new point to the compressed data and generate a signal indicating which human-like decision to use to correspond to the state of the operator.

Abstract: This invention relates to an artificial memory system and a method of continuous learning for predicting and anticipating human operator's action as response to ego-intention as well as environmental influences during machine operation. More specifically the invention relates to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the network implementation comprising an artificial hierarchical memory system.

Abstract: A computer-based method for simulating a human-like decision in an environmental context, comprising: capturing environmental data with at least one sensor, realising a computer based method for realising a bi-directional compression of high dimensional data by compressing the data into a lower-dimensional map, if environmental data are captured during a learning phase of a computer-based model, evaluate the map of compressed data by determining the quality of the map by how well it separates data with different properties, the captured data corresponding to known pre-recorded data that have been pre-evaluated, if environmental data are captured after the learning phase, add new point to the compressed data and generate a signal indicating which human-like decision to use to correspond to the state of the operator.

Abstract: A measurement system (1) of the relative position between two structural parts (2a, 2b) of a building, that have separated for example following the formation of a crack, comprising a cylindrical permanent magnet (3) adapted to generate a magnetic field (B) and a sensor (5), the sensor (5) being fixed and the magnet (3) being moveable according to a direction perpendicular to its axis of symmetry (h) so as to vary the direction of the lines of force of the magnetic field B which cross the sensor 5.

Abstract: A measurement system (1) of the relative position between two structural parts (2a, 2b) of a building, that have separated for example following the formation of a crack, comprising a cylindrical permanent magnet (3) adapted to generate a magnetic field (B) and a sensor (5), the sensor (5) being fixed and the magnet (3) being moveable according to a direction perpendicular to its axis of symmetry (h) so as to vary the direction of the lines of force of the magnetic field B which cross the sensor 5.

Abstract: This invention relates to an artificial memory system and a method of continuous learning for predicting and anticipating human operator's action as response to ego-intention as well as environmental influences during machine operation. More specifically the invention relates to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the network implementation comprising an artificial hierarchical memory system.