A method for estimating the position of a rotor of a synchronous electrical machine, includes a rotor and a stator coupled to an inverted synchronous electrical machine via a rectifier comprising the following steps: measurement of a current i.sub.abc circulating in the stator of the synchronous electrical machine; determination of two signals in quadrature i.sub.α; i.sub.β according to a stationary reference frame from the current i.sub.abc and isolation of two filtered signals i.sub.αh; i.sub.βh from the two signals in quadrature i.sub.α; i.sub.β; demodulation of the two filtered signals i.sub.αh; i.sub.βh in order to obtain two demodulated signals i.sub.αobs, i.sub.βobs, obtaining of an estimated position {circumflex over (θ)} of the rotor from the two demodulated signals i.sub.αobs, i.sub.βobs.

A method determines the total mass of an automotive vehicle on the basis of data of a communication network and parameters of the vehicle, in which an estimation of the total laden mass (mv,est) of the vehicle, of the speed of the vehicle (vest) and of the slope of the road (αest) is determined at an instant (k) by applying the fundamental equation of dynamics and as a function of the values of the total mass of the vehicle, of the speed of the vehicle and of the slope of the road at a previous instant (k−1).

A method for estimating the position of a rotor of a synchronous electrical machine, includes a rotor and a stator coupled to an inverted synchronous electrical machine via a rectifier comprising the following steps: measurement of a current i.sub.abc circulating in the stator of the synchronous electrical machine; determination of two signals in quadrature i.sub.α; i.sub.β according to a stationary reference frame from the current i.sub.abc and isolation of two filtered signals i.sub.αh; i.sub.βh from the two signals in quadrature i.sub.α; i.sub.β; demodulation of the two filtered signals i.sub.αh; i.sub.βh in order to obtain two demodulated signals i.sub.αobs, i.sub.βobs; obtaining of an estimated position {circumflex over (θ)} of the rotor from the two demodulated signals i.sub.αobs, i.sub.βobs.

A method for evaluating sensor data. In the method, firstly, raw sensor data and/or processed sensor data from at least one sensor are input and measurement data determined from the raw sensor data and/or the processed sensor data. The measurement data are then corrected on the basis of a mathematical model, wherein, on correction, drift of the raw sensor data and/or of the processed sensor data is determined and removed from the measurement data. The corrected measurement data are furthermore output.

Embodiments described herein include processes for generating a hybrid model for modeling processes in semiconductor processing equipment. In a particular embodiment, method of creating a hybrid machine learning model comprises identifying a first set of cases spanning a first range of process and/or hardware parameters, and running experiments in a lab for the first set of cases. The method may further comprise compiling experimental outputs from the experiments, and running physics based simulations for the first set of cases. In an embodiment, the method may further comprise compiling model outputs from the simulations, and correlating the model outputs with the experimental outputs with a machine learning algorithm to provide the hybrid machine learning model.

A hardware downscaler and an architecture for implementing a FIR filter in which the downscaler can be arranged for downscaling by a half in one dimension. The downscaler can comprise: hardware logic implementing a first three-tap FIR filter; and hardware logic implementing a second three-tap FIR filter; wherein the output from the hardware logic implementing the first three-tap filter is provided as an input to the hardware logic implementing the second three-tap filter.

A hardware downscaling module and downscaling methods for downscaling a two-dimensional array of values. The hardware downscaling unit comprises a first group of one-dimensional downscalers; and a second group of one-dimensional downscalers; wherein the first group of one-dimensional downscalers is arranged to receive a two-dimensional array of values and to perform downscaling in series in a first dimension; and wherein the second group of one-dimensional downscalers is arranged to receive an output from the first group of one-dimensional downscalers and to perform downscaling in series in a second dimension.

Methods and apparatuses for determining operational parameters of a blood pump comprising a rotor which transports the blood are provided. The change in the behaviour of at least one first and one second operational parameter, independently from each other, of the pump, is determined. A determination of the flow through the pump and/or the difference in pressure across the pump and/or the viscosity of the blood takes into account the determined change in behaviour of the at least two operational parameters. A modelling for a dynamic model of the known quantities may be carried out and an estimation method using a Kalman filter may be used.

Device (1) and method for detecting battery cell states, BZZ, and/or battery cell parameters, BZP, of at least one battery cell (BZ), comprising a dual Kalman filter (2) which includes a state estimator (2A) for estimating battery cell states, BZZ, and a parameter estimator (2B) for estimating battery cell parameters, BZP, and comprising a determination unit (3) which is suitable for determining noise components (n, v) of the state estimator (2A) and of the parameter estimator (2B) on the basis of a stored characteristic parameter behaviour of the battery cell (BZ), wherein the battery cell states, BZZ, and the battery cell parameters, BZP, can be adapted automatically to a specified battery model (BM) of the battery cell (BZ) by means of the dual Kalman filter (2) on the basis of the noise components (n, v) determined by the determination unit.

Provided are an earth satellite attitude data fusion system and method, applicable to an earth satellite space environment to estimate attitude data of a satellite. When the earth satellite attitude data fusion system of the present invention is used to perform the earth satellite attitude data fusion method, the first step is to perform a body rates/quaternion attitude data processing operation. Then, the next step is to perform an attitude/rates data fusion processing operation, wherein an attitude data fusion algorithm module receives a first IAE result data from a first EKF, and a second IAE result data from a second EKF, and performs an attitude/rates data fusion algorithm in a subsystem level to evaluate an attitude estimation IAE performance based on the first IAE result data, and the second IAE result data.