A spurious outlier detection-system is provided. The system includes a memory having computer-readable instructions stored therein and a processor configured to execute the computer-readable instructions to receive time-series data from one or more sensors and/or applications, process the time-series data to detect one or more change points based on a pre-defined cost function. The processor is configured to identify data chunks between the change points using pre-determined window sizes and to estimate smooth reconstructed values (SRVs) for each of the change point data chunks between two consecutive change points to identify one or more global outliers from the SRVs. The processor is configured to determine distribution of the global outliers using kernel density for each change point data chunk and identify one or more true outliers from the distribution of the global outliers based upon a skewness of the distribution.

The invention relates to a method for the automated determination of characteristic curves and/or characteristic maps of devices, which comprises the following method steps: acquisition of a measurement data set, execution of an iteration method with the iteration steps calculation of an iteration result from the measurement data set using a neural network, acquisition of a termination parameter, checking the termination parameter and terminating the iteration method if the termination parameter matches a termination criterion, as well as the optical visualization of the iteration result and the measurement data set and repeating the iteration steps.

Provided is an interpolation technique in which command values chronologically input can be interpolated without increasing a jerk and with less follow-up delay with respect to a command. A control unit (10) of a servo driver (20) has a function of sequentially generating, on the basis of four command values from x(k−2) to x(k+1), a kth interpolation function for calculating command values in a kth (≥3) time interval and a function of generating, as the kth interpolation function, a fifth-order equation with respect to time in which function values at a start time and an end time of the kth time interval match x(k) and x(k+1), respectively, and in which a second derivative value at a start time of the kth time interval matches a second derivative value at an end time of a (k−1)th time interval corresponding to a (k−1)th interpolation function.

A computing program product and method for interpolating wavelets coefficients and estimating spatial varying wavelets using the covariance interpolation method in the data space over a survey region having multiple well locations, are disclosed. The method and computing program product, embodied in a non-transitory computer readable device, that stores instructions for performing by a device are based on interpolating coefficient models in the data space domain using covariance analysis methods to overcome inaccuracy and instability issues commonly observed during wavelet estimation and interpolation.

In a general aspect, an optimization problem is solved using a hybrid computing system. A classical processor unit receives a first data structure that represents the optimization problem. The classical processor unit executes a branch-and-bound process on the first data structure to generate values for a first subset of elements of a solution to the optimization problem. A second data structure is generated based on the first data structure and the first subset of elements. The second data structure represents a reduced version of the optimization problem. A quantum processor unit and a classical processor unit are used to execute a quantum approximate optimization algorithm (QAOA) on the second data structure to generate values for a second subset of the elements of the solution to the optimization problem. The first subset and second subset are combined to obtain the solution to the optimization problem.

A computing program product and method for interpolating wavelets coefficients and estimating spatial varying wavelets using the covariance interpolation method in the data space over a survey region having multiple well locations, are disclosed. The method and computing program product, embodied in a non-transitory computer readable device, that stores instructions for performing by a device are based on interpolating coefficient models in the data space domain using covariance analysis methods to overcome inaccuracy and instability issues commonly observed during wavelet estimation and interpolation.

A computer-implemented method of an arithmetic processing, the method including: identifying maximum absolute values of individual dimensions by projecting a maximum absolute value in a direction of each of the individual dimensions of a tensor represented by a multidimensional array, the tensor in which a value is set for each of elements of the array; identifying a minimum value that indicates a minimum maximum absolute value among the maximum absolute values of the individual dimensions; and setting a quantization range for the tensor on a basis of the minimum value.

A plurality of sets of sample data points from a vehicle sensor are determined, each set including a predetermined number of consecutive sample data points. Respective differences between pairs of consecutive sample data points in each set are determined. The differences are stored in respective subspaces of one of a plurality of memory spaces of the memory, each memory space in the plurality of memory spaces storing differences of one and only one of the plurality of sets of sample data points. The collected data are restored based on the stored differences in the plurality of memory spaces. A vehicle component is actuated based on the restored data.

A strict reverse navigation method for optimal estimation of fine alignment is provided. The strict reverse navigation method including: establishing an adaptive control function; performing a forward navigation calculation process; performing a reverse navigation calculation process; and performing the adaptive control for a number of forward and reverse calculations. The strict reverse navigation method shortens an alignment time for the optimal estimation of fine alignment while ensuring an alignment accuracy. The strict reverse navigation method provided effectively solves a problem that an error of an initial value of filtering in an initial stage of the optimal estimation of fine alignment affects convergence speeds of subsequent stages. In the initial stage, a larger number of the forward and reverse navigation calculations are adopted to reduce an error of the initial value as much as possible and increase a convergence speed of the filtering.

An estimation apparatus includes a memory; and a processor configured to execute: taking a set of observation points representing events observed in a space region of a predetermined dimensionality in an observation, an observation count representing the number of times the observation is performed, a first function satisfying a predetermined condition, and a parameter of the first function as inputs; and analytically estimating a rate function for obtaining occurrence rates of the events in the space region.