7 results about "Re sampling" patented technology

The invention discloses a target data selection method based on an approximate Shapley value, electronic equipment and a medium. The target data selection method comprises the steps of obtaining a ratio of a source data set to a target utility, and calculating a total utility value and a target utility value. And screening to obtain an extended sample and a feature set. And sample optimization: carrying out Monte Carlo sampling on the expanded sample set to generate random arrangement, forming a plurality of third subsets and calculating utility values of the third subsets. And after a sample Shapley value is calculated, selecting a subset with the minimum error to carry out iterative sample replacement. And if the error reduction degree is smaller than a second threshold value, re-sampling, otherwise, taking a subset of which the error is smaller than a third threshold value as an initial candidate. And if the final error still exceeds the tolerance threshold, turning to feature optimization. And feature optimization: the process is similar to sample optimization, and feature iteration and replacement are guided through a Shapley value. And alternately optimizing the sample subsets and the feature subsets until the utility value error of the combined candidate set (the candidate sample subsets + the candidate feature subsets) is smaller than an error tolerance threshold.

The processing device and method for adaptive extension lookup table provided by the embodiments of the present application store a first lookup table with high precision and large quantity in a first storage unit, and dynamically maintain a second lookup table with low precision and small quantity and an anchor scaling factor in a second storage unit. A processing unit dynamically resamples the first lookup table according to the actual value distribution of parameter input data, updates the output value of the second lookup table, and calculates the corresponding anchor scaling factor. When processing subsequent data, the input value is first mapped to the coverage range of the second lookup table by using the anchor scaling factor, and then the output value corresponding to the second mapping value is determined by table lookup. Thus, in the case of limited hardware storage resources, the value interval most active for the current data can be adaptively concentrated, that is, the interpolation precision of the main data region is improved without increasing the hardware cost, and the waste of storage resources caused by the fixed anchor distribution is avoided.

A system determines that an asset of an engineering system has transitioned from a quasi-steady degradation stage to an accelerated degradation phase based on sensor measurements received from an asset. During the accelerated degradation phase, features are extracted from the sensor measurements that are indicative of wear of the asset. A conformal mathematical filter is applied to the features that causes the features to conform to a wear curve formulation associated with the asset. An output of the filter is resampled to form a noise-reduced signal. The noise-reduced signal is input into a sequence machine learning model. A loss function of the sequence machine learning model uses an increased penalty to overprediction and a relaxed penalty for underprediction. An output of the sequence machine learning model is used to predict a remaining useful life (RUL) of the asset.

The invention discloses a construction equipment interactive operation method and system based on time phase independence, and the method comprises the steps: firstly obtaining an original track of a construction mechanical arm during operation, and carrying out the continuous processing; based on a spatial sampling algorithm, performing resampling on the continuous trajectory according to a set arc length step length, and eliminating time distortion and staying noise; the obtained sampling track is subjected to regularization; fitting the regularized track point set into a smooth geometric curve function; and finally, generating a time phase function according to a required working mode, generating a target trajectory in a time domain based on a geometric curve function, and realizing adaptive deployment under different component sizes. According to the method, the space arc length is used as a trajectory parameter, an independent variable is irrelevant to time, and the geometric trajectory established through equal-arc-length resampling and kernel function fitting can keep the space structure of the trajectory stable and unchanged. The trajectory and the time are thoroughly decoupled, and the trajectory robustness is higher.

The invention provides a micro-expression recognition method and system based on balanced adaptive grouping sampling, and relates to the technical field of micro-expression recognition, and the method comprises the steps: obtaining a data set; the similarity among a plurality of samples of the same student is calculated based on multi-scale structural similarity measurement, so that the identification difficulty of the samples is judged. According to the method, the structural similarity between the samples is calculated through multi-scale structural similarity measurement, so that the recognition difficulty of the samples is judged, grouping sampling is performed according to the recognition difficulty, and a micro-expression recognition model is trained based on a data set obtained through sampling. And meanwhile, the identification difficulty is adjusted according to a model output result, and sampling is performed again. Thus, re-sampling and model training are periodically performed, and the sampling weight of the sample is continuously updated according to the information fed back during model training, so that continuous reinforcement and feature optimization of the specific sample can be realized during training of the model, and the stability and generalization performance of the model are improved.

The application provides a signal transmission method with a resampling mechanism, comprising: obtaining sampling data of a data signal; calculating a time difference between an actual sampling time point and an ideal sampling time point corresponding to target sampling data according to operating frequencies of a signal sending end and a signal receiving end; selecting a preset time point closest to the ideal sampling time point from preset time points in a sampling interval according to the time difference; selecting a plurality of groups of operation sampling data within a preset range before and after the target sampling data in the sampling data; selecting a group of response items from a pre-stored query table according to the closest preset time point, so that the response items and the time difference are substituted into a parameter operation formula to generate resampling response parameters; and calculating corresponding to the operation sampling data and the resampling response parameters to generate a resampling value of the target sampling data.

This invention belongs to the field of engineering optimization design technology, specifically relating to a method, program, equipment, and storage medium for uniform experimental design within a design space with complex constraints. By accurately representing the boundary of the constrained design space and gradually reducing the relaxation of constraints based on the sequential Monte Carlo method, this invention ensures that after a finite number of iterations, the selected priority sample points fully satisfy all constraints. This prevents individual sample points from not belonging to the actual constraint space due to inaccurate boundary condition definitions, thus avoiding unreasonable sample point selection. By introducing the "maximize minimum distance" criterion, uniform and orthogonal sampling can be achieved for a given number of samples, ensuring good representativeness of the sample selection. Progressive sampling can be implemented, preventing the need for complete resampling due to insufficient initial sample point selection, while simultaneously ensuring that all sample points maintain good uniformity and orthogonality after progressive sampling.