4 results about "Re sampling" patented technology

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 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.