7 results about "Multi scale entropy" patented technology

The invention relates to the technical field of information processing, and discloses a social robot identification method based on multi-scale entropy features, a terminal and a medium. The method comprises the following steps: acquiring social network behavior data of a to-be-detected user, and respectively constructing an initial binary time sequence for predefined behavior types; setting a plurality of time windows with different granularities, and mapping the initial binary time sequence into a plurality of coarse-grained behavior sequences with different time scales; for the coarse-grained behavior sequence of each time scale, calculating a behavior information entropy corresponding to the coarse-grained behavior sequence to form a behavior entropy set; based on the behavior entropy set, calculating variable coefficients of entropy values among different time scales so as to quantify dynamic fluctuation characteristics of user behaviors; and fusing the behavior entropy set and the variable coefficient to construct a comprehensive feature vector, inputting the comprehensive feature vector into a trained supervised classification model, and outputting a classification result that the user is a real user or a social robot. According to the invention, the recognition capability for the complex camouflage behavior of the social network robot is improved.

The application relates to the technical field of information processing, and discloses a social robot identification method based on multi-scale entropy features, a terminal and a medium. The method acquires social network behavior data of a user to be detected, constructs initial binary time series for each predefined behavior type, sets a plurality of time windows with different granularities, maps the initial binary time series into coarse-grained behavior sequences with different time scales, calculates the behavior information entropy of each coarse-grained behavior sequence with a time scale to form a behavior entropy set, calculates the variation coefficient of the entropy values between different time scales based on the behavior entropy set to quantize the dynamic fluctuation features of the user behavior, fuses the behavior entropy set and the variation coefficient to construct a comprehensive feature vector, inputs the comprehensive feature vector into a trained supervised classification model, and outputs the classification result of the user as a real user or a social robot. The application improves the identification ability for the complex camouflage behavior of social network robots.

The invention discloses an extremely low bit rate image compression method based on mixed attention and multi-scale entropy modeling, and the method comprises the steps: obtaining a standard image data set which comprises a training set and a test set; the method comprises the following steps: constructing an extremely low bit rate image compression network model based on double-branch mixed attention and multi-scale entropy modeling, constructing a loss function of an extremely low bit rate image compression network, and in a deployment stage, inputting an original image in a test set into the extremely low bit rate image compression network loaded with an optimal weight, and obtaining a coded and compressed binary code stream and a decoded and reconstructed image. According to the method, a multi-scale attention fusion mechanism is introduced into an autoregression entropy model, the problem of dimensionality fragmentation of feature representation is solved through three parallel branches, and cross-dimensional interaction, local spatial correlation and grouping channel correlation are modeled to jointly enhance context features. Therefore, potential space redundancy in the compression process is reduced.

This invention provides a reservoir dam safety monitoring system, the core mechanism of which includes: using a pole-symmetric mode decomposition algorithm to perform multi-scale decomposition on the dam structure response time series data to obtain several intrinsic mode functions; adaptively filtering each intrinsic mode function through permutation entropy; performing multi-scale grouping and weighted reconstruction of effective structural feature modes through dispersion entropy to obtain multiple sets of reconstructed sequences at different scales; constructing a multi-scale entropy feature vector corresponding to the dam structure response; coupling and analyzing the entropy feature sequence obtained by the same processing of the running command time series data with the multi-scale entropy feature vector to output the dam safety state coefficient; performing target detection, tracking, and behavioral risk analysis on the panoramic video stream data of the dam area to output the personnel behavioral risk coefficient; and performing a fusion risk assessment based on the dam safety state coefficient and the personnel behavioral risk coefficient to calculate the comprehensive risk level and trigger the corresponding level of prevention and control early warning.

The invention discloses a weighted multi-scale entropy-based electroencephalogram signal analysis method and a cognitive function state evaluation system, and the method comprises the steps: carrying out the preprocessing of collected original electroencephalogram signals, so as to remove noise and artifacts; multi-scale entropy calculation is carried out on the preprocessed electroencephalogram signals, and entropy value sequences of the electroencephalogram signals under multiple time scales are obtained; calculating energy distribution of the preprocessed electroencephalogram signals on different frequency bands through a power spectral density function, wherein the energy distribution is used for quantitatively representing activity intensity of each electroencephalogram frequency band; determining a weight factor corresponding to each time scale based on the energy distribution on different frequency bands; and carrying out weighted fusion on the entropy value sequence by using a weight factor, and calculating to obtain a weighted multi-scale entropy. In this way, the complexity measurement can represent the dynamic characteristics on the time scale and the structural change of the frequency energy dimension at the same time, and therefore time-frequency double-domain fusion analysis of the electroencephalogram signals is achieved.

The invention discloses a new energy full direct current data center multi-stream mutual fusion method based on multi-scale entropy, and belongs to the technical field of energy internet and intelligent data center operation control. The method comprises four steps of multi-scale information acquisition, adaptive decomposition and calculation, system quantitative identification and multi-flow optimization and regulation, and is a collaborative regulation method based on the deep fusion of the computing power flow, the power flow and the cold flow of the full-direct-current power supply architecture. And multi-scale entropy analysis is combined to realize multi-resource flexible potential identification, system complexity evaluation and active response optimization under different time scales. According to the method, aiming at the problem of lack of unified description and dynamic coordination among computing power flow, electric power flow and cold flow, multi-scale sample entropy is introduced to a time sequence signal level, and four stages of state modeling, complexity quantification, collaborative optimization and active regulation and control are formed to form a complete closed loop; and multi-scale identification, multi-level flexible potential regulation and control and robust optimization control of system complexity under multi-energy flow are realized.