37 results about "Transfer entropy" patented technology

The invention discloses a method for analyzing electroencephalogram and electromyographic coupling among multiple time-frequency scales on the basis of wavelet-transfer entropy. The method includes an electroencephalogram and electromyographic signal synchronous acquisition portion and a signal processing portion. The electroencephalogram and electromyographic signal synchronous acquisition portion includes acquiring electroencephalogram signals and acquiring electromyographic signals; the signal processing portion includes preprocessing the signals and carrying out processes for analyzing the electroencephalogram and electromyographic wavelet-transfer entropy. The method has the advantages of applicability, admissibility and important value in the field of rehabilitation medicine.

The invention discloses a quantitative cardiopulmonary system interaction analysis method which includes the steps: acquiring multi-lead physiological signals of a subject in nocturnal sleep; marking sleep stages of the signals and extracting an RR interval sequence, a PP interval sequence, a PTT sequence and an RA interval sequence; synchronously segmenting the four sequences according to 5-minute duration in the same sleep state and then extracting three characteristic parameters: low-frequency Shannon entropy, high-frequency Shannon entropy and transfer entropy; training a neural network, determining neural network parameters and building a neural network cardiopulmonary interaction evaluation model; evaluating the cardiopulmonary function of the subject by the trained neural network and outputting evaluation results. Cardiopulmonary coupling is analyzed by multi-variable time sequence analysis technology, multiple variables such as cardiac cycle, respiration and blood pressure are analyzed by a frequency domain and information analysis method, analysis deficiency of single variable is effectively remedied, so that a complicated regulation mechanism between cardiopulmonary systems is more accurately and comprehensively quantified, the coupling strength of cardiopulmonary interaction is quantified, and the direction of cardiopulmonary interaction can be judged.

The invention discloses a brain section coupling analysis method based on synchronous screening. The method comprises first synchronously acquiring EEG signals in 32 channels and EMG signals in 12 channels when different gripping power is output; in order to research a coupling relation of an EEG motor area and a sensory area in a gripping power output process, analyzing the EEG signals and brachioradialis signals of multiple channels, such as leads C3, C4, CP5, and CP6, in the motor area and the sensory area of the brain; then extracting the synchronous information of the EEG and the EMG by using a synchronous screening algorithm to obtain SSEM (Synchronous Screening of EEG Signals Based on EMG Signals); and finally calculating the symbol transfer entropy of the SSEM to determine its coupling relation. The method can remove data unrelated to motion and reduce a data size.

Described is a system for predicting system trajectories toward critical transitions. The system transforms a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series. Then pair-wise time series of a transfer entropy (TE) measure are determined, wherein the TE measure quantifies the amount of information transfer from a source to a destination in the complex system. An associative transfer entropy (ATE) measure is determined which decomposes the pair-wise time series of TE to associative states of asymmetric, directional information flows, wherein the ATE measure is comprised of an ATE+ influence class and a ATE− influence class. The system estimates ATE+, TE, and ATE− trajectories over time, and at least one of the ATE+, TE, and ATE− trajectories is used to predict a critical transition in the complex system.

The invention discloses a voltage transformer error prediction method based on transfer entropy and a wavelet neural network. The method comprises the following steps: acquiring environmental parameters, electrical parameters and error data of operation of an electronic voltage transformer; through a transfer entropy theory, calculating transfer entropy values of environmental parameters and electrical parameters on error data, selecting main influence quantities according to entropy values and positive and negative values, calculating transfer entropy values of contrast differences and angular differences of five influence factors in the environmental parameters and the electrical parameters respectively, and screening the influence factors with strong correlation; and normalizing the influence factors obtained by screening to enable the data to be in an order of magnitude, taking the processed data as an input quantity, and respectively establishing a ratio difference prediction model and an angular difference prediction model through a wavelet neural network; and calculating an error between the prediction curve and the expected curve, and representing the precision of the errorprediction method by an average absolute error. According to the invention, errors of the electronic voltage transformer under different voltage levels can be predicted, and the method has good adaptability.

The invention relates to a micro blog secluded key user analysis method based on topic transferring entropy. The method comprises the steps that direct influence of a micro blog user is measured through a micro blog average spread scale, and according to the direct influence, obvious key users and alternative secluded key users are found; micro blog topic sequences of the two types of users are extracted; the topic transferring entropy from the alternative secluded key users to the obvious key users is calculated, and the influence degree of the alternative secluded key users on the obvious key users is measured; based on the direct influence of the alternative secluded key users on the obvious key users and the influence degree of the alternative secluded key users on the obvious key users, the seclusion importance degree of the alternative secluded key users is analyzed, and the secluded key users are identified. The micro blog secluded key user analysis method based on the topic transferring entropy has the advantages that based on micro blog topic similarity and timing sequence relativity, the micro blog topic transferring entropy from the non-obvious key users to the obvious key users is obtained, the seclusion influence degree of the non-obvious key users on the obvious key users is measured, the own direct influence of the non-obvious key users and obvious key users arecombined, the seclusion importance of the users is evaluated, and the accurate identification of the micro blog secluded key users is achieved.

The invention discloses a transfer entropy-based multi-terminal DC transmission line protection method, which comprises the following steps of (S1) collecting positive and negative current of a multi-terminal DC transmission line on a DC circuit breaker of a fault point; (S2) calculating a transfer entropy drop of the positive and negative current; (S3) judging whether a fault appears or not according to the transfer entropy drop and a transfer entropy criterion; (S4) calculating zero-module current on the DC circuit breaker of the fault point by using current decoupling, and carrying out fault judgment according to the zero-module current and a fault pole selection criterion to obtain the fault type; (S5) finding out a fault pole according to the fault type, collecting a current signal ofthe fault pole and calculating a Kurtosis factor; and (S6) judging whether the fault appears in the line or not according to the Kurtosis factor and a Kurtosis factor criterion. According to the transfer entropy-based multi-terminal DC transmission line protection method, the problems of low practicability, a complicated system structure and a poor fault identification effect and the problem thatprotection cannot be carried out in an offline state in the prior art are solved.

The invention discloses a two-layer real-time monitoring and alarm source tracing method based on dynamic and static coordinated differential analysis. The upper-layer monitoring algorithm can effectively comprehensively consider the process volatility and the dynamic and static angles of the process state from a small scale; and in combination with the source tracing method based on the contribution figure and the transfer entropy, the alarm priority of the current lower-layer DCS system can be determined, and a fault source can be effectively positioned. According to the method provided by the invention, the controller adjustment capacity, and the dynamic and static online monitoring indexes of the process running state are synthesized to serve as the selection basis of an alarm management method, the interference alarm is reasonably suppressed, the key problem of alarm flooding is effectively solved, and thus the safe and efficient operation of the industrial process are ensured.

The invention discloses a mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF. The TE transfer entropy is used to perform a causal correlation analysis on interference and measure results, an error source is determined and a multi-interference imbalance degree is measured, and the directivity of the TE transfer entropy is used for leading out a quantification standard and a determination method having a non-substantial causal correlation. A virtual observation method is provided for realizing multiple deconstruction of a unit observation sequence, an ANN error prediction model is used to realize compensation of a single interference channel virtual observation sequence, and a multiple adaptive weight fusion method is employed for fusion reconstruction on a multiple virtual observation sequence. By aiming at a weight convergence problem in a fusion algorithm, an index forgetting method is introduced in a model to combine the good weight pre-estimatedcapability of TE and the weight adaptive adjustment of AWF, and the dynamic performance of a error compensation process is improved.

The invention discloses a chemical process fault diagnosis method based on transfer entropy. Firstly, a process monitoring model is established by adopting related information entropy; then, judging areal-time working condition state by utilizing the established process monitoring model, and finally, for an abnormal working condition, establishing a fault root cause-fault characteristic variabledatabase by utilizing historical fault data and introducing transfer entropy, and judging a fault root cause by utilizing the fault root cause-fault characteristic variable database. According to thechemical process fault diagnosis method based on the transfer entropy, the defect that a principal component analysis method processes nonlinear data is overcome. The fault diagnosis accuracy is improved. The transfer entropy is introduced, and the fault occurrence mechanism can be described.

Described is a system for detecting cyber intrusions based on analysis of network traffic. During operation, the system performs a statistical analysis of message timing on network traffic to produce a temporal dependency matrix representative of temporal dependency between different message types in the network traffic. The sets of temporal dependency matrices are decomposed into component matrices, where at least one component matrix represents typical properties of these matrices and at least one other component matrix represents atypical properties of the matrices. A new temporal dependency matrix is generated based on new network traffic. Finally, anomalous behavior is detected in the new network traffic by comparing component matrices of the new temporal dependency matrix with component matrices of the temporal dependency matrices under normal operation conditions.

The invention discloses an error compensation method for mobile source emission gas remote sensing measurement based on transfer entropy and adaptive fusion estimation. The error compensation method organically combines priori knowledge and an optimal estimation theory of a measured object and can obtain the optimal estimation of a true value from a noisy observation sequence. The error compensation method comprises the following steps: firstly, establishing a remote sensing measurement error prediction model under multiple interferences through an over-limit learning machine method; then, providing a virtual observation decomposition model and performing multi-sequence decomposition on the observation sequence by utilizing a virtual observation decomposition model; after that, transforming the actual measurement process into the multi-sensor virtual observation process and establishing a mathematical model for the multi-sensor virtual observation process; finally, performing fusion and reconstruction on multiple virtual observation sequences through introducing a transfer entropy and an adaptive Kalman filter to obtain the optimal estimation of a measurement sequence. The error compensation method disclosed by the invention can effectively compensate measurement errors caused by external environment interference and improve the environment applicability and the anti-interference ability of a remote sensing detection technology.

The embodiment of the invention provides a power dispatching automation system fault tracing method based on an information difference graph model, and the method comprises the steps: selecting historical data of a power dispatching automation system before and after alarm, obtaining a clustering center through a k-means algorithm, and taking the clustering center as an end point of interval division, wherein the mean value of each interval serves as a discretization result of continuous features; calculating the information entropy of the components of the power dispatching automation systemand the transfer entropy between the components, establishing an information correlation matrix with or without an alarm section, measuring the difference degree before and after the alarm through thechange rate, and obtaining an information difference matrix by adopting a normalization technology; extracting features with relatively high alarm information change of a power dispatching automationsystem and interaction information among the features, further constructing an information difference graph model combining a bidirectional graph and node self-information, and fitting fault degree indexes to sort fault degrees. According to the technical scheme provided by the embodiment of the invention, the fault tracing performance of the power dispatching automation system is improved.

The invention relates to a TE process time sequence prediction method based on transfer entropy and a long short-term memory network. In order to solve the problems of low time sequence prediction precision and low training speed caused by high relevance among TE process variables and easiness in introducing redundant information into a prediction model, the asymmetry of a transfer entropy algorithm is used for variable selection, an upstream variable which greatly affects the temperature of a reactor is selected from TE process reactor unit variables, and the interference of downstream irrelevant variables is eliminated, so that the complexity of a time sequence prediction model is reduced. By using the excellent performance of the LSTM in the aspect of time sequence prediction, an LSTM time sequence prediction model is established based on a variable selected by the transfer entropy, and the future time sequence of the temperature of the reactor is predicted.