53 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 an analysis method of multi-channel brain electrical coupling based on variable scale symbolic transfer entropy, which firstly collects the multi-channel electroencephalogram (EEG) signals and the surface electromyography (EMG) signals on the relevant muscle groups of different gripping actions at the same time. Then the variable scale parameters are selected to mark the EEG signals of the left and right hand under the same grip, and the entropy of the symbolic sequence is calculated. By synthetically analyzing the average value and standard deviations of the transfer entropy and calculating the used time, a suitable and effective symbolic scaling parameter is selected for further analysis. The left hand / right hand movements, EEG signals and corresponding EMG signals of different gripping actions and multiple channels are further analyzed and compared. Finally, according to the change of transfer entropy of EEG to EMG and EMG to EEG, a representation method of coupling intensity of EEG signals is proposed, which reflects the coupling strength between the cortical muscle and the motor muscle objectively and quantitatively.

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 provides a method and system for discovering causality among social network users combining a behavior sequence and text information, which comprises the following steps: S1) data acquisition; S2), preprocessing the data at equal intervals with the smallest time unit; 3) optimize that objective function to find the optimal interval by using the time sequence behavior data; S4) reconstructing the text data in the way of merging the text at the time of merging, and representing the text vectorization; S5) calculating the transfer entropy of the text vector sequences of the two users; 6) prune to obtain a user causality network; 7) store and deriving that us causal network; S8) user causality inquiry and visualization. The invention solves the problem of calculating the transferentropy caused by the sparse user activities; infer the user causality of the social network by using the text data, and the amount of information is more abundant than the pure behavior data; and aninteractive system for inferring, inquiring and deriving the user causality is provided.

The invention discloses a quantitative analysis method of aviation delay propagation, belonging to the technical field of aviation delay analysis. Firstly, the arrival delay time series from airport ito airport j and the departure delay time series of airport j are constructed, and the transfer entropy TE (Y(right arrow)X) of the t-th time slice is calculated to quantify the shift of the generalized Markov condition, and the causality of arrival delay to departure delay is tested. Then several arrival delay time series and departure delay time series with the same statistical characteristicsare reconstructed as substitute data, and the causality of each substitute data is destroyed; the transfer entropy (TE) is calculated with the substitute data, and the significance test is carried out. Pairs of tests are conducted between any two airports to establish the weighted edge, build the network model of aviation delay propagation, and use the network model to analyze flight delay. The invention only needs to analyze the causality between the delay time series, and can quantitatively calculate the delay propagation situation between the airports.

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 relates to a gearbox fault diagnosis method based on multi-element modal decomposition-transfer entropy, and belongs to the technical field of gear fault analysis. According to the technical scheme, a multi-element modal decomposition noise reduction algorithm is adopted to extract system main characteristics firstly, then transfer entropy is used for describing system complexity, and is used for gear fault diagnosis. According to the gearbox fault diagnosis method, noise-assisted multi-element empirical modal decomposition-transfer entropy is used to analyze gearbox fault vibration information transmission characteristics, nonlinear coupling and information transmission characteristics between output shaft end and input shaft end signal frequency bands under a gearbox faultcondition are quantitatively described so that a transmission path of gearbox fault vibration signals can be explored, a gearbox state evaluation index based on the multi-element empirical modal decomposition-transfer entropy is established, and a new effective means is provided for fault diagnosis, performance degradation state identification and trend prediction of a rotary machine.

The invention provides an electroencephalogram-electromyogram coupling research method based on variational mode decomposition-transfer entropy and belongs to the technical field of information. The research method of the invention comprises the steps of 1, allowing a testee to complete experimental operations according to experimental instructions, and acquiring electroencephalogram and electromyogram during the experimental process; 2, preprocessing data of the electroencephalogram and electromyogram; 3, subjecting the preprocessed electroencephalogram and electromyogram to variational modedecomposition to obtain a plurality of different intrinsic mode functions; 4, performing transfer entropy calculation on pairs of the electroencephalogram and electromyogram acquired via variational mode decomposition, and observing electroencephalogram-electromyogram coupling intensities; and 5, performing transfer entropy calculation on the intrinsic mode functions having different properties, different components and different directions again at different moments of time, so that interference to transfer entropy calculation between different subsequent frequency waves due to frequency bandoverlapping can be lessened, and accuracy of experimental results can be improved.

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 intermuscular coupling analysis method based on the transfer entropy and the generalized partial directional coherence, aiming at accurately analyzing the intermuscular functional coupling strength under different movement modes. As the traditional coherence analysis can neither effectively describe the intramuscular coupling direction and non-linear coupling characteristics nor exclude the influence of indirect connection, the method introduces the transfer entropy and the generalized partial directional coherence to the intermuscular coupling analysis to investigate the coupling characteristics of the intermuscular bi-directional function of electromyographic signals from relative muscles in the upper limb movement in the time-frequency domain. The intermuscular coupling analysis method has the advantages of providing an effective observation means to explore the movement control mechanism of central nervous system, and having great application value in thefield of rehabilitation medicine.

The invention provides a production line equipment fault prediction method and system, and relates to the technical field of fault prediction.The method comprises the steps that multi-source time sequence signals are synchronously collected, a multi-dimensional health feature sequence is extracted, the comprehensive sensing capacity covering multiple states of equipment is established, and further, the fault prediction efficiency is improved. By selecting a key health feature sequence for each device and performing linear fitting to quantify the performance degradation severity, accurate description and early recognition of the performance degradation trend of the device are realized, and in addition, the causal influence intensity between the devices is quantified by adopting a transfer entropy algorithm, so that the accuracy of the performance degradation trend is improved. Scientific modeling and visual analysis of a fault propagation path and a linkage effect in a production line are realized, and a comprehensive risk index is generated through weighted fusion of performance degradation severity and a global impact factor, so that a comprehensive risk assessment index is formed. Based on the cumulative failure probability function, the comprehensive risk index is converted into a fault probability curve changing along with time, a visual mathematical expression of the risk trend is formed, and a scientific basis is provided for decision making.

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 invention discloses a bridge structure damage positioning method based on transfer entropy of double sensor information. The method comprises the following steps that two acceleration sensors areinstalled at any two different positions of the bridge in a perpendicular mode; the acceleration response of the vehicle load passing through the bridge is measured, and acceleration signals a (t) andb (t) of the acceleration sensor are obtained respectively; a moving time window is defined; a moving time window is used for carrying out synchronous signal interception on the two measuring point acceleration signals, and the transmission entropy is calculated for two sections of signals in the window, and the time sequence of the damage index transmission entropy Ki is obtained; the damage ofthe beam-type bridge structure can be positioned by transmitting the entropy K curve at the bridge damage position. According to the method, due to the fact that the windowing transfer entropy betweenthe two collected two-column acceleration signals is changed before bridge damage, the beam-type bridge structure damage positioning is carried out, and only two sensors need to be used for measuringthe acceleration data, and then the transfer entropy value is calculated, the damage position of the bridge is positioned, and the number and the cost of the sensor can be reduced.

The embodiment of the invention discloses a data network transfer monitoring method and device and a communication system. The data network transfer monitoring method comprises the steps that a transfer request of a client side for transferring virtual assets to a second account number from a first account number is received; legitimacy verification reference information of the second account number is acquired, and the legitimacy verification reference information includes the transferred entropy of the second account number and / or the overlap ratio of the consumption account number of the second account number; whether the second account number is a suspected illegal account number is determined according to the legitimacy verification reference information of the second account number; and virtual asset transfer risk management and control operation is performed under the condition that the second account number is determined to be the suspected illegal account number according to the legitimacy verification reference information of the second account number. According to the scheme, the monitoring effectiveness of disposal of stolen profits through the network can be enhanced.

Described is a system for detecting cyber intrusions based on analysis of network traffic. During operation, the system performs a statistical analysis of message tuning on network traffic to producea 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 tinder normal operating conditions.

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 discloses an automatic wharf operation delay propagation prediction method fusing anomaly perception and transfer entropy analysis, which comprises the following steps: on the basis of an operation time sequence of operation equipment, introducing anomaly characteristics of the operation equipment, constructing an enhanced time sequence set, and enhancing the response capability to sudden disturbance in a prediction process; a candidate condition variable set is selected through a recursive feature elimination algorithm, so that the calculation complexity and the over-fitting risk caused by a high-dimensional variable space are effectively reduced, and the problem of curse of dimensionality is relieved; by constructing the delay propagation atlas, the mutual relation between operation equipment can be indicated, and intuition is achieved; a multi-stage operation delay conduction model is constructed according to the transformation probability of the operation equipment in different states, so that the influence of the equipment in different states on the port transportation process can be reflected more accurately; by using the causal relationship between the operation devices in the delay propagation map, how to propagate between the operation devices during delay propagation can be more clearly understood.

The invention discloses an interactive network modeling method of complex electromechanical system in process industry based on adaptive symbol transfer entropy, the symbolic common parameters of timeseries are obtained on the basis of multivariate space reconstruction, the probability density and distribution of the original time series are estimated by using the adaptive kernel density estimation method, and divide the sequence into equal probability, by obtaining the best number of symbols and dividing intervals, coarse-grained symbolic representation of the original sequence is implemented, in order to improve the accuracy of the measurement of interaction information between variables, the symbolic sequence of monitoring variables is analyzed by transfer entropy, and the net information transfer quantity is calculated, so as to obtain the basic parameters needed for system interaction network modeling, and establish the network model reflecting the interaction mechanism of the actual system bottom layer. The network model will provide a basis for system state assessment, fault propagation analysis and diagnosis decision-making, so as to improve the scientific and intelligentdecision-making level of complex electromechanical systems in process industry under complex operating conditions.

A strategy adaptive protein conformation space optimization method based on a transfer entropy is disclosed. The method comprises the following steps of 1) giving input sequence information and a protein force field model; 2) initializing; 3) generating a background point; 4) carrying out clustering operation; 5) calculating the transfer entropy; 6) carrying out strategy adaptive operation; 7) carrying out selection operation; and 8) determining whether a termination condition is satisfied, terminating if condition is satisfied and output all optimal solutions. In the method, a conformation solution space is divided into subspaces corresponding to different local optimum solutions, the historical evolutionary information of population is combined to establish the transfer entropy so as tomeasure the exploration degree of the population to the conformation solution space, and then, an entire search process is adaptively divided into two phases, and a phase-specific conformation generation strategy is adopted to improve the prediction accuracy of a protein structure prediction method. By using the strategy adaptive protein conformation space optimization method based on the transferentropy provided in the invention, prediction accuracy is high.

The invention relates to the technical field of forecast correction, in particular to a multi-mode forecast correction method and system based on ensemble learning, and the method comprises the following steps: S1, mapping a multi-mode original forecast field of a target region to a phase space, and generating a phase space state vector comprising key elements; s2, on the basis of the phase space state vector in the S1, calculating a state mutual coupling matrix between modes through a transfer entropy algorithm; and S3, when the maximum entropy value in the state mutual coupling matrix in the S2 exceeds a bit threshold value, triggering a phase space trajectory corrector to reconstruct a forecast trajectory and output a correction result, otherwise, directly outputting an original forecast optimal integration. According to the method, systematic risks such as typhoon peripheral vortex pollution and frontal resonance errors which are difficult to detect by a single mode are effectively identified, and scientific quantification of correction triggering is realized.

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.

Apparatus for determining stroke volume, cardiac output and systemic inflammation by fuzzy logic combination of features extracted from a voltage measured over the thorax, electrocardiogram and electroencephalogram comprising a) sensor for measuring electroencephalogram; b) sensor for measuring the electrocardiogram; c) calculating the heart rate variability from the electrocardiogram; d) two electrodes (1, 2) for measuring the voltage generated by a constant current generator in two other electrodes (1, 2); e) calculating the area under the curve of voltage for each heart-beat; f) calculating the derivative of the voltage curve; g) calculating the transfer entropy between EEG and ECG; h) combining at least 3 extracted parameters using an Adaptive Neuro Fuzzy inference system (ANFIS) or any other fuzzy reasoner into a final index of cardiac output; i) combining at least 3 extracted parameters using an Adaptive Neuro Fuzzy Inference System or any other fuzzy reasoner into a final index of systemic inflammation.