202 results about "Time domain correlation" patented technology

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

The invention relates to the technical field of wireless communication, and discloses an intelligent channel prediction method based on a deep multi-layer perceptron architecture in order to solve the problem of high-dimensional near-field cascade channel prediction in a reconfigurable intelligent surface assisted low-orbit satellite communication system. Designing a frequency domain MLP architecture to learn global time sequence characteristics of a cascade channel; secondly, considering high-dimensional spatial domain characteristics of a near-field cascade channel, decomposing the cascade channel into a plurality of feature dimensions, and proposing a multi-branch MLP architecture to realize global spatial domain correlation modeling; and finally, by taking the frequency domain MLP and the multi-branch MLP as basic network feature extraction modules, designing a channel prediction network model based on an encoder-decoder architecture. According to the method disclosed by the invention, the accurate prediction of the downlink CSI can be realized by utilizing the historical channel state information estimated by the uplink communication, and the proposed channel prediction model has better channel prediction precision and model convergence speed.

The invention relates to the technical field of industrial laser precision measurement, in particular to a laser centering device for a coupler and a using method of the laser centering device for the coupler. Light spot displacement signals and environment vibration signals are synchronously collected through a laser transmitter and an optical sensor array, and a frequency domain feature decoupling module is adopted to separate a shafting displacement component and an interference component; the time domain correlation verification module is used for calculating signal time sequence correlation and generating a deviation vector, and finally the adaptive compensation decision module is used for analyzing the deviation vector and outputting an adjustment instruction. According to the invention, effective separation of real misalignment and transient noise in a dynamic environment is realized, the technical problem of measurement misalignment of an existing laser centering device in a complex industrial environment is solved, and the coupling installation precision and the equipment operation stability are improved.

The invention discloses a time domain orthogonal frequency coding waveform design and interference suppression method, equipment and a medium, and relates to the technical field of radar signal processing. The method comprises the following steps: constructing a time domain correlation function of interference and a target based on a radar echo signal model, and solving the time domain correlation function to obtain an optimized intra-pulse frequency coding sequence; and on the basis of an intra-pulse frequency coding waveform, intercepting different time-frequency signals for the echo signal through a time-frequency domain window so as to obtain a new time-frequency domain matrix, and carrying out inverse transformation on the time-frequency domain matrix and then realizing target identification and interference suppression according to time-domain correlation. Aiming at a common technical problem of intermittent sampling forwarding interference resistance in the radar anti-interference field, the method comprises the following steps: firstly, designing a frequency coding waveform, and optimizing the waveform through a genetic algorithm to reduce time domain correlation between a target and intermittent sampling interference; and then target identification and intermittent sampling forwarding interference suppression are realized by using a time-frequency domain window function based on an echo signal obtained by the waveform.

The invention discloses a high-precision Beidou satellite navigation and positioning data acquisition method and system, particularly relates to the technical field of satellite navigation and positioning processing, and is used for solving the problem of positioning error increase caused by receiver hardware delay drift in the prior art. Inverting the first dynamic reference value by using carrier phase integral displacement, and extracting a third-order intermodulation component through coherent demodulation to generate a second dynamic reference value; adopting mutual information entropy analysis to select an effective reference value to calculate a residual error; fitting a theoretical value according to the carrier phase change rate to calculate deviation; marking a hardware drift error when the time domain correlation continuously exceeds a threshold value; finally, the ionosphere residual error is dynamically compensated to generate a corrected observation value, effective separation of the hardware drift error and the ionosphere residual error is realized, and the precision and reliability of long-time observation in a dynamic environment are improved.

The invention relates to the technical field of fruit preservation, and particularly discloses a winter jujube preservation control method and system based on IoT and AI, and the method comprises the steps: carrying out the data analysis of a near infrared spectrum image and a plurality of preservation parameters of winter jujubes through employing an artificial intelligence technology, respectively capturing the quality characteristics of the winter jujubes and the time domain correlation mode of the multi-mode preservation parameters, and carrying out the analysis of the quality characteristics of the winter jujubes; according to the method, the fresh-keeping parameters of the winter jujubes and the fresh-keeping parameters of the winter jujubes are analyzed through fine-grained interactive response, so that the internal relation between the quality of the winter jujubes and the fresh-keeping parameters is excavated, the fresh-keeping duration of the winter jujubes is intelligently predicted, and when the predicted fresh-keeping duration is lower than the expectation, a fresh-keeping quality early-warning prompt is given out. In this way, accurate control over the winter jujube fresh-keeping process can be achieved, the fresh-keeping efficiency is effectively improved, unnecessary waste is reduced, and the labor cost is reduced.

A terminal device obtains M channel estimation results. The terminal device jointly compresses the M channel estimation results by using a first encoder network, to obtain N pieces of compressed information and first state information, where the first state information represents downlink channel information, the downlink channel information includes information about downlink channels corresponding to the M channel estimation results, and the first state information is used to determine next input information of the first encoder network. The terminal device sends the N pieces of compressed information to an access network device. Downlink channels are correlated in time domain. When compressed information is obtained, in addition to a current downlink channel, a historical downlink channel is also referenced.

A method for determining a tightening class of a tightening operation performed by a tightening tool is provided. The method comprises acquiring time-domain representations of sets of observed torque and angle values for fasteners having been tightened by the tightening tool, acquiring frequency-domain representations of the observed sets of torque values, associating a tightening class with each acquired time-domain and frequency-domain representation identifying a tightening operation, training a machine-learning model with the acquired time-domain representations and frequency-domain representations and the tightening class and supplying the trained machine-learning model with a further acquired time-domain representation frequency-domain representation for a fastener having been tightened by the tightening tool, wherein the trained machine-learning model outputs an estimated tightening class for the supplied set of observed torque and angle values.

A communication method and a related device. In the method, when a network device determines third channel information of a higher dimension by means of a first neural network, input data of the first neural network may comprise first channel information of a lower dimension and Q pieces of second channel information, and the position of a time-domain resource carrying Q second reference signals is before the position of a time-domain resource carrying a first reference signal. Therefore, the first neural network can use a time-domain correlation between the Q pieces of second channel information and the first channel information, and can also use a space-domain correlation and / or a frequency-domain correlation between the first channel information and the third channel information, thereby improving the accuracy of obtaining by the neural network the channel information of the higher dimension on the basis of the channel information of the lower dimension.

The invention discloses a dynamic three-dimensional scene representation method based on Gaussian splashing, and belongs to the field of machine vision. A first time feature constructed through a first neural network based on the time index is fused with the Gaussian feature of the original Gaussian point cloud, and then decoding is performed to obtain various parameters of the original Gaussian point cloud; and fusing a second time feature obtained through second neural network coding based on the time index with the feature obtained by the micro renderer, so that time domain information is introduced in the image reconstruction process to assist in constructing the incidence relation of adjacent video frames, and the time domain expression capability of the model is further enhanced. According to the method, the convolutional neural network is combined with Gaussian splash, the modeling capability of a model for a dynamic scene can be effectively enhanced by fusing time domain features, and efficient expression of a dynamic three-dimensional scene is realized; time domain redundancy is eliminated by modeling a time domain incidence relation, and efficient expression of a dynamic scene is achieved.

The invention relates to the field of intelligent detection, and particularly discloses an intelligent detection system for a safe electrical device, which adopts a neural network model based on deep learning to process real-time operation state parameters of monitored electrical equipment and various state parameters marked as fault states. According to the method, the time domain correlation characteristics of the equipment state multi-modal parameters of the electrical equipment in the real-time operation state and various fault states are extracted respectively, and the real-time state parameter characteristics and the fault state parameter characteristics of the electrical equipment are further inquired, matched and analyzed, so that whether the state of the monitored electrical equipment is abnormal or not is intelligently identified.

The invention discloses a transformer monitoring method and system based on a multi-dimensional signal, and relates to the technical field of transformer monitoring, and the method comprises the steps: obtaining the multi-dimensional signal and working condition parameters of a transformer, carrying out the time domain feature extraction of the multi-dimensional signal, obtaining a corresponding time domain correlation feature, carrying out the frequency domain feature extraction of the multi-dimensional signal, obtaining a corresponding frequency domain correlation feature, and carrying out the time domain feature extraction of the multi-dimensional signal; fault analysis is carried out according to the working condition parameters and the frequency domain correlation characteristics, and a corresponding initial fault detection result and an electromagnetic coupling residual value are obtained; and if so, carrying out fuzzy evaluation on the time domain correlation feature, the frequency domain correlation feature, the electromagnetic coupling residual value and the multi-dimensional signal based on a pre-trained fault detection model to obtain a corresponding fault monitoring result. The technical problems that a traditional transformer monitoring method mainly depends on electrical signal analysis, but key parameters such as oil temperature are not included in a monitoring system, so that potential fault hidden dangers cannot be found in time, and the operation reliability of the transformer is reduced are solved.

The invention discloses a wind power blade structure health monitoring method based on multi-field decoupling optical fiber sensing, particularly relates to the technical field of wind power equipment state monitoring, and is used for solving the problem of health state misdiagnosis caused by coherent interference of environmental noise and damage signals. The method comprises the following steps: collecting vibration, strain and temperature signals and generating a background noise vector; calculating a frequency domain coherence function of the vibration signal and the noise vector to identify an interference danger frequency band; analyzing the polarization angle distribution of the vibration signal in the frequency band, and inverting the physical path topology of the noise transmitted into the blade; when the path passes through a predefined weak area, improving a damage weight coefficient of a strain signal of a corresponding frequency band and carrying out Hilbert phase shift reconstruction to generate an anti-interference strain feature vector; calculating the energy dissipation rate of the vector in a dangerous frequency band, and judging an energy leakage event caused by damage by combining a viscoelastic constitutive threshold value of the composite material; the damage type and the spatial position are judged according to the time domain correlation between the energy leakage event and the temperature signal, and the hidden damage detection reliability is improved.

The present invention provides a variational mode decomposition optimization method based on a time-frequency correlation coefficient joint factor, comprising: S1, initializing the K value, setting K=2; S2, performing VMD decomposition on the signal to obtain K IMF components; S3, calculating the time domain correlation coefficient ρ between each IMF component and the original signal K,i and frequency domain correlation coefficient #imgabs0#Calculate the time-frequency correlation coefficient joint factor δ K,i ; S4, let K = K + 1, perform VMD decomposition again, and obtain K + 1 IMF components; S5, calculate the time domain correlation coefficient ρ between each IMF component and the original signal K+1,j and frequency domain correlation coefficient #imgabs1#Calculate the time-frequency correlation coefficient joint factor δ K+1,j ; S6, calculate the time-frequency correlation coefficient joint factor difference β between each IMF component when K takes different values ​​and the same decomposition number K,r ; S7, when β K,r ≤θ(θ=0.05), it is considered as an effective factor, otherwise it is considered as an invalid factor; S8, when K=m, the number of continuous effective factors reaches the maximum value, that is, when K=m+1, the number of continuous effective factors is less than K=m, it is considered that the decomposition is optimal, and K=m is finally selected as the optimal value. The present invention is simple to operate, the correlation feature extraction is comprehensive and reliable, and is less affected by the factors of the signal itself.

The invention discloses a fault identification and network efficiency improvement method for an intelligent micro-grid, particularly relates to the technical field of cross-domain cooperative avoidance, and is used for solving a resonance frequency band problem caused by coupling of an existing SDN flow table updating strategy and micro-grid power fluctuation. The method comprises the following steps: acquiring micro-grid power fluctuation data and SDN flow table updating parameters in real time, and performing timestamp calibration; extracting a power fluctuation frequency domain characteristic and a flow table updating time domain correlation coefficient; fusing frequency domain integration and time domain convolution through a cross-domain coupling model to generate a dynamic resonance risk index; predicting a future fluctuation main frequency band based on the phase change and generating a constraint condition in combination with a historical avoidance frequency band; dynamically generating a frequency avoidance weight according to the real-time network load and the frequency band effectiveness; triggering the period adjustment of the asymmetric flow table to realize the frequency domain avoidance of the update frequency and the fluctuation main frequency band; resonance risk identification and active avoidance of power and communication cross-domain data fusion are realized, and the transient stability and the network resource utilization rate of the micro-grid are remarkably improved.

The invention belongs to the technical field of human body electroencephalogram signal processing and brain regulation and control, and provides a multi-modal coupling type closed-loop electrical stimulation sleep synergy signal regulation and control device and system.The technical scheme includes that multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals are obtained; screening and preprocessing the obtained multi-modal physiological signal to obtain a preprocessed multi-modal signal; calculating the time domain correlation between the EEG slow wave power and the cerebral blood flow and the frequency domain coherence between the cerebral blood flow and the cerebrospinal fluid flow signal; introducing regulatory factors of time domain correlation of EEG slow wave power and cerebral blood flow and frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; and optimizing the signal stimulation intensity by combining a signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to obtain the optimized signal stimulation intensity. And an accurate sleep regulation and control signal is generated.

The invention discloses a power distribution network fault line selection method and system based on modal decomposition and adaptive noise. The method comprises the steps that whether a power system breaks down or not is judged according to zero-sequence voltage; if a fault occurs, a transient zero-sequence current signal containing noise on the feeder line is obtained; selecting a main frequency mode component from the intrinsic mode components according to energy, frequency spectrum and time domain correlation of the intrinsic mode components; calculating the difference between the energy of the main frequency modal component of each feeder and the energy of the sum of the main frequency modal components of other feeders, and the sum of the correlation coefficients of each feeder and other feeders; if the feeder line not only meets the minimum energy difference value, but also meets the condition that the sum of the correlation coefficients is less than zero, the feeder line is a fault feeder line. According to the method, the fault feeder is screened through the energy and the correlation coefficient, and the accuracy of power distribution network fault line selection judgment is improved.

The invention provides a measurement reporting method and device, a terminal and network equipment. The method comprises the following steps: a terminal determines a report quantity according to a plurality of pieces of measurement resource related information; reporting the report quantity to a network device, or reporting the report quantity and indication information of multiple pieces of measurement resource related information corresponding to the report quantity to the network device; the report quantity comprises at least one of the following items: time domain related parameters; frequency domain related parameters; and phase-related parameters. The terminal determines the report quantity according to the multiple pieces of measurement resource related information, and reports the report quantity and / or the indication information of the multiple pieces of measurement resource related information corresponding to the report quantity to the network equipment, so that the network equipment can compensate signals based on the reported information, parameter differences between the TRPs are eliminated, the signals between the multiple TRPs received by the terminal are synchronized, and the user experience is improved. And the transmission performance of the CJT is ensured.

The invention discloses a distributed photovoltaic power prediction method and device based on dynamic spatial correlation, and relates to the field of distributed photovoltaic technology, and the method can reflect the real influence relation between photovoltaic power stations at different moments in real time through constructing dynamic graph structure data which can reflect the spatial relation between the photovoltaic power stations more comprehensively and accurately. And the prediction result of the distributed photovoltaic power is improved. The method comprises the following steps: acquiring an influence factor feature set of distributed photovoltaic power prediction; by taking the distributed photovoltaic power station as a node, constructing dynamic graph structure data of the photovoltaic power station according to the influence factor feature set and the spatial weight matrix fused with the meteorological condition information; performing spatial correlation extraction on the dynamic graph structure data by using a pre-trained first network to obtain spatial correlation characteristics of the distributed photovoltaic power station; and performing time domain correlation extraction on the spatial correlation characteristics by using a pre-trained second network to obtain a point prediction result of the distributed photovoltaic power.

Methods and apparatuses for reporting of time-domain channel correlation properties are provided. A method includes receiving a configuration about a CSI report. The configuration includes information about (i) M non-zero power (NZP) CSI reference signal (RS) resources for tracking, (ii) a number (K) of time-domain correlation values, and (iii) delay values τk for k=0, 1, . . . , K−1. The method further includes, based on the configuration: measuring the M NZP CSI-RS resources for tracking, determining, based on the measurement, the K time-domain correlation values, wherein a k-th correlation value is between two CSI-RS transmission occasions that are separated by one of the delay values τk, and quantizing a value for an amplitude of each of the K time-domain correlation values. The method further includes transmitting the CSI report including an indicator, for each of the K time-domain correlation values, indicating the quantized value of the amplitude.

Various aspects of the present disclosure relate to training dataset updates. A training dataset is partitioned into multiple dataset groups and each dataset group includes one or more training datapoints. Each dataset group is associated with a first label and a second label. The first label corresponds to a temporal or time-domain related parameter, such as a time stamp or a time duration. The second label is at least one of a weight or a value associated with a characteristic of the dataset. The training dataset is updated based on at least one of the first label or the second label, such as by updating a subset of values of the second label, removing a dataset group, or adding a new dataset group to the training dataset. Updated information corresponding to the updated training dataset can then be sent from one device to another.

The invention provides a hydraulic turbine set valve fault diagnosis method, system, equipment and product, and is applied to the field of hydroelectric equipment fault diagnosis. On one side of the edge end, an initial voiceprint signal, an initial vibration signal and temperature data of a hydraulic turbine set valve are collected; taking the initial voiceprint signal as a reference signal, and performing noise reduction processing on the initial vibration signal based on the time domain correlation between the fluid noise in the initial voiceprint signal and the first noise in the initial vibration signal; by taking the vibration noise reduction signal as a reference signal, performing noise reduction processing on the initial voiceprint signal based on the frequency domain correlation between the mechanical resonance noise in the vibration noise reduction signal and the second noise in the initial voiceprint signal; and based on the vibration noise reduction signal, the voiceprint noise reduction signal and the temperature data, performing fault diagnosis on the hydraulic turbine set valve to obtain a fault diagnosis result. Therefore, fault diagnosis is carried out through cooperative noise reduction of the voiceprint signal and the vibration signal in combination with multi-dimensional data, and the fault diagnosis accuracy of the hydraulic turbine set valve is improved.

The invention discloses a port mapping method and device of sounding reference signal (SRS) resources and a terminal, and belongs to the technical field of communication, the port mapping method of the SRS resources in the embodiment of the invention comprises the steps that the terminal sends the SRS resources, and port mapping of the SRS resources is realized based on target parameters; wherein the target parameter comprises at least one of a first parameter and a second parameter, the first parameter comprises a Doppler domain related parameter or an orthogonal cover code (OCC) sequence related parameter, and the second parameter comprises a time domain related parameter.

A communication method and related devices, in which a first communication device may transmit the second channel information, enabling a second communication device to communicate based on the second channel information. Wherein the second channel information is determined according to the first channel information, the time information of the first channel information and the characteristic information of the historical channel information, namely, the second channel information can reflect the time domain correlation corresponding to the time information of the first channel information and can reflect the time domain correlation corresponding to the time information of the historical channel information. And one or more of time domain correlation, space domain correlation and frequency domain correlation corresponding to the characteristic information of the historical channel information can be reflected. Therefore, the accuracy of the second channel information can be improved by utilizing various correlations, so that the performance of data transmission based on the second channel information is improved. In addition, the scheme can be applied to a channel information compression feedback scene, and the overhead of channel information feedback can be reduced.

The present application relates to the technical field of communications. Disclosed are a port mapping method and apparatus for a sounding reference signal (SRS) resource, and a terminal. The port mapping method for an SRS resource in the embodiments of the present application comprises: a terminal sending an SRS resource, port mapping of the SRS resource being realized on the basis of a target parameter, wherein the target parameter comprises at least one of a first parameter and a second parameter, the first parameter comprises a parameter related to a Doppler domain or a parameter related to an orthogonal cover code (OCC) sequence, and the second parameter comprises a parameter related to a time domain.

The invention discloses a new energy sending-out line single-terminal grounding protection method and system, and the method comprises the steps: obtaining a voltage instantaneous value and a current instantaneous value at a protection installation part, and the line length of a setting point, calculating the differential of the voltage and zero-sequence current of the setting point, forming a new energy sending-out line single-terminal grounding protection criterion through the calculation of a correlation coefficient, and obtaining a new energy sending-out line single-terminal grounding protection criterion. The judgment module judges whether a fault point is in the area; the new energy sending-out line single-ended grounding protection method and system using the sampling values to form time domain correlation discrimination are not affected by short-circuit current amplitude limitation, non-power-frequency frequency and phase angle control after rapid adjustment of power electronic equipment, and can effectively solve the problem of transient overrun after the new energy sending-out line is grounded through a transition resistor.

The embodiment of the invention discloses a power grid line state monitoring method and device based on vibration acoustics. The method comprises the following steps: acquiring a power grid power transmission line vibration signal and a sound wave signal; according to the topological structure of the power transmission line, the spatial distribution of adjacent lines and the measuring point positions of different sensors in the same line, constructing a signal propagation model, and calculating a multi-dimensional correlation index between the vibration signal and the sound wave signal, the correlation index including a time domain correlation coefficient, a frequency domain power spectral density and a phase difference feature; simulating signal propagation characteristics in a normal operation mode by using the signal propagation model, generating a reference correlation index, and identifying whether the power transmission line has an abnormal state; and carrying out accurate positioning on the abnormal state by adopting a spatial positioning algorithm, and carrying out multi-level evaluation according to the abnormal confidence coefficient and the influence range to generate a line abnormal report. According to the invention, real-time monitoring and accurate positioning of the abnormal state of the power transmission line are realized.

The present disclosure relates to the technical field of wireless communications. In particular, an information transmission method and apparatus, and an electronic device and a computer program product are provided. The method comprises: on the basis of a first system message, determining at least one first occasion, or on the basis of the first system message and a second system message, determining at least one first occasion, wherein the first system message is related to a beam; or on the basis of at least one of a third system message, a time-domain-related parameter of the third system message, and a time-domain-related parameter of a synchronization signal block (SSB), determining at least one first occasion; and performing information transmission at the at least one first occasion. In the present disclosure, a terminal device determines an information transmission occasion, and thus energy consumption waste caused by transmission failures and repeated transmissions of the terminal device is reduced.

The invention discloses a CAN bus abnormal flow detection method based on multi-scale and frequency domain characteristics, and relates to the technical field of Internet of Vehicles security, and the method comprises the following steps: carrying out the preprocessing of the original message data of a CAN bus, dividing a continuous message sequence according to a preset time window, and constructing a time sequence sample; inputting the time sequence sample into a feature extraction and fusion model, and sequentially executing multi-scale time sequence feature extraction, time sequence correlation modeling and frequency domain feature enhancement processing to obtain feature representation fusing time domain correlation information and frequency domain structure information; and constructing a classification model based on the feature representation, and carrying out anomaly detection and classification on the CAN bus flow. According to the CAN bus abnormal flow detection method based on the multi-scale and frequency domain characteristics, effective fusion of time domain correlation enhancement and frequency domain noise suppression is realized, the detection robustness and accuracy in a complex vehicle-mounted noise environment are improved, and the false alarm rate in the complex vehicle-mounted noise environment is reduced.

The invention belongs to the field of helicopter strength design, and relates to a time domain correlation atlas analysis method for bending moments of different cross sections of a helicopter rotor shaft. The method comprises the following steps: extracting actual measurement data of rotor shaft bending moment measurement strain gauges on all cross sections under a specific working condition, and constructing a bending moment data matrix Mm of each cross section according to geometric arrangement of the rotor shaft bending moment measurement strain gauges; wherein each cross section has actually measured data with two orthogonal channels; forming a measuring point horizontal coordinate and a measuring point vertical coordinate of a Cartesian coordinate system by the data at the same moment in the two groups of measured data of the channels in the same direction of the different cross sections, or forming the measuring point horizontal coordinate and the measuring point vertical coordinate of the Cartesian coordinate system by the data at the same moment in the two groups of measured data of the channels in the different directions of the different cross sections; drawing positions of all measuring points on the time domain to obtain a time domain correlation graph; obtaining a helicopter rotor shaft bending moment correct measurement data time domain correlation graph normal form with the same working condition and the same number of rotor support arms; and comparing the time domain correlation graph with the time domain correlation graph normal form.