37 results about "Signal statistics" patented technology

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

Technology is disclosed for an optical receiver. The optical receiver may include an optical and digital signal processing (ODSP) device. The ODSP device may include a photodiode operable to convert an optical signal to an electrical signal; a digital signal processor operable to generate a digital signal based on the electrical signal; a quantization component operable to generate a quantized output signal based on the digital signal; and a processing device. The processing device may be operable to measure one or more signal statistics of the digital signal; and identify one or more quantization thresholds, wherein the one or more quantization thresholds are computed based on the one or more signal statistics.

The present application relates to the technical field of energy storage battery, in particular to a forward-looking energy storage frequency modulation power distribution method and system; the method has a double-time scale structure of minute-level strategy adjustment + second-level real-time control. On the minute-level time scale, an AGC signal statistical feature extraction and prediction framework is constructed to predict the AGC signal characteristics and dynamically adjust and optimize the weight; on the second-level time scale, multi-objective optimization is performed every 2 seconds to distribute the charging and discharging power among the battery units, minimizing the AGC tracking error, battery degradation and SOC deviation. The scheme can significantly reduce the battery degradation cost while maintaining good frequency modulation performance and SOC consistency; by capturing the time statistical characteristics of the AGC signal, the potential impact of the AGC signal on the battery degradation is quantified, that is, the influence of the change trend of the AGC signal on the battery energy storage system is considered, providing forward-looking support for the control strategy of power distribution.

The embodiment of the present disclosure provides a loudspeaker audio classification method under a plurality of heterogeneous devices, and belongs to the technical field of data identification, and specifically comprises the following steps: step 1, calling a built-in acceleration sensor of a mobile phone to collect an initial vibration signal played by a same on-board loudspeaker; step 2, performing a pretreatment operation on the initial vibration signal; step 3, removing interference signals in the pretreated initial vibration signal to obtain a target vibration signal; step 4, screening signal statistical features highly related to audio content and independent of devices; and step 5, classifying the audio content of the target vibration signal by using an adversarial neural network according to the signal statistical features. Through the scheme of the present disclosure, the efficiency and accuracy of speech recognition are improved.

The application discloses a valve tester verification method and system of reverse resistance type IGCT driving function, and relates to the technical field of direct current transmission.The valve control host detects the driving up-sent IGCT forward voltage signal, and the delay input / exit test mode is put into operation.The valve tester is divided into multiple rounds of statistical stages and trigger stages, and the forward voltage signal statistics are realized, so that multiple test function verifications are realized.The valve tester verification method and system of reverse resistance type IGCT driving function are aimed at the characteristics that the IGCT driving energy taking time is long and cannot be automatically turned off, realize the effect that the valve tester identifies test items and matches control time sequences under the condition that the valve tester does not directly communicate with the valve control host or the IGCT driving, and satisfies the test requirements of comprehensive testing of the reverse resistance type IGCT driving performance.

Techniques for an optical receiver are disclosed. The optical receiver may include an optical digital signal processing (ODSP) device. The ODSP device may include: a photodiode operable to convert an optical signal into an electrical signal; a digital signal processor operable to generate a digital signal based on the electrical signal; a quantization component operable to generate a quantized output signal based on the digital signal; and a processing device. The processing device is operable to: measure one or more signal statistics of the digital signal; and identifying one or more quantization thresholds, wherein the one or more quantization thresholds are calculated based on the one or more signal statistics.

The present application belongs to the field of aviation manufacturing and intelligent processing technology, and discloses a laminated interface online identification method based on an adaptive statistical discrimination mechanism. The method preprocesses the machining state signal, judges whether to enter the stable drilling machining stage based on the change characteristics of the machining state signal in the sliding time window, and calculates the dynamic mean value of the machining state signal in the stable drilling machining stage. The bilateral cumulative sum statistics are constructed based on the dynamic mean value, and the interface is judged in combination with the first and second judgment thresholds which are adaptively adjusted based on the statistical characteristics of the historical signal. When the preset interface judgment condition is met, it is judged that the machining state at the current drilling position has a sudden change, and the interface position is identified. The method does not need to establish a complex signal model and parameter calibration, can adaptively compensate the slow trend in the machining process, and is suitable for interface online identification under complex working conditions of multi-material laminated structure.

The invention provides a post-injury rehabilitation tracking system and method based on high-density surface electromyographic signals, and belongs to the technical field of sports medicine and rehabilitation engineering, and the system comprises a synchronous collection module which is used for synchronously collecting the high-density electromyographic signals and joint torque signals; the signal preprocessing module is used for preprocessing the electromyographic signals; the neural decoding module is used for constructing a space-time expansion vector, updating signal statistical characteristics and separating to obtain a discharge sequence of the motion unit; the function evaluation module is used for calculating and optimizing a nerve density index; the rehabilitation decision output module is used for calculating indexes of the indexes and generating personalized training suggestions according to whether the indexes reach the standard or not; and the parameter adjusting module is used for receiving the personalized training suggestions and continuously optimizing the suggestions based on the execution feedback information. And the efficiency, the accuracy and the individuation degree of clinical rehabilitation evaluation and training control are remarkably improved.

The application relates to the technical field of visual detection, and discloses a drowning prevention early warning method and system based on visual detection, which comprises the following steps: acquiring a water surface video and generating an optical flow distribution map; separating a foreground motion region based on optical flow statistical characteristics; extracting reflection noise features for denoising treatment to obtain pure motion data flow; performing time-frequency and extreme value analysis on the pure motion data flow to determine preliminary signs of struggling behavior; performing displacement analysis on multiple frames of images to obtain abnormal behavior tracking results; accumulatively calculating a comprehensive score of a drowning event, and generating an early warning signal when the score exceeds a threshold value; and counting the number of receiving terminals, and triggering an event processing procedure when the number of receiving terminals meets a threshold value. The method can effectively suppress water surface reflection interference and realize high-reliability drowning prevention early warning.

The present application relates to the technical field of physical testing, in particular to an ultrasonic flaw detection method, system and equipment for isostatic pressing graphite finished products, the method comprising: determining local fluctuation synchronization features of a target position based on ultrasonic time domain signals of the isostatic pressing graphite finished product at the target position and its spatiotemporal neighborhood positions; determining multi-scale scattering correlation features of the target position by using mutual information between adjacent scale component signal features of the ultrasonic time domain signals; determining scattering events based on local signal-to-noise ratios of the ultrasonic time domain signals, determining event state transition probability matrices based on scattering event features, and obtaining sequence signal statistical evolution features of the target position; determining a comprehensive structure continuity parameter of the target position by combining the above multiple features of the target position, and obtaining a flaw detection result by adjusting separation constraint criteria of ICA based on the comprehensive structure continuity parameter. Through the technical scheme of the present application, substantial improvement in accuracy and reliability of graphite material defect detection is achieved.

This invention discloses a method and system for predicting industrial equipment faults based on deep learning, belonging to the field of fault diagnosis and prediction technology. The method includes: constructing an equipment topology graph model, abstracting sensor data channels as graph nodes, and constructing an adjacency matrix based on physical mechanical connections and signal statistical correlations to characterize stress transmission and vibration coupling paths; extracting spatial feature vectors through a multi-layer graph convolutional neural network in a parallel architecture, and simultaneously extracting temporal feature vectors through a multi-head self-attention mechanism; concatenating the spatiotemporal features and extracting equipment health feature vectors through a nonlinear mapping module containing parameterized linear rectifier units; outputting the posterior probability of the fault and triggering an early warning. The system includes a topology graph model construction module, a parallel feature extraction module, a feature fusion and mapping module, and a fault prediction output module. This invention can achieve highly sensitive identification and early warning of early faults in industrial equipment.

The invention discloses a partial discharge identification method and device based on multi-dimensional waveform feature extraction, and belongs to the technical field of state monitoring and intelligent diagnosis of high-voltage electrical equipment, and the method comprises the steps: obtaining the voltage and current waveform data of a transformer under the impact voltage, and carrying out the preprocessing of the voltage and current waveform data, voltage and current waveform preprocessing data are obtained; original signal statistical characteristics, partial discharge event characteristics and voltage structure characteristics are extracted; standardized features are obtained through standardized processing; constructing a neural network model based on the standardized features; training the neural network model by using a labeled sample set to obtain a trained neural network model; and inputting a signal acquired in real time into the trained neural network module for calculation so as to realize identification of the partial discharge signal. According to the invention, the features are trained and classified by using the multilayer perceptron neural network, and automatic identification of different discharge stages is realized.

The invention discloses a flow feature confusion method based on an AI virtual human, and the method comprises the steps: S1, configuring the basic attributes of the virtual human and a preset behavior strategy, and automatically obtaining the fingerprints of Internet access equipment of a user; s2, virtual human environment fingerprint camouflage is carried out by configuring a containerized operation environment and adjusting browser fingerprints; s3, dynamically generating behavior parameters for determining a virtual human online task based on LLM drive of preset parameters; and S4, the virtual human calls a local browser driver on line based on the behavior parameters obtained in the S3, simulates human to access the target site and generates feature traffic. According to the method, the virtual human Agent is driven by the AI, the mixed flow is generated by autonomously acting on the network, the user identification and tracking technology of the network access target based on the flow signal characteristics is effectively interfered, the signal statistics characteristics of the user internet surfing flow can be effectively mixed, and the method is suitable for the network privacy protection requirements in various scenes.

A FDSS parameter configuration method includes obtaining, by a User Equipment (UE), first information, and dynamically switching or configuring, by the UE, an FDSS-related parameter based on the first information. The FDSS-related parameter is a parameter used for performing FDSS-based uplink transmission. The first information includes at least one of the following: an association relationship between the FDSS-related parameter and a first parameter, the first parameter being a parameter used for performing uplink transmission; a first indication information, wherein the first indication information is used for indicating the FDSS-related parameter that needs to be dynamically switched or configured; or a signal measurement value or a signal statistics value.

The invention discloses a wireless air interface abnormal data processing system, and the system comprises a wireless network information collection and analysis module which is used for collecting wireless network communication data and carrying out the analysis of the data, and obtaining the analysis data; the abnormal event and behavior detection module is used for carrying out feature extraction according to the analysis data so as to detect abnormal events and behaviors; the wireless network attack identification module is used for identifying denial of service attack, replay attack and camouflage attack in the wireless network; and the abnormal behavior reasoning module is used for carrying out behavior intention feature modeling according to the collected behavior features so as to predict a target behavior intention. According to the wireless air interface abnormal data processing system, a feature extraction method based on information entropy and a detection method based on a time sequence diagram are adopted, signal statistics of all time periods is regarded as a random process, features in a wireless network can be rapidly and effectively extracted, and abnormal events and behaviors can be detected.

The invention provides a dose determination method and device based on a time sequence signal, equipment and a storage medium, and relates to the field of physics and computers. The method comprises the following steps: acquiring a plurality of sample CT images and determining dose distribution information and nuclide distribution information of each sample CT image; for each sample CT image, generating a PET accumulated signal image corresponding to each signal statistics window based on nuclide distribution information; training the first initial model by taking the sample CT image and the PET accumulated signal image corresponding to the sample CT image as a first training data set to obtain a first target model; training a second initial model by taking the dose distribution information, an output result of the first target model and part of training data in the first training data set as a second training set to obtain a second target model; and sequentially inputting the target CT image and the PET accumulated signal image of the target time window into the first target model and the second target model to obtain dose information output by the second target model.

The invention relates to the field of pipeline leakage detection, and discloses a digital leakage listening detection system based on stepless frequency and multi-band bandwidth adjustment, the system comprises a signal acquisition module, a user adjustment module, a main control module, a display module, an audio output module and a power supply module, and the system constructs a real-time simulation auditory sense and asynchronous digital vision dual-channel architecture; the core of the method is that a main control module calculates a leakage signal steady-state index by fusing an energy stability index representing signal energy time stability and a statistical randomness index representing signal statistical randomness in an asynchronous digital visual path; the index provides an objective and quantitative visual navigation basis for operators, and continuous leakage signals and transient and periodic interference can be effectively distinguished. According to the invention, auditory experience and objective data analysis of operators are considered, and the accuracy, reliability and efficiency of leakage detection are remarkably improved.

This application discloses a method, apparatus, medium, and product for estimating network packet loss rate, relating to the field of transmission and bearer technology. The method includes: acquiring signaling statistics data collected at a preset collection point in a first link, including the total number of request packets in a first direction and a second direction of the first link, and the number of retransmissions of request packets in the corresponding directions; wherein the first link includes a first end and a second end; calculating a first overall packet loss rate in the first direction and a second overall packet loss rate in the second direction of the first link based on the signaling statistics data; determining the segmented packet loss rate of segmented links of the first link based on the first overall packet loss rate and the second overall packet loss rate, wherein the segmented links include a first segmented link between the first end and the preset collection point; and a second segmented link between the second end and the preset collection point. The solution of this application solves the problem that existing packet loss rate assessment schemes cannot accurately and quantitatively estimate packet loss rates.

The invention relates to the technical field of energy storage batteries, in particular to a prospective energy storage frequency modulation power distribution method and system. The method has a dual-time scale structure of minute-level strategy adjustment and second-level real-time control. On a minute-level time scale, an AGC signal statistical feature extraction and prediction framework is constructed to predict AGC signal features and dynamically adjust and optimize weights; on a second-level time scale, multi-objective optimization is performed once every 2 seconds to allocate charge and discharge power among battery cells, minimizing AGC tracking error, battery degradation, and SOC deviation. According to the scheme, the battery degradation cost can be remarkably reduced, and meanwhile, good frequency modulation performance and SOC consistency are kept; by capturing the time statistical characteristic of the AGC signal, the potential influence of the AGC signal on battery degradation is quantified, that is, the influence of the change trend of the AGC signal on the battery energy storage system is considered, and forward-looking support is provided for the control strategy of power distribution.

A method and a system for generating a durable load spectrum of an electrically driven parking system belong to the field of parking system testing, and comprise the steps of extracting parking related signals, counting working condition frequencies of flat ground static parking, ramp parking, high-speed parking, low-speed parking and collision parking, and recording working condition parameters; according to the working condition parameters, the parking torque corresponding to each working condition is calculated in combination with real vehicle test data; the statistical result and the working condition parameters are integrated into a load spectrum table, the table comprises the environment temperature, the working condition type, the loading direction, the cycle index and the parking torque value, and a bench test is executed according to the preset temperature change sequence. According to the method, the P gear engaging times in different scenes are obtained through the real vehicle data, the force needing to be borne by the parking system in each scene is calculated in combination with the working condition parameters and the real vehicle test data, the statistical result and the calculation parameters are integrated into the table, the test times, the loading direction and the stress magnitude of each scene are determined, time delay is arranged according to the preset temperature sequence, and the accuracy of the test is improved. And verifying the reliability of the parking system.

The invention discloses a full pulse number acquisition method and device in a partial discharge signal, electronic equipment and a storage medium, and the method and device are applied to the electronic equipment and specifically used for solving a short-time conjugate product signal of the partial discharge signal. Establishing a smooth difference function sequence based on the short-time conjugate product signal; and solving a full pulse parameter calculation result of the partial discharge signal based on the smooth difference function sequence to obtain a full pulse number in the partial discharge signal. Conjugate correlation processing is adopted to carry out noise reduction and energy gain improvement on continuous signals; meanwhile, performing energy focusing at a starting moment and a cut-off moment on the partial discharge signal through differential operation; the protection unit is adopted for judgment, the influence of accidental interference on signal statistical characteristic collection is avoided, finally, system analysis is conducted through threshold setting and judgment of the protection unit, the full pulse number of the partial discharge signals is determined, and the adverse effect of accidental interference signals on full pulse number collection can be avoided.

This invention relates to the field of mechanical equipment condition monitoring technology, and discloses a vibration signal denoising method and system based on a self-supervised convolutional neural network. The method involves preprocessing the original vibration signal during the operation of the mechanical equipment to train a constructed one-dimensional convolutional-deconvolutional neural network model. Instead of relying on manually labeled clean samples, it utilizes signal statistical characteristics such as kurtosis, envelope spectrum sparsity, and root mean square amplitude to construct a self-supervised loss function. This allows the model to automatically learn to distinguish between impact components and noise, guiding the one-dimensional convolutional-deconvolutional neural network model to automatically separate impact components from noise. The convolutional-deconvolutional structure enables temporal feature extraction and signal reconstruction, thereby achieving signal denoising while maintaining consistency with the original amplitude and effectively enhancing impact characteristics.

A circuit includes a first digital pre-distortion (DPD) corrector and a second DPD corrector. The first DPD corrector has an input, and an output. The second DPD corrector has an input coupled to the input of the first DPD corrector, and an output. A signal combiner has a first input coupled to the output of the first DPD corrector, a second input coupled to the output of the second DPD corrector, and an output. The second DPD corrector is configured to provide a signal at the output of the second DPD corrector based on a signal at the input of the second DPD corrector and one or more signal statistics related to the signal at the input of the second DPD corrector.

The invention relates to the technical field of physical testing, in particular to an ultrasonic flaw detection method, system and equipment for an isostatic pressing graphite finished product, and the method comprises the following steps: based on ultrasonic time domain signals of the isostatic pressing graphite finished product at a target position and a space-time neighborhood position thereof, determining local fluctuation synchronism characteristics of the target position; determining a multi-scale scattering correlation feature of the target position by using mutual information between adjacent scale component signal features of the ultrasonic time domain signal; determining a scattering event based on a local signal-to-noise ratio of the ultrasonic time domain signal, determining an event state transition probability matrix based on scattering event characteristics, and obtaining a sequence signal statistical evolution characteristic of the target position; the comprehensive structure continuity parameters of the target position are determined by combining the multiple features of the target position, and the flaw detection result is obtained by adjusting the separation constraint criterion of the ICA based on the comprehensive structure continuity parameters. By means of the technical scheme, the accuracy and reliability of graphite material defect detection are substantially improved.

This invention relates to the field of data processing technology and discloses a multi-granularity respiratory signal statistical inference and sleep staging decision-making system. The system comprises a signal acquisition and buffering module that segments a continuous respiratory acoustic signal sequence into continuous analysis windows; a multi-dimensional feature extraction module that extracts multi-dimensional observation feature vectors containing respiratory rate variation features and cross-window respiratory trend features; a dynamic transition probability generation module that acquires global sleep onset time parameters and continuous dwell time parameters, performs numerical updates on the initial state transition probability matrix, and generates a dynamic state transition probability matrix; and inputs the multi-dimensional observation feature vectors and the dynamic state transition probability matrix into a Markov model to calculate the posterior probability and output the classification result. This invention overcomes the aftereffect defect of static Markov models by reconstructing the state transition matrix through spatiotemporal dual variables, achieving accurate anti-disturbance and state locking against transient noise disturbances.

The invention discloses a periodic error correction method based on particle filtering and polynomial fitting. The periodic error correction method is used for correcting periodic errors. Kalman filtering takes the known first-order and second-order statistical properties of the signal as the premise and the minimum mean square error as the criterion, can automatically track the non-stationary change of the statistical properties of the signal, and is an algorithm with a recursive property. In the method, a Kalman filter firstly linearizes a model on each new value and enters a prediction step, then estimates a current state by using a previous state and an error covariance, then adjusts the estimated state by using current observation information, perfects the estimated state by measuring a residual error and establishes a feedback loop, and enables an elliptic coefficient to converge through recursive iteration, so as to obtain a prediction result. Therefore, the ellipse is optimally estimated and the periodic error is corrected.

The application provides a data fusion processing method for frequency use behavior description based on adaptive reachable density, signal frequency parameters, signal statistical spectrum, signal communication parameters and signal direction degree are calculated through intermediate frequency spectrum, IQ and measurement data in spectrum monitoring; parameter measurement results of single sensing position and single measurement are formed by reorganizing signal frequency parameters, signal statistical spectrum, signal communication parameters and signal direction degree based on sensing position, collection time and collection frequency; signal parameters of single sensing position and single measurement are extracted from the parameter measurement results, signal parameters are classified and normalized through adaptive reachable density clustering, and normalized results of signal parameters of single sensing position and at least twice measurement are formed; signal parameter fusion is performed on each electromagnetic target based on the normalized results of signal parameters of single sensing position and at least twice measurement and corresponding electromagnetic targets. The problem that monitoring results of multiple measurements and multiple sensing positions cannot be fused is solved.

The invention discloses an audio reverberation space clue parameter estimation method and device and a storage medium, and relates to the technical field of audio processing. The method comprises the following steps: acquiring a target wet sound signal, and carrying out dry and wet sound separation on the target wet sound signal to obtain a dry sound signal and a pure reverberation signal; obtaining parameter estimation rules corresponding to a plurality of target audio space cue parameters used for representing reverberation space characteristics; the parameter estimation rule is pre-constructed based on the correlation between the signal statistical characteristic of the audio signal and the audio space clue parameter; and according to the parameter estimation rule, analyzing the target wet sound signal, the dry sound signal and the pure reverberation signal to obtain a target audio space clue parameter of the target wet sound signal. Automatic audio reverberation space clue parameter estimation can be realized, the audio space clue parameter estimation efficiency and accuracy are improved, and the method is suitable for linear and nonlinear reverberation effects.

The invention discloses a seismic magnetic field error correction method and system based on magnetic feature constraint, and relates to the field of geophysics, and the method comprises the steps: collecting the original three-component magnetic field time sequence data of a target region and all geomagnetic stations around, the synchronous data of a stable reference station, and the theoretical value of a background field, and carrying out the standardization preprocessing, obtaining standardized three-component magnetic field time sequence data; calculating a regional dynamic stress field by using crustal deformation and seismic activity data, identifying a stress attention area and a target time period from the regional dynamic stress field according to a stress change rate threshold, and obtaining a target station list located in the stress attention area; and extracting a training sample set from the standardized three-component magnetic field time sequence data based on the target station list and the target time period. According to the method, the anomaly detection threshold is obtained based on the historical calm period signal statistics, anomaly recognition and visual output are performed on the corrected signal, and the accuracy and reliability of seismic magnetic signal extraction are effectively improved.

Technology is disclosed for an optical receiver. The optical receiver may include an optical and digital signal processing (ODSP) device. The ODSP device may include a photodiode operable to convert an optical signal to an electrical signal; a digital signal processor operable to generate a digital signal based on the electrical signal; a quantization component operable to generate a quantized output signal based on the digital signal; and a processing device. The processing device may be operable to measure one or more signal statistics of the digital signal; and identify one or more quantization thresholds, wherein the one or more quantization thresholds are computed based on the one or more signal statistics.