243 results about "Time domain waveforms" patented technology

The invention discloses a resting electroencephalogram quality evaluation method and system based on double-branch comparative learning, and the method comprises the steps: collecting an original EEG signal X, carrying out the data preprocessing and data enhancement, generating two different enhanced views, transmitting the two different enhanced views to a double-branch encoder in parallel, respectively extracting a time domain waveform and a time-frequency domain rhythm feature, and carrying out the deep fusion, the fused features X1 and X2 are sent to a projection head, and through a self-supervised contrast learning mechanism, the network weight is optimized by using contrast loss; a small amount of labeled fine-tuning data sets including X and corresponding labels y are adopted, after flowing through a pre-trained double-branch encoder, the fine-tuning data sets are directly sent to a classification head connected with the back of the double-branch encoder so as to output a prediction result of an input data segment, and a mixed loss function including classification loss and comparison loss is adopted for training in the optimization process. According to the invention, a complete online real-time quality control system is established, and end-to-end real-time closed loop from data acquisition to quality evaluation is realized.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention, which belongs to the technical field of electromagnetic signal monitoring, relates to a rapid judgment method for a spatial spill electromagnetic signal, and the method specifically comprises the following steps: S1, receiving a spill electromagnetic signal in a space; s2, dividing the monitoring total frequency band into a plurality of sub-frequency bands with the same width, rapidly analyzing the obtained electromagnetic signal frequency spectrum in real time based on multi-scale spectrum entropy analysis, and positioning the frequency band where the characteristic signal is located; and S3, converting the frequency spectrum of the frequency band where the characteristic signal is located into a time domain signal, and simultaneously displaying a spectrogram and a time domain waveform. Through spectrum segmentation, multi-scale spectrum entropy analysis, adaptive threshold judgment and frequency domain-time domain combined display, the problems of poor real-time performance, weak anti-noise capability and single dimension in the prior art are solved.

The invention discloses an electric arc detection method based on differential increase and structured attention, and the method comprises the steps: firstly carrying out the collection and preprocessing of a current signal, carrying out the differential enhancement according to a sample type, carrying out the strong enhancement of an electric arc sample, improving the generalization capability, and carrying out the weak enhancement of a normal sample, thereby avoiding the overfitting; the problem of class imbalance is relieved, and the model generalization ability is improved; secondly, obtaining six complementary feature representations of time domain waveform, frequency domain frequency spectrum, time frequency analysis, envelope features, statistical distribution and related features from the differentially enhanced current signal through a multi-modal feature extraction method, and fusing to generate a multi-modal image; then, designing a deep learning model integrated with structured attention, carrying out distinguished attention on different feature analysis areas of the multi-modal image, and directionally enhancing arc features; and finally, dynamically quantifying the trained model, reducing the size of the model and reasoning delay, and supporting efficient deployment of various edge computing devices.

The invention discloses a cutter state real-time monitoring and compensating method, system and equipment and a medium, and relates to numerical control precision machining. In the method, a cutting edge optical image, an acoustic emission signal time domain waveform, a vibration acceleration parameter and real-time temperature data of a cutting area of a cutter in the machining process are collected; measuring the maximum width and depth of a wear belt through an image to form a wear characteristic quantized value, and judging a wear over-limit state; judging the damage state based on the continuous ultralimit time of the instantaneous mutation rate of the acoustic emission signal; judging the abnormal vibration grade and state through vibration spectrum analysis; and inputting the vibration spectrum and the temperature data into a prediction model to obtain a position offset prediction quantity. And calculating a size deviation value according to a wear / damage state, calculating a coordinate offset according to vibration abnormity, generating a cutter geometric compensation instruction or a motion trail compensation instruction, and completing real-time compensation operation. According to the scheme, the fine abnormal change of the cutter in the machining process can be captured in time, and compensation is conducted according to the fine abnormal change.

The invention provides a voice reconstruction method and system based on entropy coding residual quantization and frequency spectrum restoration, and relates to the technical field of artificial intelligence voice signal processing, and the method comprises the steps: obtaining an original voice waveform, inputting the voice waveform into a neural voice coding and decoding model, firstly entering a coder to map the input voice waveform into acoustic potential representation, and then entering a frequency spectrum restoration model; performing residual quantization on the acoustic potential characterization layer by layer through a residual vector quantization module, introducing a gating-based dynamic layer number selection mechanism and entropy regularization constraint, enabling bits to be adaptively distributed among different voice segments, reconstructing reconstructed acoustic features of the potential characterization, inputting the reconstructed acoustic features into a decoder, restoring the reconstructed acoustic features into a time domain waveform, and outputting the time domain waveform. And mapping to a logarithmic magnitude spectrum domain through a spectrum repairing module, predicting a residual error in the logarithmic magnitude spectrum domain and performing confidence gating fusion to obtain a complex spectrum, and outputting after time domain synthesis to obtain reconstructed speech. According to the invention, high fidelity, intelligibility and transmission reliability of the voice can be considered at an extremely low bit rate.

The invention discloses an underwater acoustic target classification and identification method based on multi-domain feature deep fusion and multi-platform sonar data learning. The method comprises the following steps: firstly, preprocessing target tracking beam data collected by a multi-platform sonar, and constructing a time domain waveform sample set and a time-frequency spectrogram sample set; secondly, constructing an underwater acoustic target classification and recognition network model which comprises a primary feature extraction parallel double-branch network module and a feature deep fusion and classification recognition module, the primary feature extraction parallel double-branch network module extracts time domain and time frequency features respectively, and the feature deep fusion and classification recognition module realizes feature deep fusion and classification through a bidirectional cross attention mechanism; training the model by adopting a multi-stage training strategy, wherein the training comprises pre-training, special training, end-to-end training and fine tuning and incremental training; finally, unknown target data is preprocessed and then input into the trained model for forward reasoning, and a classification recognition result is output according to the majority voting principle. According to the method, target multi-domain information can be fully mined, multi-source data are effectively utilized, and the model generalization ability and the recognition precision are improved.

The invention discloses an interference suppression measurement method for a grounding resistor, and relates to the technical field of interference resistance, and the method comprises the steps: injecting a detection current into a grounding electrode, and synchronously collecting an original voltage signal at two ends of the grounding electrode and a time domain waveform of the detection current; performing time domain segmentation on the original voltage signal, performing linear frequency modulation wavelet transform on the signal in each time period, generating an initial time-frequency matrix, extracting a time-frequency ridge line from the initial time-frequency matrix, identifying interference information, obtaining an interference-removed time-frequency matrix, optimizing the interference-removed time-frequency matrix through robust kernel principal component analysis, and obtaining an interference-removed time-frequency matrix; a synchronous redistribution operator is used for carrying out signal purification, a pure time-frequency matrix is obtained, linear frequency modulation Z transformation is carried out on the pure time-frequency matrix, voltage amplitude and frequency are extracted, an energy centroid method is used for correcting the voltage amplitude and frequency, interference can be effectively restrained, and the grounding resistance measurement precision is remarkably improved.

The invention relates to the technical field of signal processing, and provides a time domain waveform reproduction signal smooth processing method, which is used for detecting the fatigue durability of automobile parts in a vehicle vibration acquisition system, and comprises the following steps: S1, obtaining the initial impedance of the automobile parts and multiple frames of to-be-reproduced signals; s2, calculating a first frame first driving signal, and smoothing the first driving signal to form a target driving signal; s3, calculating and generating a first driving signal of a corresponding frame based on the to-be-reproduced signal of each subsequent frame and the updated impedance corresponding to the previous stage in sequence, and forming a target driving signal; and S4, performing stress analysis on the multi-frame response signals, quantifying the damage accumulation process of the automobile part under the simulated working condition, and evaluating the fatigue durability of the automobile part. According to the invention, the generation of response pulses can be avoided, and the signal waveform reproduction precision is improved, so that the accuracy of fatigue durability detection of automobile parts is improved.

The invention discloses a high-frequency and high-speed wire harness test system data analysis processing method, and relates to the technical field of signal processing crossing, and the method comprises the steps: inputting a three-dimensional fusion tensor into a space-time-frequency three-dimensional convolutional neural network model, capturing time sequence features through a bidirectional LSTM, and obtaining defect features through a multi-head attention mechanism; fusing the time sequence features and the defect features to generate time sequence-defect fusion features, and performing comparative analysis on the time sequence-defect fusion features and historical defect data to obtain a risk defect area; by generating a three-dimensional fusion tensor and acquiring defect features through a multi-head attention mechanism, high-dimensional coupling of multi-modal data and adaptive focusing of key defect modes are realized; based on a three-dimensional joint feature space, a quantum coding mapping fusion method is utilized to perform row mapping combination on a time domain waveform sequence, a spatial heat distribution matrix and a frequency domain S parameter spectrum, so that collaborative coding of a time process, a spatial position and frequency change information is realized, and the joint characterization capability of a multi-source signal is improved.

The invention relates to a fault detection method and device of a converter transformer, computer equipment and a medium. The method comprises the steps of obtaining a vibration time-domain waveform signal and a current time-domain waveform signal of a winding of the converter transformer in a preset time period, determining vibration frequency response data of the winding according to the vibration time-domain waveform signal and the current time-domain waveform signal, and determining the current time-domain waveform signal of the winding according to the vibration frequency response data and preset reference vibration frequency response data. And determining a fault detection result of the winding. By adopting the method, the timeliness of fault detection of the converter transformer can be improved.

This invention discloses a signal post-processing method and apparatus for improving the accuracy of train passing identification, belonging to the field of high-speed railway condition monitoring technology. Based on candidate train passing events initially identified by existing methods, the method first performs time-domain waveform screening to eliminate events whose time-domain waveforms do not conform to a preset impact pattern. Then, for the screened events, the frequency domain features of their vibration signals are extracted. Finally, a second screening is performed based on the comparison results of the frequency domain features and a preset threshold to distinguish real train passing events. This invention, through a two-stage screening architecture of first the time domain and then the frequency domain, can effectively capture and utilize the essential differences in time-domain waveforms and frequency-domain energy distribution between transient train passing impacts and persistent interference, thereby significantly improving the accuracy and reliability of train passing identification and producing a high-purity train passing event dataset.

The invention discloses a time-frequency fusion power line noise classification modeling method, system and device based on deep learning, and a medium, and belongs to the technical field of power line noise classification modeling, and the method comprises the steps: constructing a data set containing multiple types of typical power line noise signals; performing short-time Fourier transform on the noise signal to obtain a spectrogram, and extracting local texture features in the spectrogram; extracting global time sequence characteristics of the original time domain waveform of the noise signal; splicing the local texture features and the global time sequence features to form a fusion feature vector, inputting the fusion feature vector into a full-connection neural network, and outputting a multi-label noise prediction result; and training the full-connection neural network, and updating network parameters by optimizing a binary cross entropy loss function. According to the method, different characteristics and generation mechanisms of each type of noise are considered, through fusion of time domain information and frequency domain information, the characteristics can be extracted more comprehensively, the space relation and mode of the time domain and the frequency domain are effectively captured, and the adaptability and accuracy of the model to a noise classification task are improved.

The invention discloses a traction motor bearing fault online detection method and system, a medium and equipment. The method comprises the steps that S1, a working condition signal and a leakage current signal of a traction motor under a stable working condition are acquired; s2, performing adaptive decomposition on the leakage current signal, and capturing a sensitive mode corresponding to the state change of the bearing, namely a corresponding high-frequency mode component of the traction motor bearing in the leakage current signal; s3, extracting a peak-to-peak value Apeak of a main wave crest in the high-frequency modal component time domain waveform, a fluctuation period Ta, a resonant frequency fc of a high-frequency modal component frequency spectrum and energy Ef corresponding to the resonant frequency fc; and S4, inputting the Apeak, Ta, fc, Ef and working condition signals into a pre-constructed and trained bearing fault multi-feature fusion diagnosis model, and outputting a motor bearing diagnosis result. The method has the advantages that the fault features of the motor bearing can be effectively and sensitively obtained, and accurate traction motor bearing fault early warning is achieved.

The invention provides an AI-based loudspeaker nonlinear compensator parameter determination method, system and device, and the method comprises the steps: carrying out the real-time processing of an input audio sequence corresponding to a calibration audio based on a nonlinear compensator, and carrying out the sound amplification output; performing compensator parameter updating according to a preset updating beat until a set convergence condition is met, and determining a target compensator parameter; wherein the compensator parameter updating comprises the following steps: acquiring a reference digital sequence corresponding to the same time window, a current compensator parameter and an acoustic feedback sequence corresponding to the output of a loudspeaker to be calibrated acquired by a microphone, and processing the acoustic feedback sequence, the reference digital sequence and the current compensator parameter corresponding to the same time window through a preset AI updating network to obtain an iterative compensator parameter. The method inputs the original time domain waveform data, does not need to carry out explicit time delay alignment, synchronous compensation and other feature extraction steps, and has the characteristics of flow automation, individual difference adaptation, fast convergence, strong engineering landing and the like.

The invention discloses a power system forced oscillation source positioning method based on ICEEMDAN-ITEO, and belongs to the technical field of power system stability analysis and control. In a signal adaptive decomposition and modal extraction part, an ICEEMDAN algorithm is utilized to decompose a wide-area measurement signal, suppress modal aliasing and noise interference and obtain a modal component with physical significance; the energy weight of each modal component is calculated by adopting ITEO, the identification capability of the dominant forced oscillation component is enhanced through an adjustable resolution parameter, and the time domain waveform of the dominant forced oscillation component is reconstructed; in the oscillation source judgment part based on the dissipation mode energy flow, the dissipation mode energy flow of each generator port is calculated according to the dominant oscillation mode component, and accurate judgment of a disturbance source is realized through linear fitting of slope characteristics of the dissipation mode energy flow; in the verification and application part of the oscillation source positioning result, the accuracy and robustness of the method in different noise and disturbance scenes are verified through a test system comprising a network-forming fan.

The invention discloses a speech spectrum reconstruction method and system combining time-domain half-wave rectification and a weighted Gaussian mixture model decoder, a terminal and a medium, and relates to the technical field of speech signal processing, the method comprises the following steps: acquiring a low-resolution audio, executing time-domain half-wave rectification, and executing weighted Gaussian mixture model decoding; obtaining a mixed amplitude spectrum based on the amplitude spectrum of the low-resolution audio and the amplitude spectrum of the rectification signal; outputting GRU features based on an encoder, inputting the GRU features into a weighted Gaussian mixture model decoder, and generating a plurality of groups of Gaussian component parameters through three parallel linear layers and applying three constraint designs; and calculating a frame-level frequency distribution weight, combining the frame-level frequency distribution weight with the mixed amplitude spectrum to obtain a model output amplitude spectrum, inputting the model output amplitude spectrum and the amplitude spectrum of the low-resolution audio into a frequency band guide masking module to obtain a final amplitude spectrum, and reconstructing an audio time domain waveform based on the final amplitude spectrum and the phase of the low-resolution audio. According to the method, high-fidelity and high-frequency reconstruction can be realized while the complexity of the model is greatly reduced.

The application discloses an active direct wave suppression method, device, computer and storage medium, which filters a received signal by using a time domain blocking matrix, suppresses an interference frequency component, and forms a new sampling sequence; and performs a discrete Fourier transform on the new sampling sequence generated after filtering to obtain a power spectrum signal after interference suppression. According to the active direct wave suppression method, device, computer and storage medium, the uniformity of a single-frequency signal in time domain sampling is utilized, alignment on a time domain waveform is realized through phase compensation, and a difference cancellation process is performed on a reference signal and an original signal, so that the frequency component is cancelled, and static reverberation is suppressed. The method is convenient to calculate, has good single-frequency direct wave interference and reverberation suppression capability, and can be used for active target detection in a single-frequency long pulse signal transmission mode, and the effective distance is improved.

The invention discloses a hardware-in-loop simulation method based on a high-order generalized average model and a related device, and belongs to the technical field of power electronic converter simulation. The method comprises the following steps: listing a piecewise linear state equation of a target power electronic converter; performing moving Fourier expansion on the piecewise linear state equation to obtain a high-order generalized average model of the power electronic converter; calculating moving Fourier coefficients of a switching signal and an input signal of the power electronic converter, and substituting the moving Fourier coefficients into a high-order generalized average model of the power electronic converter to obtain a differential equation of each order of moving Fourier coefficient about a circuit variable; and solving the differential equation, calculating to obtain a mobile Fourier coefficient of each variable in the circuit, and further reconstructing a time domain waveform of each circuit variable at the center moment to realize simulation of the target power electronic converter. According to the method, the simulation step length of the model is greatly increased, and computing resources are saved; meanwhile, the precision requirement is met, and the steady-state and transient-state behaviors of the circuit can be accurately predicted.

The application discloses a high-speed link signal equalization method based on a three-dimensional memristor type equalizer, which comprises the following steps: obtaining a distorted signal and generating an ideal received signal, and then constructing a data set; training a recurrent neural network by using the data set to obtain a trained weight parameter; obtaining a conductance value matrix in the three-dimensional memristor type equalizer by mapping the weight parameter; deploying the conductance value to the three-dimensional memristor type equalizer according to the conductance value matrix; and finally inputting the distorted signal into the three-dimensional memristor type equalizer to obtain an optimized signal after compensation distortion. Compared with the traditional feedforward equalizer and decision feedback equalizer method, the application effectively reduces the time domain waveform distortion, improves the high-speed signal integrity, has wide applicability, can be used for the training and deployment application of the memristor type equalizer of various complex high-speed links, and deploys the above-mentioned conductance value to the three-dimensional memristor cross array, so as to improve the integrated density and reduce the area occupied by the equalizer.

The application relates to a relay protection misoperation root cause analysis method, device, equipment, medium and program product. The method comprises the following steps: in response to detecting a relay protection misoperation event, acquiring a corresponding observation data set; extracting and locking an exogenous noise vector of the relay protection misoperation event by inverse operation of a mixed driving structure equation; calculating average causal effects of each environmental state variable on relay protection action behavior, and generating a root cause quantitative analysis result according to the average causal effects; receiving an intervention operation instruction input by a user for a target environmental state variable, keeping the locked exogenous noise vector unchanged, inputting the modified target environmental state variable into a structural causal model, performing forward prediction and waveform synthesis through a mixed deduction engine, generating a virtual fault waveform and corresponding relay protection logic simulation results; and comparing the time domain waveform and the action logic to generate a comparison result. The method can quickly reproduce an accident scene and quantitatively locate a misoperation root cause.

The application discloses a kind of multi-element fusion carotid vascular monitoring method, system, electronic equipment and storage medium, it is related to medical monitoring field.The method comprises: obtaining the time-domain acoustic signal of carotid artery of the individual to be measured, and the time-domain acoustic signal is carried out frequency domain transformation, and frequency-domain acoustic signal is obtained;Nonlinear flow state feature and topological form feature are extracted based on the frequency-domain acoustic signal;The nonlinear flow state feature and the topological form feature are compared with the corresponding baseline feature in the historical acoustic baseline of the individual to be measured, and feature deviation result is obtained;Risk assessment is carried out based on the feature deviation result, when the feature deviation result exceeds preset range, risk early warning is triggered.The application suppresses the interference such as power frequency, baseline drift, respiratory body movement and environmental noise mixed in time-domain waveform, thereby reducing the false positive rate of carotid vascular acoustic monitoring, and realizing early risk early warning of vascular lesion.

The application discloses a dynamic bit and power loading method suitable for power line carrier communication, which comprises the following steps: a sending end and a receiving end train and prestore at least one subcarrier allocation table (SAT) set through channel detection; mode indication information of a Payload part is set in frame control information of a physical layer of the sending end, and when the mode indication information is in a dynamic loading mode, dynamic loading parameters required by the dynamic loading mode are determined; corresponding dynamic loading mode SAT_ID parameters are selected according to the dynamic loading parameters, and the corresponding SAT_ID parameters are configured in corresponding bit positions of the frame control information; after the corresponding SAT_ID parameters are configured, a load time domain waveform is generated according to the selected dynamic loading parameters from the subcarrier allocation table (SAT) set; the load time domain waveform, a preamble and a frame control time domain waveform are combined to form a complete physical layer data packet time domain waveform, and the complete physical layer data packet time domain waveform is transmitted to a power line channel for transmission.

The invention discloses a TCN-based vehicle-mounted sound equipment equalizer compensation method and system, and relates to the technical field of vehicle-mounted electronic equipment, and the method comprises the steps: playing a test signal in a vehicle-mounted environment, collecting a distorted time domain waveform affected by an acoustic environment in a vehicle through a microphone, and carrying out the preprocessing of the distorted time domain waveform; inputting the processed waveform into a time convolution network, extracting multi-scale time sequence features, and predicting to obtain a first half segment coefficient of a linear phase FIR filter after global average pooling and full connection layer mapping; carrying out symmetrical overturning splicing on the front half segment coefficient, constructing a complete linear phase FIR filter coefficient, and carrying out direct current gain normalization processing; and finally, carrying out one-dimensional convolution operation on the normalized FIR filter coefficient and an original audio signal, and outputting an equalized audio signal subjected to frequency response compensation. According to the invention, the adaptive equalization compensation of the vehicle-mounted audio system is realized, and the tone quality performance and the hearing experience are remarkably improved while the linear phase characteristic is ensured.

The invention relates to the technical field of electric leakage measurement, and discloses an on-line monitoring method and system for an interface leakage current of an insulation crossing frame, and the method comprises the steps: extracting a time domain waveform of a leakage current signal in an insulator string as original leakage current data; according to the geometric topological parameters of the insulator string, constructing a surface electric field distribution gradient of the insulator string, and extracting an electric field feature vector of the surface electric field distribution gradient; separating an interface leakage current component in the electric field feature vector; the method comprises the following steps: acquiring an environmental noise sample in a state that an insulator string has no working voltage, and constructing a noise reference set of the insulator string; performing coherent filtering on the interface leakage current component according to the noise reference set to generate a purified leakage current signal; generating an interface leakage current effective value of the insulation crossing frame according to the root-mean-square value of the purified leakage current signal; according to the invention, the accuracy of online monitoring of the interface leakage current of the insulation crossing frame can be improved.

The present disclosure relates to the technical field of thin film material performance detection, and provides a method and system for testing complex permittivity of epitaxial thin film material in a terahertz frequency band, which comprises the following steps: performing transmission type testing on different transmission regions obtained by dividing a to-be-tested epitaxial thin film material sample and air, to obtain a time-domain waveform of a terahertz pulse transmission electric field; performing Fourier transform on the obtained time-domain waveform to obtain an amplitude spectrum and a phase spectrum of the electric field; calculating a complex refractive index of a substrate material according to the amplitude spectrum and the phase spectrum; constructing a transmission and reflection model for the substrate plus the epitaxial thin film material by taking into account multiple reflections inside the substrate material; obtaining a relationship between the complex permittivity of the epitaxial thin film material and the complex refractive index of the substrate material based on the transmission and reflection model, and inversely calculating the complex permittivity of the epitaxial thin film material. In the process of testing the complex permittivity of the epitaxial thin film material, the testing error introduced by multiple reflections inside the substrate material is taken into account, thereby improving the testing accuracy of the complex permittivity of the material.

This invention discloses a high-speed sweeping fiber optic light source. The specific device includes: a mode-locked pulse light source, an optical fiber amplifier, a highly nonlinear optical fiber, and a dispersion-compensating optical fiber. The passively mode-locked pulse light source outputs a high repetition rate, wide-spectrum mode-locked pulse. Its dispersion propagation follows a parabolic differential equation similar to one-dimensional paraxial diffraction, analogous to Fraunhofer diffraction. The pulse is amplified outside the cavity by an optical fiber amplifier to increase the laser pulse energy and peak power. Then, it is broadened by the highly nonlinear optical fiber to obtain a supercontinuum light source. Finally, the pulse is stretched by a highly dispersive medium provided by the dispersive optical fiber, and the pulse frequency domain spectrum is mapped to a time domain waveform, thereby realizing a high-speed sweeping frequency light source in the time domain.

The invention belongs to the technical field of nondestructive testing, particularly relates to a nondestructive testing method and system for the early strength of a prefabricated part based on an ultrasonic array, and aims to solve the technical problem that an existing nondestructive testing technology is difficult to accurately evaluate the early strength of the prefabricated part under multi-factor interference. The detection method comprises the following steps: S1, identifying the arrival time of a head wave for each time domain waveform signal, and calculating the average sound velocity by combining the geometric position of each receiving channel; s2, calculating a mean value of permutation entropies of all the receiving channels to obtain a peak pre-entropy mean value; s3, calculating a mean value of the attenuation coefficients of all the receiving channels to obtain a frequency attenuation coefficient mean value; and S4, based on the comprehensive strength indication value, determining the early strength of the prefabricated part through a preset strength mapping relation. According to the detection method disclosed by the invention, the comprehensive strength indication value is constructed, and the accuracy of nondestructive detection of the early strength of the prefabricated part is improved.

To provide a manufacturing method of an optical integrated circuit capable of obtaining the optical integrated circuit operable with desired circuit characteristics without depending on temperature adjustment.SOLUTION: The method includes a detection step S01 of inputting the inspection light L to the optical integrated circuit S and detecting the inspection light L output from the optical integrated circuit S, a calculation step S01 of calculating a feature quantity of a time waveform or a feature quantity of a waveform spectrum of the inspection light L detected in the detection step S02, and a processing step S02 of performing refractive-index adjustment processing on the waveguides Se on the basis of a comparison result between the feature quantity calculated in the calculation step S04 and a preset designed value of the feature quantity.SELECTED DRAWING: Figure 8

The present application at least describes a method for waveform synthesis. The method may include a step of transmitting a stimulus signal to a software defined radio (SDR) operating in a first state. The method may also include a step of receiving, via the SDR, an output signal based upon the transmitted stimulus signal. The method may also include a step of. determining, based upon the received output signal, a delay state of the SDR from a plurality of predetermined delay states of the SDR. The delay state may be associated with a parameter of the SDR. The parameter may include any one or more of a receive frequency, transmit frequency or bandwidth. The method may further include a step of generating a calibrated signal based upon the determined delay state. The method may even further include a step of sending the calibrated signal to the SDR to adjust a time.