384 results about "Signal transformation" patented technology

The invention relates to the technical field of data processing, in particular to a metal wire data analysis method and system. The method comprises the following steps: acquiring wire interface data of composite metal; acquiring a wire rod interface characteristic matrix based on the wire rod interface data; constructing a wire rod interface characteristic model according to the wire rod interface characteristic matrix; differential signal conversion processing is carried out on the wire rod interface characteristic model, and wire rod interface change characteristics are extracted based on a differential signal conversion result; constructing a wire rod interface defect initial indication diagram according to the wire rod interface change characteristics; performing multi-scale decomposition processing on the basis of the wire rod interface defect initial indication graph to obtain interface defect feature data; and identifying wire interface defect type features according to the interface defect feature data. According to the method, a closed-loop optimization mechanism of defect characteristics and production process parameters is established, and the interface quality and reliability of the high-performance composite metal wire are remarkably improved.

The invention provides an audio-visual voice separation method and system for a scene with a flexible number of speakers, and the method comprises the steps: extracting mixed voice signals of all speakers in audio data, converting the mixed voice signals into audio features for representation; according to the method, a multi-head self-attention branch and a multi-layer perceptron branch with convolution gating are used for coding from a mixed voice signal to obtain dependency relationships of different scales, the audio feature representation is combined to obtain final mixed voice features and block the mixed voice features, each block is separated in parallel and independently, a mask is estimated for each potential speaker, and a multi-level perceptron branch with convolution gating is obtained. The visual features are fused to obtain cross-modal fusion features of the blocks, modeling is carried out on the cross-modal fusion features on the inter-block level, and mask estimation of the blocks is obtained; and reconstructing the separated signal in the time domain according to the estimated mask, judging whether the extracted voice is effective or not, and estimating the number of speakers. The problem that most voice separation methods cannot separate speakers with the number changing dynamically can be solved at the same time.

The invention relates to the technical field of microelectronics, semiconductors and communication, and discloses a radio frequency switch chip, and the circuit structure of the radio frequency switch chip comprises a transformer type differential inductor T-L1, a transformer type differential inductor T-L2, a capacitor C1, an NMOS transistor M1, an NMOS transistor M2, a gate resistor RG1, and a gate resistor RG2. The radio frequency switch chip provided by the invention adopts a differential structure, so that the parasitic inductance effect of a grounding key and a wire during packaging and testing can be eliminated, and the insertion loss and the isolation performance are improved; compared with a single-end port, the lower impedance of the differential port is more beneficial to improving the power capacity of the switch; the differential structure allows the transformer type differential inductor to use differential signal characteristics, the equivalent quality factor of the inductor is improved, and the circuit performance is improved; the differential structure can improve the linearity of the circuit, especially the second-order intermodulation effect; the differential structure can be compatible with a modern silicon-based CMOS radio frequency system, and an additional balun is not needed for signal conversion.

The invention discloses a multi-sensor cooperative partial discharge waveform identification method and system, and the method comprises the steps: extracting an envelope line of an oscillation waveform through Hilbert transformation for a collected partial discharge signal, carrying out the Logistic function fitting and exponential decay function fitting of each sensor signal, and carrying out the recognition of the partial discharge waveform through the Logistic function fitting and exponential decay function fitting. Taking the characteristic parameters extracted by each sensor as node characteristics in a graph structure, establishing an edge connection relation in a graph according to the maximum value starting time difference and the similarity of signals between the sensors, constructing a graph neural network model, performing training and reasoning by utilizing the graph structure, and finally outputting a recognition result of whether a partial discharge event occurs or not; the system comprises a signal conversion module, a feature extraction module, a data fusion module and a partial discharge diagnosis module. According to the invention, the accuracy and adaptability of partial discharge signal identification are improved, and the on-line monitoring requirement of the transformer in a complex electromagnetic environment is met.

The invention relates to the technical field of optical current transformer detection, and provides an optical current transformer real-time calibration and health diagnosis method and system, and the method comprises the steps: carrying out the harmonic analysis of an initial digital signal sequence obtained through the conversion of a continuous modulation optical signal of a target optical current transformer, and obtaining harmonic component data; physical state evaluation is carried out according to the higher harmonic component data and preset health state baseline data to generate a physical state evaluation result, nonlinear disturbance compensation is carried out according to an error control signal generated based on the higher harmonic component data, and after a linear working point is reached, nonlinear disturbance compensation is carried out; and calculating an electrical measurement value according to the output target fundamental component data, and performing comprehensive analysis according to the physical state evaluation result and the electrical measurement value to generate a diagnosis report. According to the invention, through a measurement calibration and state diagnosis mechanism based on harmonic analysis, while the electrical performance measurement precision is effectively improved, reliable quantitative diagnosis and accurate traceability of different physical faults in the equipment are realized.

The invention relates to the field of rotating machine fault diagnosis, in particular to a rotating machine data enhancement method based on an SCF-CVAE-GAN network, and the method comprises the steps: obtaining rotating machine vibration signals in different fault modes; the rotating machinery vibration signals are converted into a two-dimensional time-frequency image, and the SCF-CVAE-GAN network is trained by adopting the two-dimensional time-frequency image; generating a two-dimensional time-frequency image sample by adopting the trained SCF-CVAE-GAN, calculating the structural similarity and the distance score of the generated sample, and evaluating the quality of the generated sample; mixing the generated sample meeting the quality requirement and the two-dimensional time-frequency image to obtain an enhanced data set; according to the method, a double-self-correction network is provided, a frequency domain regularization strategy is integrated, global information and local textures of data are deeply mined, and the diversity and fidelity of generated samples are ensured by constraining distribution of the samples in a frequency domain.

The invention discloses an intelligent start-stop control method and system for a magnetic suspension refrigeration compressor, and the method comprises the steps: obtaining a position perception enhancement source through collecting and analyzing the current response data of a magnetic bearing coil, and extracting the current response change characteristics; rotor position estimation is carried out based on current response change characteristics, a positioning precision driving source is extracted from estimation deviation, and a non-impact starting excitation current instruction is generated; a multi-sensor fusion data network is established, configuration is reconstructed through an abnormal response activation signal, and a degradation operation fault-tolerant mode is formed; converting a response preparation gain according to the shutdown instruction signal, performing shutdown state evaluation in combination with signal reconstruction configuration, and generating an emergency shutdown trigger condition; and the power supply state of a bus capacitor is detected, gradient control potential is released, the active damping control opportunity is determined, a damping force control instruction is generated, finally intelligent start-stop control over the magnetic suspension refrigeration compressor is achieved, and safe and reliable operation of the system under various working conditions is ensured.

A data storage system for managing an operation of a solid-state device (SSD) includes the SSD, an adaptor coupled to the SSD via a first bus protocol and including a non-volatile memory storing a power configuration table. The protocol converter is configured to: couple to the adaptor using the first bus protocol, convert first signals of the first bus protocol to second signals for a second bus protocol, and couple to a host using the second bus protocol. The power configuration table includes power profiles for the SSD each corresponding to an interface type for the second bus protocol. The protocol converter is further configured to: determine an interface type of the second bus protocol, read the power configuration table from the non-volatile memory to determine a power profile corresponding to the determined interface type, and cause the SSD to operate using the determined power profile.

In one embodiment, a method includes accessing symbol sequences with each including multiple symbols, generating a respective frequency-domain signal for each of the symbol sequences based on a respective signal transformation algorithm that is based at least in part on a Fourier transform, mapping each of the frequency-domain signals to a respective subset of subcarriers out of all subcarriers in an allocated bandwidth, generating a waveform based on the frequency-domain signals and their respective subset of subcarriers based at least in part on an inverse Fourier transform, and transmitting the waveform to a second electronic device.

The invention relates to the technical field of precision shell contour metering, and discloses an online metering method for a precision shell contour, and the method comprises the steps: obtaining an original sampling point cloud and a feature point group of a to-be-measured workpiece in a conveying state; resolving a real-time pose matrix of the to-be-measured workpiece relative to a preset measurement reference by using a spatial topology constraint relationship of the feature point groups; carrying out differential processing on a real-time pose matrix in the sampling sequence, and synthesizing an instantaneous motion vector corresponding to a sampling moment; in combination with a signal response time delay constant of the sampling system, phase compensation is carried out on a motion displacement deviator induced by signal conversion delay, and a corrected sampling point cloud is generated; according to the method, the geometric distortion of the point cloud generated by response delay of the sensor is solved, the problem of instantaneous drift of the measurement reference in an unsteady state conveying state is solved, and the contour reduction precision in a complex working condition is improved.

The invention discloses a natural gas pipeline leakage detection method based on novel wavelet basis transformation and singular value decomposition in a two-dimensional convolutional neural network. Firstly, a sound signal collected by a sound wave sensor is converted into a digital signal; secondly, in the data preprocessing stage, singular value decomposition is carried out on the digital signals to effectively eliminate background noise interference, and then batch normalization is carried out on the processed data; then, converting the one-dimensional time sequence signal into a two-dimensional time-frequency image by adopting a self-defined Morlet wavelet basis function; and finally, based on the time-frequency images, constructing and training a 2D-CNN model for fault classification, and presenting a diagnosis result through a confusion matrix and a comparison graph. According to the method, 97.55% of fault recognition accuracy is obtained in a public data set, and compared with other competitive methods, the method shows more excellent noise robustness and classification performance, and has higher accuracy and wide application prospects in pipeline leakage diagnosis in a complex noise environment.

The gas turbine fault prediction and diagnosis method comprises the steps of collecting measurement parameters of a gas turbine; converting the non-stationary signal into a frequency domain feature, and converting the slowly varying signal into a time domain feature; inputting the time domain features and the frequency domain features into a feature screening model, and outputting screened time-frequency domain features; calculating a dynamic correlation coefficient matrix among the screened time-frequency domain characteristics, and calculating a characteristic relation vector based on the dynamic correlation coefficient matrix; inputting the screened time-frequency domain features and the feature relationship vector into a feature coupling model, and outputting a coupling feature vector; performing time sequence feature extraction on the measurement parameters to obtain a time sequence feature vector; and inputting the time sequence feature vector and the coupling feature vector into a fault prediction model to obtain a predicted gas turbine fault type. A time-frequency characteristic coupling relation is quantified through a dynamic correlation coefficient matrix, TCN-FNN dual-channel fusion time sequence and coupling characteristics are realized, and accurate positioning and early prediction of a fault are realized.

The invention discloses an unmanned aerial vehicle control method integrating satellite navigation and inertial navigation and related equipment, and relates to the technical field of integrated navigation. According to the method, the control system collects the satellite quality intensity of the unmanned aerial vehicle at the current navigation point and predicts the satellite quality intensity of the unmanned aerial vehicle at the next navigation point, the reliability degree of satellite navigation is evaluated in advance, and the signal conversion type from the current navigation point to the next navigation point and the corresponding navigation strategy are determined accordingly. According to the self-adaptive navigation method based on prediction, preparation can be made before the satellite signal quality changes, and the problems of navigation precision abrupt change and positioning drift easily occurring when the signal abrupt change occurs in a traditional fixed weight fusion scheme are solved. Meanwhile, the control system carries out fusion calculation on satellite navigation data and inertial navigation data and flight control parameter adjustment based on deviation, it is ensured that the unmanned aerial vehicle can stably fly according to a preset route, and the unmanned aerial vehicle is particularly suitable for being used in complex urban environments and mountainous terrains with unstable satellite signals.

The invention discloses a video intelligent monitoring system and method based on cross-modal time-frequency fusion, and relates to the technical field of new energy power monitoring and safety management, and the method comprises the following steps: obtaining multi-modal physical data; detecting a waveform characteristic of the electrical signal, and when the waveform characteristic of the electrical signal meets a preset electrical abnormal condition, performing high frame rate sampling and frame rate compensation on the visible light video to obtain video data with enhanced time sequence continuity; converting the video data, the thermal infrared image, the acoustic signal and the electrical signal to a time-frequency domain to obtain a multi-modal video feature, and inputting the multi-modal video feature to a cross-modal fusion network to obtain a fusion feature vector; and performing anomaly type prediction on the fusion feature vector based on a long-short term memory network in combination with a semantic map model to obtain an anomaly type recognition result and a risk level judgment result. According to the method, the problem that the multi-modal data lacks a unified time reference and fusion mechanism can be solved.

The present invention relates to bridge detection and discloses a real-time automated detection and positioning method for typical damage of bridge bearings, comprising: jacking the main beam, arranging an ultrasonic sensor on the upper surface of the bearing, obtaining a first detection signal after sending an initial excitation signal, converting the damage signal intercepted from the first detection signal into a secondary excitation signal using a time reversal method, obtaining a corresponding inverted focus signal, and then calculating a damage characteristic index, distinguishing bearing damage based on the damage characteristic index, and when it is determined that damage has occurred, obtaining a probability value of damage occurring at a point on the upper surface of the bearing by establishing a probabilistic damage model, and imaging the bearing damage based on the probability value. The present invention establishes a bearing damage characteristic index, which does not require an initial excitation signal and has improved resistance to environmental interference; and uses a threshold method to eliminate tail wave data in the detection signal, thereby reducing data processing complexity, eliminating the need to interrupt traffic, and improving detection efficiency while ensuring detection accuracy.

The invention provides an automobile instrument display blurred screen anomaly detection method and system, a medium and equipment, and relates to the technical field of instrument detection.The method comprises the steps that a to-be-detected image is preprocessed, feature points contained in the image are detected, and the number of the feature points is counted; taking the number of the feature points in each frame of image as a collected signal and converting the collected signal to a time domain; and analyzing the signals converted to the time domain by using a sliding window method, setting the size of a sliding window, calculating variance values of all the signals in the window to judge the dispersion degree of the signals, and when the calculated variance values exceed a preset value, determining that the blurred screen abnormal phenomenon of the display screen occurs. According to the method, the requirement for manual intervention can be reduced, and the credibility and effectiveness of overall detection are improved.

The invention discloses an electric energy meter operation state inspection and evaluation method and system, and relates to the technical field of electric power metering safety, and the method comprises the steps: collecting and preprocessing the electrical working data of an electric energy meter, obtaining the reference current and working current of the electric energy meter, extracting error features, and generating a current error signal; carrying out voiceprint collection on the electric energy meter, and capturing a voiceprint signal of the ammeter; converting the voiceprint signal into a discrete pulse sequence through a coding algorithm, and obtaining a standardized pulse sequence through dimension conversion and optimization compression; performing feature extraction on the standardized pulse sequence by applying a pulse neural network, and obtaining fault probability distribution through synaptic weight calculation and neuron state updating after obtaining pulse sequence features; the feedback control signal and the electric energy meter evaluation report are generated based on the fault probability distribution, and the problem of poor real-time performance of fault mode recognition is solved.

The invention discloses a satellite-ground enhanced signal conversion method and a satellite-ground enhanced signal conversion model based on PPP-RTK, which are used for solving the problems that conventional RTK in a ground-free network area fails, and satellite-based PPP-RTK depends on a special terminal and is slow in convergence. Generating an RTCM-OSR correction number by analyzing the SSR correction number, fusing the orbital clock correction, ionosphere and troposphere correction and performing weighted interpolation; the ambiguity is solved in combination with the observation value of the base station, dual-path verification and weighted fusion are carried out in combination with M-W and LAMBDA algorithms, and the wide lane ambiguity fixing reliability is improved; and when the ground-based signal is interrupted for more than 1 second, inheriting and maintaining Kalman filtering continuity by using the state vector and generating a switching identifier. The model integrates a signal processing module, a correction generation module, an ambiguity verification module and a switching control module, so that a conventional RTK terminal obtains centimeter-level positioning (level 2.3 cm and elevation 4.1 cm) without a public network, the interruption recovery is less than or equal to 1.5 seconds, and the scene upgrading cost of power inspection, power transmission monitoring, wind power plant monitoring and the like is reduced.

The invention discloses a laser powder bed fusion printing state monitoring method based on multi-scale sound signals, which comprises the following steps of: firstly, installing a high-sensitivity microphone in a printing cabin, acquiring sound signals in a printing process, and transmitting the sound signals to a data acquisition system for storage and analysis; in order to improve the signal quality, high-pass filtering is adopted to suppress low-frequency noise, and a one-dimensional sound signal is converted into a gray matrix and a spectrogram through a sliding window and short-time Fourier transform so as to extract time-frequency features; after data preprocessing, dividing a training set and a test set in proportion, and inputting the training set and the test set into a multi-scale acoustic signal monitoring model for feature extraction and recognition; according to the model, the weight is optimized through training, the recognition capacity for different printing states is improved, the generalization performance is evaluated through a test set, finally, the optimized MSA model can accurately recognize the LPBF process state, and technical support is provided for online monitoring and quality control.

The invention relates to the technical field of industrial automation, and particularly provides an equipment operation state intelligent diagnosis method based on current component detection. The method comprises the steps that a current sensor and a data acquisition card are adopted, non-intrusive sampling is carried out at an equipment power supply loop, and a high-frequency current signal is acquired; preprocessing the collected high-frequency current signal to obtain a preprocessed time-domain current signal; converting the preprocessed time domain current signal into a frequency domain signal by adopting fast Fourier transform (FFT), calculating the amplitude and phase of each harmonic wave from a fundamental wave to a higher harmonic wave, carrying out feature engineering, and constructing a feature vector representing the state of the equipment; comparing the feature vector with historical harmonic data to obtain an operation state result; according to the operation state result, a dynamic health baseline model is constructed, online monitoring and abnormity diagnosis are carried out, the method can adaptively monitor the operation state of the equipment, the accuracy and reliability of state diagnosis are improved, and the manual intervention degree is reduced.

A signal generation system includes a first computing device configured to generate a trajectory signal representing a planned trajectory of a vehicle; and a second computing device configured to generate an output word signal representing word information to be given to an occupant of the vehicle, by inputting an input word signal representing an utterance of the occupant in natural language format into a trained language model. At least one of the first computing device and the second computing device converts at least one of the sensor signal and a signal generated based on the sensor signal to a trajectory data signal represented in a format that can be inputted into the trained language model. The second computing device generates a trajectory word signal related to the planned trajectory as the output word signal, based on the trajectory data signal.

The invention discloses a pneumatic control valve fault diagnosis method and device based on microwave vibration measurement, and relates to the technical field of industrial process control. The method comprises the steps that a non-contact control valve rod vibration signal is collected; constructing a multi-scale time-frequency domain dual-channel feature fusion network structure, and extracting time sequence dynamic features of a valve rod vibration signal in a time domain branch through combination of multi-scale one-dimensional convolution and a bidirectional gating circulation unit; the frequency domain branch converts the one-dimensional signal into a two-dimensional time-frequency image, and uses a multi-scale two-dimensional convolutional network to extract spectrum texture features; introducing a channel attention mechanism to adaptively learn feature importance weight, and adopting a cross attention mechanism to deeply fuse time-frequency domain features; and based on a preset control valve fault category, performing fault classification on the fused control valve time-frequency features to obtain a control valve classification result. According to the method, complementary information of different modes is fully excavated, fault features of the control valve are fully extracted, and therefore accurate diagnosis of the faults of the control valve is achieved.

The application discloses an AI sound online analysis and identification method and system based on optical fiber sensing, which comprises the following steps: capturing sound signals of a target area by a distributed optical fiber sensing unit and converting the sound signals into optical fiber transmission optical signals, and completing photoelectric signal conversion to form an acoustic electric signal stream through a sound signal conversion transmission unit. Hierarchical voiceprint feature extraction is sequentially performed through an optical fiber voiceprint feature self-encoding model, feature refinement and redundancy elimination are realized by using a voiceprint feature distillation optimization algorithm, time sequence feature correlation is constructed through a sound signal time sequence prediction identification model, and finally online analysis and category determination are completed by a sound signal AI real-time identification engine. The application fully adapts to the characteristics of optical fiber sensing signals, strengthens the synergy of feature processing and time sequence modeling, improves the identification response speed and accuracy, effectively solves the problems of insufficient feature synergy and time sequence adaptation in the prior art, and can be widely applied to online analysis of sound signals in complex scenes such as industrial monitoring and security warning.

The invention discloses a borrowing type successive approximation analog-to-digital converter adopting a back propagation neural network for calibration. The borrowing type successive approximation analog-to-digital converter comprises a system composed of a positive and negative capacitance digital-to-analog converter adopting a three-stage split bridge type capacitor array, a bootstrap sampling switch, an automatic return-to-zero comparator, an SAR logic control unit, an original-to-binary module and a BPNN calibration engine. Bootstrap sampling switches are respectively arranged at the tail ends of the positive and negative capacitance digital-to-analog converters, a comparator is connected with the output ends of the two, and the output is connected with an SAR logic control unit; the original-to-binary module converts the code generated by the SAR logic control unit into a binary code; and a BPNN calibration engine receives the code and performs calibration calculation by using a trained back propagation neural network model. By implementing the converter disclosed by the invention, hardware implementation can be simplified while high performance is ensured, so that the ADC realizes efficient and accurate signal conversion under a 180-nanometer BCD process, and power consumption and cost are reduced.

This invention discloses a method for storing and providing early warning of vibration acceleration data of motor bearings. The system collects vibration acceleration signals from motor bearings using a vibration acceleration sensor, and uses this data to determine the cause of bearing failures and provide early warnings. Simultaneously, a data feedback terminal provides feedback on the actual cause of the failure to supplement and correct the vibration fault database, improving the accuracy of vibration fault diagnosis and early warning. The system is characterized by including a vibration acceleration sensor, an edge cloud, a data storage backend, and a data feedback terminal. The vibration acceleration sensor establishes signal interaction with the data storage backend, the data storage backend establishes signal interaction with the edge cloud, and the data feedback terminal establishes signal interaction with the edge cloud. The vibration acceleration sensor is positioned near the bearing on the motor for continuous data collection of vibration acceleration signals. The data storage backend includes a signal conversion module and a data processing module for data conversion and comparative analysis of the vibration acceleration signals.

Embodiments of the present application disclose a kind of integrated circuit digital back-end physical optimization method, equipment, medium and product.The method comprises: the pin node in circuit diagram and the interconnection between nodes in the circuit diagram are obtained by pre-processing to be optimized circuit diagram;According to pin node and interconnection, the wiring estimation of circuit diagram is carried out;According to the pre-processing result and the wiring estimation result of circuit diagram, the layout optimization index and timing index in circuit diagram are determined;Layout optimization index includes at least one of the following: power consumption parameter, gate circuit area parameter, buffer area parameter and half perimeter line length;Timing index includes at least one of the following: load violation parameter, signal conversion violation parameter and timing margin parameter;According to layout optimization index and timing index, the target optimization function of circuit optimization is determined, the circuit layout optimization in integrated circuit optimization, gate size adjustment and buffer layout adjustment are carried out, and optimization result is obtained, and circuit layout, gate size adjustment and buffer layout can be optimized.

Techniques for anomaly detection are disclosed. An anomaly detection system transforms observation signals received from signal generators into multidimensional representations. The system extracts portions of the multidimensional representation indicative of anomalies. Based on the extracted portions of the multidimensional representation, the system determines metrics representing the observation signals and determines whether the metrics satisfy anomaly detection criteria.

The invention discloses a method and a system for unvarnished transmission of logic signals on a two-wire power supply circuit, and the system comprises a power supply, a power isolator, the two-wire power supply circuit, a load isolator, an impedance matcher, at least one transmitting node and at least one receiving node. One of the sending nodes sends the logic signal, encodes the sending logic signal into a first narrow pulse code signal, and then converts the first narrow pulse code signal into a second narrow pulse code signal on the two-wire power supply line for transmission; and the receiving node receives the logic signal, converts a second narrow pulse code signal on the two-wire power supply line into a third narrow pulse code signal, and decodes the third narrow pulse code signal into a receiving logic signal. The method has the advantages of high transmission rate, strong real-time performance, high reliability, low cost, wide application range and the like.

A power transmission line fault location method, device and equipment and readable storage medium. The method comprises: acquiring a first time domain signal of a traveling wave collected by a first collection device and a second time domain signal of a traveling wave collected by a second collection device, wherein the first collection device and the second collection device are located on both sides of a fault point; converting the first time domain signal into a first frequency domain signal and the second time domain signal into a second frequency domain signal; obtaining a time difference of the traveling wave reaching the first collection device and the second collection device according to the first frequency domain signal and the second frequency domain signal; and performing fault location based on the time difference. Through the present application, the time when the traveling wave reaches the two devices is no longer calculated separately, but the time difference of the traveling wave reaching the two devices is directly calculated, avoiding the situation that the error of the power transmission line fault location result is amplified due to the calculation of two time points in the traditional method, improving the accuracy of the power transmission line fault location result, and having a larger application range.