17 results about "Butterworth filter" patented technology

This invention provides a method for suppressing cogging torque in a permanent magnet synchronous motor based on FxLMS, ​​comprising the following steps: The unfiltered torque demand waveform output from the PI speed loop is obtained; the torque demand is processed by a static low-pass second-order Butterworth filter to obtain a filtered torque demand waveform; the torque demand is simultaneously processed by a static high-pass second-order Butterworth filter to obtain a waveform containing only the cogging torque component, forming a noise reference signal; this signal is input into a modified FxLMS filter to obtain an anti-noise signal; this signal is input to a summing module and superimposed with the filtered torque demand waveform to generate a combined torque demand for cogging torque cancellation; the combined torque demand is input to a gain module to generate a q-axis current reference value; this reference value is input to the PI current loop; the combined torque demand is simultaneously fed back to the PI speed loop and combined with the actual speed error of the motor to generate a new torque demand value; the system then returns to the beginning, forming a closed-loop operation.

The application relates to the field of avionics, and discloses a lifting speed filtering method and system based on a second-order Butterworth filter, which comprises the following steps: collecting an atmospheric pressure signal of an airplane in real time, calculating an air pressure height according to the atmospheric pressure signal, calculating a lifting speed signal according to the air pressure height, performing amplitude-frequency characteristic analysis on the lifting speed signal to determine a cutoff frequency of the second-order Butterworth filter, dynamically adjusting the cutoff frequency of the second-order Butterworth filter according to the flight state of the airplane, inputting the lifting speed signal into the second-order Butterworth filter with the adjusted cutoff frequency, and obtaining a filtered lifting speed signal. The application realizes scientific design of filter parameters by performing amplitude-frequency characteristic analysis on the lifting speed signal and obtaining a digital filter structure through denormalization processing and bilinear transformation, determines the cutoff frequency based on the physical characteristics of the signal, and avoids the disadvantages of selecting parameters by experience in the traditional method.

This invention relates to a method for measuring the rotational speed of a fully enclosed compressor using a multi-signal processing technique, comprising the following steps: establishing a current signal speed measurement model; acquiring the current signal, which originates from the current signal of the fully enclosed compressor; preprocessing the current signal, which involves first removing the DC component of the signal, then passing it through a third-order low-pass Butterworth filter, followed by extracting the rotational speed signal using a HILBERT transform; setting a refined frequency band interval, then performing wavelet transform for spectral refinement, and then performing spectral correction using a ratio correction method; then using a Kalman filter to optimally estimate the rotational frequency of the fully enclosed compressor after correction, in order to smooth the rotational frequency of the fully enclosed compressor, and finally obtaining the rotational speed value and displaying it on a host computer; using the above method, the problems of low measurement accuracy and high computational resource consumption of fully enclosed compressor rotational speed can be solved, thereby improving measurement accuracy and computational speed.

The invention discloses a muscle fatigue intelligent detection method and system based on multi-signal acquisition, and relates to the technical field of muscle fatigue detection, and the method comprises the following steps: S1, synchronously acquiring a surface electromyogram signal and a multi-channel pressure signal; step S2, carrying out preprocessing including dynamic Butterworth filtering and wavelet soft threshold denoising on the acquired signals; s3, performing continuous wavelet transform on the preprocessed signal, and extracting frequency characteristics; s4, dynamically distributing fusion weight according to the real-time signal-to-noise ratio of each channel; s5, fusing the weighted feature information based on a Bayesian inference model, and outputting a muscle fatigue state judgment result; through efficient filtering, accurate feature extraction and effective multi-information fusion, the accuracy and reliability of muscle fatigue detection are improved by using a Bayesian reasoning method.

The invention discloses a bank security abnormal behavior detection method and device, equipment and a medium, and relates to the field of machine learning, and the method comprises the steps: carrying out the preprocessing of a current original image in a monitoring video of a bank security scene, and obtaining a current target image; processing the current target image by using a feature extractor of the target image classification model to obtain an initial activation value; if the initial activation value is not smaller than the first preset threshold value, correcting the initial activation value to a preset activation value interval by using a Butterworth filter to obtain a target activation value; if the initial activation value is smaller than the first preset threshold value, determining the initial activation value as a target activation value; inputting the target activation value into a classifier of the target image classification model to output an abnormal behavior detection result; the abnormal behavior detection result represents that the operation behavior corresponding to the current target image belongs to or does not belong to the abnormal behavior in the bank security scene. And the accuracy of abnormal behavior detection of bank security and protection is improved.

The invention discloses a self-adaptive filtering method for frequency division multiplexing ultrafast imaging reconstruction, which belongs to the technical field of computational optical imaging and comprises the following steps of: simulating and generating a large number of multi-framing frequency division multiplexing ultrafast imaging original images by adopting a method of randomly generating a matrix and interpolating and combining a grating modulation principle; correspondingly removing an unmodulated random complex amplitude image from each original image so as to obtain a zero-order item-free image; the pix2pixGAN generative adversarial network is improved and trained; inputting a shot actual multi-framing frequency division multiplexing image into the trained network, outputting a multi-framing frequency division multiplexing image without a zero-order item, performing Fourier transform to obtain a spectrogram without the zero-order item, performing binarization processing on the spectrogram, and applying a K-Means + + clustering algorithm to obtain center coordinates of positive and negative first-order items of a frequency spectrum, so as to obtain a multi-framing frequency division multiplexing image without the zero-order item; single-frame image information corresponding to the center coordinates is extracted through a Butterworth filter, inverse Fourier transform is carried out, a frequency division multiplexing single-frame image is obtained, and self-adaptive filtering is achieved.

The invention provides a gearbox fault prediction and diagnosis method, which comprises the following steps of: acquiring sensor data of a preset point location of a gearbox, storing the sensor data according to a standardized format, and filtering interference noise such as strong electromagnetism and vibration of a wind power plant by customizing parameters of a Butterworth filter; inputting the standardized data set into a multi-branch parallel convolutional neural network model to extract feature sub-segments of each preset point location, and splicing global high-dimensional feature vectors; point feature weights are dynamically adjusted through point and segment training, fault high-incidence parts are focused, irrelevant data interference is reduced, fault types and severity scores are output in combination with an adaptive parameter random forest model, and the problems that a traditional model is poor in generalization ability and cannot adapt to gearboxes of different models / working conditions are solved. High-precision recognition and high-time-efficiency early warning across early faults of the gearbox are achieved, and the problem of gearbox fault monitoring and early warning lagging is solved.

The invention discloses a maneuvering performance off-road vehicle core part simulation design modeling system and method. The system comprises an excitation spectrum analysis module for executing four-order Butterworth filtering and two-dimensional Fourier transform on terrain scanning data to generate a vehicle wheel center input load spectrum in a frequency domain; the geometric topology mapping module is used for deducing modal characteristics of the core component, calculating a spectrum overlapping coefficient in combination with a load spectrum and generating a space sensitivity field; the self-adaptive hybrid modeling module discretizes a core part into a hybrid topology of a high-order entity unit and a reduced-order super unit in space according to the sensitivity field, and realizes interface coupling through a multipoint constraint equation based on reverse isoparametric mapping; and the parallel solving module is used for monitoring the strain energy density in real time in the calculation process so as to trigger local grid reconstruction. According to the method, through adaptive mapping of the frequency domain energy and the grid density, the problem that the simulation calculation efficiency and precision of the off-road vehicle core part are difficult to consider at the same time under the complex working condition is solved.

This invention discloses a multi-core fiber ring resonator nested single-longitudinal-mode narrow-linewidth fiber laser and its linewidth measurement method. The laser includes a pump source, a combiner, an erbium-doped fiber, a Butterworth filter, a first single-mode bare fiber, a first four-core bare fiber, a second four-core bare fiber, a four-core three-ring nested coupled fiber resonator ring, a second single-mode bare fiber, a polarization controller, a first single-mode fiber coupler, a fiber isolator, a spectrometer, and an optical power meter. This invention, through a fiber laser structure with a four-core three-ring nested coupled fiber resonator ring and a temperature-controlled stable Butterworth filter inside the ring, can simultaneously achieve single-longitudinal-mode, narrow-linewidth, and power-enhanced output while simplifying structural design.

The application discloses a kind of solid-state low-frequency signaling machine matching filter circuit, comprising: first intermediate slot, Butterworth filter and second intermediate slot are electrically connected in sequence;Butterworth filter is used for impedance matching and inhibiting high harmonic component;First, second intermediate slot each includes capacitor frame group and adjustable inductance coil group in series with each other;Capacitor frame group and adjustable inductance coil group are connected with corresponding capacitor or inductance into matching filter main loop as needed by the setting of switch switch.The application especially increases the first intermediate slot, can adjust the impedance characteristic of signaling main machine access loop to make access impedance approximately reach pure resistance state, reduce the voltage and current transient impact that power device of solid-state signaling machine is subjected to when working;By closing and opening operation of switch switch, the working frequency band segmentation tuning of solid-state low-frequency signaling machine can be realized;Therefore it has the characteristics of access impedance matching and segmented tuning simultaneously.

The invention provides a power distribution room complex sight distance human body wrist tracking method based on a single commercial millimeter wave radar, and belongs to the technical field of signal detection. The method comprises the following steps: firstly, starting a radar MIMO function, and reconstructing an original signal into a three-dimensional structure; then, through an LSE-BBSR method based on least square estimation, body-background components with strong interference are eliminated from the radar signals by using a pre-acquired pure body-background signal spectrum, and arm signals embedded with wrist position information are purified; then, inputting the purified signal into a CE-GCrossViT model integrated with a multi-scale convolution word coding module and a global cross attention mechanism, so as to estimate a two-dimensional coordinate of a wrist relative to a screen cabinet plane; and finally, smoothing the trajectory by using a Butterworth filter. According to the method, the technical problem that the wrist of the human body is difficult to track in a non-contact and high-precision manner in a complicated NLOS environment of a power distribution room is solved, visual equipment or wearing of a sensor is not needed, and excellent robustness and tracking precision are shown under LOS and NLOS conditions while privacy is protected.

This invention discloses a method and system for locating a transformer oil sampling robot based on multi-source data fusion. The method includes: acquiring IMU, optical flow sensor, and GPS data; preprocessing the data using a second-order Butterworth filter; and automatically selecting a fusion algorithm based on the scene: indoors, complementary filtering is used to dynamically fuse optical flow and IMU data, and tilt interference is compensated through weight factor adjustment and error feedback closed-loop; outdoors, extended state Kalman filtering is used to fuse GPS and IMU data, and electromagnetic interference is suppressed by introducing accelerometer zero-bias compensation and dynamic noise covariance adaptation. This invention effectively solves the problems of insufficient robot positioning accuracy, weak anti-interference capability, and scene switching discontinuity in the complex environment of converter stations, achieving high-precision continuous positioning with high positioning error indoors, and meeting the operational requirements for precise docking of transformer oil sampling ports.

The application provides a dance video generation method based on multi-modal music driving and frequency-space dual-flow decomposition, multi-granularity music features are extracted through a composite encoder (Librosa+Jukebox), and a beat-gated attention mechanism is used to strictly align key actions such as lifting hands and kicking legs with music beat points, and through test data set verification, the synchronization error is reduced to 118 ms; in view of the problem of visual detail loss, a frequency-space dual-flow decomposition architecture is proposed, a reference image is decoupled into a low-frequency energy graph and a high-frequency residual by using a Butterworth filter bank, and a dual-flow diffusion mechanism is used to optimize global posture and local details; in view of the generation stability under the shielding scene, a joint confidence prediction module is introduced, a time domain sliding window weighted fusion strategy is used to dynamically correct the motion trajectory of an abnormal joint point, and reasonable actions conforming to ergonomics can still be generated under a 50% limb shielding rate.

This invention relates to the field of electronic data digital processing technology, and discloses a method for vehicle monitoring and positioning data processing based on multi-source data fusion. The method includes: acquiring GNSS, IMU, and odometer data; constructing a four-dimensional feature vector containing the number of satellites, carrier-to-noise ratio statistics, and vertical acceleration vibration energy, and identifying the driving environment state in real time using a decision tree classifier; dynamically activating the corresponding fusion processing pipeline (such as tightly coupled, loosely coupled, dead reckoning, or enhanced pipeline with vibration compensation) according to the state type; wherein, under vibration interference conditions, a gyroscope bias compensation and adaptive Butterworth filtering mechanism based on a lookup table is introduced. The system includes data acquisition, environment classification, pipeline selection, and fusion processing modules. This invention improves the positioning robustness and all-scenario adaptability in bumpy scenarios, optimizes the balance between power consumption and accuracy through an on-demand activation strategy, and ensures trajectory continuity through hysteresis switching and covariance propagation.

The invention relates to the technical field of avionics, and discloses a lifting speed filtering method and system based on a second-order Butterworth filter, and the method comprises the steps: collecting an atmospheric pressure signal of an airplane in real time, and calculating the atmospheric pressure height according to the atmospheric pressure signal; calculating a lifting speed signal according to the air pressure height; amplitude-frequency characteristic analysis is carried out on the lifting speed signal, and the cut-off frequency of a second-order Butterworth filter is determined; dynamically adjusting the cut-off frequency of the second-order Butterworth filter according to the flight state of the aircraft; and inputting the lifting speed signal into a second-order Butterworth filter after cut-off frequency adjustment to obtain a filtered lifting speed signal. According to the method, amplitude-frequency characteristic analysis is carried out on the lifting speed signal, a digital filter structure is obtained through normalization removal processing and bilinear transformation, scientific design of filter parameters is achieved, the cut-off frequency is determined based on physical characteristics of the signal, and the defect that parameters are selected according to experience in a traditional method is overcome.

The invention discloses a quick-start crystal oscillator stability test method, which comprises the following steps of: installing a tested crystal oscillator on an adaptive clamp, and electrifying to output a clock signal as a phase-locked loop reference input; the power supply receives an instruction of the upper computer and synchronously outputs a power-on time sequence signal as a timing starting point; the phase-locked loop tracks the reference clock through negative feedback, and outputs a locking time sequence signal when the frequency stability reaches a preset threshold value; and the time measuring unit records stable time, uploads the stable time to the upper computer for data verification and statistical analysis, and automatically powers off after a report is generated. According to the method, a phase-locked loop is taken as a core, an Arron variance statistical mechanism is combined, and the frequency jitter sensitivity reaches a ppb level; in cooperation with a vernier method and Butterworth filtering, the time measurement precision reaches a microsecond level, and the problem that the precision of a traditional method is insufficient is effectively solved. Parameterized configuration and automatic testing are supported, waveform acquisition and data tracing functions are achieved, and the testing efficiency and reproducibility are remarkably improved.

The invention relates to the technical field of electronic data digital processing, and discloses a multi-source data fusion vehicle monitoring and positioning data processing method, which comprises the following steps of: acquiring GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and odometer data; constructing a four-dimensional feature vector containing the number of satellites, the carrier-to-noise ratio statistics and the vertical acceleration vibration energy, and recognizing the driving environment state in real time through a decision tree classifier; dynamically activating a corresponding fusion processing assembly line (such as tight coupling, loose coupling, dead reckoning or an enhanced assembly line with vibration compensation) according to the state type; wherein a gyroscope bias compensation and adaptive Butterworth filtering mechanism based on a lookup table is introduced in the vibration interference state. The system comprises a data acquisition module, an environment classification module, an assembly line selection module and a fusion processing module. According to the method, the positioning robustness and the full-scene adaptability in a bumpy scene are improved, meanwhile, the power consumption and precision balance is optimized through an on-demand activation strategy, and the track continuity is guaranteed by means of hysteresis switching and covariance transmission.