42 results about "Transient noise" patented technology

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

The present application relates to pipeline system damping and noise reduction technical field, provide a kind of active noise cancellation pressure relief valve, comprising: valve body, with the liquid inlet cavity and pressure relief cavity being communicated are formed;Bypass pipe, with liquid inlet cavity is communicated;Piezoelectric actuator and pressure relief component, pressure relief component includes adjusting piece, pressure relief elastic piece and pressure relief valve core, pressure relief valve core and pressure relief elastic piece are set in pressure relief cavity, adjusting piece is movably arranged on valve body, piezoelectric actuator is arranged on adjusting piece, pressure relief elastic piece is pressed between piezoelectric actuator and pressure relief valve core;Piezoelectric actuator is configured to drive pressure relief elastic piece, to eliminate the transient noise caused by pulsating pressure when pressure relief valve core moves.The active noise cancellation pressure relief valve provided by the present application introduces active noise cancellation technology into the active noise cancellation pressure relief valve pulsating noise control by arranging piezoelectric actuator, can be adjusted by pressure relief elastic piece The force exerted on the pressure relief valve core can eliminate the transient noise caused by pulsating pressure when the pressure relief valve core moves.

The invention relates to the technical field of digital image processing and computer vision, and discloses a strip detection method based on image data processing, which comprises the following steps: acquiring an original image matrix of the surface of a carrier to be detected; dynamically adjusting diffusion flux according to a pixel neighborhood gradient modulus value, and processing an original image matrix to generate a reference matrix; applying spatial perturbation to the reference matrix and generating virtual residual distribution; performing differential operation on the original image matrix and the reference matrix to construct a feature matrix; and calculating an energy offset vector between the feature matrix and the virtual residual distribution, identifying a non-target noise cluster according to the linearity difference of the energy offset vector relative to a preset displacement amount, and reducing the pixel energy weight of the non-target noise cluster. According to the method, decoupling of physical entity features and transient noise features is realized, and weak damage features are reserved while non-uniform illumination and water mist noise are suppressed.

The invention relates to the technical field of power equipment on-line monitoring and intelligent diagnosis, in particular to an extra-high voltage transformer multi-modal data conflict fault misjudgment analysis method based on a DS evidence theory and graph enhancement. According to the invention, under the unified time reference and the electromagnetic-thermal physical topology constraint, the consistency fusion of the three-mode data of electromagnetic, acoustic vibration and distributed optical fiber temperature is realized, and the layered suppression and traceable positioning are carried out on evidence conflicts caused by coupling uncertainty and transient noise in the fusion process. Therefore, reliable fault confidence evaluation is output, and an accurate misjudgment traceability basis is provided. A traceable fault confidence coefficient is output in a unified time reference and a physical topology constraint through an electromagnetic-thermal dynamic coupling map which is synchronously acquired by hardware-level three modes and is updated in real time along with working conditions, and a layered D-S evidence conflict suppression and traceability algorithm.

This invention discloses a preprocessing method for highly polluted pediatric EEG data, belonging to the field of EEG signal processing technology. The invention proposes a robust motion artifact detection method based on a multi-channel voting mechanism. This method overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision-making, automatically identifying time periods requiring restoration. A comprehensive preprocessing workflow integrating PCHIP interpolation restoration, adaptive Bayesian wavelet denoising, bandpass filtering, and independent component analysis is designed. This workflow is validated using real pediatric continuous task EEG data. This invention demonstrates excellent performance in preserving frequency band information and improving the weighted signal-to-noise ratio, achieving an average accuracy of 96.4% (205 test cases) and a maximum of 100% based on permutation entropy feature classification. This invention effectively solves the preprocessing challenge of pediatric EEG data in high-noise environments, significantly improving the accuracy and reliability of subsequent classification tasks.

The present application relates to the technical field of new energy vehicle NVH testing, and discloses a range extender transient noise sound quality evaluation method based on physiological signal correction, which constructs a double-branch neural network by using in-vehicle noise audio signals, subject EEG signals and subjective scores under the transient working condition of the range extender, the audio branch outputs the objective benchmark score, and the EEG branch outputs the subjective bias correction amount; the model is trained by using a joint loss function that fuses the score fitting loss, the objective consistency constraint loss and the bias regularization loss; in the inference stage, the objective sound quality score can be output by only inputting the audio features. The present application decouples the noise objective quality and the evaluator's physiological bias, effectively eliminates the interference of individual differences, emotions and fatigue states on the evaluation results, improves the evaluation stability and repeatability, realizes efficient, accurate and robust objective evaluation of the range extender transient noise without relying on EEG, and is suitable for NVH performance testing and optimization in the vehicle development stage.

The application provides a kind of intelligent voice interaction system and method, it is related to electronic information technical field, including: acquisition calibration module, for by multichannel acoustic sensor array real-time acquisition mixed audio signal in environment, and set four reference positioning points located at corner point on sensor array, form quadrilateral structure;Based on the area characteristics of quadrilateral generation geometric correction value, and synchronous extraction noise frequency band feature and speech short-time energy distribution characteristics, and utilize geometric correction value to extract the feature and calibrate;Noise identification module is used for based on noise frequency band feature and speech short-time energy distribution characteristics, identify noise type, including wide-band high-intensity noise, low-frequency persistent noise, multi-source human voice interference and burst transient noise, generate label vector, and dynamically adjust noise reduction strategy according to label vector.The application is collected and calibrated audio signal by multichannel acoustic sensor array, improves the accuracy and fluency of voice interaction.

The application discloses a micro bearing production and manufacturing detection data analysis method and system based on big data, relates to the technical field of data processing, and pre-trains a recurrent time sequence network through collection of vibration signals of bearings in a known state, obtains an evolution feature vector of bearings in the same batch through the network combined with a transient noise suppression operator, extracts a static feature vector combined with a machining time sequence and an end face image, corrects a visual missing sample after splicing, constructs a reconstructed kernel feature matrix, obtains a feature transformation and a mapping matrix based on two-way alternate optimization of the matrix and a real label, establishes a bearing state database, collects multi-modal data of a bearing to be detected to generate an initial to-be-detected feature vector, calculates the similarity of the to-be-detected feature vector with database samples after mapping, and finally outputs a detection result. The application deeply fuses multi-modal features, effectively overcomes interference such as workshop visual pollution, thermal expansion and group tolerance drift, and greatly improves detection accuracy and robustness.

The invention relates to a pipeline leakage detection method, in particular to a pipeline leakage detection method based on deep learning. The method comprises the following steps: acquiring a pipeline inspection video; capturing a leakage target; comparing the current frame and the historical frame of the leakage target; capturing the change and spatial position of the leakage target in the time dimension, and carrying out feature fusion; carrying out modeling and motion trail prediction on the motion trail of the leakage target; performing feature enhancement on the leakage target after the motion trail prediction to obtain enhanced features; and carrying out detection head monitoring on the enhanced features, and outputting the leakage probability. According to the invention, a temporarily appearing false target and continuously existing real leakage can be intelligently distinguished, the space-time fusion detection mechanism significantly improves the anti-interference capability of the system, and the problem that the transient noise is sensitive and the continuous leakage is slow in the prior art is solved.

The application discloses a pre-processing method based on high-pollution children's electroencephalogram data and belongs to the technical field of electroencephalogram signal processing. The application proposes a robust motion artifact detection method based on a multi-channel voting mechanism, overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision, and automatically identifies the repair period. A set of comprehensive preprocessing procedures are designed, which integrate PCHIP interpolation repair, adaptive Bayesian wavelet denoising, band-pass filtering and independent component analysis. The procedures are verified based on real children's continuous task electroencephalogram data. The application performs excellently in preserving frequency band information and improving weighted signal-to-noise ratio, and the average accuracy rate based on permutation entropy feature classification reaches 96.4% (205 test sets), and the highest is 100%. The application effectively solves the pre-processing problem of children's electroencephalogram data in a high-noise environment, and significantly improves the accuracy and reliability of subsequent classification tasks.

The invention provides an active noise reduction method and device, and relates to the technical field of data processing.The method comprises the steps that real-time driving state data of a vehicle are input into a mode recognition model, the current vehicle condition mode category is determined, and the real-time driving state data at least comprise the engine rotating speed; obtaining a target filter coefficient corresponding to the vehicle condition mode category from a preset filter coefficient lookup table; the target filter coefficient is utilized to update a filter coefficient of an adaptive filter in the active noise reduction system, and the adaptive filter generates an anti-noise signal to offset engine noise based on the updated filter coefficient and a reference signal corresponding to the engine rotating speed. The active noise reduction system can respond quickly when the working conditions of the vehicle are switched quickly, and the transient noise reduction performance and the stability of the overall noise reduction effect under the complex driving working conditions are remarkably improved.

The application discloses a wet granulator fault identification method and relates to the technical field of industrial process control. Through a dynamic scaling factor and a sliding window mechanism, the application can adapt to normal fluctuations of raw material properties in real time, significantly reduce the false alarm risk caused by slight changes in particle size or moisture content, adopt an incremental learning and forgetting strategy for an online updated multivariate statistical process control model, effectively handle data drift and working condition switching, avoid model rigidity, and improve stability in continuous production. Through continuous monitoring and double threshold value design, an abnormal score and hysteresis alarm mechanism can distinguish between transient noise and real faults, reduce alarm jitter, and ensure the timeliness of fault identification. The model robustness and transferability are further enhanced by integrating multi-source data such as near-infrared spectral characteristics and referring to experience tolerance in a knowledge base, and reliable monitoring can be realized without hardware modification.

The invention discloses a rotating machine fault classification method based on semi-supervised transfer learning, and relates to the field of fault diagnosis, and the method comprises the following steps: S1, collecting a vibration signal containing transient noise and a small number of fault labels; s2, positioning and removing transient noise by using a first-order Markov model, and dividing a 80% training set and a 20% test set; s3, building a semi-supervised transfer learning model; s4, training a semi-supervised transfer learning model; s5, the model is tested, and it needs to be ensured that the accuracy rate is larger than or equal to 95% under the label rate of 5 And S6, performing online application. According to the method, semi-supervised and transfer learning are fused, the non-label data value is effectively mined, transient noise is suppressed, the blank of low-label and high-interference working condition diagnosis is filled, a data driving method is promoted to move from a laboratory to an industrial complex scene, and key technical support is provided for field improvement of fault detection accuracy and reliability and predictive maintenance upgrading.

The invention discloses a real-time ball target detection method and system based on artificial intelligence. The method comprises the steps of initial detection of a ball target, bounding box zooming, ball adaptive matching degree enhancement, ball credibility index enhancement, ball motion orbit diameter repair, motion orbit diameter smoothing and output of a real-time detection result. The invention belongs to the field of target detection, and particularly relates to a real-time ball target detection method and system based on artificial intelligence. According to the scheme, scaling factors are dynamically adjusted based on credible indexes, direction sensitive scaling is introduced, and insufficient scaling in the high-speed motion direction is avoided; reducing matching failures during rate mutation based on dynamic scaling distance enhancement; shape misjudgment caused by insufficient pixels is reduced based on micro weighted shape optimization; introducing previous three frames of historical credible exponential attenuation items, and filtering instantaneous noise by using historical trajectory continuity; and focusing candidate matching based on the survivability and carrying out rate enhancement fusion, thereby improving the ball target detection effect.

This application discloses a method and system for real-time inference and parameter inversion of gravitational wave events based on deep learning. The method includes: constructing a physics-instrument dual-manifold decoupled network; extracting physics signal features and instrument noise features from time-frequency spectra; introducing physical manifold constraints and orthogonally decoupling the features in the latent space; performing parameter inversion on the decoupled physics features using a conditionally normalized flow model; and verifying the consistency of the inference results by regenerating the waveform in reverse and analyzing the residuals. This application distinguishes between high-eccentricity gravitational wave signals and detector transient noise, improving recognition accuracy and reducing false alarm rate.

This invention relates to a keyword detection method in noisy environments based on multi-task joint learning, belonging to the fields of speech signal processing and natural language processing. It extracts multi-layer acoustic representations from noisy signals using a pre-trained audio encoder model, then performs temporal and inter-layer aggregation through global average pooling layers to generate highly compact and noise-resistant shared features, eliminating local interference from transient noise. Finally, the features are input in parallel into detection and classification branches, and a multi-task joint optimization strategy is used for training to enhance the model's ability to distinguish between speech and background noise. A keyword detection test dataset in a real noisy environment is constructed, and a cascaded decision logic of detection followed by classification is adopted in the inference stage to effectively filter invalid inputs. This invention combines the generalization representation power of the pre-trained model with a multi-task collaborative learning mechanism, significantly improving the model's recognition accuracy, system stability, and engineering practicality in low signal-to-noise ratio and real non-stationary noise environments.

The invention provides an intelligent voice interaction system and method, and relates to the technical field of electronic information, and the system comprises a collection calibration module which is used for collecting mixed audio signals in an environment in real time through a multi-channel acoustic sensor array, and setting four reference positioning points located at corner points on the sensor array to form a quadrilateral structure; generating a geometric correction value based on the area characteristics of the quadrangle, synchronously extracting noise frequency band characteristics and voice short-time energy distribution characteristics, and calibrating the extracted characteristics by using the geometric correction value; and the noise identification module is used for identifying noise types, including wide-band high-intensity noise, low-band continuous noise, multi-source human voice interference and sudden transient noise, based on the noise band characteristics and the voice short-time energy distribution characteristics, generating a mark vector, and dynamically adjusting a noise reduction strategy according to the mark vector. According to the invention, the audio signal is collected and calibrated through the multichannel acoustic sensor array, and the accuracy and smoothness of voice interaction are improved.

The application discloses a karaoke audio processing method based on human voice separation and repair, which comprises the following steps: acquiring a time-frequency matrix of a karaoke audio signal, and judging whether noise components exist in the karaoke audio signal; identifying a noise basis matrix and an effective audio basis matrix, calculating the overlap coefficients of noise and effective audio on different frequency bands, and determining the cross frequency bands; identifying transient noise and steady noise according to the noise basis matrix and the corresponding activation matrix; designing a filter to perform real-time filtering on noise of each frame; judging the lost phase information, using linear difference interpolation to fill the missing area phase, and compensating the time delay through time-frequency matrix translation; judging the denoising effect and the audio repair effect, and formulating and executing a karaoke audio signal processing improvement scheme. The application can effectively solve the problems of noise interference, audio distortion, phase loss and time delay compensation existing in the current karaoke application, significantly improve the audio quality, and provide users with a more high-quality and personalized karaoke experience.

The invention discloses a full-scene video conference guarantee platform and method, and relates to the technical field of communication. According to the method, the speaking rotation model is established in the conference proceeding process, the intention speaking signal is fused, the next potential speaker is recognized in advance, and when the trigger threshold value is met, video stream booking is initiated in advance and written into the background buffer area, so that buffered data are preferentially used to enter a rendering link during view switching; therefore, switching waiting is reduced, and visual follow-up coherence of an observer is improved; besides, rotation statistical characteristics and local intention evidences are fused, and a switching cost item related to the current conference view is introduced into a fusion score, so that probability output is insensitive to short-time insertion and transient noise, meaningless frequent skipping is reduced, and a view focus more stably fits a real speaking rhythm.

This invention relates to the field of audio processing technology, and in particular to a method and system for anti-feedback processing of broadcast audio channel selection. The method includes receiving audio data frames via a digital bus and parsing the audio type flag and audio data stream from the audio data frames; initiating a crossfade transition period with a preset transition duration; obtaining mixing coefficients according to a predetermined gradient curve during the crossfade transition period; and weighted mixing the processed speech stream with a delayed music stream based on the mixing coefficients to obtain the output audio stream; and synchronously updating the current anti-feedback parameter set according to the mixing coefficients during the crossfade transition period, so that the current anti-feedback parameter set gradually changes with the change of the mixing coefficients. This invention solves the transient noise problem caused by independent parameter switching in the prior art, and while ensuring the anti-feedback effect of speech amplification, it achieves high-fidelity direct pass-through of the music signal, making the switching process smoother and more seamless.

The application relates to the technical field of language data processing, and discloses a language data preprocessing method for multiple languages and complex scenes, a speech data preprocessing system for multiple languages and complex scenes based on an AutoPrep framework, and five modules of speech enhancement, speech segmentation, speaker clustering, target speech extraction and quality filtering are integrated to realize automatic and structured processing of speech data. The scheme realizes differentiated suppression of stable-state and transient-state noises in multiple language speech signals, especially in small language (such as Kazakh and Tagalog) scenes, effectively improves the speech signal-to-noise ratio and the language independence of speech characteristics, overcomes the problem that the existing technology lacks a special phonetic system processing module for small languages, resulting in a high phoneme mapping error rate, and enhances the availability and processing effect of low-resource language data.

The present application relates to a kind of based on noise sensor's transfer point control dust removal linkage device and linkage control method, belong to industrial dust control and automation control technical field.The device includes linkage control unit, noise sensor, actuator and power module;Linkage control unit is connected between noise sensor, actuator by communication cable;Linkage control unit and power module are connected by power cable.Actuator is spray dust suppression mechanism or closed dust removal mechanism, and selection is carried out according to application scene.The present application can distinguish the working condition of transfer point transport belt by the composite criterion of double threshold value and duration analysis, realizes adaptive control dust removal adjustment, and can effectively filter transient noise interference, reduces the probability of equipment false start or miss start, and reliability is high;In addition, the present application uses noise sensor non-contact signal acquisition, avoids the problems such as sensor wear, collision damage, and improves economic benefit.

This invention discloses a data analysis method and system for the production and testing of miniature bearings based on big data, belonging to the field of data processing technology. The invention pre-trains a recursive time-series network by collecting vibration signals from bearings in known states. This network, combined with a transient noise suppression operator, obtains the evolutionary feature vectors of bearings in the same batch. Static feature vectors are extracted by combining processing time sequences and end-face images. After splicing, visually missing samples are corrected, and a reconstructed kernel feature matrix is ​​constructed. Based on this matrix and real labels, bidirectional alternating optimization is performed to obtain a feature transformation and mapping matrix, establishing a bearing state database. Multimodal data of the bearings under test are collected to generate initial feature vectors to be detected. After mapping, the similarity with database samples is calculated to output the final detection result. This invention deeply integrates multimodal features, effectively overcoming interference from workshop visual contamination, thermal expansion, and group tolerance drift, significantly improving detection accuracy and robustness.

The present application relates to the technical field of intelligent livestock breeding, and particularly relates to a livestock behavior recognition method and system based on a brain-like network. The method comprises the following steps: acquiring collar-end data; performing gravity component estimation and motion component separation according to the collar-end data to obtain gravity component data and motion component data respectively; performing trigger determination on the gravity component data and the motion component data to obtain trigger determination data; performing pulse coding according to the trigger determination data to obtain pulse coding data; constructing a brain-like network according to the pulse coding data to obtain a brain-like network model; and performing behavior state machine fusion according to the brain-like network model to obtain a livestock behavior recognition model. The present application realizes long-term stable recognition of livestock behavior at a low-power collar end, effectively reducing the energy consumption burden caused by continuous calculation. Through gravity gating inhibition and state machine fusion, the anti-interference ability to head-shaking interference, loose wearing and transient noise is enhanced.

The utility model relates to the technical field of server signal coupling conduction, in particular to a detection device for preventing server signal coupling conduction interference, which comprises a shielding shell. The device further comprises a mounting base, the mounting base is fixedly connected into the shielding shell, a first driving motor is fixedly connected to the shielding shell, the output end of the first driving motor is fixedly connected with a first driving rotating shaft, the first driving motor is used for driving the first driving rotating shaft to rotate, and the first driving rotating shaft is rotationally connected to the shielding shell. The other end of the first driving rotating shaft is fixedly connected with a worm; according to the utility model, the server body is subjected to omnibearing detection through a plurality of driving pieces and detectors, high-frequency transient noise, low-frequency ground loop interference and radio-frequency signals coupled by a cable bundle can be captured at the same time, the situation that a traditional device has a detection blind area is avoided, a hidden interference source is found in advance, the system safety is enhanced, and the service life of the server is prolonged. The interference source can be quickly positioned, the troubleshooting time is shortened, and the method has both technical perspectiveness and economical efficiency.

This invention discloses a method for bidirectional linkage between pipeline sensor data and a 3D GIS scene, comprising the following steps: constructing a 3D GIS engine kernel and an IoT sensing engine kernel, and formulating ID encoding rules for all pipeline network entities; utilizing the multi-protocol adaptation framework of the IoT sensing engine kernel; executing a forward data-scene linkage process; executing a reverse scene-device linkage process; and constructing a closed-loop management and control system for pipeline network operation status based on the full-link data interaction of forward and reverse linkages. This invention, through forward data-scene linkage and reverse scene-device linkage, enables the completion of the entire process of data viewing, equipment management, and emergency response within a 3D GIS scene without the need for cross-system switching; it employs a spatiotemporal dual threshold validity verification mechanism, simultaneously verifying data validity from the perspectives of temporal abrupt changes and spatial gradient rationality, effectively filtering out transient noise and abnormal data caused by sensor malfunctions.

The invention relates to the technical field of noise monitoring, in particular to an engineering construction noise real-time monitoring method and system, and the method comprises the steps: collecting a noise signal in real time in a construction region of an electric power construction project through noise monitoring equipment; obtaining the instantaneous noise degree of each noise data in each noise signal, and obtaining the steady-state noise degree of each transient noise interval and each steady-state noise interval in the noise signal; obtaining a feature approximation degree between any two noise monitoring devices, and classifying all the noise monitoring devices; for any type, the position of a noise pollution source is obtained through the feature approximation degree and the noise signal difference between different noise monitoring devices; acquiring an attenuation factor at each noise monitoring device; and obtaining a noise grade score of the noise pollution source. The invention aims to improve the recognition precision of the noise pollution source and more comprehensively and accurately evaluate the sound environment pollution severity at the noise pollution source.

The invention relates to the technical field of electric digital data processing, discloses a high-speed data communication and noise suppression method for a digital isolator, and provides a cross-layer collaborative optimization scheme for solving the problem that data distortion is caused by the fact that an existing digital isolator is easily influenced by noise during high-speed communication. The method comprises the following steps: at a sending end, compensating high-frequency attenuation of an isolation channel by adopting a self-adaptive pre-emphasis technology, and carrying out channel coding on data to be transmitted; and at a receiving end, sampling the signal by using a dynamic threshold comparator to obtain soft decision data containing amplitude information, and performing Viterbi decoding based on the soft information to correct a transmission error. According to the invention, a hybrid automatic retransmission mechanism is combined, and the coding rate is adaptively adjusted according to the channel quality. According to the invention, through joint optimization of the physical layer and the link layer, the data transmission rate and reliability of the digital isolator in a high common-mode transient noise environment are significantly improved, and the data integrity is effectively ensured.

Implementations generally relate to noise cancelation for loud ambient transient sound. In some implementations, a method includes capturing, by a one or more microphones coupled to one or more earbuds, ambient sounds from an environment of the user while the user is wearing the one or more earbuds. The method further includes enabling, by the one or more earbuds, the user to hear the ambient sounds and media content sounds. The method further includes attenuating the ambient sounds based on one or more noise cancellation policies.

The invention provides a GaN small-size charger and a low-noise design method thereof. Belongs to the cross technical field of high-frequency power electronics, electromagnetic compatibility (EMC) and acoustic engineering. The method comprises the following steps: performing switch transient waveform analysis on the circuit topology of the GaN charger to generate transient noise source distribution data; designing a GaN gate drive waveform shaping strategy according to the transient noise source distribution data, and generating optimized drive waveform parameters; dynamically modulating the GaN switching tube based on the optimized driving waveform parameter to generate low-noise switching behavior data; through collaborative design of GaN switch transient shaping, three-dimensional magnetic integration layout, piezoelectric active offset and acoustic metamaterial packaging, the method can reduce audible frequency band noise to below 35dBA while keeping high power density of the charger, and obviously improves mute experience of a user in use.