263 results about "Filter noise" patented technology

The invention relates to the technical field of urban infrastructure intelligent detection, and discloses an urban drainage pipe network damage detection system based on intelligent analysis. A multi-source data acquisition module acquires pipe network static structure parameters and real-time operation monitoring data through a distributed sensor network; generating a topological characteristic value, a dynamic operation state characteristic value and a dynamic detection threshold set of each damage type; the data preprocessing module filters noise of the monitoring data, eliminates abnormal values and extracts time domain and frequency domain feature vectors; the feature fusion module fuses and generates a multi-dimensional fusion feature matrix based on the topology and operation feature values; the intelligent analysis engine calculates and outputs a damage type diagnosis result through mode matching; the spatial positioning module generates three-dimensional coordinate positioning data of the damaged area in combination with the topological characteristic value; and the dynamic learning module optimizes the detection rule according to the maintenance data. The system realizes intelligent damage detection and accurate positioning, improves the detection accuracy and efficiency, adapts to a complex environment, and is high in intelligent level.

The invention discloses a method for detecting the quality of a functional layer of a building outer wall by an unmanned aerial vehicle, and particularly relates to the technical field of building outer wall detection.The method comprises the steps that firstly, an infrared image of a building outer wall facing object is collected through the unmanned aerial vehicle, pixel points and remaining pixel points of a serious hollowing area are determined, and the abnormal degree of the remaining pixel points is calculated; clustering is carried out by adopting a defect probability-based region growing method; based on seed point screening of morphological preprocessing, structural elements of different sizes are comprehensively determined to be used through weighted summation calculation according to the jitter frequency of the unmanned aerial vehicle and the image resolution, and noise is filtered step by step. Generating a multi-scale pyramid for the original image, and detecting candidate seed points at each level; sorting and preferentially selecting the candidate seed points based on the morphological closure degree and the shape regularity of each candidate seed point; pixel points in the neighborhood of the growth seed points are merged to obtain all hollow defect connected domains; and judging the quality condition of the building outer wall functional layer according to the area of the hollowing defect connected domain.

The invention provides a casting industry abnormal sound detection and grading response method based on voiceprint recognition, and belongs to the technical field of casting industry detection. A high-temperature-resistant microphone array is arranged at an easy-to-leak part of cast aluminum equipment, and three-stage filtering noise reduction and amplitude normalization preprocessing are adopted, so that the problem of poor signal quality caused by noise interference in a complex environment is effectively solved; the characteristics of the molten aluminum leakage sound in different frequency bands and different stages can be captured through variable window long-short time Fourier transform and extended Mel frequency cepstrum coefficient in combination with extraction of an energy change rate and a frequency spectrum gravity center; a Transform-CNN hybrid deep learning model based on an attention mechanism is constructed, and feature screening is optimized through principal component analysis and recursive feature elimination, so that the recognition and generalization ability of the model to the abnormal sound in the casting industry is significantly improved; and meanwhile, graded response measures are made based on the detection result, so that the abnormal conditions of molten aluminum leakage with different severity degrees are processed.

The invention discloses a vehicle detection and identification method and system based on laser sensing, and relates to the field of vehicle detection.The method comprises the steps that firstly, obstacle reflection signals are obtained through a multi-band laser array, preliminary classification is conducted through wavelength, time difference and intensity difference, and point cloud features are constructed; a lightweight network is adopted to filter noise points, and point cloud holes and shielding areas are intelligently complemented; then, performing hierarchical voxelization processing according to the obstacle distance, performing coarse-grained downsampling on a long-distance target, and keeping details on a short-distance target fine-grained; and finally, through a double-stage verification mechanism of a lightweight PointPill network and a three-dimensional convolutional network, precise grading detection and target reliability rechecking of far and near targets are realized, real-time performance and accuracy are considered, the problems of poor adaptability, low precision and insufficient dynamic scene adaptability of an existing laser radar detection technology are solved, and the method has the advantages of high detection precision and high reliability. According to the invention, high-precision and high-real-time vehicle detection and identification can be realized.

The invention discloses a cross-modal remote sensing image unsupervised adaptive method based on unreliable pseudo tag guidance. The method comprises the following steps: step 1, acquiring data of a source domain and a target domain and preprocessing the data; 2, constructing a teacher-student self-training framework, generating a pseudo tag for a target domain by a teacher network, and filtering noise through confidence evaluation; 3, dividing the image into reliable pixels and unreliable pixels according to the confidence coefficient, and generating a mask; 4, constructing positive samples, negative samples and anchor point features; 5, designing an unreliable sample guide pixel contrast loss function; 6, optimizing the loss function training model until the optimal performance is achieved; and 7, predicting test set data by adopting the trained model to obtain a semantic segmentation result. According to the method, the potential of pseudo labels of which modals are difficult to label is fully mined, the problem that unreliable pixels are insufficient in utilization during training is solved, and finally more effective cross-modal domain alignment and better cross-modal unsupervised domain adaptive semantic segmentation precision are realized.

The invention discloses a power transmission line fault identification method and device based on artificial intelligence, and relates to the technical field of power transmission line fault identification. Determining line data of the target area, performing data acquisition according to the line data to obtain an original traveling wave signal, and filtering the original traveling wave signal to obtain a time domain signal; performing adaptive variational mode decomposition on the time domain signal to obtain a mode component, and performing calculation according to the mode component to obtain a fault point; and determining an inspection scheme according to the fault point, executing the inspection scheme to obtain a fault point image, and performing alarm judgment on the fault point image through artificial intelligence. Line data and traveling wave signals are accurately collected, noise is filtered by adopting an advanced filtering technology, and the signal quality is improved; adaptive variational mode decomposition is utilized to accurately extract a mode component, and the fault point positioning precision is enhanced; faults are quickly and accurately judged by combining artificial intelligence image recognition, automatic operation and maintenance are realized, and the operation and maintenance efficiency of the power transmission line and the stability of the power system are improved.

The invention discloses a multi-sampling and cross-domain matching-combined roadside multi-laser radar point cloud splicing method, which comprises the following steps of: firstly, inputting roadside point cloud data at the same moment and at different visual angles, and adaptively adjusting a sampling proportion by analyzing point cloud density and noise level so as to enhance effective information and filter noise; then, geometric and shape features are extracted based on neural networks such as DGCNN and Point Transform, and preliminary cross-domain feature matching and coarse registration are carried out through algorithms such as GeoTransform, so that an initial transformation matrix is obtained; and carrying out fine point pair matching and rigid body transformation optimization in combination with RANSAC and ICP algorithms to realize high-precision point cloud registration. And finally, finishing point cloud merging by using the final transformation matrix, removing residual noise through bilateral filtering, and outputting a spliced high-quality point cloud image. According to the process, the splicing precision and robustness of the heterogenous laser radar point cloud are effectively improved.

The invention provides an underground ore body positioning identification method and system based on a ground penetrating radar, and relates to the technical field of ore body detection, and the method comprises the steps: inputting a corrected multi-dimensional point cloud feature set into a pre-trained closed-loop convolutional neural network, optimizing the network parameters through transfer learning, and obtaining a multi-dimensional point cloud feature set; outputting the category confidence and the spatial position probability distribution point cloud of the ore body target; on the basis of the spatial position probability distribution point cloud, generating ore body depth and horizontal positioning point cloud through a reverse projection algorithm in combination with an electromagnetic wave two-way travel time calculation rule and a Kanay resistivity measurement result; and performing multi-scale two-dimensional empirical mode decomposition on the reverse projection positioning point cloud, and filtering noise components to extract a high-confidence target point cloud. According to the invention, the accuracy and stability of ore body identification are improved.

The invention discloses an electromagnetic spectrum prediction method and system based on fractional Fourier transform. The method comprises the following steps: constructing historical spectrum observation data in a target area; mapping the collected spectrum observation data to a corresponding fractional order Fourier domain by using adaptive fractional order Fourier transform, and adaptively adjusting fractional order parameters of a fractional order Fourier transform layer to output a fractional order Fourier domain containing predictable spectrum components; in the mapped fractional order Fourier domain, non-predictive noise in the converted data is filtered out; performing feature extraction and prediction on the frequency spectrum data of the fractional order Fourier domain after noise filtering by using a complex valued linear network to obtain a prediction result of the fractional order Fourier domain; and mapping the prediction result of the fractional Fourier domain back to the original time domain by adopting inverse fractional Fourier transform, and generating final frequency spectrum prediction output. According to the method, the accuracy of weak periodic spectrum prediction is effectively improved through fractional Fourier transform and a filtering strategy.

The invention relates to the technical field of radar echo denoising, in particular to a radar echo signal filtering noise self-adaptive suppression method and a radar echo signal filtering noise self-adaptive suppression system. Acquiring radar station position and beam pointing data, and synchronously acquiring forest vegetation data and environment interference data of a beam pointing target area to form radar environment data; the radar environment data are identified through the multi-band decision model, and multi-modal waveform parameters are obtained; based on the multi-modal waveform parameters, radar equipment is controlled to perform periodic detection on the target area, and radar echo data are obtained; the method comprises the steps of identifying radar echo data and radar environment data, extracting dynamic spectrum characteristics in the radar echo data, performing clutter suppression processing on the dynamic spectrum characteristics by using the radar environment data, calculating the confidence probability that an early fire exists in a target area, and executing space-time consistency joint verification; according to the invention, fire early warning is carried out through the confidence probability of the fire.

The invention discloses a hysteresis compensation system and method for low-frequency compensation of a magnetoelectric vibration sensor. The compensation system comprises a difference to single-end circuit, a second-order lag compensation circuit, a single-chip microcomputer, an ADC conversion module, a first-order high-pass filtering module and a second-order low-pass filtering module which are sequentially connected with the magnetoelectric vibration sensor. The differential-to-single-end conversion circuit receives voltage signals output by the magnetoelectric vibration sensor and converts the voltage signals into single-end electric signals, the second-order lag compensation circuit receives the single-end electric signals of the differential-to-single-end conversion circuit for low-frequency compensation, the single-chip microcomputer receives low-frequency compensation signals, the ADC conversion module converts the low-frequency compensation electric signals into digital signals, and the digital signals are transmitted to the magnetoelectric vibration sensor. The first-order high-pass filtering module filters noise with the frequency lower than f1 out of the digital signals, and the second-order low-pass filtering module filters noise with the frequency higher than f2 out of the digital signals output by the first-order high-pass filtering module. According to the invention, the amplitude response of the magnetoelectric vibration sensor during low-frequency signal measurement can be improved.

The invention provides an intelligent anomaly detection method and system based on a convolutional flow adversarial network, and relates to the technical field of anomaly detection, and the method comprises the steps: collecting a full-life-cycle vibration signal of equipment, processing the full-life-cycle vibration signal into a time-frequency diagram, and dividing the time-frequency diagram into a normal training set and a full-cycle test set; constructing a convolutional flow adversarial network comprising a generator and a discriminator; pre-training the generator, and alternately optimizing the discriminator and the generator; and constructing an abnormal state detection model by using the trained generator to realize full-cycle test set online detection. The method can accurately capture normal features, is sensitive to early abnormality, reduces the risk of mode collapse, is high in stability, provides a probability reference to reflect fault evolution, can filter noise, and is good in robustness.

The invention provides an image video retrieval method based on a domain fine-tuning large language model, which comprises the following steps: performing fine-tuning on a pre-training model to obtain a fine-tuning pre-training model for intention classification and keyword extraction; performing dynamic iteration screening on an optimal prompt template through Monte Carlo tree search in combination with a hidden Markov model (HMM); performing noise filtering on the keyword list, and predicting category labels of the filtered keywords through a conditional random field model to obtain a keyword enhancement set; combining with the user intention to generate a query condition, and obtaining a candidate resource set; and according to the similarity between the user query text and the candidate resource set, and in combination with the optimal prompt template, obtaining the resource path with the highest matching score between the user query and the candidate resource, and obtaining the retrieved image or video, so that the identification deviation possibly occurring when a general model processes proper nouns and terminologies can be effectively solved, and the user experience is improved. And the retrieval accuracy and response speed are improved, so that the retrieval accuracy and professional adaptability are improved.

The invention provides a motor stator fault detection system and method, and relates to the technical field of data processing, and the method comprises the steps: synchronously collecting a three-phase current signal and a stator winding temperature signal during the operation of a motor in real time through a current sensor and a temperature sensor; performing time-frequency domain analysis and spectrum feature extraction on the three-phase current signal to obtain a current unbalance degree and a current harmonic distortion rate, and forming a current feature parameter set; performing filtering noise reduction and trend elimination processing on the stator winding temperature signal to obtain a processed signal; on the basis of the processed signals, temperature abnormal points are recognized through a k-nearest neighbor clustering algorithm of the three-dimensional Euclidean distance, the temperature instantaneous change rate is calculated according to the temperature abnormal points, and a temperature characteristic parameter set containing the abnormal point density and the temperature rise rate is obtained. According to the invention, stator fault detection and differential early warning are realized.

The invention discloses an ultrasonic phased array weld defect intelligent identification method based on adaptive filtering, and belongs to industrial nondestructive detection image identification methods, the technical scheme is that the method comprises the following steps: sampling and sorting pipeline weld PAUT detection images; dividing the collected image samples into a training set and a test set; pixel classification and filtering noise reduction are carried out on the training set images through a hybrid adaptive filter; performing abnormal signal labeling on the preprocessed training set image and the unprocessed test set image respectively; training a target recognition network through the preprocessed training set; and collecting a new fan-shaped scanning image of the pipeline weld defect as network input, and identifying the weld defect. The ultrasonic phased array weld defect intelligent identification method based on self-adaptive filtering has the beneficial effect that the ultrasonic phased array weld defect intelligent identification method based on self-adaptive filtering is provided.

The invention discloses a root cause alarm analysis method and system based on time sequence association and multi-modal fusion, and the method comprises the steps: taking digital twin dynamic threshold cleaning as a core, fusing multi-source data time sequence alignment verification, and forming a multi-source data set of time sequence alignment and preliminary restoration through exception handling and cross-system time consistency verification; a cleaning threshold is optimized in combination with a working condition to filter noise, a label confidence weighting method is fused, confidence is given to a label through twin simulation, and a multi-modal data set with a multi-dimensional label is constructed. A learning model is constructed and trained, exclusive feature extraction branches are designed for multi-source data, a twin working condition adaptation enhancement module is introduced to strengthen core modal extraction, and the proportion of abnormal modals in fusion is reduced by means of a dynamic attention filtering mechanism. Based on the model and a twinning system, root cause penetration type positioning is completed through dynamic sequencing of root cause confidence in combination with a twinning reduction time sequence. And the root cause analysis efficiency and accuracy are improved.

The invention relates to the technical field of traffic information collection, in particular to a tunnel traffic flow detection method based on a video cloud networking technology, and the method comprises the following steps: obtaining video flow data collected by each monitoring camera in a tunnel in real time; performing compression coding processing on the video stream data; performing noise filtering and frame synchronization preprocessing on the coded video data; in a preset edge computing node, inputting the structured video data into a vehicle flow detection model to extract vehicle feature parameters in the video frame; performing tracking association on the target vehicle in the video frame through an asymmetric Kalman filtering algorithm, and comparing the vehicle flow detection data with a preset vehicle flow threshold; and in the cloud analysis platform, analyzing the structured detection data through the tunnel traffic flow prediction model. According to the invention, noise filtering and time-space alignment can be carried out on the collected vehicle operation video data, and the accuracy of tunnel traffic flow detection is improved.

According to the classic-quantum wavelength division multiplexing noise reduction method and system provided by the invention, by using a polarization filtering system, Raman scattering noise generated by classic optical signals in a quantum communication system can be attenuated, and the signal-to-noise ratio and the common-fiber transmission performance are improved; in combination with data post-processing, the polarization coding QKD system can still perform normal decoding after polarization filtering, and the safety of the QKD system is not affected. Through the combination of the polarization filtering system and the traditional filtering noise reduction technology, the requirement for power attenuation of classical optical signals can be reduced to a certain extent, or the requirement for pulse width control and the like of the classical optical signals can be reduced, so that the communication risk of the classical optical signals is reduced.

The invention discloses a monitoring system and method for determining an abnormal geologic body based on distributed optical fiber sound wave vibration, and relates to the technical field of distributed optical fiber sound wave detection.The monitoring system comprises a distributed optical fiber sensing unit, a signal excitation unit, a photoelectric demodulation unit, a signal processing and analyzing unit, a result recording and outputting unit and the like; the distributed optical fiber is used for continuously collecting stratum vibration response; the signal excitation unit applies controllable disturbance to a known reference position to induce a potential abnormal geologic body to generate an inactive excitation response signal; the photoelectric demodulation unit performs phase demodulation on backscattered light transmitted back by the optical fiber to form distributed vibration measurement data; and the signal processing and analyzing unit is used for filtering and denoising the data, establishing a quantitative mathematical model of a signal incident angle and signal intensity, solving an epicentral distance and an incident angle and calculating the spatial position of an abnormal geologic body by combining propagation attenuation correction and least square inversion, and is suitable for dangerous section identification and avoidance decision before mining and construction.

The invention discloses a self-adaptive air pressure regulation and control system of quartz sand unpowered powder selecting equipment, and relates to the technical field of automatic control. According to the method, wind pressure and airflow disturbance signals are collected, a high-resolution map is generated through Hampel filtering and noise reduction, features are extracted through the convolutional neural network, key influence areas are identified, the problem of unstable wind pressure caused by airflow disturbance in a mountainous area is solved, and the particle separation precision is improved; a natural potential energy distribution matrix is constructed based on building floor height difference, velocity field reconstruction and airflow channel optimization are driven, technological parameters are adjusted in combination with a gradient descent algorithm, precise utilization of natural potential energy is achieved, energy consumption is reduced, uneven efficiency is improved, airflow distribution is simulated through a fluid dynamic model, and sedimentation behaviors are analyzed and predicted through particle trajectory. A support vector machine is used for classifying tracks and generating regulation and control instructions, PID control is used for stabilizing air pressure to form a closed loop, the problems that fine powder is mixed with coarse powder, coarse particles are left and the like are avoided, the equipment blockage risk is reduced, and the product purity stability is improved.

The invention discloses a single-axis tracking support driving system fault diagnosis method and device based on EEMD-CWT-CNN. The method comprises the following steps: carrying out working condition classification on a single-axis tracking photovoltaic support according to an operation mode and a fault type; collecting operation current of the single-axis tracking support system under various working conditions; ensemble empirical mode decomposition is adopted to filter noise caused by the environment in the running current; performing feature extraction on the current signals by adopting continuous wavelet transform, and converting various current signals into corresponding time-frequency images; establishing an identification model, inputting a time-frequency image with working condition characteristic information, training, and carrying out state identification on the single-axis tracking support system through the trained model; according to the operation characteristics of the single-axis tracking support system, effective feature extraction can be performed on the operation current of the support, the operation state can be monitored, and support driving, diagnosis and communication functions can be completed through the core controller.

The invention discloses an ammonia gas leakage multi-sensor fusion automatic monitoring method and system, and the method comprises the steps: deploying a plurality of gas detection sensors in a monitoring region, synchronously collecting the ammonia gas concentration and environmental parameters, and forming a multi-source data set; carrying out noise filtering and drift correction by utilizing the edge computing node, and extracting spatial-temporal characteristics to identify concentration anomaly; determining a leakage source coordinate through a data fusion algorithm, simulating a diffusion path and a trend, and constructing a risk diffusion model; and finally, an emergency trigger signal is extracted, ventilation equipment and valve control are activated, and real-time linkage response is achieved. According to the method, data layer fusion and a risk diffusion model are combined, so that the high efficiency of accurate positioning and emergency response of a leakage source is ensured, the safety and reliability of harmful gas monitoring and control are remarkably improved, and a technical guarantee is provided for gas leakage risk management in a complex environment.

The invention discloses an intelligent electric power measurement and decision-making method based on an Internet of Things architecture, and aims to solve the problems of large temperature measurement deviation, poor dynamic adaptation, unquantified aging influence and extensive meter change decision-making in the prior art. The method comprises the following steps: collecting multi-source data, denoising and fusing, and generating a fused temperature observation value; inputting historical time sequence data formed by the data and the fusion temperature into the TCN, and outputting a state matrix increment to correct a Kalman filtering state equation; optimizing a filtering noise covariance matrix through PSO (Particle Swarm Optimization); based on the correction equation and the optimal noise matrix, the initial temperature of the contact is obtained through improved Kalman filtering; in combination with an SVR model, the aging compensation amount is calculated by taking the multi-source denoising data and the initial temperature as input, and the final temperature is obtained through superposition; and calculating an aging influence coefficient according to the aging compensation amount and the final temperature, and executing early warning or meter replacement. According to the invention, temperature prediction precision and long-term stability are improved, intelligent meter changing decision is realized, and equipment safety and operation and maintenance efficiency are guaranteed.

The invention discloses a city safety monitoring and early warning method and system, and the technical key points are that the method comprises the following steps: S1, multi-source data collection: collecting heterogeneous city safety monitoring data through monitoring terminals distributed in all scenes of a city, the heterogeneous data comprising real-time video stream data, structured numerical data and semi-structured log data; s2, data preprocessing and fusion: inputting the heterogeneous data acquired in the step S1 into a multi-source data fusion model based on an attention mechanism, filtering noise data through a data cleaning module, and extracting spatial-temporal features of each data source through a feature extraction module; s3, performing dynamic risk assessment; s4, grading early warning and collaborative linkage are carried out; and S5, performing closed-loop optimization. According to the method, the multi-source data fusion precision can be improved by more than 35%, the weights of different data sources are automatically identified by constructing a heterogeneous data fusion model based on an attention mechanism, the problem of data islands is solved, and a reliable basis is provided for risk assessment.

The invention discloses a dynamic sleep memory intervention method and system based on an ultrathin eyeshade, the system comprises an ultrathin eyeshade body and a cloud platform, and the eyeshade integrates electroencephalogram acquisition, memory content playing, master control and communication modules. The electroencephalogram acquisition module adopts an eight-channel super-soft spandex conductive electrode and a noise reduction unit combining adaptive notch filtering with wavelet threshold filtering; the memory playing module is a hemispherical dome miniature bone conduction loudspeaker, and silent leakage outside 30cm is realized; a dynamic sleep memory dry prediction algorithm is built in the main control module, and playing parameters can be adjusted according to delta wave intensity; the cloud stores personalized memory content and intervention records, and push content can be intelligently updated. The method comprises the steps of equipment starting initialization, electroencephalogram collection and noise reduction, deep sleep recognition, dynamic playing and data encryption synchronous closing, the problems that existing equipment is heavy and intervention is rigid are solved, non-inductive wearing, accurate intervention and privacy protection are achieved, and the method is suitable for sleep memory consolidation.

This invention discloses a weakly supervised semantic segmentation method and system for remote sensing images based on adversarial background interference, belonging to the fields of pattern recognition and machine learning. The method includes: First, prototyping a background prototype mining approach. By constructing a pure background prototype library and calculating pixel-level similarity, a background mask is generated to suppress false activations of interfering backgrounds from a priori level. Second, foreground-background decoupling is designed. A contrastive loss based on prediction entropy weighting is introduced, driving the model to aggregate background features and move away from foreground features in the feature space, enhancing semantic discriminative power. Finally, a pseudo-label optimization strategy is employed. Through hole filling and dual-threshold screening, the structural integrity of pseudo-labels is repaired and noise is filtered, providing high-quality supervisory signals. This method aims to solve the technical problems in existing weakly supervised semantic segmentation of remote sensing images, where severe background interference leads to high pseudo-label noise and incomplete foreground target segmentation.

The invention discloses an RGB-T counting method and device based on semantic perception complementary feature mining. The method comprises the steps that firstly, images in a training data set are preprocessed; then constructing an RGB-T counting network based on semantic perception complementary feature mining, wherein the RGB-T counting network comprises a feature extractor based on Transform, a mode feature adaptive method from coarse to fine and a semantic perception agent module; the feature extractor based on the Transform is used for extracting high-level semantic representations of two different modal images; the mode feature adaptive method from coarse to fine is used for filtering noise features and mining fine complementary features; and the semantic perception agent module is used for enhancing semantic consistency perception in network output and the feature extractor, and predicting a density map and a counting result through a density regression layer. According to the method, the RGB-T crowd counting task can be efficiently completed, and the counting result is better than that in the prior art.

The invention relates to a touch control processing method and device, electronic equipment and a storage medium. The touch control processing method comprises the following steps: in response to a detected touch control operation, obtaining a first induction signal corresponding to the touch control operation; acquiring a correction parameter associated with the touch screen to which the first induction signal belongs; wherein the correction parameter is used for filtering noise introduced when the sensing signal of the touch screen is filtered, the correction parameter is obtained by carrying out noise reduction processing on a sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs; correcting the first induction signal by using the correction parameter to obtain a second induction signal; and determining a touch position according to the second sensing signal. According to the embodiment of the invention, the noise or redundant information in the first induction signal can be effectively reduced, and the principal component in the first induction signal is reserved, so that the quality of the second induction signal is improved, and the user experience is improved.

An example medical device includes a memory; and processing circuitry coupled to the memory, the processing circuitry is configured to: receive, from one or more electrodes coupled to the medical device, a cardiac signal of a patient; determine an initial indication of a cardiac event based on the cardiac signal; determine whether a particular feature of the cardiac signal satisfies a filter threshold; and in response to the particular feature of the cardiac signal satisfying the filter threshold, activate a filter to filter noise from the cardiac signal.

The invention discloses a streaming video subtitle generation method and system based on a localized large model, and a storage medium, and belongs to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: constructing an asynchronous streaming audio extraction queue, and realizing parallel processing of millisecond-level response and background fragmentation of a first audio segment of a video; a decoupled voice activity detection model is adopted to carry out refined slicing on the audio stream, and noise filtering and muting are carried out; text decoding is carried out through a speech recognition model subjected to fine tuning of synthetic data, and term enhancement in professional fields is supported; carrying out multi-modal fusion and time sequence calibration on an identification result to realize sound and picture synchronization and text standardization; and finally, rendering subtitles through a real-time callback mechanism and a cross-platform interface. The method solves the problems of high delay of long video subtitle generation, time axis drift and low terminology recognition rate, is especially suitable for Linux / Windows cross-platform localization deployment, and has high privacy security and low-cost field adaptive capability.