34results about How to "Solve noise interference" patented technology

The invention discloses a child massage teaching auxiliary system based on artificial intelligence, and particularly relates to the technical field of massage auxiliary teaching, a Kalman filtering technology is adopted to effectively filter out large-amplitude interference in hand motion data, time-frequency analysis is carried out in combination with fast Fourier transform or wavelet transform, and frequency domain characteristics of microfibrillation signals are accurately captured; through fine micro-tremor signal analysis, noise interference is removed, the fine change of micro-tremor of the hands of a student is accurately identified, and the evaluation precision of the massage action stability is remarkably improved; and the integrated learning progress management module automatically adjusts teaching contents according to the learning progress and skill mastering conditions of the students, provides real-time feedback and forward excitation, provides excitation when the students have good performance, prompts and optimizes follow-up actions in real time when deviation occurs, helps the students to continuously improve skills, avoids error accumulation, and effectively improves the teaching effect.

The invention relates to the field of artificial intelligence and image processing, in particular to a dynamic portrait verification and check result generation system based on machine learning, which comprises multi-modal data acquisition, recursive feedback and dynamic feature optimization, feature completion and correction, and spatial-temporal feature verification and correlation analysis. Performing time sequence optimization through a recurrent neural network, complementing missing features through a generative adversarial network, and correcting time-space consistency by using Bayesian reasoning; the system is combined with a Markov chain Monte Carlo method to dynamically adjust the weight of the multi-modal data, and more accurate time sequence characteristic data is generated; the space-time relevance of the feature data is analyzed through the graph neural network, a final verification result is generated, the identity verification accuracy and consistency in the dynamic scene are ensured, and the method is particularly suitable for passenger verification and security systems in high-flow dynamic scenes such as civil aviation.

The invention belongs to the technical field of blood oxygen detection, and particularly relates to a non-invasive blood oxygen detection method. The main machine shell is arranged on the upper portion of the blood oxygen machine, a blood oxygen sensor is arranged below the blood oxygen machine and electrically connected with the main machine shell, the main machine shell comprises a single chip microcomputer, an analog front end driving module, a display module, a circuit module, a signal receiving module, a digital signal processing module and a storage module, and meanwhile a lithium battery is arranged in the main machine shell. A power supply is provided for the oximeter; the blood oxygen sensor is directly connected with the signal receiving module to form a blood oxygen signal receiving module, and the blood oxygen signal receiving module is connected with the digital signal processing module to convert a voltage signal into a digital signal; according to the invention, the problem that detection signal pulse wave output signals are interfered by serious noise such as environmental noise and hardware circuits is solved; the problems of signal loss and signal superposition caused by a traditional filtering method are solved.

The invention discloses a hyperspectral image super-pixel spatial-spectral fusion recognition method and system and a medium, and the method comprises the following steps: S101, extracting a super-pixel feature map of a to-be-recognized hyperspectral image, and carrying out the classification recognition of the super-pixel feature map, extracting a spatial-spectral feature map from the hyperspectral image to be recognized, and carrying out classification recognition on the spatial-spectral feature map; and S102, fusing the two obtained classification identification results to obtain a final surface feature category identification result. The invention aims to realize super-pixel and spatial-spectral fusion recognition of the hyperspectral image, so as to solve the problem of poor recognition effect caused by noise and scarce sample quantity in the hyperspectral image, and improve the precision of surface feature category recognition of the hyperspectral image.

The invention belongs to the technical field of cable insulation evaluation, and provides an on-service cable insulation evaluation method and system fused with multivariate target optimization in order to solve the problem that it is difficult to comprehensively and accurately evaluate the cable insulation state in the prior art. The active service cable insulation evaluation method fused with multivariate target optimization comprises the following steps: determining a time domain incident signal and a frequency domain incident signal; variational mode decomposition is carried out on the measured time domain reflection signal to obtain a time domain actual reflection signal; designing a cable propagation filter by using an initial propagation constant, and estimating a time domain reflection signal by combining a frequency domain incident signal; determining a target function according to energy and position differences between the estimated time domain reflection signal and the actual time domain reflection signal, and optimizing a propagation constant in a set range to minimize a target function value under each frequency; and calculating a dielectric loss factor according to the optimized relationship between the propagation constant and the dielectric constant, and evaluating the cable insulativity.

The invention relates to the technical field of signal transmission, in particular to a wireless while-drilling pulse signal transmission system under a high-temperature condition. The system comprises a signal generation part, a signal transmission part and a signal receiving part, the signal generation part is used for sensing changes of temperature, pressure or vibration of an underground environment through a piezoelectric crystal, generating an initial electric signal, forming a mechanical signal and converting the mechanical signal into an initial pulse signal in an electric signal form again; the signal transmission part is used for performing mud pressure modulation on the initial pulse signal to obtain a phase change function; performing nonlinear correction to obtain a correction result; performing signal coding modulation based on the correction result; the modulation signal is sent to a signal receiving part at a far end; and the signal receiving part is used for receiving the modulation signal and demodulating the modulation signal to obtain a demodulated output signal. A complete, efficient and reliable solution is provided for wireless while-drilling pulse signal transmission under the high-temperature condition.

The utility model discloses a sound absorption structure of an earphone shell, and belongs to the field of earphones. An earphone shell sound-absorbing structure comprises a head-mounted ring, connecting ends are fixed to the ends of the two sides of the head-mounted ring, earmuffs are fixed to the inner sides of the connecting ends, a sound-absorbing component is bonded to one side of each earmuff through quick-drying glue, an earphone body is bonded to one side of each sound-absorbing component through quick-drying glue, and the earphone body is fixed to the head-mounted ring through the quick-drying glue. According to the utility model, the sound absorbing cotton is arranged in the hush pipe, and the hush pipe is connected with the sound absorbing group through the components such as the sleeve, the reserved opening and the cavity shell, so that sound can enter the hush pipe and is absorbed by the sound absorbing cotton, and the sound absorbing cotton can effectively absorb redundant sound and lingering sound; the transmission and reflection of noise are reduced, the silencing effect is achieved, and meanwhile the problems of sound quality loss and noise interference are solved.

The invention discloses a cultural relic image processing method and system based on an image recognition technology. The method comprises the following steps: carrying out preliminary preprocessing operation on an original literature and blog image to obtain a first image; using a VGG16-Unet Weibo image preprocessing model based on deep learning to carry out noise identification and removal operation on the first image to obtain a second image; performing segmentation processing on the second image by using an RT-DETR model-based literature image segmentation model to obtain a third image containing target information; and performing target information identification on the third image by using an improved ResNet-101-based literature and blog image target information extraction model, and determining target information contained in the original literature and blog image. According to the scheme, the definition of the cultural relic image is improved, the accuracy of target information segmentation and recognition is greatly improved, and the method is suitable for various application scenes such as cultural relic protection, digital archiving and academic research.

The invention belongs to the technical field of water quality monitoring, and particularly relates to an urban water supply network water quality monitoring and early warning method which comprises the following steps: S1, setting water quality monitoring points of a water supply network; s2, acquiring original time sequence data; s3, processing the original time sequence data through complete adaptive noise ensemble empirical mode decomposition CEEMDAN to obtain a plurality of IMF component subsequences; s4, calculating a sample entropy value of each IMF component sub-sequence by using a sample entropy SE, and carrying out merging reconstruction; s5, importing the merged and reconstructed IMF component subsequences into a water quality prediction model to obtain a prediction result of the water quality data; and S6, according to a prediction result of the water quality data, judging whether a water quality early warning condition is met, and if so, generating early warning information. According to the method, the water quality data of the water supply network can be effectively predicted, so that the water quality problem of the water supply network is effectively warned in advance, and the influence of the water quality problem on normal water use of residents is reduced as much as possible.

The invention discloses an online damage detection method and system based on an acoustic emission index, and belongs to the field of mechanical indexing table damage detection.The online damage detection method comprises the steps that acoustic emission waveforms of equipment to be detected are collected; extracting AE events based on the original waveform of the acoustic emission waveform, recording the starting point and the ending point of each AE event, and counting the duration of the AE events; calculating and counting an acoustic emission index of each AE event, and constructing a data set; carrying out absolute value and normalization processing on the constructed data set to obtain a new data set; calculating a damage evaluation index of each AE event in the new data set; and dividing the damage grade of the to-be-detected equipment according to the numerical value interval of the damage evaluation index. According to the invention, online dynamic detection can be carried out on the running equipment, a detailed damage grade evaluation report can be given, the time and the damage degree of the equipment damage can be monitored in time, and then fault prevention can be carried out in advance.

The invention discloses a grinding wheel abrasive particle image segmentation method based on machine vision, and the method comprises the following steps: inputting a grinding wheel abrasive particle sample image, converting the sample image into a gray-scale image, and carrying out the preprocessing of image enhancement on the gray-scale image; performing operation on the gray-scale map line by line and column by column by two-dimensional Gaussian filtering; calculating the partial derivative of each pixel point in the x-axis direction and the y-axis direction, and obtaining the gradient of each pixel point according to the partial derivative; performing non-maximum suppression according to the gradient; acquiring a global threshold by using an Otsu algorithm, and introducing the global threshold into an adaptive function for setting image segmentation by high and low thresholds of a Canny algorithm to realize edge connection; lag compensation is carried out, and middle edges are accepted and rejected; and morphologically processing irrelevant areas and filling holes. According to the method, edge connection is achieved by setting the adaptive function of image threshold segmentation, the problem of noise interference caused by the grinding wheel base body area and the environment is effectively solved, the boundary of the grinding wheel abrasive particle image contour can be accurately divided, and good flexibility is achieved.

The invention relates to a multi-modal medical image fusion method based on image denoising and illumination enhancement. The method comprises the following steps: inputting a multi-modal medical image into an illumination and contrast enhancement network for illumination intensity correction to generate an enhanced image; adding random noise to the multi-modal medical image to obtain a noise image; inputting the noise image into a denoising and reconstruction network to carry out denoising and reconstruction tasks; using the enhanced image and the multi-modal medical image as labels to jointly supervise and train a denoising and reconstruction network to obtain a feature encoder and a feature decoder with denoising and illumination enhancement functions, using the trained feature encoder to respectively extract features of different modal images to obtain modal feature maps, and using the modal feature maps to obtain the multi-modal medical image; and carrying out feature fusion on the modal feature maps of the different modal images, and reconstructing the fused feature maps by adopting a trained feature decoder to obtain a multi-modal medical image fusion result. The problems that image fusion is interfered by noise, illumination is degraded and fusion efficiency is low are solved.

The invention belongs to the technical field of computer vision, and discloses a multispectral imaging-based intelligent file investigation and identification method and device for a text detector, and the method comprises the steps: firstly carrying out the distortion correction of a file, and extracting the corrected file from an image; segmenting the text line into continuous complete characters by using a text line character segmentation method improved based on a projection method; then detecting the geometric center of each character of the text line, and calculating a center curve of the text line; and finally, the row height and the row spacing are calculated, and the abnormal row height and the abnormal row spacing are detected. In the whole process, not only is global distortion correction carried out on the document, but also direction detection is carried out on each character of each text line, so that detection errors caused by file distortion are effectively reduced.

The invention relates to the field of disaster relief communication, and particularly discloses a mining disaster relief communication system and a use method, the mining disaster relief communication system comprises a communication terminal module, a communication base station module, a signal transmission module, a signal processing module, a communication path optimization module, a terminal positioning module and an electric quantity management module; according to the mining disaster relief communication system provided by the invention, the communication terminals communicate with each other through the micro emergency communication base station installed in a mining area, a noise elimination algorithm is provided, noise elimination of signals of the communication terminals is carried out, and optimal communication path selection of the communication terminals and the communication base station in an emergency state is carried out through path optimization of network communication.

The application relates to the technical field of wind turbine fault detection, and provides a wind turbine transmission chain fault detection method and system, which comprises the following steps: acquiring multi-dimensional monitoring data of a wind turbine transmission chain; performing standardized preprocessing on the multi-dimensional monitoring data based on operation condition characteristics and signal transmission characteristics to obtain effective monitoring data; performing feature extraction on the effective monitoring data based on time-frequency feature analysis and fault feature enhancement processing to obtain transmission chain full-link fault features; combining historical operation data and real-time operation trends through data statistics and time sequence analysis algorithms to construct and update adaptive detection thresholds; and comparing the full-link fault features with the adaptive detection thresholds based on multi-index fusion decision and transmission chain fault conduction mechanism analysis to obtain fault detection results. The application solves the problems of multi-source data asynchronization and weak fault identification difficulty, and realizes high-precision fault detection and accurate positioning under variable conditions.

The invention belongs to the technical field of signal processing and deep learning, and particularly relates to a pressure signal reconstruction method based on deep learning, which comprises the following steps: classifying an input dynamic pressure signal, and judging whether the signal is complete or not; for an incomplete signal, missing data completion is carried out by using a time-frequency feature fusion filling method based on LSTM and GAIN, and then noise filtering is carried out by using a noise reduction method based on CEEMDAN-wavelet packet fusion LSTM; for a complete signal, a noise reduction method based on CEEMDAN-wavelet packet fusion LSTM is directly applied to remove noise interference; and outputting the reconstructed complete and clear dynamic pressure signal. The method can effectively solve the problems of signal loss and noise interference in an extreme environment, provides reliable technical support for research and development, testing and performance evaluation of industrial equipment, and has important industrial application value and wide market prospect.

The application discloses a kind of multi-modal sentiment analysis method and device based on feature decoupling and guiding correction, it is related to sentiment analysis technical field, including: build initial multi-modal sentiment analysis network, initial multi-modal sentiment analysis network includes multi-modal feature extraction mapping block, cross-modal interaction enhancement coding block, feature decoupling block, guiding formula gate semantic correction block and low rank fusion sentiment analysis block, determine the total loss value of training sentiment analysis result of training multi-modal sentiment data, according to total loss value to initial multi-modal sentiment analysis network is optimized iteration, until determining target multi-modal sentiment analysis network, using target multi-modal sentiment analysis network exports the target sentiment analysis result of the multi-modal sentiment data to be analyzed.Based on the above scheme, help to improve multi-modal fusion sentiment recognition precision.

This invention provides a method for constructing and detecting remote photoplethysmography (PPG) signals and heart rate. The construction method includes: acquiring and preprocessing a sequence of facial video images as an initial dataset; acquiring PPG signals as a label set; constructing a PPG signal and heart rate detection model, using the initial dataset as input and the label set as output to train the PPG signal and heart rate detection model, and the model outputting the final remote PPG signal. This invention adds a multi-level feature fusion module and a multi-scale enhancement module to the encoder and decoder, respectively, to ensure that effective physiological features highly correlated with the temporal information of the remote PPG signal are retained during feature extraction, thereby recovering accurate remote PPG signals and improving model performance. The signal estimation module performs filtering operations on the predicted signal, which can better adapt to waveform distortion values ​​to a certain extent and improve the model's prediction accuracy.

The application discloses an advertisement click rate prediction system, comprising a data acquisition module for real-time acquisition of user behavior data, attribute data, context data and biological characteristic signal data; a multi-modal feature extraction module for extracting multiple feature vectors; a dynamic feature fusion module for dynamically weighting and fusing multi-modal features through an adaptive attention fusion network; a graph neural network coding module for coding a heterogeneous graph structure to generate a graph embedding feature vector; an adaptive routing prediction module for dynamically selecting an expert subnetwork through a router network for prediction; a real-time feedback calibration module for online parameter updating based on real click feedback and generating a calibration signal; and a multi-objective optimization decision module for outputting an advertisement recommendation list based on multi-objective optimization. The application realizes accurate prediction and intelligent decision through adaptive routing and multi-objective optimization, and significantly improves the accuracy and scene adaptability of advertisement click rate prediction.

The invention provides a switching device, a vacuum interconnection sample transfer system and a use method, and relates to the technical field of vacuum analysis instruments.The switching device comprises a first flange, a second flange, a locking assembly and a telescopic piece, the first end of the first flange is suitable for being communicated with first vacuum equipment, and the second end of the second flange is suitable for being communicated with second vacuum equipment; the first end of the second flange is provided with a first position communicating with the second end of the first flange and a second position spaced from the second end of the first flange. The locking assembly can lock the second flange and the first flange when the first end of the second flange is located at the first position. The second end of the second flange is connected with the telescopic piece, and the end, deviating from the second flange, of the telescopic piece is suitable for communicating with second vacuum equipment. By adjusting the length of the telescopic piece, the on-off states of the first flange and the second flange can be conveniently switched, so that the first vacuum equipment and the second vacuum equipment are controlled to be communicated to transmit samples or separated to isolate noise, the overall structure is simple, and the vibration isolation effect is good.

The present application relates to the technical field of enterprise management and risk monitoring, and specifically discloses a method and system for penetrating risk monitoring of invested enterprises based on supply chain conduction, which accurately reflects the dynamic changes of the supply relationship between enterprises by constructing a supply chain meta-network containing dynamic conduction impedance tags; calculates the propagation tendency of risks in the supply chain using a risk gravity model, dynamically generates a risk propagation chain pointing to the invested enterprise, and realizes forward-looking early warning of risks; focuses on high-risk conduction paths to improve monitoring efficiency and accuracy, and solves the problem of analysis delay caused by massive data; at the same time, the system has closed-loop adaptive learning capability, and updates the supply chain meta-network parameters in reverse according to the actual risk conduction results, continuously improving the accuracy of the risk assessment model. The present application can identify potential risks in advance, provide timely and accurate risk early warning for investment institutions, and effectively improve the initiative of risk management.

The utility model discloses a kind of mute magnetic shaft and keyboard, wherein, a kind of mute magnetic shaft includes shell, button piece, magnet piece and spring, shell includes the upper cover and base connected, the upper cover is equipped with avoiding hole, button piece is slidably worn in avoiding hole, button piece is used to be connected with keycap, button piece and upper cover at least one is equipped with first buffer, magnet piece is located in button piece, magnet piece and base at least one is equipped with second buffer, spring is located between base and button piece, wherein, when pressing drive button piece moves down, magnet piece can be through second buffer and base and abutment collision;Spring drives button piece to reset and move up, button piece can be through first buffer and upper cover and abutment collision. By the above structure, first buffer and second buffer cooperate to effectively weaken or eliminate the collision sound energy generated by button piece at the end of up-down stroke, so that the collision sound can be greatly reduced or eliminated.

The invention discloses a sensor-based multi-channel bridge measuring device, which comprises a signal acquisition module, a signal conditioning module, a data processing module and a communication interface module, and is characterized in that the signal acquisition module is composed of 8-32 independent bridge circuits, each bridge circuit is connected with a resistance strain type sensor, and a full-bridge or half-bridge structure is adopted; the signal conditioning module comprises a multiplexer, an instrument amplifier and a filter circuit; and the multiplexer adopts a low-on-resistance analog switch. According to the multi-channel bridge measuring device based on the sensor, through multi-channel synchronous measurement (8-32 channels can be selected), the problem that a traditional device is low in single-point measurement efficiency is solved, the detection efficiency is improved, and through multiple anti-interference design (optoelectronic isolation, shielding and a filtering algorithm), the detection efficiency is improved. The problem of industrial field noise interference is solved, and measurement precision is guaranteed.

The application discloses a kind of meridian tire X-ray image disease flaw automatic detection method based on improved YOLO-v5 model, belong to meridian tire detection technical field, including following steps: step 1, meridian tire X-ray image is collected and is segmented processing, uniform resolution, model training sample data are made;Step 2, for the image strip, block missing caused by X-ray machine instability, thereby affecting the detection effect Problem, image restoration processing is carried out;Step 3, design improved YOLO-v5 model, including increasing the fourth detection layer, increase attention module, and improve loss function;Step 4, using meridian tire disease flaw data carries out model training;Step 5, using the model of training completion carries out actual application scene tire disease flaw detection.The application is a kind of automatic detection method, based on improved model can automatically identify multiple disease flaws, and different disease flaws are detected and classified, and detection efficiency and accuracy are higher.

The invention discloses a cross-modal pedestrian re-identification method and device, and relates to the technical field of computer vision. The invention discloses a cross-modal pedestrian re-identification method, and the method comprises the steps: obtaining a to-be-identified visible light image and a to-be-identified infrared image, and carrying out the preprocessing, and obtaining an input image; inputting the input image into the trained pedestrian re-identification network to obtain an identification result; the pedestrian re-identification network comprises a convolutional neural network, a structure perception enhancement module arranged in a shallow layer of the convolutional neural network and a collaborative low-rank decomposition module arranged at the tail end of the convolutional neural network; the structure perception enhancement module is used for extracting shallow layer features; the collaborative low-rank decomposition module is used for extracting deep features. According to the method, the problems of cross-modal feature misalignment and noise interference are effectively solved, and the precision and robustness of cross-modal pedestrian re-identification can be remarkably improved.

The invention relates to the technical field of computer vision, and discloses an image data enhancement preprocessing system for gesture recognition, which comprises an image acquisition unit, a data enhancement unit, a feature extraction unit and a preprocessing output unit. The image acquisition unit receives multi-source input of visible light, infrared and depth sensors and the like, and performs de-weighting and resolution calibration; the data enhancement unit implements geometric transformation and color adjustment based on gesture features; the feature extraction unit identifies contour edges, textures and joint points; and the preprocessing output unit verifies the data quality and packages and outputs the data in a standardized manner. According to the system, through multi-source data fusion, semantic perception enhancement and multi-dimensional feature extraction, the training data quality of a gesture recognition model is remarkably improved, the problems of data isomerism, feature distortion and the like in a traditional method are solved, and the system has the advantages of being high in processing efficiency and adaptability.

This application discloses an on-board oxygen supply device and method, and a vehicle. The on-board oxygen supply device includes an air compressor, which is suspended from the vehicle floor and used for filtering and compressing air; an oxygen chamber molecular sieve assembly, which is connected to the air compressor to receive compressed air and perform gas separation to produce oxygen; and a first oxygen delivery pipe, the inlet end of which is connected to the oxygen outlet of the oxygen chamber molecular sieve assembly, and the outlet end of which is connected to the vehicle's interior cavity. This on-board oxygen supply device suspends the air compressor from the vehicle floor, achieves air separation and oxygen production through the oxygen chamber molecular sieve assembly, and delivers the oxygen to the vehicle's interior cavity through the first oxygen delivery pipe. The structure and layout are reasonable, which can not only stably realize the oxygen production and supply function and ensure the health needs of drivers and passengers, but also effectively reduce equipment operation interference and improve driving comfort.

The present invention discloses a method and system for processing cultural and historical images based on image recognition technology. The method comprises performing preliminary preprocessing operations on the original cultural and historical images to obtain a first image; using the VGG16‑Unet cultural and historical image preprocessing model based on deep learning to perform noise recognition and removal operations on the first image to obtain a second image; using the cultural and historical image segmentation model based on the RT‑DETR model to perform segmentation processing on the second image to obtain a third image containing target information; using the cultural and historical image target information extraction model based on the improved ResNet‑101 to identify the target information of the third image, and determine the target information contained in the original cultural and historical images. The scheme of the present invention not only improves the clarity of cultural and historical images, but also greatly improves the accuracy of target information segmentation and recognition, and is suitable for a variety of application scenarios such as cultural relics protection, digital archiving, and academic research.

The invention discloses a power equipment fault prediction method based on vibration analysis. The method comprises the following steps: collecting vibration signal data of power equipment; preprocessing the vibration signal data and converting the vibration signal data into vibration analysis data; segmenting the vibration analysis data into a plurality of sub-segments according to a time sequence, the sub-segments including continuous vibration analysis data points, and extracting feature data from the vibration analysis data in the sub-segments; according to the feature data in the sub-segments, calculating dynamic adjustment coefficients of the sub-segments, performing dynamic smoothing processing on the vibration analysis data in the sub-segments by using the dynamic adjustment coefficients of the sub-segments, and calculating a deviation value between a predicted value and an actual value of the vibration analysis data in each sub-segment; and presetting a deviation value threshold value, comparing the deviation value with the deviation value threshold value, and judging whether the vibration analysis data in the sub-segment is abnormal or not. According to the invention, the early warning of the power equipment fault is realized, the operation safety and reliability of the power system are improved, the accuracy is higher, and powerful support is provided for the monitoring and management of the power equipment.

The present invention provides an aviation compressor fault diagnosis method and system, which belongs to the field of aviation compressor fault diagnosis; the method steps are: data collection and preprocessing; generating virtual samples through adaptive multi-features; constructing a multimodal fusion convolutional network model, and training and verifying: using the fault data in the original sample to verify the multimodal fusion convolutional network model, and adjusting the domain adaptive parameters to ensure the generalization ability of the model in different application scenarios. The present invention mainly solves the problems of data scarcity, insufficient feature extraction and poor diagnostic generalization ability existing in the existing methods.