28 results about "Simulation noise" patented technology

The invention discloses a three-dimensional noise mapping method and system based on point cloud, and belongs to the technical field of urban environmental noise prediction.The method comprises the steps that point cloud data driven three-dimensional modeling and parameter extraction are carried out, an urban three-dimensional model with a geometric topological structure is constructed through laser scanning point cloud data, and related parameters are extracted; based on sound field calculation of ISO 9613 standards, a multi-path propagation model including an atmospheric effect, a ground effect and obstacle diffraction is established, a green infrastructure noise reduction effect is especially considered, and propagation attenuation of noise in a three-dimensional space is simulated; and performing visual expression of three-dimensional noise, and constructing fusion expression of a sound pressure level contour surface and a three-dimensional city model. In view of difficulty in simulation of three-dimensional reconstruction and sound waves in a spatial propagation process in the prior art, the method improves the precision of quantitative prediction, and fuses a green infrastructure noise reduction effect to realize rapid and efficient regional three-dimensional reconstruction and noise mapping.

A feature extraction and simulation method for sound signals of an autonomous navigation ship tunnel thruster. A digital filter is used to process measured signals to remove spike signals, and the characteristics of residual wideband floor noise signals are analyzed. A finite impulse response filter is designed, and the impulse response of the finite impulse response filter is convolved with white Gaussian noise, so that simulated noise is matched with frequency-domain characteristics of floor noise signals. A spike signal amplitude is calculated, and a group of sine wave signals having a corresponding frequency are generated as spike signals. Narrowband spike signals and wideband floor signals are superposed and synthesized into simulated tunnel thruster noise. A feasible solution is provided for research and analysis of sound signals of a tunnel thruster. The sound characteristics of the tunnel thruster are accurately extracted, a predicted sound level is improved, and a tunnel thruster sound source database is established, thus providing valuable data support for signal quality analysis of a ship autonomous navigation system, fault diagnosis and improvement of a control strategy for the tunnel thruster.

The invention discloses an intelligent denoising and enhancing method and system for ground penetrating radar signals, and relates to the technical field of ground penetrating radar data processing.The method comprises the following steps that forward modeling software is used for generating a synthetic radar profile, analog noise is added to the radar profile, a noisy profile is generated, and a training data set is constructed; a DRSN-ATT-UNET network model is constructed and trained, the network model takes U-Net as a basic framework, a deep residual shrinkage module is integrated in an encoder, and an attention gating module is integrated in jump connection; actual ground penetrating radar profile data to be processed are loaded, and standardized preprocessing is carried out; inputting the preprocessed profile data into the trained network model, and outputting a de-noised and enhanced radar profile; and carrying out anti-standardization post-processing on the model output, and outputting a radar image with a high signal-to-noise ratio. According to the method, the ground penetrating radar noise in complex environments such as mines can be effectively suppressed, the data quality is remarkably improved, and a reliable basis is provided for accurate geological interpretation.

The invention relates to the technical field of pipeline deformation monitoring, and discloses a buried steel pipe deformation degree detection method and system based on multi-sensor fusion. The method comprises the steps that data of an accelerometer and a magnetometer are collected and processed to obtain an initial space orientation angle estimated value, the initial space orientation angle estimated value is compared with a preset steel pipe axis model, and if deviation exceeds a threshold value, dynamic deviation compensation is conducted through gyroscope data to obtain a real-time posture correction vector; extracting a multi-point detection sequence fitting deformation curve to obtain a continuity parameter set, and screening a low-consistency paragraph fusion neural network to obtain a multi-point deformation data set; and determining a final deformation evaluation value through underground interference simulation, noise modeling and particle adjustment, matching the final deformation evaluation value with historical records, and generating an alarm and risk early warning report when the matching degree is low. The method can improve the detection precision and data consistency, and guarantees the safe operation of the buried steel pipe.

The invention discloses a noise simulation method and device of a transformer, a storage medium and electronic equipment. The method comprises steps that geometric information of a reference emission surface of a target transformer, an operation condition of the target transformer, and distance information between the target transformer and a sound measurement point are acquired, and the reference emission surface is a main radiation area of sound waves of the target transformer; based on the geometric information and the distance information, the sound pressure level of the target transformer under the operation condition is determined, and the sound pressure level is used for representing the sound pressure level of the simulation noise at the sound measurement point; based on the sound pressure level and the operation condition, adopting a pre-established standard spectrogram library to generate initial simulation noise; and performing correction processing on the initial simulation noise by adopting the gain coefficient to obtain target simulation noise of the target transformer. The technical problem that the accuracy of the generated analog noise of the transformer is low in the prior art is solved.

The application discloses a 12-lead electrocardiosignal denoising method and system based on a dual generative adversarial network, which comprises the following steps: obtaining time-adjacent clean electrocardiosignal segments and noisy electrocardiosignal segments; constructing a noise generative adversarial network, wherein the generator takes a random noise vector as input and simulates noise as output, the discriminator takes the noisy electrocardiosignal segment as a positive sample and a synthetic signal composed of the clean electrocardiosignal segment and the simulated noise as a negative sample for adversarial training, and a noise generator snapshot library is generated; performing dynamic noise injection on the clean electrocardiosignal segment to obtain 'noisy-clean' paired data; constructing a denoising network comprising a denoiser; obtaining the 12-lead electrocardiosignal containing noise and inputting it into the denoiser to output the denoised 12-lead electrocardiosignal. The application aims to overcome the defects in the prior art, such as the lack of real 12-lead electrocardiosignal denoising paired data and the poor model generalization ability caused by excessive dependence on the Gaussian noise assumption.

The application provides a kind of based on the shear wave transformation of Meyer window function's sonar image noise reduction method and system.The method comprises: the transformation of reverberation noise model to Gaussian additive noise model is combined with shear wave transformation;In the process of shear wave transformation, considering the continuity, smoothness and compact support performance shown by wavelet function and scale function in time domain and frequency domain, Meyer window function based on the performance shown in time domain and frequency domain is proposed to construct shear wave filter;In order to avoid the influence of gray overflow in sonar image, and realize better reverberation suppression effect, noise variance estimation based on weak texture block is proposed, and the root mean square coefficient obtained by the shear wave transformation of simulated noise is combined to obtain adaptive filtering threshold in each scale and direction.Experiments show that the method proposed in the application can more effectively suppress reverberation noise, and has better edge preservation effect.

The present invention discloses a method and device for evaluating the noise reduction effect of a generator set. The method comprises: after applying a noise reduction method to the generator set, testing and obtaining a noise reduction value set of the generator set; obtaining a noise reduction method technical indicator set; the noise reduction method technical indicator set includes a structural noise reduction method technical indicator subset, a material noise reduction method technical indicator subset, a simulation noise reduction method technical indicator subset and an overall design noise reduction method technical indicator subset; constructing a noise reduction quantitative evaluation model; based on the noise reduction value set and the noise reduction method technical indicator set, solving the noise reduction quantitative evaluation model to obtain a solved noise reduction quantitative evaluation model; based on the solved noise reduction quantitative evaluation model, obtaining noise reduction effect evaluation values ​​of various noise reduction methods.

The invention provides an acoustic echo cancellation test method and device for webpage real-time communication, and relates to the technical field of data processing, in particular to the technical field of artificial intelligence, echo cancellation and communication. The specific implementation scheme is as follows: configuring an original first test audio as a virtual sound source of a web browser; audio loopback processing is carried out on the virtual sound source to obtain second test audio, and the first test audio and the second test audio are both mixed audio of test voice and analog noise; sending a second test audio to the webpage server, and performing acoustic echo cancellation (AEC) processing on the second test audio based on the analog noise to obtain a target audio; and according to the target audio and a reference audio of the first test audio, performing quality evaluation on the AEC component of the webpage server, the reference audio including the test voice.

Automatic exposure with simulated histograms may include obtaining a noise-blur exposure duration value in accordance with a minimal simulated noise-blur cost value obtained in accordance with simulated noise data and simulated blur data, obtaining a saturation exposure value in accordance with a minimal simulated saturation cost value obtained in accordance with simulated black saturation data and simulated white saturation data, comparing the noise-blur exposure duration value and the saturation exposure value, and obtaining a target gain value and a target exposure duration value based on the comparison, wherein the comparing may include obtaining the target exposure duration value in accordance with a minimal simulated blur-saturation cost value obtained in accordance with the simulated blur data and the simulated black saturation data or in accordance with a minimal simulated noise-saturation cost value obtained in accordance with the simulated noise data and the simulated white saturation data.

The invention relates to the technical field of defect detection, and provides an industrial defect data generation method based on a visual language large model, and the method comprises the steps: extracting a visual feature vector of an industrial product image and a semantic feature vector described by a defect text through the visual language large model, and mapping the visual feature vector and the semantic feature vector to a unified semantic space; a semantic guidance vector is dynamically generated in combination with a cross attention mechanism; directional simulation noise is generated based on the visual form of the defect, a semantic guide vector is coded into a modulation signal through a language branch of a diffusion model, and deep fusion of noise adding visual features and the modulation signal is achieved in a denoising decoder; the semantic matching degree of the defect image and the defect text description is verified through a visual language large model, the positioning accuracy of a defect area is verified by means of a class activation graph, a verification result serves as a reward signal, diffusion model parameters and weight parameters of a cross attention mechanism are alternately optimized through reinforcement learning, and the defect recognition accuracy is improved. And the finally generated industrial defect data set is stable in quality and reliable in attribute.

The application discloses a kind of ground penetrating radar signal intelligent denoising and enhancement method and system, it is related to ground penetrating radar data processing technical field, including the following steps: using forward simulation software to generate synthetic radar profile, and adding simulated noise in radar profile, generate noisy profile, construct training dataset;Build and train DRSN-ATT-UNET network model, the network model is with U-Net as basic architecture, and integrated depth residual shrinkage module in encoder, integrated attention gate module in jump connection;Load actual ground penetrating radar profile data to be processed, and carry out standardization preprocessing;Profile data after preprocessing is input into the network model that has been trained, and output denoising and enhanced radar profile;After model output is carried out anti-standardization post-processing, output high signal-to-noise ratio radar image.The application can effectively suppress ground penetrating radar noise under complex environment such as mine, significantly improve data quality, and provide reliable basis for accurate geological interpretation.

The present application relates to a kind of bearing degradation system level data generation method based on component level test, it is related to bearing signal measurement technical field, solve the technical problem that must carry out system level test in order to obtain bearing system level degradation signal in prior art.The present application first uses dynamics simulation software or the assembly level simulation noise signal of bearing, then based on bearing component level test device, obtains the component level vibration signal of bearing in full life cycle degradation;Then by extracting high sampling rate, narrow bandwidth under the rotating frequency characteristic signal, obtain the filter signal of simulation signal and component level test signal;On this basis, by the correct peak value that can represent the motion characteristics of bearing obtained by the proposed screening method, finally with these peak values as reference datum, obtain bearing degradation system level data, can avoid the problem that traditional method needs to carry out system overall assembly level test, improve model training data generation efficiency and reduce economic cost simultaneously.

The application relates to a noise adjustment method of an audiometer and a noise generator thereof, comprising the following steps: constructing a noise database, the types of the noise including simulated noise, random noise and mixed noise; acquiring frequency range and amplitude data when pure tone is output, and generating a preselected scheme of masking noise adapted to the pure tone from the corresponding type according to an input instruction, the preselected scheme including the duration, the expected frequency range and the expected amplitude of the selected masking noise; superimposing and outputting the selected masking noise and the pure tone, and acquiring the mixed tone segment signal again at the eardrum, and generating a waveform spectrum through time-frequency conversion; based on the waveform spectrum, removing the waveform spectrum calculated according to the frequency range and the amplitude data of the pure tone, and recalculating the actual frequency range and the actual amplitude of the masking noise, and adjusting the frequency range and the amplitude of the masking noise output according to the actual frequency range and the actual amplitude, so that the actual frequency range and the actual amplitude are infinitely close to the expected frequency range and the expected amplitude respectively.

The invention relates to a bearing degradation system-level data generation method based on a component-level test, relates to the technical field of bearing signal measurement, and solves the technical problem that a system-level test must be carried out in order to obtain a bearing system-level degradation signal in the prior art. The method comprises the following steps: firstly, adopting dynamic simulation software or an assembly-level simulation noise signal of a bearing, and then acquiring a degraded part-level vibration signal of the bearing in a full life cycle based on a bearing part-level test device; then, obtaining a simulation signal and a filtering signal of a component-level test signal by extracting a frequency conversion characteristic signal under a high sampling rate and a narrow broadband; on the basis, correct peak values capable of representing the motion characteristics of the bearing are obtained through a proposed screening method, and finally, bearing degradation system-level data are obtained by taking the peak values as reference bases, so that the problem that an assembly-level test of the whole system needs to be carried out in a traditional method can be avoided; meanwhile, the model training data generation efficiency is improved, and the economic cost is reduced.

Automatic exposure with simulated histograms may include obtaining a noise-blur exposure duration value in accordance with a minimal simulated noise-blur cost value obtained in accordance with simulated noise data and simulated blur data, obtaining a saturation exposure value in accordance with a minimal simulated saturation cost value obtained in accordance with simulated black saturation data and simulated white saturation data, comparing the noise-blur exposure duration value and the saturation exposure value, and obtaining a target gain value and a target exposure duration value based on the comparison, wherein the comparing may include obtaining the target exposure duration value in accordance with a minimal simulated blur-saturation cost value obtained in accordance with the simulated blur data and the simulated black saturation data or in accordance with a minimal simulated noise-saturation cost value obtained in accordance with the simulated noise data and the simulated white saturation data.

The invention discloses a natural gas station pressure regulating valve noise reduction spacing determination method and terminal, and the method comprises the following steps: building a physical model according to geometric parameters of a pressure regulating valve and a pipeline structure connected with the pressure regulating valve, and simulating a noise generation process in the physical model; selecting a corresponding calculation model, and calculating to obtain a flow field numerical value; selecting a corresponding calculation model, determining a plurality of key points as pulsating pressure monitoring points, converting the flow field into a dipole sound source of a valve and a pipeline, and obtaining sound field time domain and frequency domain analysis results; according to the sound field time domain and frequency domain analysis results and the corresponding pressure regulating mode, the distance between the pressure regulating valves and the manifold or the distance between the pressure regulating valves is regulated, and the optimal distance is determined. According to the method, the noise generation process is subjected to simulation calculation by adopting a method based on numerical simulation, and the noise is digitized, so that the optimization method for setting the process spacing of the pressure regulating valve is formulated, the noise influence of the pressure regulating valve is effectively reduced, and the physical and psychological health of station yard personnel is guaranteed.

The application provides an electroencephalogram data synthesis method, device and equipment and a storage medium, and belongs to the field of biological signals. The method comprises the following steps: in response to an electroencephalogram data synthesis task, loading corresponding head model information, wherein the head model information is biophysical basic information; synthesizing electroencephalogram scalp signals according to the head model information and the electroencephalogram data synthesis task to obtain scalp signal data, and outputting electroencephalogram signal causality during the synthesis of the electroencephalogram scalp signals to obtain causality feature data; generating simulated noise for electroencephalogram extraction according to the head model information and the electroencephalogram data synthesis task to obtain noise signal data; and synthesizing electroencephalogram data according to the electroencephalogram data synthesis task, the scalp signal data, the noise signal data and the causality feature data. By adding the electroencephalogram signal causality and the simulated noise for electroencephalogram extraction into the electroencephalogram data, the generated electroencephalogram data has a causal true value and is more accurate.

This invention discloses a method and apparatus for grading the sugar content of kiwifruit by combining multi-scale feature extraction and global inference. Near-infrared spectral and sugar content data from two batches of kiwifruit samples spanning two years are collected as independent datasets. Abnormal spectral data and corresponding sugar content data entries are removed. The remaining spectral data is preprocessed and mixed with simulated noise. Sugar content is graded and used as label values ​​for training the model. A multi-scale attention residual convolutional network is used to extract fine spectral features related to sugar content. A parallel long-distance correlation encoding and decoding model is used to capture the long-pathway dependence of sugar content traits implicit in the spectral data. The trained and tested stable model is deployed to the kiwifruit grading device for online non-destructive testing and automatic grading. This overcomes the problems of low grading accuracy and easy confusion of critical sugar content grades caused by the limited local receptive field in existing technologies, which makes it difficult to capture fine spectral morphology and lacks modeling of long-pathway global dependencies.

Systems and methods for training a neural network for generating a reasoning statement are provided. In one embodiment, a method includes receiving raw data regarding a raw simulation for an ego agent. The raw simulation includes at least one dynamic object. The method also includes generating a noisy simulation by applying a first input noise of a plurality of input noises to the raw simulation. The method further includes predicting one or more object trajectories for the at least one dynamic object. The method yet further includes generating an ego trajectory for the ego agent in the noisy simulation based on the one or more object trajectories. The method includes causing the ego agent to execute the ego trajectory.

A machine learning network is trained to generate a noise field image from a radiographic (x-ray) image. The training includes accessing a number of previously acquired radiographic images, duplicating the previously acquired radiographic images, and conditioning each of the duplicated images with simulated noise content to form a plurality of simulated low-exposure images. Each of the simulated low-exposure images is paired with its corresponding previously acquired image to form a learning pair. The machine learning network is trained to generate a noise field image using the learning pairs of images. A noise suppressed image of an object can be generated by applying a scaling factor to at least a portion of the corresponding noise field image and combining the scaled noise field image with a current captured image of the object.

The invention relates to a terahertz medical image denoising method based on an SE module and a DnCNN neural network, and the method comprises the steps: carrying out the simulation generation of a simulation noise which is similar to the statistics of an observation noise for an observed first noise-containing image, defining a second noise-containing image as a noise-containing image added with the simulation noise, inputting the noise-containing image into a DnCNN-SE model, carrying out the noise reduction processing, and training an NAC strategy of a self-supervision network, thereby achieving the denoising of a terahertz medical image. And training a DnCNN-SE model by taking a mean square error MSE between the denoised image and the first noisy image as a loss function, and directly carrying out denoising processing on the original noisy image y to obtain a final denoised image. An external clean sample is not needed in the process, an original noise image is regarded as a training target, and additional noise is superposed on the original noise image to serve as network input. By simulating the noise process, the model learns the general denoising capability, and the noise level does not need to be estimated in advance. And the SE module adaptively emphasizes channels with rich information and inhibits channels dominated by noise through extrusion and excitation operations, so that key information of the medical image is better reserved.

The present invention relates to the technical field of soil property detection, and provides a soil spectral detection method and device based on reversible computing. The method includes: adding simulated noise to soil spectral data based on reversible computing, and mapping the soil spectral data with added simulated noise to a latent space to extract various soil spectral features; screening the various soil spectral features through the inverse operation of reversible computing to obtain target features, so as to detect the content of at least one of total nitrogen and organic matter in the soil spectral data. The method of the present invention improves the accuracy of soil spectral detection, and thus can provide rapid, efficient and accurate measurement for the soil properties of different soil types.

This application relates to the field of defect detection technology and provides a method for generating industrial defect data based on a large visual language model. The method extracts visual feature vectors from industrial product images and semantic feature vectors from defect text descriptions using a large visual language model, maps them to a unified semantic space, and then dynamically generates semantic guidance vectors using a cross-attention mechanism. Directional simulated noise is generated based on the visual morphology of the defect, and the semantic guidance vectors are encoded into modulation signals through the language branch of a diffusion model. Deep fusion of the noisy visual features and the modulation signals is achieved in a denoising decoder. The semantic matching degree between the defect image and the defect text description is verified using a large visual language model, and the accuracy of defect region localization is verified using a class activation map. The verification results are used as a reward signal, and reinforcement learning is used to alternately optimize the parameters of the diffusion model and the weight parameters of the cross-attention mechanism, ensuring that the final generated industrial defect dataset is of stable quality and reliable attributes.

The invention discloses a method and device for evaluating the noise reduction effect of a generator set, and the method comprises the steps: carrying out the testing of a noise reduction value set of the generator set after a noise reduction method is used for the generator set; obtaining a noise reduction method technical index set; the noise reduction method technical index set comprises a structure noise reduction method technical index subset, a material noise reduction method technical index subset, a simulation noise reduction method technical index subset and an overall design noise reduction method technical index subset; constructing to obtain a noise reduction quantitative evaluation model; based on the noise reduction value set and the noise reduction method technical index set, solving the noise reduction quantitative evaluation model to obtain a solved noise reduction quantitative evaluation model; and based on the solved noise reduction quantitative evaluation model, obtaining noise reduction effect evaluation values of various noise reduction methods.

The application discloses an audio data processing method and device, a medium, equipment and a program product, which are applied to AI noise reduction, AI echo cancellation and the like in artificial intelligence (AI) and machine learning. The method comprises the following steps: acquiring original audio data collected, wherein the original audio data comprises pure speech audio data and noise audio data; generating simulation noise data according to the pure speech audio data and the noise audio data in the original audio data; generating target audio data used for simulating changes of audio after the audio is transmitted through a space according to the original audio data or the simulation noise data; and performing a speech enhancement operation to obtain enhanced target audio data, wherein the speech enhancement operation comprises performing first-order speech enhancement on the original audio data, regenerating updated target audio data based on the data of the first-order speech enhancement, and performing second-order speech enhancement on the target audio data and / or the updated target audio data, so as to improve diversity of a simulation audio data set.

A method for synthesizing arbitrary distribution noise signal includes two parts. An upper computer fits H given distribution function ƒ(x) to obtain H pluralities of K pairs of means μkh and variances σkh, by using Expectation Maximization algorithm based on a weakened Gaussian Mixture Model. An FPGA stores H pluralities of K pairs of means μkh and variances σkh into RAM, function values that box-muller transformation needs into ROM 1 and ROM 2, then read out two function values and multiply them to obtain random number xn, means μkh and variances σkh are read out from RAM to synthesizing kth Gaussian noise signal yknh, after amplitude limitation and normalization, obtained analogy signal is amplified Amax−Amin times and set bias voltage to Amin to obtain analogy noise signal with amplitude interval. An arbitrary distribution noise signal with specific distribution is outputted.

The present invention relates to a system-oriented bearing fault prediction method using a noise reduction autoencoder, which belongs to the technical field of bearing fault prediction and solves the problem in the prior art of failing to consider the influence of complex system noise on bearing fault prediction. The method comprises the following steps: obtaining parameters and operating condition information of a transmission chain to be tested, and determining a dangerous bearing as a target bearing; performing a dynamic simulation of the transmission chain to be tested, and obtaining simulation result data as simulated noise; performing a component-level degradation test, and obtaining a vibration signal during the degradation process as test data; performing signal mixing on the test data and the simulation result data, and obtaining a noisy signal; constructing and training a noise reduction autoencoder model, and outputting denoised bearing vibration data; constructing a fault prediction model, dividing the degradation process into a healthy stage, a slow degradation stage, and a fast degradation stage, and inputting the denoised bearing vibration data into the three independent models respectively to obtain the prediction value of the highest-precision model among the three independent models.