44 results about "Noise assessment" patented technology

The technique of the invention converts an object RGB image into an image in a YIQ color space, calculates a luminance variation at each target pixel from Y channel values of the target pixel and an adjacent pixel adjoining to the target pixel, and computes a smoothness degree of luminance variation at the target pixel as summation of absolute values of differences between luminance variations at the target pixel and adjacent pixels. A block noise evaluation value B is obtained as a ratio of an average smoothness degree ave(psx), ave(psy) of luminance variation for boundary pixels located on each block boundary to an average smoothness degree ave(nsx), ave(nsy) of luminance variation for inner pixels not located on the block boundary. The block noise evaluation value B closer to 1 gives an evaluation result of a lower level of block noise, whereas the block noise evaluation value B closer to 10 gives an evaluation result of a higher level of block noise.

The invention discloses a noise separation-based power transformer noise assessment method. The steps include: 1) building a voiceprint feature database in advance according to voiceprint feature information corresponding to noise signals generated by a fan component, an iron core component and a winding component when a standard power transformer is in different running states; 2) collecting in real time sound wave signals sent when a transformer to be tested runs, so as to obtain noise signals to be assessed; 3) separating noise signals generated by the fan component, the iron core component and the winding component from the noise signals to be assessed; and 4) extracting voiceprint feature information of each noise signal, identifying the running state of the transformer to be tested, and assessing the noise level of each component according to the running state. The noise separation-based power transformer noise assessment method provided by the invention can realize assessment of the noise level of each component in the power transformer utilizing acoustic characteristics, and has the advantages of easy operation implementation and high assessment precision and efficiency.

The invention discloses a channel pace-making signal detection method of an electrocardiogram machine, comprising the following steps: (1) carrying out sampling on original signals and carrying out differential processing on the sampled signals; (2) setting necessary conditions C1 for judging pace-making signals on the basis of a first threshold TH and a second threshold TH_Trail, detecting differential signals y(n) and filtering pulse spikes according to the necessary conditions C1; and (3) counting the number of spikes with high slope and low slope around the filtered pulse spikes, and carrying out noise evaluation on the filtered pulse spikes on the basis of the counted number. By utilizing the channel pace-making signal detection method, the noise and correct pace-making signals can be effectively distinguished, the accuracy of magnetic resonance angiography (QRS) detection and noise level evaluation is improved, therefore, the channel pace-making signal detection method can be conveniently applied on detection processing of channel pace-making signals of the 12-lead electrocardiogram machine, thus having good improvement effect in the process of detecting and analyzing electrocardiosignals.

The invention discloses a noise assessment method for a zero-difference observation value of a Beidou receiver. The noise assessment method comprises the following steps: reading Beidou observation data in an observation document; respectively obtaining Beidou carrier phase observation values and pseudo-range observation values of B1, B2 and B3 three frequencies; based on theoretical analysis, respectively calculating coefficients of three-frequency carrier phase combination and three-frequency pseudo-range combination, and utilizing the corresponding coefficients to form a combined observation value; utilizing the three-frequency carrier phase combination coefficient to perform cycle slip probe and mark on the carrier phase observation values; calculating the noise conditions of the carrier phase observation values and the pseudo-range observation values of the B1, B2 and B3 three frequencies respectively. Compared with the conventional method, the noise assessment method disclosed by the invention only requires the observation data of a single receiver to be processed with simple linear combination, and then independently evaluates the noise of the carrier phase observation values and the pseudo-range observation values of a single satellite, so that the noise assessment method can evaluate the noise of the original observation values of the Beidou receiver more simply and objectively.

Noise assessment is important to image quality evaluation as well as image processing. For example, the noise level estimation is used as criteria for terminating an iterative noise reduction process. To determine a meaningful noise level, the pixels in featureless regions are separated from the rest of the image. A new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates. Furthermore, a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram. The first peak in filtered histogram gives the most representative noise measure as a desired approximation of true standard deviation.

Autocorrelation coefficients for three dimensions defined by the horizontal direction, the vertical direction, and the time direction of evaluation target moving image data are acquired. A plurality of noise amounts are calculated by executing frequency analysis of the acquired autocorrelation coefficients for the three dimensions and multiplying each frequency analysis result by a visual response function representing the visual characteristic of a spatial frequency or a time frequency. The product of the plurality of calculated noise amounts is calculated as the moving image noise evaluation value of the evaluation target moving image data.

A high-frequency receiver alleviates an influence of adjacent interference waves in a location where an identical set of broadcast channels can be received with different broadcast waves which are broadcast or relayed in frequency bands different from one another by satellites and ground repeater(s). The I and Q signals are passed to a pair of variable LPFs. A demodulation unit is provided for each of said different broadcast waves to demodulate the I and Q signals derived from the broadcast wave into a demodulated signal. One of the demodulated signals which has a better reception quality is selected for output. Based on a reception level and a noise evaluation information (e.g., the bit error rate) for a selected demodulated signal, a controller controls the bandwidths of the variable LPFs so as to optimize the reception level and the noise evaluation information for the selected demodulated signal.

The invention discloses a priority considered power quality and noise comprehensive evaluation method for a convertor transformer. The method comprises the following steps: (1), voltage, current and noise signals of the convertor transformer are acquired; (2), index values are calculated according to the acquired signals; (3), evaluated values are calculated according to the index values and standard values; (4), the evaluation priority is divided, and the operation state of the convertor transformer is evaluated. The convertor transformer is evaluated by priority division of the evaluated values, so that the operation state of the convertor transformer can be judged rapidly; meanwhile, internal relation of the power quality and noise of the convertor transformer is reflected through the power quality and noise evaluated values, power quality and noise comprehensive evaluation of the operation state of the convertor transformer can be realized online, and reference is provided for judging potential problems of the convertor transformer.

Disclosed herein in one embodiment is an adaptive coding threshold algorithm for a videoconference system which alleviates problems due to the erroneous recoding of stationary areas of images caused by image noise. The algorithm, which is preferably implementable on a videoconference encoder, assesses the coding parameters for a current macroblock and the coding parameters for that same macroblock the last time it was coded. If for both macroblocks the coding was non-intra, with a [0,0] motion vector, and with a low quantization parameter, a determination is made that the current macroblock depicts a stationary image. Accordingly, the algorithm increases the coding threshold (T1) for the blocks within that macroblock, so that it becomes more difficult for those particular blocks to be recoded. This renders the block with stationary content less susceptible to erroneous recoding, while preserving system bandwidth and improving image quality. Additionally, related noise assessment techniques can be employed at the encoder or decoder without the adjustment of the coding threshold T1.

The invention discloses a big data statistical analysis based transformer substation factory boundary noise prediction and evaluation method, which is characterized in that information acquisition is performed on main equipment of a transformer substation, and core influencing factors influencing transformer substation factory boundary noise are acquired by using big data statistical analysis; data of a newly built / to-be-tested transformer substation is sent to an evaluation system to analyze through building a noise evaluation model in the evaluation system, and prediction and evaluation for the transformer substation factory boundary noise are realized by adopting a fuzzy evaluation method. The transformer substation factory boundary noise prediction and evaluation method provides reference proposals for transformer substation construction or renovation.

The invention provides a ship underwater radiation noise assessment method. The method comprises the following steps: 1, inputting ship length, equipment type selection and total number of ship conceptual schemes, and equipment to ship bow distance; 2, identifying the exciting force of the mass center position of the equipment; 3, calculating a far-field underwater radiation noise sound source level and a frequency band sound source level of single-machine equipment of the ship; 4, calculating the total sound level of each point from the bow to the stern at the transverse a-meter position of the ship, drawing a characteristic curve of longitudinal distribution of sound pressure along the ship body as a passing characteristic curve of the ship, and determining the equivalent sound center ofthe ship; 5, estimating an underwater radiation noise source level, a frequency band sound source level and a sound pressure spectrum source level of the ship; and 6, comparing the parameters obtained in the step 5 with a design demand limit value to obtain a ship underwater radiation noise index argumentation and evaluation result. The far-field sound pressure value of the ship under the navigation working condition can be estimated according to the ship concept scheme at the demonstration stage, and demonstration evaluation is carried out on the far-field sound pressure value according to the demand limit value.

An image noise estimation method based on human eye visual features and a partitioning analysis method comprises the steps of utilizing a human eye contrast sensitivity function for processing an original image with noise to obtain an initially-processed image; utilizing a watershed partitioning algorithm for conducting approximation region partitioning on the initially-processed image to obtain a plurality of partitioned image region blocks, and obtaining a region partitioned image; conducting noise-free image approximation rebuilding on partitioned regions of the region portioned image to obtain a rebuilt estimated noise-free image of the whole image; according to the original image with the noise and the rebuilt estimated noise-free image, obtaining a distribution diagram of intensity-noise pairs, and utilizing the distribution diagram of the intensity-noise pairs for obtaining a noise label of the original image with the noise. According to the image noise estimation method, due to the combination of the human eye visual features, partitioning analysis and noise estimation are carried out on the original observation image, a single comprehensive estimated label value is obtained finally, and the result is quite approximate to a human eye visual system.

A system assesses a column of gas in an atmosphere. The system includes an optical transmitter for transmitting (a) an online signal tuned to an online wavelength of gas, (b) an offline signal tuned to an offline wavelength of the gas, and (c) an optical signal for providing altimetry data. A modulator is included for modulating the online signal with a first stepped chirp waveform and modulating the offline signal with a second stepped chirp waveform. A processor assesses the column of gas, after receiving a returned online signal, a returned offline signal and a returned optical signal. A lock-in amplifier is included for (a) multiplying a returned signal with the first stepped chirp waveform to obtain a detected online signal, and (b) multiplying the returned signal with the second stepped chirp waveform to obtain a detected offline signal. The processor is configured to remove ground reflections from the optical signal to obtain scattered noise, and assess the column of gas based on the scattered noise. The processor is also configured to (a) multiply an estimated returned online signal with the first stepped chirp waveform to obtain a simulated cloud only online signal, and (b) multiply an estimated returned offline signal with the second stepped chirp waveform to obtain a simulated cloud only offline signal. In addition, the processor is configured to assess the column of gas by performing regression between the detected online and offline signals and the simulated online and offline signals.

The invention discloses an image noise evaluating system and an evaluating method thereof, which are used for evaluating the noise of current images, wherein, a storage device stores the former image of the current images; a multi-windowed noise evaluating device carries out noise evaluation on areas of the current images and on the corresponding area of the former image; a noise evaluating index and a noise adjustment evaluating index which are corresponding to the windows are output; a comparing device is used for comparing multiple noise adjustment evaluating indexes, choosing the minimum value thereof and outputting a window index at the same time; the window index works as the window of the minimum noise adjustment evaluating index. When the minimum noise adjustment evaluating index is smaller than a critical value, a detecting device which can move in whole domain outputs the noise evaluating index which is corresponding to the minimum noise adjustment evaluating index as a noise evaluating value of the current image.

The invention provides a noise assessment and noise optimization method and device for an electric reactor. The noise assessment method for the electric reactor comprises the steps that basic parameter information of each coil in a to-be-measured electric reactor is acquired; according to the basic parameter information, the maximum displacement of each coil is calculated respectively; according to the maximum displacement and the basic parameter information, an effective value of the vibration velocity of each coil is calculated; according to the effective value of the vibration velocity of each coil, the noise level of the to-be-measured electric reactor is evaluated. The noise assessment and noise optimization method and device for the electric reactor have the advantages that the problem that the noise cannot be evaluated in the early design phase of the electric reactor, the assessment method is simple and effective, a basis is provided for performing noise optimization on the electric reactor subsequently, and the noise assessment and noise optimization method and device have guiding significance for noise reduction design of a smoothing reactor.

The invention provides an experimental device for wind tunnel flow field noise assessment and an assessment method, and belongs to the field of experimental aerodynamics. The experimental equipment for wind tunnel flow field noise assessment includes an inlet valve, a stable section, a spraying pipe section, an experimental section and a diffusion section which communicate successively, the inletvalve is detachably connected with the stable section, the stable section is detachably connected with the spraying pipe section; and one end of the spraying pipe section extends into the experimentalsection and is detachably connected with the experimental section; and the experimental section is connected with one end of the diffusion section, the other end of the diffusion section is used forbeing connected with a vacuum tank, a control assembly is detachably installed in the stable section, and the control assembly includes a first control part for decreasing the flow field noise or a second control part for increasing the flow field noise. The experimental device further includes a first measuring probe and a second measuring probe, the first measuring probe is installed in the stable section and used for measuring the noise at anoutlet of the stable section, and the second measuring probe is installed inside the experimental section and used for measuring the noise at an outletof the spraying pipe section.

The invention relates to a machine learning-based noise image saliency detecting method which comprises the following steps: 1, a plurality kinds of denoising parameters are adopted for a noise image of each amplitude, and an optimal denoising parameter for each amplitude is obtained; 2, each noise image is subjected to characteristic extracting operation via a noise assessing algorithm, noise value characteristics are obtained, and a noise value characteristic set is formed; 3, the noise value characteristic set is used as a machine learning algorithm characteristic set, and a noise amplitude prediction model is obtained via a machine learning algorithm and a quinquesection cross validation method; 4, a noise image corresponding to the noise amplitude prediction model is adopted for prediction, and predicted noise amplitude value is obtained; 5, predicted noise amplitude value of each noise image and a corresponding optimal denoising parameter are used for denoising operation, and a denoised image set can be obtained; 6, images in the denoised image set is subjected to saliency detecting operation via a saliency detection method, and a final salient image can be obtained. According to the machine learning-based noise image saliency detecting method, noise image detecting performance can be improved.

The invention discloses a database-based Raman spectrum preprocessing method, which comprises the following steps: collecting Raman spectra of various samples and constructing a Raman spectral database, wherein a spectral matrix formed by Raman spectral data contained in the Raman spectral database is low-ranking; under certain preset integration time conditions, collecting original spectra of thesamples with the same components; when the noise of the original spectra exceeds a threshold, extracting appropriate spectral data from the Raman spectrum database to construct a temporary database,combining the original spectra and the temporary database into a spectral matrix, and using a low-rank spectral optimization algorithm to process the spectral matrix; extracting the Raman spectrum optimized by the algorithm and evaluating the noise of the Raman spectrum; if the noise does not exceed the threshold, obtaining the Raman spectrum of which the effect of the noise is eliminated and thesignal-to-noise ratio is satisfactory; otherwise, performing next iteration, and performing data optimization process again. The method effectively shortens the acquisition time of the spectral data and provides a broader application prospect for the Raman spectrum detection.

Noise assessment is important to image quality evaluation as well as image processing. For example, the noise level estimation is used as criteria for terminating an iterative noise reduction process. To determine a meaningful noise level, the pixels in featureless regions are separated from the rest of the image. A new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates. Furthermore, a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram. The first peak in filtered histogram gives the most representative noise measure as a desired approximation of true standard deviation.

The invention belongs to the technical field of semi-submersible type ocean platform vibration and noise reduction assessment, and particularly relates to an underwater radiation noise assessment method for an overall scheme of a semi-submersible ocean platform. The method comprises the following steps: performing semi-submersible type ocean platform overall scheme design analysis and structure modeling parameter combing; establishing a semi-submersible type ocean platform geometric structure simulation model; establishing a semi-submersible ocean platform structure statistical energy analysisand evaluation model based on a statistical energy analysis method; performing single machine radiation noise assessment; and performing combined condition underwater radiation noise assessment. Themethod provided by the invention is reasonable and easy to implement, overcomes the uncertainty of a conclusion obtained based on an artificial experience method in the existing semi-submersible oceanplatform overall scheme underwater radiation noise assessment, and can greatly improve the rationality and accuracy of an underwater radiation noise assessment result of the semi-submersible ocean platform overall scheme.

The method includes acquisition of an original image; low-frequency filtering of the original image to obtain an estimated image; a noise image development as a difference between the original and estimated images by morphologic filtering elimination of noise image pixels corresponding to sharp changes in the original image; dividing an intensity range of the estimated image into intervals, wherein each pixel of the estimated image relates to an appropriate interval; accumulating some noise image pixels corresponding to estimated image pixels; calculating interval estimations of noise dispersion using accumulated noise image pixels; improving interval estimations by removing noise pixels according to σ3 criteria, approximating interval estimations of noise dispersion resulting in tabular function of noise vs. signal intensity; calculating the base of estimated image and obtaining tabular function of the dependence of noise on signal intensity the noise map as a pixel-by-pixel noise dispersion estimation of the digital original image.

The invention discloses a parameter design method of an electronic system comprehensively considering manufacturing and temperature noise. The method comprises following steps: establishing a robustness parameter design analysis table from controllable factors and basic data of first parameter noise factors and second parameter noise factors; calculating a target simulation result of the analysis table; performing second parameter statistics of inner and external table to obtain center value and variance; performing normalization; determining first parameter noise weight and second parameter noise weight to obtain second parameter noise assessment value; filling the second parameter noise assessment value in the inner and external table and performing statistics on the signal to noise ratio and sensitivity characteristic value corresponding to the inner table; performing variance analysis on the signal to noise ratio and sensitivity characteristic value to determine optimized parameter level combination; obtaining output distribution under second parameter of different designs before and after optimization through simulation; if the optimization scheme satisfies requirements, then optimization is stopped; otherwise, adjusting the first parameter noise weight and the second parameter noise weight and optimizing the parameter level combination again.

The invention provides a noise comprehensive treatment system for a gas-driven generator set and a distributed energy station. The noise comprehensive treatment system comprises a sound source monitoring system and noise comprehensive treatment equipment; the sound source monitoring system collects the information of sound source videos and noise grades; the combined type sound-absorption wall bodies of the noise comprehensive treatment equipment can be combined into a room body, and the sound source is integrally coated; a pipeline sound-absorption sleeve is arranged on a wall penetrating pipeline in a sleeving mode, the wall penetrating pipeline can be prevented from being in contact with the wall body, the noise is reduced, and meanwhile, the situation that the noise is spread through the wall body is reduced; and a damping composite partition plate type sound absorber is adopted, the double-layer characteristics of a folded plate type silencer and a damping silencer are achieved, and the exhaust noise can be greatly reduced. According to the noise comprehensive treatment system, sound source monitoring and noise evaluation can be facilitated, intelligent setting can be carriedout according to the type of the sound source, the noise reduction amount reaches more than 30 dB (A), noise pollution can be greatly reduced, and the health, lives and working states of surrounding people can be improved.

The invention discloses a rotating workpiece noise assessment device. The rotating workpiece noise assessment device comprises a base, wherein the base is matched with a bearing pedestal and a bearingpush rod which are respectively placed at two sides through a motor output shaft which is vertical to the base so as to complete running-in and detection for a bearing outer ring. The rotating workpiece noise assessment device has the advantages that the assembling is simple, structures are in rigid connection, the running-in for the bearing outer ring can be guaranteed, the rotating workpiece noise assessment device is applicable to bearings with different outer diameters, the size is small, the device can be detached into parts, the rotating workpiece noise assessment device has advantagesand remarkable effects compared with previous large bearing running-in devices, and the device can set the size of an axial load for the bearing through being matched with a torque sensor so as to better test the quality of the bearing.

The invention discloses a capillary flow noise analysis and structural transformation design system, which comprises the following steps of S1, building a parameterized model of an outlet of a capillary; S2, starting an optimization flow; S3, calculating a one-dimensional mathematical model of the capillary, giving import and export conditions of an outlet end of the capillary, calculating a simulation result of fluid mechanics to obtain flowing information, adopting the information as an input condition for calculating acoustic analysis, carrying out accurate sound field simulation, and adopting a final result of acoustic analysis as a noise assessment target of a current capillary structure; S4, optimizing an algorithm; S5, judging whether a noise structure meets an optimal target value or not, if yes, adopting the noise structure as an optimal result and outputting, if not, returning to the S4 step for re-optimizing the algorithm until the noise structure meets the optimal target value, and completing the result output of the whole system. The invention provides a universal, accurate and low-cost capillary noise reduction improvement system independent of people, and achieves the aim of engineering application.