216 results about "Separation result" patented technology

LED packages, and LED lamps and bulbs, are disclosed that are arranged to minimize the CRI and efficiency losses resulting from the overlap of conversion material emission and excitation spectrum. In different devices having conversion materials with this overlap, the present invention arranges the conversion materials to reduce the likelihood that re-emitted light from a first conversion materials will encounter the second conversion material to minimize the risk of re-absorption. In some embodiments this risk is minimized by different arrangements where there is separation between the two phosphors. In some embodiments this separation results less than 50% of re-emitted light from the one phosphor passing into the phosphor where it risks re-absorption.

The application discloses a role separation method based on voice, comprising the following steps: extracting feature vectors frame by frame from a voice signal to get a feature vector sequence; assigning role tags to the feature vectors; using the feature vectors with role tags to train a deep neural network DNN model; and judging a role sequence corresponding to the feature vector sequence according to the DNN model and a hidden Markov model HMM trained using the feature vectors, and outputting a role separation result, wherein the DNN model is used to output the probability of each role according to an input feature vector, and HMM is used to describe the jump relationship between roles. The invention further provides a role separation device based on voice. According to the method provided by the invention, as the DNN model with strong ability of feature extraction is used to model speaker roles, the method has stronger characterization ability than a traditional GMM (Gaussian Mixture Model), role characterization is more detailed and accurate, and therefore, a more accurate role separation result can be obtained.

The invention discloses a satellite attitude control system failure diagnosis device based on a state observer and an equivalent space and a satellite attitude control system failure diagnosis method based on the state observer and the equivalent space, which belong to the field of aerospace and aim to solve the problems of high hardware complexity, low control accuracy and low failure diagnosis algorithm effectiveness of the conventional failure diagnosis method. The method provided by the invention comprises the following steps that: 1, a failure diagnosis observer outputs a satellite triaxial angular rate residual according to output signals of an actuator and a gyro sensor; 2, an equivalent vector space description module constructs equivalent space descriptions of the gyro sensor according to the output signal of the gyro sensor, wherein an output equivalent vector p is used for judging whether the gyro sensor fails or not; and 3, a failure diagnosis and isolation module outputs a failure separation result indicating that the actuator or the gyro sensor fails according to the satellite triaxial angular rate residual obtained by the step 1 and the equivalent vector p obtained by the step 2, and further judges which axis of the failing part fails.

It relates to the after potential exploration of an oil filed, with the ability of analyzing the oil distribution, left resource quantity, and so on. It comprises the data inspection device, data preprocessing device, KH value separation device, oil well water absorption preprocessing device, water absorption section separation device, dynamic personnel analysis separation device, small layer weighting separation device, single sand separation result accumulation device, data base, water filling effect, strata HK, and weighting of the oil reserve weighting by the dynamic personnel, determining the strata output through the water absorption section, water content of the strata through well fluid section, with the whole process being computerized and accurate.

A multicell cyclone with a plurality of cyclone cells and a method for the production thereof, in which the cyclone cells of the multicell cyclone are combined into groups from which deposited particles are cleaned out by vacuum suction through channel structures. This permits better control of the discharge of separated particles. Vacuum suction of the separated particles can be effected, for instance, via openings (19) which are jointly provided for the group (21) of cyclone cells. The group of cyclone cells can furthermore be produced as a module, which can, for instance, be assembled from two parts (12, 13) in which like structural elements of a group of cyclones are combined into one respective part. This makes it possible to optimize the separation result with little structural complexity and at low production cost.

A voice recognition system (10) for improving the toughness of voice recognition for a voice input for which a deteriorated feature amount cannot be completely identified. The system comprises at least two sound detecting means (16a, 16b) for detecting a sound signal, a sound source localizing unit (21) for determining the direction of a sound source based on the sound signal, a sound source separating unit (23) for separating a sound by the sound source from the sound signal based on the sound source direction, a mask producing unit (25) for producing a mask value according to the reliability of the separation results, a feature extracting unit (27) for extracting the feature amount of the sound signal, and a voice recognizing unit (29) for applying the mask to the feature amount to recognize a voice from the sound signal.

The invention discloses a matrix-transformation-based method for underdetermined blind source separation. The method comprises the following steps of: gradually transforming a mixed matrix to obtain a transformed matrix, then acting the transformed matrix on an observation signal to gradually remove each source signal, and constructing multi-stage binary shielding templates by zero points generated in each observation signal to gradually separate the source signal. By the method, the requirement on sparsity of the source signal is reduced; aliasing of at most M-1 paths of source signals is prevented (wherein M is the number of sensors); source signals for forming each time frequency point can be clearly known; the underdetermined separation problem of music signals and noise signals is solved; the statistical property requirement on the source signal is low; the underdetermined separation problem of Gaussian signals and related signals is solved; processing stages are adjusted according to the requirement of separation precision; when the processing level is more, the separation result is better; the separation process is realized by aid of the matrix transformation; and the complexity is relatively low.

The present application relates to separation processes and systems and more specifically to hybrid carbon dioxide separation processes. In one embodiment, the system for the separation or removal of carbon dioxide comprises an apparatus for a selective separation of carbon dioxide (CO2) from flue gas—typically exhaust gases, syngas or natural gas streams—using one or more so-called CO2 reverse selective membrane(s) in the first separation unit to enrich a feed gas stream which contains carbon dioxide with CO2 and by separating other constituents of the gas stream. Thus, the feed gas stream is separated in the first separation unit by CO2-reverse-selective separation into a CO2-lean gas stream and a CO2-enriched gas stream. The CO2-enriched gas stream is fed to a second separation unit which is a CO2-selective separation unit. The second separation results in a purified CO2-rich gas stream and a remaining CO2-lean gas stream.

The invention relates to a mechanical vibration fault characteristic time domain blind extraction method, and belongs to the technical field of mechanical equipment status monitor and fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method includes: firstly, expanding a vibration observation signal into a high dimension signal subspace; then, obtaining a low dimension signal; afterwards, performing FastICA independent component analysis, calculating normalization kurtosis of all independent components, figuring out a component signal corresponding to the minimum normalization kurtosis, and using an orthogonal matching pursuit algorithm to reconstitute periodic signals; subsequently, removing the reconstituted periodic signal from each independent component, and then using an improved KL distance algorithm to calculate a distance matrix among the independent components after the periodic signals are removed from the independent components, and performing dynamic particle swarm clustering so as to obtain an estimation signal; finally, analyzing an envelope demodulation spectrum of the estimation signal, and performing fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method is suitable for processing a long convolution data problem, can effectively reduce influences from periodic ingredients on a blind separation result, and simultaneously can solve blind separation result order uncertainty problems, and finally achieves bearing fault characteristic extraction.

A signal processing device includes a signal transform unit which generates observation signals in the time frequency domain, and an audio source separation unit which generates an audio source separation result, and the audio source separation unit includes a first-stage separation section which calculates separation matrices for separating mixtures included in the first frequency bin data set by a learning process in which Independent Component Analysis is applied to the first frequency bin data set, and acquires a first separation result for the first frequency bin data set, a second-stage separation section which acquires a second separation result for a second frequency bin data set by using a score function in which an envelope is used as a fixed one, and executing a learning process for calculating separation matrices for separating mixtures, and a synthesis section which generates the final separation results by integrating the first and the second separation results.

The invention discloses an algorithm for separating vibration signal blind sources under a strong noise environment. The method comprises the following steps: 1, de-noising a group of given mixed signals containing noise through a time delay autocorrelation method to acquire de-noised mixed signals; 2, performing mean value removal and steady whitening pretreatment on the mixed signals acquired in the first step to further reduce the influence of the noise signals on the separation result; and 3, calculating second-order and fourth-order cumulated amount of the initial separate signals, using the sum of diagonal elements of second-order and fourth-order cumulated amount matrixes as a cost function, and maximizing the cost function to similarly diagonalize the joints of the cumulated amount matrixes so as to realize the separation of the signals of the independent sources and acquire orthogonal separation matrixes. The method combines the conventional de-noising method and the blind separation algorithm to realize the separation of the mixed signals under the strong noise environment, and has the advantages of good separation effect, high convergence rate and de-noising effect free from the limitation of the set threshold value compared with the conventional algorithm.

An image processing apparatus comprises: a conversion unit that converts an original into image data by scanning the original; a detection unit that detects a blank page in the image data converted by the conversion unit; a separation unit that deletes image data of the blank page detected by the separation unit from the converted image data; and a transmission unit that transmits to an external apparatus (a) the converted image data from which the image data of the blank page is separated by the separation unit, (b) the separated image data of the blank page, and (c) notification information formed based on a separation result by the separation unit.

The invention relates to an unmanned aerial vehicle longitudinal flight control system fault detection and separation method based on nonlinear adaptive observers. According to the invention, based onan unmanned aerial vehicle longitudinal nonlinear model, a set of nonlinear adaptive observers are designed for actuator and sensor faults of a longitudinal flight control system; a fault separationproblem is transformed into a model matching problem; by referring to the idea of a contribution analysis method, a fault estimation value and the fault direction, which are acquired by each adaptiveobserver, are used to contribute a contribution function, and standardization is carried out; the optimal matching model is determined by selecting the maximum standardized contribution function; andfinally, the maximum standardized contribution function is compared with a threshold value, and fault detection and separation results are acquired. According to the invention, a new reference index is provided for model matching by referring to the contribution analysis method; an analytical model is used to design the nonlinear adaptive observers to acquire a reliable fault estimation value andfault direction; and the problem that the fault direction is difficultly determined in a data driving method is overcome.

The invention provides a wave field separating method and device. The method comprises the steps that variation function values of anisotropy parameter distribution of initial models are calculated and fitted to obtain variation functions of the initial models; the variation functions and a reference point search strategy are utilized for selecting N reference models from the initial models; a vector elastic wave field of a spatial domain is converted into a wave number domain; following operation is carried out on each reference model selected from the N reference models, pseudo differential operators are truncated, the wave field separation is carried out on the wave number domain, the vertical and horizontal wave field is reversely converted into the spatial domain, and the wave field separation results of the reference models are obtained; the variation functions are utilized for calculating the weight coefficients of all the reference models relative to the initial models; weighting interpolation processing is carried out on the wave field separation results of all the reference models in the spatial domain to obtain the wave field separation results of the initial models. According to the method, on the basis of ensuring the accuracy of the wave field separation results, the effect of reducing the calculation amount is achieved.

The invention discloses a MR (Measurement Report) data indoor and outdoor separation method based on a statistic model. The MR data indoor and outdoor separation method comprises the steps of: aiming at MR sampled data of each cell of an outdoor macro station, carrying out characteristic value statistics of received signals, which comprises statistics of a main cell level; and carrying out separation of a mixed Gaussian distribution and probability calculation, and according to an indoor probability, obtaining a corresponding indoor and outdoor separation result. Moreover, the MR data indoor and outdoor separation method supports combination of various factors of the main cell level, a main and adjacent level difference, the number of adjacent cells and the like, and by a combined model, an indoor and outdoor judgment result is judged and output; and judgment methods at other angles also can be integrated. According to the MR data indoor and outdoor separation method disclosed by the invention, data characteristics which are shown by signal intensity and a signal number of the MR data in a statistical sense and are combined by two indoor and outdoor normal distributions are utilized, and probabilities that different signal intensity and different signal numbers occur in a door or outside a door can be calculated, so that the method of separating the MR data according to signal characteristics is more feasible, and accuracy of indoor and outdoor separation of the MR data is improved.

A voice recognition system (10) for improving the toughness of voice recognition for a voice input for which a deteriorated feature amount cannot be completely identified. The system comprises at least two sound detecting means (16a, 16b) for detecting a sound signal, a sound source localizing unit (21) for determining the direction of a sound source based on the sound signal, a sound source separating unit (23) for separating a sound by the sound source from the sound signal based on the sound source direction, a mask producing unit (25) for producing a mask value according to the reliability of the separation results, a feature extracting unit (27) for extracting the feature amount of the sound signal, and a voice recognizing unit (29) for applying the mask to the feature amount to recognize a voice from the sound signal.

The invention discloses a brain electrical signal independent component extraction method based on convolution blind source separation. The brain electrical signal independent component extraction method based on the convolution blind source separation includes concrete steps: building a brain electrical signal independent component extraction system based on the convolution blind source separation, which comprises an AD (analog to digital) sampling module, a short time Fourier transformation module, a frequency domain instantaneous blind source separation module, a sequence adjustment module and a short time inverse Fourier transformation module; using the AD sampling module to sample brain electrical signals; using the short time Fourier transformation module to transform the brain electrical signals from a time domain to a frequency domain; using the frequency domain instant blind source separation module to separate instantaneous mixing signals in the frequency domain; using the sequence adjustment module to perform sequence adjustment on independent components in a vector on each frequency domain segment; using the short time inverse Fourier transformation module to transform a frequency domain separation result into an independent component on the time domain. The brain electrical signal independent component extraction method based on the convolution blind source separation extracts the independent components of brain electrical signals based on a true convolution mixing model, uses a convolution blind source separation frequency domain algorithm, and is simple to achieve, good in separation effect, and low in calculation complexity.

This invention involves separation of brown algae; is a method of extract phycophacin from brown algae. The specific process is: brown algae as raw material, by 60-100% ethanol extraction, concentration, filtration to gain precipitation, macroporous resin column chromatography, spray drying to obtain products. Products content of 1% -20% (HPLC: High Performance Liquid Chromatography, HPLC). This invention is simple, separation result nice, and suitable for industrial production. The invention also involves brown algae in the health-food products, food and cosmetics field applications.

The invention discloses a gear case coupling modulation signal separation method based on inner product transformation and correlation filtering. According to the method, on the basis of the structure parameter (like the number Z of teeth) and the operational state (like the rotating frequency fn), a parsing dictionary Ds(t) capable of representing a stable modulation signal is constructed; an inherent frequency fd and a damping ratio Xi that correspond to the impact are identified in a gear case vibration acceleration signal x(t) by using a correlation filter method and an adaptive dictionary Dc(t) capable representing an impact modulation signal is constructed; and according to inner product transformation and correlation analyzing of the x(t), the Ds(t), and the Dc(t), sparse decomposition is carried out on the x(t) so as to obtain a primary function DS(t) and a coefficient Ai that are capable of expressing a stable modulation signal as well as a primary function DC (t) and a coefficient Bi that are capable of expressing an impact modulation signal, and separation is carried out to obtain a stable modulation signal Dpw(t) that is equal to sigma Ai DS (t) and an impact modulation signal Dcj(t) equal to sigma Bi DC (t). The method for constructing a parsing dictionary has advantages of both the parsing method and the adaptive method; and the physical significance is explicit. No complete orthogonality is required and the universality is high. The coupling modulation separation result is precise and reliable; and the anti-noise performance is good. A defect that the weak impact can not be extracted easily according to the traditional method can be overcome.

An energy supplying method and an energy balancing-allocating method for producing refuse derived fuel are provided, wherein, the energy supplying method for producing refuse derived fuel comprises: firstly, crude refuse is separated according to different components; secondly, according to separation results, the refuse is fed into corresponding systems for treatment respectively; finally, the energy generated by a refuse fermenting system is supplied to a refuse derived fuel manufacturing system for use. The energy balancing-allocating method is that: according to the surplus conditions of a refuse derived fuel factory in energy use, partial organic refuse originally used for the fermenting system is also used as the raw materials in the production of refuse derived fuel, so as to realize the supply of the fermenting system and the energy balance needed in the production process of refuse derived fuel. The present invention has the advantages that: RDF production cost is reduced; RDF heat value is improved; the amount of biodegradable component used for fermentation can be flexibly adjusted according to the concrete conditions of energy balance.

The invention provides a method for separating a vehicle running track, and includes the steps of: (1) acquiring satellite positioning data of operation of a vehicle in a set period of time, the satellite positioning data including the acquisition time, the vehicle position and the passenger carrying state corresponding to the satellite positioning data; (2) screening the acquired satellite positioning data according to the passenger carrying sate of each satellite positioning data and forming a corresponding passenger carrying running track according to a screening result; and (3) determining the positions of time jump points and space jump points of the passenger carrying running track, and dividing the passenger carrying track into a plurality of sub-running tracks according to the positions of the time jump points and the space jump points. The method for separating the vehicle running track completes division in sequence according to the time jump points and the space jump points, is easy to realize, and fully considers the characteristic of the vehicle running track, thereby enabling a finally obtained track separation result to be more accurate, and accuracy of a result of performing motion mode mining using the track separation result can be effectively improved.

The invention discloses an audio separation method and device, electronic equipment and a storage medium. The audio separation method comprises the following steps of acquiring mixed audio to be processed; extracting the audio features of the mixed audio; inputting the audio features into a pre-trained audio separation neural network model, wherein the audio separation neural network model comprises a dense module Dense Block structure and a sand clock Hourglass structure, wherein the Dense Block structure and the Hourglass structure are used for outputting accompaniment audio features and human voice audio features corresponding to the mixed audio step by step according to the audio features; obtaining the accompaniment audio features and the human voice audio features corresponding to the mixed audio output by the audio separation neural network model; and based on the accompaniment audio features and the human voice audio features, acquiring accompaniment audio and human voice audiocorresponding to the mixed audio, and taking the accompaniment audio and the human voice audio as a sound mixing separation result of the mixed audio. According to the method, the mixed audio is input into an audio separation neural network model comprising the Dense Block structure and the Hourglass structure, so that pure accompaniment and pure human sounds can be accurately separated, and theaudio separation effect is improved.

The invention discloses an audio single tone separation method, an audio single tone separation device, computer equipment and a storage medium. The invention is applied to the technical field of audio processing, and is used for solving the problem that single tone separation cannot be realized in the prior art. The method provided by the invention comprises the following steps: acquiring a target audio to be subjected to audio separation; determining each tone type required to be separated for the target audio; selecting one LSTM neural network corresponding to each tone type from each pre-trained LSTM neural network, wherein the LSTM neural networks serve as target LSTM neural networks, each LSTM neural network is obtained by pre-training audio samples corresponding to different timbretype combinations, and each timbre type combination is composed of more than two timbre types; inputting the target audio as input into a target LSTM neural network to obtain each output target spectrogram; and performing time domain transformation on each target spectrogram to obtain a target single-tone audio corresponding to each target spectrogram, and taking the target single-tone audio as anaudio separation result of the target audio.

The invention relates to a volume rendering method for highlighting the target area in volume data, and belongs to the technical field of scientific visualization direct volume rendering in computer graphics. The method includes the steps that firstly, multiple characteristics of original data are calculated, characteristic data are estimated, and proper characteristics are selected to form a transmission function; two-dimensional histogram images are segmented according to a normalization segmentation method, and the target data are separated; if the target data are not finely separated, proper characteristics continue to be selected to further separate a current result, and then the fine separation result is acquired step by step; then, the final transmission function is synthesized through the separation result; finally, the target area is highlighted through the transmission function in the volume rendering process, and an ideal visualization effect is acquired. By the adoption of the method, characteristic distinguishing capacity of the transmission function is enhanced, an interaction mode based on two-dimensional histogram image segmentation and hierarchical clustering is utilized, and operation is convenient and quick. The method is suitable for various data.

The invention discloses an X-ray chest radiograph image quality determination method and device. The method comprises: conducting normalization processing on an X-ray chest radiograph image; inputtingthe normalized image into a deep learning model for image separation and recognition to obtain at least one separation result; wherein the deep learning model at least comprises one of the followingformulas: a pulmonary lobe segmentation model, a spine segmentation model, a shoulder blade segmentation model and a foreign matter detection model; and determining the image quality of the X-ray chest radiograph based on the separation result. The X-ray chest radiograph image quality is fully automatically evaluated, and the image quality determination speed is high. Moreover, a technician can behelped to control the image quality of the chest radiograph, and the radiograph reading accuracy is indirectly improved.

The invention provides an SCSS (Single Channel Speech Separation) algorithm based on a DNN (Deep Neural Network). The SCSS algorithm based on the DNN mainly comprises the following steps of preprocessing a training speech sample, and extracting feature information of the training speech sample; training the DNN by using a loss function so as to obtain a DNN model; preprocessing a speech sample tobe tested; extracting feature information of the speech sample to be tested; performing speech separation through the trained DNN model; and then obtaining a separation result through speech reconstruction. The DNN is trained by using the nonlinear relationship between input and output. Compared with a conventional separation method based on single-output DNN, the SCSS algorithm has the advantagesthat the combined relationship between the output is sufficiently mined; the separation efficiency is high; two source speech signals can be separated in one step; the voice distortion rate is effectively reduced; and meanwhile, the understandability of the separation speech is improved.

The invention provides a foreground image separation method. By the method, dynamic foreground and static background can be separated in continuous input images with automatic white balance or abrupt luminance change. The method comprises the following steps of: receiving an input image from a camera; performing white balance or luminance compensation on the input image according to a reference image to generate a compensation image, which has a background color and background luminance which are roughly the same as that of the reference image; and performing a background subtraction algorithm on the compensation image to generate a background separation result. The background subtraction algorithm can be a Gaussian MixtureModel-based algorithm. In the method, the images continuously input by the camera are treated, so that foreground image separation results are continuously generated and the reference image is updated according to the foreground image separation results to ensure that a monitoring system can adapt to the change of the lighting condition.

The invention relates to a method for preparing lactuca indica and lactucin, which is a method for using two separation processes to obtain lactuca indica and lactucin from cichorium glandulosum by a high-speed countercurrent chromatography. The method has the advantages of large fractional dose, small sample loss, high recovery rate, mild separation environment and solvent saving. A great amountof lactuca indica and lactucin crude products can be placed in a countercurrent chromatograph, the separation result achieves the high purity and good separation effect. The method is not only suitable for preparing the product with high purity from plant crude extract, but also suitable for purifying lactuca indica and lactucin crude extracts obtained by various approaches.

The invention relates to an automatic formation and separation method of nickel-metal hydride batteries. The automatic formation and separation method of nickel-metal hydride batteries comprises the following steps of: (1) self-adapting charging process: charging the batteries in the process; (2) constant current discharging process: discharging the charged batteries by constant current and then entering a third step after finishing discharging; (3) separation process: adopting two steps to carry out separation, wherein the first step of separation is carried out according to voltage changes V of each battery in the step (1); the voltage change V is Vt-V0, Vt is a voltage value after the batteries are charged, and V0 is a voltage value before the batteries are charged; the second step of separation is further thinning separation based on a separation result of the first step and is carried out according to a battery capacity value Q measured in the first step; Q is sigma delta f(i)*deltat; and f(i) expresses an average current value within the time of deltaT. The separation is exact by adopting the method.