30results about How to "Conform to the distribution characteristics" patented technology

The invention relates to a method for optimizing running parameters of a thermal power unit based on a fuzzy set association rule. The method comprises the following steps of: (1) selecting data; (2) preprocessing the data and dividing a working condition; (3) constructing a fuzzy set; (4) extracting the fuzzy set association rule; and (5) performing regression analysis. According to the method provided by the invention, the optimal parameter and working condition of the thermal power unit which can be achieved under the present running working condition are considered; the main controllable parameters and the historic data influencing the running optimization of the thermal power unit are analyzed; the relevant association rule is excavated by utilizing an excavating technology for automatically constructing the fuzzy set data; a target value for the running parameter optimization when the power supply coal consumption of the thermal power unit is lower is confirmed; and the regression analysis is performed, thereby obtaining a running optimization curve of each parameter under a confirmed working condition. The method provided by the invention has high practical value and wide application prospect in the technical field of energy-saving optimization control during a thermal process of a thermal power plant.

The present invention discloses a children speech emotion recognition method, comprising: creating a training voice database, classifier training and recognizing emotion. The classifier training comprises the step of extracting the speech emotion features, which includes performing pretreatment on the analog speech signal, including sampling, denoising and other basic signal treatments, performingHHT transformation on the pretreated emotion sentences, obtaining the Hilbert spectrum of the emotion speech; performing amplitude feature extraction, intonation feature extraction, tone feature extraction and rhythm feature extraction, and obtaining the emotion feature; reducing dimension of the emotion feature, and obtaining different quaternionic feature of emotion space; performing calculation on the sample set of the training speech database, obtaining the projection of a series of quaternionic features in national emotion state in the emotion space i, thus forming an emotion feature projection set; and carrying out classification training based on the SVM / HMM mixed system by using the data in the emotion feature projection set.

The invention discloses a non-local mean value image denoising method based on a filter window and parameter adaption. According to the invention, firstly, noise is detected, and a noise calibration matrix is established according to a detection result; the size of the noise calibration matrix is consistent with the size of an image, and the matrix value at a corresponding position of each noise point is set to be 1, and the matrix value at a corresponding position of each non-noise point is set to be 0. Then, each pixel of a noise image is successively taken as a reference point, and centric to the point, a predetermined number of non-noise reference points are taken in a counterclockwise direction to be involved in computation. Finally adaptive weighting parameters are determined according to the locations of the reference points, and a weighted result is calculated and a restored pixel value is obtained; the corresponding element in the noise calibration matrix is set to be 0 and the pixel point after denoising can be used as a reference point of other noise points. Compared with traditional image denoising methods, the method provided by the invention is added with the noise detection and noise point screening, thus improving algorithm accuracy, changing a reference point selection window and improving algorithm adaptability.

The invention discloses an iterative eye fundus image blood vessel segmentation method based on the distance modulation loss, which comprises the steps of (0) collecting a color eye fundus image to form an original image; (1) performing normalization processing on the original image; (2) iteratively training a dense convolution neural network based on the distance modulation loss; and (3) carryingout iterative blood vessel segmentation by using the trained dense convolution neural network. The method can process color eye fundus images under different collection conditions, can provide interactive blood vessel segmentation experience for ophthalmologists, is more robust for blood vessel detection and provides a reliable guarantee for subsequent auxiliary diagnosis.

The invention discloses an RGB-D image variable metric super voxel segmentation method. By adopting Poisson disk sampling, seed points are selected in data, and iteration clustering is carried out according to color distances and spatial distances between data points and the various seed points, and then an initial super voxel segmentation result is acquired. The super voxels acquired by the initial segmentation are used as vertexes, and the adjacency relations of the super voxels are used as edges to form an undirected graph, and the super voxel integration is carried out by adopting a method based on a graph theory. Super voxel segmentation results having different dimensions are acquired in the same RGB-D image, and the variable metric super voxel segmentation is realized. The RGB-D image variable metric super voxel segmentation method belongs to data pretreatment, and by using the acquired super voxels by segmentation, the large-scale super voxels are acquired in the area having high data consistency, and the small-scale super voxels are acquired in the area having the poor data consistency, and the method is capable of satisfying a human visual cognition characteristic in a better way.

The invention belongs to the technical field of image processing, and discloses a hyperspectral open set classification method based on Euclidean distance and deep learning. The method comprises the following steps: firstly, constructing a category center of each known category, a category prediction function based on an Euclidean distance and a loss function based on the Euclidean distance, and training and optimizing a deep learning network model; secondly, combining a box line graph method and an extreme value theory Weibull distribution model, analyzing and fitting the distance between input data and the category center of each known category, thereby achieving constraint on each known classification boundary, and then achieving judgment on unknown categories. Specifically, a box linegraph is used for providing the number of abnormal points needed for fitting the Weibull distribution model; and judging whether a Weibull model is used or the upper edge of the box line graph is usedfor judging unknown classes according to the number of the abnormal points. Aiming at the open set classification problem in the hyperspectral field, the method is simple, practical, high in classification precision and high in robustness.

The invention discloses a speech emotion recognition method and system based on semi-supervised adversarial variation self-encoding, and the method comprises the steps: S1, constructing a generative adversarial network, and constructing a speech emotion recognition model through the combination of a semi-supervised variation self-encoding model and the generative adversarial network, wherein data with emotion labels in input data and corresponding emotion labels are used as input, data without emotion labels in the input data are used as emotion label attribute missing types for processing, feature probability distribution of the input data in a hidden layer is learned through the generative adversarial network, and an SSAVAE model is constructed; S2, training the constructed SSAVAE model by using a training set; and S3, inputting to-be-processed speech emotion data, and inputting the to-be-processed speech emotion data into the trained SSAVAE model to obtain an emotion recognition result. The method has the advantages of being simple in implementation method, high in recognition precision, good in generalization ability and data disturbance resistance and the like.

The invention discloses an object-oriented remote sensing image data space-time fusion method, a system and a device, which are suitable for being used in the technical field of remote sensing. The method comprises the following steps: firstly, acquiring a high-resolution image and a low-resolution image of a first time phase and a low-resolution image of a second time phase; downscaling the two time-phase low-spatial-resolution images to the same resolution as the first time-phase high-resolution image by using a bicubic interpolation model to obtain an interpolation image; segmenting the high-resolution image ground object of the first time phase by using image segmentation; in each segmentation block, inputting the interpolation image and a high-resolution image of a first time phase into a pre-established linear interpolation model to obtain a preliminary fusion result; in each segmentation block, searching spectral similar pixels of a target pixel pixel pixel by pixel, and takingan intersection of the two images as a final spectral similar pixel; and performing spatial filtering through inverse distance weighting in combination with spectral similar pixel information, so thata final fused image can be obtained. The steps are simple, and the obtained spatio-temporal data fusion result is better.

The invention provides a digital rock core reconstruction method and system based on a generative adversarial neural network. The method comprises the following steps: executing iterative processing:processing historical noise data according to a weight matrix of a generator and bias of the generator to obtain a generated sample; processing the generated sample according to the weight of the discriminator and the bias of the discriminator to obtain a generated sample discrimination probability; processing the real sample according to the weight of the discriminator and the bias of the discriminator to obtain a real sample discrimination probability; determining a loss function according to the generated sample discrimination probability and the real sample discrimination probability; whenthe current number of iterations reaches a preset number of iterations; processing the current noise data according to the weight matrix of the generator and the bias of the generator in the currentiteration to obtain a digital rock core model; otherwise, updating the weight matrix of the generator, the bias of the generator, the weight of the discriminator and the bias of the discriminator according to the loss function, so that the reconstruction efficiency can be improved, and the digital rock core conforms to the distribution characteristics of the real rock core.

The invention provides an image restoration method, an image restoration system and a flat panel detector, and the method comprises the steps: obtaining a first image, carrying out the connected domain analysis of the first image, and extracting all defect pixels in the first image; dividing the defect pixels into isolated point defects, cluster defects and broken line defects according to the size and shape of a connected domain; and outputting the second image and repairing the second image according to the type of the defective pixel. According to the method, defective pixels can be effectively repaired, and details of an original image can be well reserved. And repairing the second image output by the detector in real time according to the classification of the defective pixels to realize the rapid repair of the defective pixels of the image. The method is only related to the type and size of the defective pixel, the applicability is high, and the working efficiency is remarkably improved. The image restoration system is integrated in the detector, the hardware design does not need to be changed, the product cost can be reduced, and only software needs to be optimized and upgraded in subsequent upgrading and maintenance.

The invention discloses a digital elevation model (DEM)-based water system extraction method, relates to the field of digital elevation model-based water system extraction methods, and belongs to the field of geosciences, and the digital elevation model-based water system extraction method comprises the following steps: step (1): preprocessing to-be-operated data, logging in the geographic space data cloud network platform to cut and download the selected area data, performing embedding processing on the obtained area data, and adding an administrative region boundary as a reference; and step (2), performing depression filling processing on the original DEM data by using a built-in system toolbar of the ArcGIS, performing flow direction extraction on the filled depression, taking the flow direction as input and output flow data, and performing calculation of a flow direction accumulation grid. According to the method, on the basis of the DEM, the river, the water outlet and the drainage basin are extracted by simulating the runoff forming process of surface water, convenience and rapidness are achieved, and extraction of the small drainage basin is more accurate and clearer.

The invention discloses a method for detecting a hidden object in a millimeter-wave human body image based on block mixture Gaussian low-rank matrix decomposition.The method mainly solves the problemsof low imaging quality caused by weak scatter echo of the hidden object and low detection accuracy of a gray scale value of the hidden object and a human body similarity in the prior art. An implementing scheme of the method comprises the following steps of 1, removingabnormal points in an imaging region background in an original millimeter-wave human body image, and dividing the human body imageinto six parts according to proportions of human body parts; 2, decomposing all regions of thehuman body based on a block mixture Gaussian low-rank matrix decomposition algorithm to obtain a low-rankpart and a sparse part; and 3, binaryzing the sparse part by using a typology method, and removing small noise points to obtain a final detection result graph. The method increases the detection rateof various complicated small targets in the millimeter-wave human body image without a large number of training samples, so that the detected hidden object is more complete; and the method can be used for detecting hidden objects carried by people in public places such as an airport and a bus station.

The invention discloses a rainfall weather phenomenon comprehensive identification method based on multi-source observation data. The method comprises the following steps of firstly, extracting minute rainfall particle spectrum distribution (particle size and number concentration) and falling speed information detected by a raindrop spectrometer, and carrying out quality control to obtain rainfall particle spectrum distribution and falling speed after quality control; and then based on the particle spectrum distribution and falling speed information observed by a raindrop spectrometer and the environment temperature information at a meteorological station position, introducing a fuzzy logic identification method to identify different types of rainfall weather phenomena; and finally, for the identified rainfall type, further according to intensity and continuous changes, subdividing specific rainfall sub-types, and realizing more refined weather phenomenon identification. The method is based on a multi-source observation means and a fuzzy logic algorithm, can solve a problem that a current raindrop spectrometer is too high in weather phenomenon false recognition rate, and achieves accurate recognition of a rainfall phenomenon.

The invention discloses a software defect prediction data processing method. According to the method, an independent feature distribution model is established for each feature of a defect sample; andpart of the features are replaced by adopting a random variation mode so as to obtain a new defect sample, and new samples are supplemented continuously until the ratio of a non-defect sample to thedefect sample is balanced so as to obtain a processed software defect prediction data set for subsequent model training. The invention further provides a software defect prediction data processing device based on the method and a machine storage medium, the problem that in the prior art, due to the fact that the number of defect samples is smaller than that of non-defect samples, the defect samplerecognition capacity is insufficient is solved, and the software defect prediction precision is effectively improved.

The invention discloses a complete set of energy-saving equipment for recovery and utilization of pressure swing adsorption tail gas. The equipment comprises an air inlet pipeline, a compression heatregeneration drying device, a gas conveying pipeline, a tail gas regeneration pipeline and an oxygen-nitrogen separation device, wherein the compression heat regeneration drying device comprises a first drying tower and a second drying tower, and the outlet of the first drying tower and the outlet of the second drying tower are separately connected with the tail gas regeneration pipeline through afourth check valve and a third check valve; and when the first drying tower or the second drying tower is depressurized, evacuation is performed firstly through a small-diameter pneumatic valve, andthen evacuation is performed through a large-diameter pneumatic valve. In addition, the invention also discloses an energy-saving method for recovery and utilization of pressure swing adsorption tailgas. No heaters are needed for heating, evacuation of a large amount of tail gas is avoided, consumption of the raw material air and consumption of power are saved, and the operating cost is reduced.

The invention discloses a software defect prediction data processing method. The method establishes an independent feature distribution model for each feature of a defect sample, and uses random variation to replace part of the features, thereby obtaining new defect samples and constantly supplementing New samples are added until the proportion of non-defective samples and defective samples reaches a balance, and the processed software defect prediction data set is obtained for subsequent model training. The present invention also provides a software defect prediction data processing device and a machine storage medium based on the above method, which solves the problem of insufficient defect sample identification ability caused by the ubiquitous defect sample number less than the non-defect sample number in the prior art, and effectively improves the software Accuracy of defect prediction.

The invention discloses a financial big data risk control platform based on a blockchain. The financial big data risk control platform comprises a financial data acquisition module, a financial data detection module, a financial data transmission module and a blockchain storage module, wherein the financial data acquisition module is used for collecting financial data; the financial data detectionmodule is used for clustering the collected financial data, judging whether the financial data in each class obtained by clustering is security data or suspicious data, abandoning the financial datain the class judged to be the suspicious data, and transmitting the financial data in the class judged to be the security data to the blockchain storage module for storage through a financial data transmission module. The financial big data risk control platform has the beneficial effects that the financial data detection module is arranged, and the financial data detection module clusters the received financial data and judges whether the financial data in each class obtained by clustering is security data or suspicious data, so that the security of the financial data when the financial dataenters the blockchain storage module is ensured.

The invention discloses a method for detecting a hidden object in a millimeter-wave human body image based on block mixture Gaussian low-rank matrix decomposition.The method mainly solves the problemsof low imaging quality caused by weak scatter echo of the hidden object and low detection accuracy of a gray scale value of the hidden object and a human body similarity in the prior art. An implementing scheme of the method comprises the following steps of 1, removingabnormal points in an imaging region background in an original millimeter-wave human body image, and dividing the human body imageinto six parts according to proportions of human body parts; 2, decomposing all regions of thehuman body based on a block mixture Gaussian low-rank matrix decomposition algorithm to obtain a low-rankpart and a sparse part; and 3, binaryzing the sparse part by using a typology method, and removing small noise points to obtain a final detection result graph. The method increases the detection rateof various complicated small targets in the millimeter-wave human body image without a large number of training samples, so that the detected hidden object is more complete; and the method can be used for detecting hidden objects carried by people in public places such as an airport and a bus station.

The present invention provides an image restoration method, an image restoration system and a flat panel detector. The method includes: acquiring a first image, performing connected domain analysis on the first image, and extracting all defective pixels in the first image; The size and shape classify the defective pixels into isolated point defects, cluster defects and bad line defects; output the second image and repair the second image according to the type of defective pixels. This method can not only effectively repair defective pixels, but also preserve the details of the original image well. The second image output by the detector in real time is repaired according to the classification of the defective pixels, so as to realize fast repair of the defective pixels of the image. The method is only related to the type and size of defective pixels, has strong applicability, and significantly improves work efficiency. The image restoration system is integrated in the detector, which does not require changes to the hardware design, which can reduce product costs. In subsequent upgrades and maintenance, only the software needs to be optimized and upgraded.

The invention discloses a non-local mean image denoising method based on filter window and parameter self-adaptation. Firstly, the noise detection is carried out, and the noise calibration matrix is ​​established according to the detection results, whose size is consistent with the image size. The matrix value corresponding to the noise point is set to 1, and the non-noise point is set to 0. Then, each pixel in the noise image is taken as a reference point in turn, and then a predetermined number of non-noise reference points are taken counterclockwise around this point to participate in the calculation. Finally, the adaptive weighting parameters are determined according to the position information of the reference point, the weighting result is calculated, and the restored pixel value is obtained, and the corresponding element of the noise calibration matrix is ​​set to 0, and the pixel point after denoising can be used as the reference point of the remaining noise points. Compared with the traditional image denoising method, this method adds noise detection and noise point screening, which improves the accuracy of the algorithm, changes the reference point selection window, and improves the adaptability of the algorithm.

The invention discloses a method for comprehensively identifying precipitation weather phenomena based on multi-source observation data. First, the minute precipitation particle spectral distribution (particle size and number concentration) and falling speed information detected by a raindrop spectrometer are extracted, and after quality control, quality control is obtained. The post-precipitation particle spectral distribution and falling velocity. Then, based on the particle spectrum distribution and falling velocity information observed by the raindrop spectrometer, as well as the ambient temperature information at the location of the meteorological station, a fuzzy logic identification method is introduced to identify different types of precipitation weather phenomena. Finally, for the identified precipitation types, the specific precipitation subtypes are further subdivided according to the intensity and continuous changes, so as to realize more refined weather phenomenon identification. The method of the present application is based on multi-source observation means and fuzzy logic algorithm, and can solve the problem that the current raindrop spectrometer has an excessively high misrecognition rate of weather phenomena, and realize accurate identification of precipitation phenomena.

The invention discloses a method for analyzing actually measured pressure data in a hydraulic transient process, which relates to the field of electric power and comprises the following steps of: extracting mean pressure to obtain instantaneously changed pressure fluctuation data; performing time-domain grid distribution division on the obtained pressure fluctuation data to divide the pressure fluctuation data into a plurality of time grid regions, analyzing the pressure fluctuation data in each time grid region by using an empirical cumulative distribution function to obtain probability distribution of each data, performing confidence probability processing, and combining processed data groups of each time grid region to obtain the probability distribution of each data; obtaining processed pressure fluctuation data; the average pressure is added back to the processed pressure fluctuation data, and the total pressure data subjected to confidence probability processing in the whole time domain are obtained.According to the method, the test actual measurement extreme value result and the transition process pressure change process can be well evaluated, the draft tube pressure fluctuation process can be well analyzed and evaluated, safe and stable operation of a unit is ensured, and the reliability of the draft tube is improved. And a solid technical guarantee is provided for safe and stable operation of a power station.

The present invention discloses a children speech emotion recognition method, comprising: creating a training voice database, classifier training and recognizing emotion. The classifier training comprises the step of extracting the speech emotion features, which includes performing pretreatment on the analog speech signal, including sampling, denoising and other basic signal treatments, performing HHT transformation on the pretreated emotion sentences, obtaining the Hilbert spectrum of the emotion speech; performing amplitude feature extraction, intonation feature extraction, tone feature extraction and rhythm feature extraction, and obtaining the emotion feature; reducing dimension of the emotion feature, and obtaining different quaternionic feature of emotion space; performing calculation on the sample set of the training speech database, obtaining the projection of a series of quaternionic features in national emotion state in the emotion space i, thus forming an emotion feature projection set; and carrying out classification training based on the SVM / HMM mixed system by using the data in the emotion feature projection set.

The invention discloses a high-fault-tolerance genome complex structure variation detection method based on a filtering strategy, and the method comprises the steps: carrying out preprocessing on an input file in an SAM format, and traversing a CIGAR field in an optimal quality comparison reading segment; according to the compared CIGAR field and variation score calculation criterion, calculatinga variation score corresponding to each site in the current reading segment, and storing the variation score in a variation score set of each site in advance; counting an average value in the variation score set of each site as a final variation score of the site and obtaining a variation score function of the sample; carrying out Kalman or Gaussian filtering on the variation score function to obtain a variation score function after filtering and noise reduction; according to the filtered variation score function, setting a threshold value and separating a structure variation region, and carrying out feature extraction; and training a support vector machine (SVM) model, and classifying the structural variation regions by using the trained SVM model to obtain a complex indel result set. According to the invention, the interference of sequencing errors on the determination of structural variation is solved.

A financial big data risk control platform based on blockchain, comprising a financial data acquisition module, a financial data detection module, a financial data transmission module and a blockchain storage module, the financial data acquisition module is used for collecting financial data, and the The financial data detection module is used to cluster the collected financial data, and to judge whether the financial data in each class obtained by the clustering is safe data or suspicious data, discard the financial data in the class judged as suspicious data, and judge it as The financial data in the secure data class is transmitted to the blockchain storage module through the financial data transmission module for storage. The beneficial effects of the present invention are as follows: a financial data detection module is provided, and the financial data detection module performs clustering on the received financial data and determines whether the financial data in each category obtained by the clustering is safe data or suspicious data, ensuring that The security of these financial data when entering the blockchain storage module.

The invention discloses a stroboscopic stripe detection and elimination method and device, camera equipment and a storage medium, which are used for detecting stroboscopic stripes of an image picture and improving the detection precision, thereby facilitating elimination of the stroboscopic stripes. The method comprises the following steps: acquiring a preprocessed initial image; carrying out binarization processing on the preprocessed initial image to obtain a binarized image; integral projection of the binary image in a preset direction is calculated, and a one-dimensional discrete signal is obtained; determining an effective wave crest and an effective wave trough in the one-dimensional discrete signal, wherein the effective wave crest and the effective wave trough are wave crests and wave troughs meeting preset effective conditions; and determining the stroboscopic state of the image according to the effective wave crests and the effective wave troughs.

The invention belongs to the field of oil gas and geophysical exploration, and particularly relates to a multichannel bipolar sparse spectrum inversion method based on Hessian matrix constraint. The method comprises the following steps: estimating the wavelength of seismic wavelets, determining the length of a time window, and segmenting multiple channels of seismic data; seismic wavelets in the time window are extracted; constructing a multi-channel bipolar sparse spectrum inversion objective function based on Hessian matrix constraint in the time window, and calculating a reflection coefficient of multi-channel seismic data in the time window; and combining the center reflection coefficient results of the time windows along a time sequence to obtain a spectrum inversion result of the multi-channel seismic data. According to the method, the real contact relation of the stratum can be reflected, the real pinching position of the stratum can be depicted, and high-resolution and richer geological information can be provided. While the seismic data resolution is improved, the method can ensure the transverse continuity of stratums in multi-channel data, and can provide more accurate and rich geological and reservoir information.