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51results about How to "To achieve the purpose of dimensionality reduction" patented technology
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Path planning method of passable area divided at unequal distance
InactiveCN103528585AOvercome memory spaceOvercome timeNavigational calculation instrumentsUndirected graphPlanning approach
The invention belongs to the technical field of path or flight path planning of robots as well as low-altitude flight aircrafts, specifically relates to a path planning method of a passable area divided at unequal distance, and is used for solving the problem that existing planning algorithm has large time complexity in time and space complexity. The path planning method comprises the following steps of: calculating convex extreme points of each barrier curve; dividing the passable area by using each convex extreme point as a horizontal line; abstracting each small area obtained by dividing into a peak of a graph; forming an undirected graph by all peaks; finding out a peak serial number corresponding to the small area at which a starting point and a final point are located; finding out all paths for the undirected graph by breadth-first or depth-first scanning; finding out an actual to-be-travelled path of a moving object according to the situation on an actual map. The path planning method disclosed by the invention has the beneficial effect of overcoming the problems of algorithms of A* and the like on memory space and operation time, and overcoming a convergence problem of an ant colony algorithm at the same time. Besides, time complexity and space complexity are improved greatly in comparison with other algorithms.
Owner:ZHONGBEI UNIV
Semi-supervised learning-based method for filtering junk users in social network
ActiveCN106294590ASatisfy the requirement of conditional independenceReduce time complexityData processing applicationsWeb data indexingLearning basedData Annotation
The invention discloses a semi-supervised learning-based method for filtering junk users in a social network. A cooperative training algorithm is applied to the detection of the junk users in the social network. Massive information in the social network is classified mainly by utilizing a supervised learning algorithm at present; and the algorithm is based on a classification model built based on annotated data, but the social information scale is huge, the labor cost required for data annotation is high, and few methods for solving the problem of user data annotation of the social network exist. A method is proposed; and by referring to the cooperative training algorithm, multiple views and multiple classifiers are applied to a large amount of non-annotated social network data or a small amount of annotated social network data, so that the classifiers on different views learn mutually and the purpose of data annotation is achieved.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Hyperspectral abnormity detection method based on digital image morphology theory
InactiveCN101916440AEasy to meet testing needsSuppress interferenceImage enhancementImage analysisDimensionality reductionAnomaly detection
The invention provides a hyperspectral abnormity detection method based on the digital image morphology theory. The method is characterized by firstly carrying out band characteristic extraction on the hyperspectral images by adopting close operation of extended morphology to reach the aim of dimension reduction, being capable of smoothening spectral data while carrying out band selection according to closed transform to remove redundancy, avoiding discontinuity of band information and effectively combining the space information of the ground objects and the information of correlation between fine spectra and space; and then carrying out abnormity detection on the hyperspectral image information undergoing dimension reduction, adopting a KRX operator to carry out abnormity detection on the images, obtaining the grayscale images of the detection results and then filtering the detection results by utilizing area close-open operation (ACO) of grayscale morphology to obtain the final detection result. The method not only can be used in combination with the KRX operator but also can be used in combination with other operators for hyperspectral image abnormity detection. The method has strong transportability and can more easily meet the requirement of hyperspectral detection.
Owner:HARBIN ENG UNIV
Fabric flaw detection method based on wavelet transformation and genetic algorithm
InactiveCN105931246AReduce resolutionPrecise Segmentation EffectImage enhancementImage analysisWavelet decompositionGenetic algorithm
The invention relates to a fabric flaw detection method based on wavelet transformation and a genetic algorithm. The method comprises the following steps: preprocessing acquired images with flaws; obtaining multi-dimensional sub-images by performing wavelet decomposition on the preprocessed images; obtaining optimal flaw edge information by performing fusion on the sub-images; calculating a threshold for the sub-images by use of the genetic algorithm, and performing threshold segmentation on the fused images by use of the threshold; and performing morphological processing on the images after threshold segmentation. According to the invention, the cloth flaw segmentation effect after processing is accurate, the segmentation speed is fast, and original forms of the flaws are well reserved.
Owner:DONGHUA UNIV
Method for classifying musical instrument signals
InactiveCN103761965ASolve the problem of high dependencyEasy to chooseSpeech recognitionFeature extractionNormal density
The invention discloses a method for classifying musical instrument signals, and belongs to the technical field of electronic information. Modules adopted in the method comprise a phase-space reconstruction module, a principal component analysis module, a feature extraction module and a flexibility neural tree module. The method is characterized by comprising the step of carrying out the phase-space reconstruction on a time sequence produced by different musical instrument sample signals, the step of removing redundant information through the principal component analysis to achieve the dimensionality reduction purpose, the step of depicting the differences of different musical instruments in the phase space through the probability density function by analyzing the features of various musical instruments, and the step of utilizing a flexible neural tree model to serves as a classifier to carry out classification. The method can effectively solve the problem of the high dependency of an artificial neural network structure, and the classification accuracy of a single musical instrument can reach up to 98.7 percent.
Owner:TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
Recommendation method based on label semantic normalization
ActiveCN106599037ATo achieve the purpose of dimensionality reductionIncrease reflectionSpecial data processing applicationsWeb data retrieval using information identifiersCo-occurrenceLabeled data
The invention relates to a recommendation method based on label semantic normalization. The method comprises the steps that a user-defined label is preprocessed to obtain the preprocessed label, and label semantic similarity is obtained according to calculation; a label resource matrix is obtained according to the preprocessed label, and then label resource co-occurrence similarity is obtained through calculation; linear fusion similarity is obtained through calculation according to the label semantic similarity and the label resource co-occurrence similarity, the linear fusion similarity is subjected to clustering operation to obtain label data after semantic normalization by a user, and collaborative filtering recommendation is performed in combination with the label data obtained after semantic normalization by the user. With respect to a plenty of redundant labels or labels with inaccurate semantic expression in a previous label system, by means of label normalization, the semantic expression of the normalized labels can be clearer; in a recommendation system, recommendation quality, namely accuracy and efficiency can be improved, and recommendation time can be shortened.
Owner:FOCUS TECH +1
Sewage treatment process monitoring method based on KPLS and RWFCM
ActiveCN110232256ATo achieve the purpose of dimensionality reductionAccurate and convenient to determineGeneral water supply conservationCharacter and pattern recognitionDimensionality reductionOutlier
The invention relates to the technical field of sewage treatment quality monitoring, and provides a sewage treatment process monitoring method based on KPLS and RWFCM. The method comprises the steps that firstly, collecting sewage treatment process data samples containing normal working conditions and abnormal working conditions wherein data of sewage treatment operation variables and data of effluent quality variables serve as an input data matrix and an output data matrix respectively, and the two matrixes are standardized; constructing a KPLS model, and solving a score matrix; then, based on an RWFCM algorithm, clustering the score matrix to obtain a membership matrix, and according to the membership matrix, carrying out abnormal working condition monitoring on the sewage treatment process; and finally, establishing a linear regression model of the membership matrix and the sample variables, solving a variable contribution matrix, and performing abnormal condition identification onthe sewage treatment process according to the variable contribution matrix. According to the invention, dimensionality reduction can be carried out on high-dimensional data, nonlinear data can be processed, the method is insensitive to outliers, and timeliness and accuracy of monitoring and identification in a sewage treatment process can be improved.
Owner:NORTHEASTERN UNIV
Novel channel estimation method of multi-user 3D MIMO system
ActiveCN110460549ASimplified Channel ModelingTo achieve the purpose of dimensionality reductionTransmission monitoringRadio transmissionEstimation methodsEngineering
The invention discloses a novel channel estimation method for a multi-user 3D MIMO system, and the method comprises the steps: carrying out the modeling through employing an included angle between a direction of arrival and an x axis and an included angle between the direction of arrival and a y axis of a planar array antenna, and projecting a 3D MIMO channel to the x axis and the y axis; in the uplink preamble stage, allocating an orthogonal pilot frequency sequence to each user, and obtaining paired spatial features and optimal rotation angles of channels projected to the x axis and the y axis by each user; grouping the obtained paired spatial features, distributing the same pilot frequency information in the groups, and distributing orthogonal pilot frequency sequences among the groups;carrying out channel estimation through the spatial feature and the optimal rotation angle of the (n-1)th coherence time in the group of the nth coherence time after the leader stage, and then dynamically updating the spatial feature and the optimal rotation angle of the user; and repeating the intra-group channel estimation method to obtain spatial features and optimal rotation angles of all users in the cell, reconstructing channels of an x axis and a y axis of all users in the cell, and generating a 3D MIMO channel through a Kronecker product.
Owner:NANJING UNIV OF POSTS & TELECOMM
Micro-blog short text classification method based on lexical chain feature extension and LDA (latent Dirichlet allocation) model
ActiveCN108920482AImprove classification performanceReduce dimensionalityNatural language data processingSpecial data processing applicationsExtension methodLexicon
The invention discloses a micro-blog short text classification method based on lexical chain feature extension and an LDA (latent Dirichlet allocation) model, and provides a lexical chain feature extension method according to short length, less content, sparse feature and the like of a micro-blog text. A basic lexical chain is generated based on Chinese thesaurus, the micro-blog text is extended by the aid of the basic lexical chain, the lexical chain can cover lexicons recorded by the Chinese thesaurus and can further cover other lexicons without being recorded by the Chinese thesaurus, and the lexical chain can be continually richened when the micro-blog text is extended. The micro-blog text is expressed by the aid of subject probability distribution of an LDA subject model according tohigh dimension and unobvious semantic feature of a vector space model in micro-blog text classification, a similarity calculation dimension is effectively reduced, and a certain semantic feature is fused. According to the method, advantages of lexical chain feature extension and the LDA model are combined, and a micro-blog classification method is provided. Experiment results show that the methodeffectively improves classification performance of the micro-blog text.
Owner:ZHEJIANG UNIV OF TECH
Cause-and-effect structure learning method based on flow characteristics
ActiveCN105719006AIndependence Test ValidReduce complexityKnowledge based modelsArbitrary distributionStructure learning
The invention discloses a cause-and-effect structure learning method based on flow characteristics. The method comprises following steps of 1: generating and distributing any new characteristic in a flow manner; 2: carrying out a correlation analysis on each of newly generated characteristics; 3: carrying out a redundancy verification analysis on a characteristic set; 4: carrying out searching orientation based on each of the characteristics; 5: repeating steps of 1-4 until the numbers of the generated characteristics exceed a limit value, finally obtaining a corresponding cause-and-effect structure. According to the invention, a cause-and-effect structure relation can be found in linearly randomly distributed data with flow characteristics and time complexity of learning is reduced, thereby satisfying timeliness requirements of online learning.
Owner:HEFEI UNIV OF TECH
Flight emergency prediction method and device based on LSTM neural network
PendingCN112633361AEnsuring Safe ExecutionStrong modeling abilityCharacter and pattern recognitionNeural architecturesFlight dataEngineering
The invention relates to a flight emergency prediction method and apparatus based on an LSTM neural network. The method comprises the steps of obtaining sample data of historical accident flight records; preprocessing the sample data, then performing dimension reduction on the sample data by utilizing an MDDM algorithm, and mapping the sample data into a multi-dimensional vector; constructing a prediction data set by taking the multi-dimensional vector as a sample; dividing the prediction data set into a training set, a verification set and a test set, and training an LSTM neural network until the error of the LSTM neural network is lower than a threshold value and tends to be stable; and inputting the current flight data into the trained LSTM neural network to obtain the flight emergency probability. According to the invention, related data of historical flight emergencies are subjected to preprocessing, dimensionality reduction and feature extraction and are used as samples to train the LSTM neural network, so that various data in the flight process are automatically and comprehensively monitored, early warning is given out for the flight emergencies in time, and safe execution of flight tasks is guaranteed.
Owner:AIR FORCE EARLY WARNING ACADEMY
Encrypted cloud data-oriented multi-keyword matching sorting search method for cloud network
InactiveCN108563732ATo achieve the purpose of dimensionality reductionReduce storage overheadSpecial data processing applicationsScoring algorithmCloud data
The invention discloses an encrypted cloud data-oriented multi-keyword matching sorting search method for a cloud network. According to the method, an index is established in a multi-keyword form, sothat multiple keywords extracted from a document are mapped to one dimension of the index through a comprehensive scoring algorithm; meanwhile, a score matching algorithm is adopted in a search process; and the score matching algorithm is combined with the comprehensive scoring algorithm, so that search results can be sorted according to matching types and the quantity of matching keywords. Therefore, the method reduces the index storage overhead and is high in sorting precision and high in encryption and search efficiency.
Owner:ZHEJIANG SCI-TECH UNIV
Feature selection method and device for model training and electronic equipment
PendingCN111104572AImprove interpretabilityReduce feature dimensionCharacter and pattern recognitionOther databases clustering/classificationFeature setEngineering
The invention discloses a feature selection method and device for model training, electronic equipment and a computer readable storage medium. The method comprises the steps:calculating the mutual information amount of to-be-selected features and labels in an original feature set to obtain the correlation degree between each to-be-selected feature and the corresponding label; calculating the average mutual information amount of the to-be-selected features and the selected feature subsets of the original feature set to obtain the redundancy of each to-be-selected feature and the selected feature subset; and selecting a feature subset from the to-be-selected features as input data for model training according to at least one of the correlation degree and the redundancy. According to the invention, the model training time can be reduced, and the training precision is improved.
Owner:BEIJING KINGSOFT CLOUD NETWORK TECH CO LTD
Power system operation state simulation method and power system operation state simulation system
ActiveCN105224774AShorten the time periodHigh precisionSpecial data processing applicationsStatistical analysisElectric power system
The invention relates to a power system operation state simulation method and a power system operation state simulation system. The method comprises the following steps of: performing statistical analysis on the basis of historical data of the operation state of a power system; obtaining probability distribution curves of three kinds of random variables including the power generator active power output, the power generator voltage and the active load of a load node of a power generator; and using a clustering analysis method for respectively clustering the probability distribution curves with similar distribution in the three kinds of random variables. The dimension reduction goal is achieved; the time period for generating mass power network operation state simulation samples can be greatly shortened; and the efficiency is improved. The clustering probability distribution curves subjected to clustering merging dimension reduction are sampled; the power flow distribution is subjected to simulation calculation; a power network operation state simulation sample is obtained; the obtained power network operation state simulation sample is similar to the power network actual operation state; the problem of low precision of the calculation result due to random sampling of each random variable in a designated range in the existing method is effectively solved; and the precision of the power flow distribution calculation result can be effectively improved.
Owner:POWER DISPATCHING CONTROL CENT OF GUANGDONG POWER GRID CO LTD +1
Continuous information forecasting method based on filter
InactiveCN101727578ATo achieve the purpose of dimensionality reductionGood effectCharacter and pattern recognitionPredictive methodsDimensionality reduction
The invention discloses a continuous information forecasting method based on a filter, comprising the following steps: on the basis of multipoint geostatistical method, a filter is utilized to realize the dimensionality reduction of training images, filter score spaces are generated by the filter, all training patterns having similar filter scores are classified into a category in the filter score spaces, training patterns belonging to the same category are extracted in a random manner in the forecasting process, and then the patterns are pasted in an area to be stimulated. In the invention, the method is applicable to the reappearance of the structure feature information of the training images and has better effect in predicting continuous variables.
Owner:SHANGHAI SECOND POLYTECHNIC UNIVERSITY
Electronic component quality detection method and system based on deep learning
ActiveCN111932511AIncrease the number of layersImprove detection accuracyImage enhancementImage analysisData setImage detection
The invention relates to a convolutional neural network electronic component quality detection method based on deep learning, and belongs to the technical field of fault diagnosis and signal processing and analysis. The method comprises the following steps: firstly, searching images of unqualified electronic components, such as component missing and wrong marking, dividing the collected images into a training set, a verification set and a test set, and carrying out unqualified region marking on the images in a data set, including coordinate information and classification information; secondly,constructing a convolutional neural network model for electronic component quality detection; training a convolutional neural network model for detecting the images of the unqualified components by utilizing the images in the training data set; performing quality detection on the unqualified component images in the test data set by using the trained convolutional neural network model for crack image detection. According to the method disclosed in the invention, the network model can effectively increase the selection of unqualified components, the speed is faster than that of a traditional multi-step image detection method, and more images can be processed in a short time; the network model can obtain finer local details; therefore, the whole network can realize effective progressive feature transmission, and the quality detection precision of the electronic components of the network model is improved.
Owner:NANJING UNIV OF TECH
Clustering thought-based multi-view dynamic brain network feature dimension reduction method
ActiveCN111325288AReduce computational complexityTo achieve the purpose of dimensionality reductionCharacter and pattern recognitionMedical automated diagnosisImaging processingComputation complexity
The invention discloses a clustering thought-based multi-view dynamic brain network feature dimension reduction method, which relates to the technical field of image processing, and comprises the following steps of: clustering vertexes and edges of a built dynamic brain network by using a clustering method, and enabling correlation time sequence rules among the vertexes distributed in the same cluster to be similar; processing each cluster by using a central moment method to obtain a central moment correlation time sequence; then, based on a central moment thought of a central moment, constructing a low-order brain network by using the central moments of the correlation time sequences; furthermore, a high-order dynamic network is constructed on the basis of correlation, and a high-order brain network is constructed by adopting the principle of a low-order dynamic network. According to the method, the reduced low-order dynamic brain network and the high-order dynamic brain network are constructed by using a clustering thought, so that the network scale and the calculation complexity are reduced to a great extent; and establishing a plurality of brain networks by utilizing the central moment characteristics, and providing diagnosis information for disease diagnosis from a plurality of perspectives.
Owner:中科信息产业(山东)有限公司
Sewage treatment process monitoring method based on KPLS and FCM
ActiveCN110232062ASolve the limitation of not being able to handle nonlinear dataTo achieve the purpose of dimensionality reductionRelational databasesSpecial data processing applicationsDimensionality reductionAlgorithm
The invention relates to the technical field of sewage treatment quality monitoring, and provides a sewage treatment process monitoring method based on KPLS and FCM. The method comprises the steps that firstly, collecting sewage treatment process data samples containing normal working conditions and abnormal working conditions, enabling data of sewage treatment operation variables and data of effluent quality variables to serve as an input data matrix and an output data matrix respectively, and standardizing two matrixes; then constructing a KPLS model, mapping an input sample to a high-dimensional feature space, introducing a Gaussian kernel function to obtain a Gram matrix K, and solving a score matrix; calculating the density value of an input sample point, calculating a construction function and drawing a construction function image to determine the number of clusters; and finally, clustering the score matrix based on an FCM algorithm to obtain a membership matrix, and monitoring the abnormal working condition in the sewage treatment process according to the membership matrix. The high-dimensional data can be subjected to dimensionality reduction, nonlinear data can be processed, the clustering number can be accurately and conveniently determined, and the monitoring timeliness and accuracy are improved.
Owner:NORTHEASTERN UNIV LIAONING
Pneumatic data analysis method based on MVVM mode
ActiveCN111680026AShorten screening timeImprove analysis efficiencyDatabase management systemsSustainable transportationData classSimulation
The invention discloses a pneumatic data analysis method based on an MVVM mode. The pneumatic data analysis method comprises the following steps that S1, building a UI interface of a pneumatic data analysis system; S2, selecting an aerodynamic data type; S3, setting an analysis parameter field of the aerodynamic data; S4, setting condition filtering parameters for the aerodynamic data after the analysis parameter fields are set; S5, judging whether pneumatic data analysis operation is executed or not, if yes, the step S6 is executed, and if not, the step S2 is executed again; S6, obtaining a visual chart through data analysis operation, and completing the analysis of the aerodynamic data. The method not only improves the analysis efficiency of aerodynamic data, but also has obvious advantages in the aspect of revealing aerodynamic characteristics by utilizing page interaction and visualization effects with rich technologies.
Owner:CHINA AERODYNAMICS RES & DEV CENT +1
CBL feature extraction and denoising webpage accurate classification method
PendingCN113516202AImprove accuracyHigh precisionCharacter and pattern recognitionWebsite content managementData setFeature extraction
According to the CBL feature extraction and denoising precise webpage classification method, feature extraction is performed on a data set, and noise data in the data set is removed. Firstly, feature extraction is performed based on a feature extraction method of a CBL model, original high-dimensional spatial features are mapped or converted into new low-dimensional spatial features, useless noise data is mapped to a weak dimension, feature items in an original space are greatly reduced, representative feature items are selected according to relevance of the feature items, and the purpose of dimension reduction is achieved. Secondly, noise data is removed based on a noise processing method of a CBL model, a data set is divided into a plurality of subsets according to categories to which the data set belongs, a probability feature topic model corresponding to each subset is constructed, information entropy values of webpages in the data set and the probability feature topic models of the subsets are calculated, and if the information entropy values of the webpages are larger than a given critical value, the webpage belongs to noise data, the junk information is cleared, and the accuracy and precision of webpage classification are greatly improved.
Owner:荆门汇易佳信息科技有限公司
Modeling method for power transmission line icing thickness prediction model based on PR-KELM
PendingCN110929611ATo achieve the purpose of dimensionality reductionImprove accuracyCharacter and pattern recognitionLocal optimumData transformation
The invention provides a modeling method for a power transmission line icing thickness prediction model based on PR-KELM, and the method comprises the steps: a first stage: converting image data intoLBP image data, carrying out the dimension reduction through employing a PCA algorithm, calculating the gray histogram cascade, and obtaining the extracted image data features; performing feature screening on the meteorological data and the mechanical data by adopting a ReliefF algorithm, and removing highly related redundant features to obtain extracted meteorological and mechanical feature data;and a second stage: forming sample data by using the feature data obtained in the first stage and the icing level in the original image data, training a PR-KELM model by using the training data, testing the trained PR-KELM model by using the test data, and finally obtaining a power transmission line icing thickness prediction model. The method has the advantages that the PR-KELM model is adoptedto predict the icing thickness, selection of the learning rate is not very sensitive, the method is not prone to falling into a local optimal solution, and therefore the accuracy of the prediction model is improved.
Owner:CHINA UNIV OF GEOSCIENCES (WUHAN)
Speech recognition method and device, computer equipment, and storage medium
ActiveCN108831494ATo achieve the purpose of dimensionality reductionTo achieve the purpose of speech enhancementSpeech analysisSingular value decompositionHankel matrix
The invention discloses a speech recognition method and device, computer equipment, and a storage medium. The method comprises the steps: carrying out the conversion of the original voice information,and obtaining a digital speech signal; obtaining a Hankel matrix based on the digital speech signal; carrying out the SVD (singular value decomposition) of the Hankel matrix to obtain at least two singular values; carrying out the inverse calculation of the SVD of at least two singular values to obtain a target speech signal; and carrying out the reduction processing of the target speech signal,and obtaining the target speech information. The method can effectively inhibit the noise interference, so as to improve the recognition accuracy of the target speech information in the speech recognition process.
Owner:PING AN TECH (SHENZHEN) CO LTD
Block level cache prefetching optimization method and system based on deep learning
The invention discloses a block level cache prefetching optimization method based on deep learning. The method comprises the steps of obtaining IO data with bytes as a unit from a test data set, converting the IO data into IO data with a block as a unit; judging whether the converted IO data is hit in a cache or not; if not, performing sequential prediction on the converted IO data to obtain a plurality of storage blocks, storing the converted IO data in an IO queue of a memory, judging whether the IO queue is full or not, if yes, inputting all the IO data in the IO queue of the memory into the trained Seq2Seq model to obtain predicted IO data, and obtaining a plurality of corresponding storage blocks according to the predicted IO data. According to the method, correlation of IO is mined through a deep learning method, prediction of an IO sequence is completed through the Seq2Seq model based on LSTM, and finally IO sequence prediction and sequential prediction are combined, so that cache prefetching is completed, and the hit rate of a cache is increased.
Owner:HUAZHONG UNIV OF SCI & TECH
Epilepsy detection integrated circuit based on sparse limit learning machine algorithm
ActiveCN109620148ATo achieve the purpose of epilepsy detectionReduce areaDiagnostic signal processingCharacter and pattern recognitionLearning machineEpilepsy
The invention discloses an epilepsy detection integrated circuit based on a sparse limit learning machine algorithm. Signals in electroencephalogram data are divided by a window containing 256 points,and the data type is a 16-bit fixed-point number comprising an 8-bit integer part and an 8-bit decimal part; four-dimensional signals are generated from the electroencephalogram data of the known type through a wavelet transform circuit module, then the generated signals are input into a feature extraction circuit module to obtain eight-dimensional feature vectors, and then the eight-dimensionalfeature vectors are input into a classifier circuit module for training, classified and output; brain electric signals of the unknown type sequentially pass through the wavelet transform circuit module and the feature extraction circuit module to obtain eight-dimensional feature vectors, and the eight-dimensional feature vectors are input into a classifier circuit module, classified and output. Lifting type wavelet transform is used for processing the electroencephalogram signals, features with different frequency bands in a signal time domain and in a frequency domain can be obtained, and results are superior to those of a traditional filter and Fourier transform.
Owner:XI AN JIAOTONG UNIV
Hyperspectral abnormity detection method based on digital image morphology theory
InactiveCN101916440BEasy to meet testing needsSuppress interferenceImage enhancementImage analysisFeature extractionDimensionality reduction
Owner:HARBIN ENG UNIV
Optical performance monitoring method and device, electronic equipment and storage medium
ActiveCN113364519AOptical Performance Monitoring RealizationTo achieve the purpose of dimensionality reductionMultiplex system selection arrangementsElectromagnetic transmissionComputation complexityEngineering
The invention provides an optical performance monitoring method and device, electronic equipment and a storage medium, and belongs to the technical field of optical performance monitoring in short-distance optical communication, and the method comprises the steps: carrying out the preprocessing of a to-be-processed SVDD signal, and carrying out the resampling; quantizing the trajectory information, and extracting hidden features in the trajectory information; and matching the hidden features. According to the method, accurate joint modulation format recognition, OSNR monitoring and RCD estimation can be realized on SVDD-BPSK, SVDD-QPSK, SVDD-8QAM, SVDD-16QAM, SVDD-32QAM and SVDD-64QAM. The requirements of short-distance communication on low calculation complexity, multi-parameter combined monitoring and high monitoring precision are met. Based on abundant difference characteristics contained in trajectory information, the method has the potential of being applied to backbone optical network and other linear or nonlinear damage monitoring.
Owner:LIAOCHENG UNIV
A Classification Method of Musical Instrument Signals
InactiveCN103761965BSolve the problem of high dependencyEasy to chooseSpeech recognitionNormal densityFeature extraction
The invention discloses a method for classifying musical instrument signals, and belongs to the technical field of electronic information. Modules adopted in the method comprise a phase-space reconstruction module, a principal component analysis module, a feature extraction module and a flexibility neural tree module. The method is characterized by comprising the step of carrying out the phase-space reconstruction on a time sequence produced by different musical instrument sample signals, the step of removing redundant information through the principal component analysis to achieve the dimensionality reduction purpose, the step of depicting the differences of different musical instruments in the phase space through the probability density function by analyzing the features of various musical instruments, and the step of utilizing a flexible neural tree model to serves as a classifier to carry out classification. The method can effectively solve the problem of the high dependency of an artificial neural network structure, and the classification accuracy of a single musical instrument can reach up to 98.7 percent.
Owner:TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
Default electricity risk model characteristic selection method and device and equipment
ActiveCN106875042ACutting costsTo achieve the purpose of dimensionality reductionForecastingResourcesElectricityRisk model
The embodiment of the invention discloses a default electricity risk model characteristic selection method and device and equipment. The method comprises the steps that S101 the default electricity tag of a user and the characteristic factor of the user are acquired; S102 an LASSO penalty function is constructed according to the default electricity tag and the characteristic factor; S103 the LASSO penalty function is solved through modified LARS to acquire the valid set of arguments of the LASSO penalty function; and S104 the arguments are filtered according to a set filtering rule and the valid set, so as to acquire a selected characteristic factor. According to the technical scheme provided by the embodiment of the invention, the selection efficiency and the effectiveness of the characteristic factor are improved.
Owner:CHINA SOUTHERN POWER GRID COMPANY +1
A Novel Channel Estimation Method for Multi-user 3d MIMO Systems
ActiveCN110460549BSimplified Channel ModelingTo achieve the purpose of dimensionality reductionTransmission monitoringRadio transmissionMultiuser systemEngineering
Owner:NANJING UNIV OF POSTS & TELECOMM
A method of filtering spam users in social network based on semi-supervised learning
ActiveCN106294590BSatisfy the requirement of conditional independenceReduce time complexityData processing applicationsWeb data indexingMultiple classifierData Annotation
Owner:CHONGQING UNIV OF POSTS & TELECOMM
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