Product
Patsnap Eureka
For R&D, Patsnap Eureka makes reading and utilizing patents & technical documents easy.
Patsnap Eureka AIR
Designed for self-driven R&D workflows. Generate viable solutions, solve complex R&D challenges, empower your innovation with AI.
Patsnap Eureka Materials
Designed for material experts only. Revolutionize your material R&D, from search, analyze, to developing new materials.
Feature
TechResearch
Generate reliable direction feasibility study reports for your R&D in just a few steps.
TechSeek
Discover and master advanced knowledge NOW. Basics, ideas, possibilities, all at once.
TechMind
As an expert in R&D Theories, TechMind can generates customized viable solutions instantly.
TechRisk
Analyze your overall solution with one click, know your potential R&D risks in advance.
TechMonitor
Get weekly tech updates, stay abreast of the latest tech innovations and key insights.
Patents
Literature
Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
37 results about "Relief algorithm" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
RELIEF Algorithm. A RELIEF algorithm is an online learning feature weighting algorithm that uses an instance based learning algorithm to assign a relevance weight to each Preditor Feature. AKA: RELIEF.
Psychological stress assessment method based on multi-physiological-parameter integration
InactiveCN103584872AEfficient integrationImprove recognition rateSensorsPsychotechnic devicesData informationCrowds
The invention discloses a psychological stress assessment method based on multi-physiological-parameter integration. The method includes: designing a reasonable stimulation program, acquiring four types of electrophysiological signals, namely electrocardiogram signals, electromyographic signals, pulse wave signals and electroencephalogram signals from people suffering psychological stress; extracting affective features of the four types of electrophysiological signals; subjecting the extracted features to feature selection by means of Relief algorithm, genetic algorithm optimization and the like; acquiring related integration functions on the basis of basic probability assignment mass. According to the method, multi-parameter signals are subjected to acquisition, preprocessed, feature selection and psychological stress affective recognition and are integrated; compared to single-parameter classified recognition or multi-parameter data-level or feature-level integration, the method allows data information to be more fully utilized and psychological stress emotions to be more accurately recognized.
Owner:YANSHAN UNIV
State monitoring and failure diagnosis method for wind generating set variable pitch system
The invention discloses a state monitoring and failure diagnosis method for a wind generating set variable pitch system. The method includes the steps that 1, in data collection, operation parameters related with the operation state of the variable pitch system are extracted from a wind generating set SCADA system; 2, according to feature parameter extraction, a Relief algorithm is utilized for extracting effective feature parameters of the variable pitch system; 3, in data analysis and prediction, historical data during normal operation of the variable pitch system are screened out to establish a health model of a fan and calculate the prediction value of a real-time signal and the difference between an actual output value and the prediction value; 4, according to a diagnosis algorithm, the operation state of the fan is judged according to residual error information transmitted by the step 3, and the main failure reason of the variable pitch system is found according to a corresponding diagnosis rule and the contribution rate of residue errors; 5, according to data storage, real-time data, prediction data and the diagnosis result are stored to facilitate failure analysis at the later period and provided as reference data for modifying the failure diagnosis rule.
Owner:北京中恒博瑞数字电力科技有限公司
Cancer-related genes finding method by using miRNA expression data
ActiveCN105701365AReduce disease riskReduce dimensionalitySequence analysisSpecial data processing applicationsStatistical analysisCancer genome
The present invention discloses a cancer-related genes finding method by using miRNA expression data, based on a pan-cancer program PanCancer under The Cancer Genome Atlas (TCGA), and uses statistical analysis and a machine learning algorithm to carry out analysis and processing on the gene expression data, and to identify complex diseases related genes. The method comprises: sorting out sample data; carrying out statistical analysis on the miRNA expression data; sorting miRNA in order of an average change rate; selecting a target gene; extracting a corresponding disease sample and normal sample; and using a Relief algorithm to sort genes in the extracted miRNA sample. The method disclosed by the present invention can find a plurality of risk genes related to cancer and other complex diseases, and has important significance to a biological target therapy, biomedical research, pathogenesis explanation, risk prediction, and the like.
Owner:XIDIAN UNIV
Driving fatigue detection method based on voice personality characteristics and models
InactiveCN106057212AReduce dimensionalitySuppressing the effects of driving fatigue detectionSpeech analysisPattern recognitionSelf adaptive
The invention provides a driving fatigue detection method based on voice personality characteristics and models. The method comprises the following steps of: firstly, extracting linear characteristics and non-linear characteristics of driver voice samples; secondly, utilizing a speaker identification algorithm based on VQ to judge the identity of a driver; then, according to the individual fatigue characteristic differences of the driver, utilizing a Relief algorithm to screen out voice characteristics fully reflecting fatigue information thereof, and constructing fatigue personality characteristic vectors; and finally, adopting an SVM classification algorithm to establish a self-adaptive fatigue detection model of the driver himself, and carrying out sample training and driving fatigue detection on the model. According to the invention, the voice linear characteristics and non-linear characteristics are combined in a complementation manner, and the voice personality characteristics fully reflecting fatigue information of the driver are screened out from the individual differences of the driver for driving fatigue detection, the influences of the individual voice differences of the driver on fatigue detection are effectively reduced, and the detection accuracy is improved.
Owner:EAST CHINA JIAOTONG UNIVERSITY
Feature selection method facing to SNP (Single Nucleotide Polymorphism) data
InactiveCN102629305ASolve the problem of low efficiency of feature selectionSolve selection inefficienciesSpecial data processing applicationsSmall sampleAlgorithm
The invention discloses a feature selection method facing to SNP (Single Nucleotide Polymorphism) data, which specifically comprises the following steps: firstly, carrying out data pre-processing; secondly, removing unrelated SNP features by using a newly-designed Relief algorithm; thirdly, carrying out critical degree sorting on the SNP features by using an improved SVM-RFE algorithm; and finally, screening the critical SNP sorting by using cross validation. The feature selection method has the beneficial effects that the advantages of Filter feature selection and Wrapper feature selection are combined, and a secondary division method is used in the machine learning process, so that the problems of a high-dimensional small sample in the SNP feature selection and a SNP pathogenic combination mode are solved, and the analysis efficiency and the accuracy are improved.
Owner:SHANGHAI UNIV
Feature selection-based arrhythmia classification method
ActiveCN106377247AHigh speedReduce dimensionalityDiagnostic recording/measuringSensorsPopulationRelief algorithm
The invention relates to a feature selection-based arrhythmia classification method. The method comprises the following steps: preprocessing an ECG (electrocardiograph) signal; extracting a morphological feature and a time-frequency feature according to a detected position R, and constructing an original feature vector; calculating feature weights, namely calculating the weight of each feature in the original feature vector by using a Relief algorithm; instructing a population to be initialized according to the feature weights, respectively performing selection, crossover and mutation operation according to a selection probability, a crossover probability and a mutation probability according to the adaptation degree of an individual to obtain a next generation, repeating the operation until the maximum number of iteration times termination condition is met, and then outputting the individual with the highest adaptation degree as an optimal feature; and enabling a plurality of di-classifiers to form an identification classifier through a multi-classification strategy to realize identification of various arrhythmias. According to the feature selection-based arrhythmia classification method, the dimensions of the features can be reduced and the accuracy of identification of the various arrhythmias can be improved.
Owner:TIANJIN UNIV
Power grid automation system and equipment running state risk assessment algorithm
The invention relates to a power grid automation system and equipment running state risk assessment algorithm. The algorithm comprises the following steps of 1.1 feature extraction based on a Relief algorithm: equipment is abnormal because of interaction of one factor or a plurality of factors of the equipment during an equipment operation process; and the feature extraction means that from a lot of information collected from the equipment, the factor influencing a type of abnormity is extracted and irrelevant factors are rejected so that latter data analysis is performed; 1.2 similar sample gathering based on kmeans algorithm: through the Relief algorithm in the step1.1, a feature index sequence influencing an equipment operation risk is acquired; and through the k-means algorithm, a threshold scope of each feature index under each risk grade is calculated. By using the algorithm, there are the following advantages that aiming at each equipment, a specific state analysis threshold knowledge database is set so that a threshold error caused by an extraneous environment and an equipment self condition is avoided; and accuracy of alarm and early warning is ensured.
Owner:STATE GRID CORP OF CHINA +2
Congestion control method based on equitable distribution of communication channel
InactiveCN104270790AAvoid false detectionAvoid missing detectionNetwork traffic/resource managementPacket lossCongestion detection
The invention discloses a congestion control method based on equitable distribution of a communication channel. The method comprises the following steps: firstly, the queue increase rate of an upstream neighbor node and the average transmission delay of a data packet are estimated according to the acquired data of the upstream neighbor node; then, the queue length of the upstream neighbor node is calculated according to the estimated queue increase rate, the total queue length of the node and the upstream neighbor node is further calculated, and the congestion detection is carried out in combination with the average transmission delay of the data packet; if congestion occurs, the network congestion degree (namely the congestion degree) is classified, the length of the congestion relief period is determined, and the node is equitably distributed with the communication channel occupation time; finally, a congestion relief algorithm is carried out according to the congestion degree so as to relieve or ease the congestion; if the congestion does not occur, no treatment is carried out. The use equity of the channel is improved, the collision and the packet loss probability are effectively reduced, and the handling capacity is effectively increased.
Owner:XIANGTAN UNIV
An aeroengine residual life prediction method
PendingCN109460574AEffectively describe the degradation processImprove robustnessInternal-combustion engine testingDesign optimisation/simulationAviationPredictive methods
The invention discloses a method for predicting the residual life of an aeroengine, The method firstly screens the measurable multi-dimensional performance characteristic parameters in the performancedegradation process of the aero-engine through the Relief algorithm, Principal component analysis (PCA) is used to reduce the correlation between features and extract more concise and effective low-dimensional orthogonal degenerative features, and Kernel smoothing is used to fit the actual degenerative trajectory. And then the similarity matching of the degradation trajectory is carried out, anda feature section set which is most similar to the track section of the to-be-predicted sample in one group of historical samples is found. In order to predict the residual life of aeroengines, the data distribution of the residual life of the similar characteristic segments is obtained by kernel density estimation, and the life prediction estimates of the samples are obtained by density weightedmethod.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Power system transient stability prediction method based on disturbance voltage trajectory cluster features
InactiveCN104578053AFast Prediction of Transient StabilityImprove applicabilityLoad forecast in ac networkVoltage amplitudeDisturbance voltage
The invention discloses a power system transient stability prediction method based on disturbance voltage trajectory cluster features. According to the method, historical information collected by a WAMS in a power system is primarily screened, time domain trajectory clusters of all key power generator node voltage amplitudes after the failure are obtained to form an original data set; 27 geometrical characteristics of the trajectory clusters are calculated according to original data; a Relief algorithm is utilized for carrying weight calculation on all characteristic quantities, a plurality of wide-range fault features strongly correlated to the system transient stability are selected and used as an input data set of a transient stability prediction algorithm; on the basis of the input data set, an SVM prediction model is built. An analysis model of the power system is not built, and according to response information, obtained through the WAMS, of the power system, the transient stability of the power system is directly predicated. The system stability can be rapidly predicted, and high adaptability and robustness are achieved.
Owner:BEIJING JIAOTONG UNIV
Distributed generation island detection method based on meta learning
InactiveCN103778569ASolve the accuracy problemSolve the problem of overfittingData processing applicationsKnowledge representationIslandingOverfitting
The invention relates to a distributed generation island detection method based on meta learning. The distributed generation island detection method based on meta learning comprises the following steps that (1) key features for island detection are recognized by using an RELIEF algorithm, so that a key feature set is obtained; (2) classification prediction is carried out on the original key feature set through a plurality of basic learning devices; (3) the classification results of the basic learning devices serve as feature items to be added to a training set, and a new sample set T is generated; (4) a meta learning device uses the sample set T as a training set, and carries out relearning on the classification results of the basic learning devices to obtain a final detection result. Compared with the prior art, the distributed generation island detection method based on meta learning solves the problems that due to weak correlation features in samples, accuracy of a classification algorithm can be reduced, and overfitting is caused, and has the advantages of being good in applicability, high in detection accuracy and the like.
Owner:SHANGHAI JIAO TONG UNIV
Feature gene selection method based on logistic and relevant information entropy
InactiveCN104598774ASimple structureAvoid lossSpecial data processing applicationsMaximum eigenvalueData set
The invention discloses a novel feature gene selection method based on logistic and relevant information entropy. The method comprises the following steps that a dataset is subjected to logistic regression, a gene variable with great influence on the classification is obtained, a Relief algorithm is used for giving a value on the gene variable, sequencing is carried out, a maximum feature value gene is added to an initial feature gene set, and the relevant information entropy is calculated. The novel feature gene selection method has the advantages that a logistic regression model in the machine study is introduced into the feature gene selection method, and a high-quality gene expression profile is obtained; the correlation between gene variables is measured by the relevant information entropy, redundant genes are deleted, and a feature gene sub set with high classification capability and fewer genes is obtained through searching a feature gene space set.
Owner:HENAN NORMAL UNIV
An Object-Oriented Change Detection Method Based on Multi-Feature Fusion
ActiveCN109409389AHigh precisionCharacter and pattern recognitionFeature extractionImage segmentation
The invention is applicable to the field of remote sensing technology, and provides an object-oriented change detection method based on multi-feature fusion, comprising the following steps of: S101, image preprocessing; S102, texture feature extraction; S103, image segmentation; S104, feature extraction of that object; S105, generating a difference image; S106, acquire an initial change detectionresult; S107, calculating feature weights; S108, the object change detection result is obtained, and each detected object is clustered into two classes of variable and invariant by fusing multi-dimensional features through weighted fuzzy C-means method. As that embodiment of the invention carry out the processing of the above step on the two-phase images, analyze and determine the weight of each dimension feature, and the weights of each dimension feature are extracted by Relief algorithm, The weighted fuzzy C-means method is formed by adding weights into the fuzzy C-means method, and the weighted fuzzy C-means method is used to fuse the multi-dimensional features, and the detection objects are clustered into two categories: variable and invariant, which effectively fuses the different features to carry out object-oriented change detection and improves the accuracy of the change detection results.
Owner:THE HONG KONG POLYTECHNIC UNIV SHENZHEN RES INST
Selection method for sensitive characteristic index of persistent attention level
The invention discloses a selection method for a sensitive characteristic index of a persistent attention level. The selection method includes: acquiring an electroencephalogram signal through a plurality of electrodes, recording a reaction time parameter corresponding to the electroencephalogram signal, and acquiring subjective fatigue evaluation data and behavior performance evaluation data; calculating an electroencephalogram entropy parameter according to the acquired electroencephalogram signal; adopting the Kruskal-Wallis detection method and the Relief algorithm to select index sets having the sensitivity different from the persistent attention level respectively; and using an intersection G of the acquired two index sets as the sensitive characteristic index of the persistent attention level. The selection method for the sensitive characteristic index of the persistent attention level can achieve selection of a persistent attention level electroencephalogram e characteristic index sensitive to a bullet train driver.
Owner:SOUTHWEST JIAOTONG UNIV
Intelligent prediction method for cutter suction dredger construction productivity
PendingCN110826790AOvercome errorOvercome accuracyEnsemble learningForecastingBoosting (machine learning)Data set
The invention discloses an intelligent prediction method for cutter suction dredger construction productivity. The method comprises the steps of (1) collecting construction monitoring data of all parts of the dredger; (2) establishing a feature selection model influencing the productivity of the cutter suction dredger according to a Relief algorithm to obtain an optimal feature set; (3) performingnoise processing on the optimal feature set according to a continuous mean denoising method to obtain a denoised feature data set; (4) selecting an extreme boosting tree ensemble learning predictionmodel according to the denoised feature data set, and predicting the productivity of the cutter suction dredger; and (5) evaluating and measuring the productivity prediction result according to various evaluation indexes. According to the method, characteristic elements which are considered to influence the productivity in traditional experience are broken through, new effective factors which influence the productivity are selected, main characteristics are selected by using a data mining method, and the characteristics are subjected to machine learning, so that the productivity of the cuttersuction dredger is accurately predicted, and a new way is found for predicting the productivity of the cutter suction dredger.
Owner:TIANJIN UNIV
Post-fault electric power system transient stability prediction method
PendingCN112952830AReduce in quantityImprove forecast accuracyAc network circuit arrangementsElectric power systemTerminal voltage
The invention provides a post-fault electric power system transient stability prediction method, which comprises the following steps of: 1, on the basis of considering power generator terminal voltage fluctuation, acquiring a feature data set through a time domain simulation method so as to analyze the influence of the power generator terminal voltage fluctuation on electric power system transient stability evaluation accuracy; 2, performing feature selection on the feature data set, and performing dimension reduction processing on an original data set by applying a Pearson correlation coefficient and combining a Relief algorithm so as to quickly screen out a better feature subset; and 3, defining an uncertain sample based on a distance value between a sample and an SVM classification hyperplane so as to reduce the calculation amount. According to the invention, the uncertain samples are screened out and are analyzed by using the feature data at the subsequent sampling moment after the fault is removed, so that the number of the uncertain samples can be gradually reduced, and the prediction accuracy of the classifier is improved.
Owner:SHANDONG UNIV OF SCI & TECH
Underground carry-scraper fault diagnosis method based on PSO-BP neural network
PendingCN113157732AImprove accuracyAccurate training dataDigital data information retrievalMechanical machines/dredgersLocal optimumOriginal data
The invention provides an underground carry-scraper fault diagnosis method based on a PSO-BP neural network, and belongs to the technical field of underground carry-scraper fault diagnosis. According to the method, firstly, original data of mine equipment operation are extracted from a database and classified and transferred according to the model of the equipment, after the obtained data are preprocessed, feature parameters of faults are mined by adopting a Relief algorithm, and a BP neural network model optimized by a particle swarm optimization (PSO) is constructed for fault diagnosis. Compared with the traditional manual screening, the invention has the advantages that the fault features are screened and filtered through the Relief algorithm, a large amount of analysis time can be saved, a more accurate analysis result can be provided; in addition, the optimized PSO-BP neural network overcomes the defect that only the BP neural network is used to converge to a local optimal solution, and the fault diagnosis accuracy of the mine equipment is improved.
Owner:UNIV OF SCI & TECH BEIJING
Method and device for identifying camouflage person by carrying multispectral camera on unmanned aerial vehicle, and medium
PendingCN114511781AReduce data volumeImprove real-time performanceCharacter and pattern recognitionPattern recognitionData set
The embodiment of the invention discloses a method and device for recognizing a camouflage person through a multi-spectral camera carried by an unmanned aerial vehicle, and a medium. The method comprises the following steps: preprocessing a DN value original image collected by a multispectral camera to complete band registration and radiation correction, and obtaining a preprocessed reflectivity image corresponding to the DN value original image; generating an original feature set based on the preprocessed reflectivity image; screening from the original feature set through cross validation based on a Relief algorithm to obtain an optimal feature subset; based on a preset data set, training the candidate supervised classifier models to obtain classification evaluation results corresponding to the candidate supervised classifier models, and selecting an optimal model from all the candidate supervised classifier models; classifying the preprocessed reflectivity image through an optimal model to obtain a predicted binary image; and identifying a camouflage person target from the prediction binary image, and determining the position of the identified target according to the position information in the DN value original image.
Owner:FOURTH MILITARY MEDICAL UNIVERSITY
Walk acceleration information segmentation-based personnel identification method
InactiveCN108596253AImprove efficiencyMeet the needs of security protectionDiagnostic signal processingCharacter and pattern recognitionFeature vectorFeature set
The invention discloses a walk acceleration information segmentation-based personnel identification method. Walk acceleration data of personnel is collected by utilizing an acceleration sensor arranged in a mobile phone; for further mining acceleration information, an eigenvector is detailed, and an acceleration change rate is explored; an HMM-based information segmentation method is proposed; andthe acceleration data is divided into a dynamic part and a steady part according to a relationship between continuous acceleration data sampling points. Features are extracted from the two parts to form dynamic and static feature sets, and the features are selected from the two sets by applying a Relief algorithm to form a new feature set, so that the personnel identification rate is increased. The personnel identification is performed by utilizing a mobile phone, so that the identification timeliness is improved and the cost is low.
Owner:CHANGZHOU UNIV
Webshell detection method and device based on Relief algorithm
InactiveCN112052453AImprove accuracyReduce false alarm rateCharacter and pattern recognitionWebsite content managementPattern recognitionFeature set
The invention relates to the field of malicious script detection, aims to solve the false alarm and high missing report rate of an existing webshell detection method, and provides a webshell detectionmethod and device based on a Relief algorithm. The method comprises the steps: collecting samples, wherein the samples comprise a webshell sample and a normal sample; extracting text features from the samples to obtain a text feature sample set, and extracting behavior features from the samples to obtain a behavior feature sample set; optimizing the text feature sample set and the behavior feature sample set based on the Relief algorithm to obtain a text feature set and a behavior feature set; training a webshell detection model according to the text feature set and the behavior feature set;and determining whether a to-be-detected file is a webshell file or not by adopting the webshell detection model. According to the method, the false alarm rate and the missing report rate are reduced,and the webshell detection accuracy is improved.
Owner:SICHUAN CHANGHONG ELECTRIC CO LTD
Coal gangue photoelectric separation image particle aggregate area shape selection identification method
The invention discloses a coal gangue photoelectric separation image particle aggregate area shape selection identification method. The method comprises the following steps: firstly, carrying out binarization processing on an obtained X-ray image; then extracting shape feature parameters of the binarized image, the convex hull binarized image, the concave defect binarized image and the edge image thereof; selecting the features by adopting a Relief algorithm to obtain an optimal shape selection feature combination; and finally, establishing a support vector machine classification model based on a linear kernel function. And the trained model is used for identifying an unknown classification aggregate area or an independent target area. The method not only can be used as a condition for judging whether the aggregate region needs to be further segmented into independent particle targets, but also can be used as a condition for judging whether the segmented aggregate region is the aggregate region.
Owner:ANHUI UNIV OF SCI & TECH
Feature selection method facing to SNP (Single Nucleotide Polymorphism) data
InactiveCN102629305BSolve the problem of low efficiency of feature selectionSolve selection inefficienciesSpecial data processing applicationsSmall sampleAlgorithm
The invention discloses a feature selection method facing to SNP (Single Nucleotide Polymorphism) data, which specifically comprises the following steps: firstly, carrying out data pre-processing; secondly, removing unrelated SNP features by using a newly-designed Relief algorithm; thirdly, carrying out critical degree sorting on the SNP features by using an improved SVM-RFE algorithm; and finally, screening the critical SNP sorting by using cross validation. The feature selection method has the beneficial effects that the advantages of Filter feature selection and Wrapper feature selection are combined, and a secondary division method is used in the machine learning process, so that the problems of a high-dimensional small sample in the SNP feature selection and a SNP pathogenic combination mode are solved, and the analysis efficiency and the accuracy are improved.
Owner:SHANGHAI UNIV
Psychological stress assessment method based on multi-physiological-parameter integration
InactiveCN103584872BImprove typicalityImprove naturalnessSensorsPsychotechnic devicesData informationCrowds
The invention discloses a psychological stress assessment method based on multi-physiological-parameter integration. The method includes: designing a reasonable stimulation program, acquiring four types of electrophysiological signals, namely electrocardiogram signals, electromyographic signals, pulse wave signals and electroencephalogram signals from people suffering psychological stress; extracting affective features of the four types of electrophysiological signals; subjecting the extracted features to feature selection by means of Relief algorithm, genetic algorithm optimization and the like; acquiring related integration functions on the basis of basic probability assignment mass. According to the method, multi-parameter signals are subjected to acquisition, preprocessed, feature selection and psychological stress affective recognition and are integrated; compared to single-parameter classified recognition or multi-parameter data-level or feature-level integration, the method allows data information to be more fully utilized and psychological stress emotions to be more accurately recognized.
Owner:YANSHAN UNIV
Cable tunnel inspection robot state detection method and device
PendingCN114580673AReal-time evaluation of comprehensive performanceImprove performanceResourcesInference methodsEvaluation resultAlgorithm
The invention discloses a cable tunnel inspection robot state detection method and device, and the method comprises the steps: collecting the historical operation data of a cable tunnel inspection robot, building a state index, and collecting and normalizing the inspection operation data of the cable tunnel inspection robot; obtaining a subjective weight and an objective weight of the forward inspection operation data by using an analytic hierarchy process and an entropy weight method, and obtaining a comprehensive weight; on the basis of a Relief algorithm, the comprehensive weight is updated and removed according to the correlation between the forward inspection operation data and the state indexes, and a factor set weight vector is constructed through the inspection operation data and the comprehensive weight after operation; performing fuzzy comprehensive evaluation on the membership function of the comprehensive comment set based on the factor set to obtain a fuzzy evaluation matrix; establishing a state detection model through the synthesis of the factor set weight vector and the fuzzy evaluation matrix; and scoring an output result of the state detection model by an expert to obtain a state detection comprehensive evaluation result. The detection method is comprehensive and reliable, and the evaluation result is real-time and accurate.
Owner:STATE GRID LIAONING ELECTRIC POWER RES INST +1
System and method for target behavior discovery and detection
Various methods, apparatuses / systems, and media for detecting a target behavior are disclosed. A processor implements a machine learning cadence model that implements an algorithm to obtain, on a per session basis, cadence data that indicates average time between each call and a standard deviation of times across each call across all active sessions of a desired target. The processor compares the cadence data to predefined background cadence data to identify whether the desired target is a new threat target or a background traffic; generates an internet protocol (IP) address of the new threat target; inputs the IP address of the new threat target into a machine learning behavior model that implements an algorithm to generate a fingerprint of all known places that the new threat target is operating; and applies a mitigation algorithm to all active sessions of the new threat target.
Owner:JPMORGAN CHASE BANK NA
Driving Fatigue Detection Method Based on Speech Personality Features and Model Adaptation
InactiveCN106057212BReduce dimensionalitySuppressing the effects of driving fatigue detectionSpeech analysisPattern recognitionSelf adaptive
Owner:EAST CHINA JIAOTONG UNIVERSITY
A Star Map Deblurring Method Based on Improved rl Deconvolution Algorithm
ActiveCN108198151BImprove real-time performanceSuppress blurImage enhancementImage analysisDefuzzificationDeblurring
The invention discloses a star map deblurring method based on an improved RL deconvolution algorithm, comprising: (1) setting basic simulation parameters in a star map simulator, using the basic simulation parameters to simulate a clear star map and corresponding fuzzy stars; (2) calculate the point spread function of each fuzzy star map according to the inertial sensor element data; (3) use the RL deconvolution algorithm to complete the defuzzification of the star map, and record the optimal number of iterations and iteration steps of the deblurring effect (4) The point spread function, number of iterations and iteration step of each blurred image are used as input for offline training of the neural network prediction model. After the training is completed, the prediction model is obtained, and the output is the number of iterations and the iteration step. The invention overcomes the disadvantage that the traditional RL deconvolution defuzzification algorithm needs to manually test the number of iterations and iteration steps to complete the defuzzification of the star map, improves the real-time performance of the algorithm and significantly improves the effect of the star map defuzzification.
Owner:SOUTHEAST UNIV
Arrhythmia Classification Method Based on Feature Selection
ActiveCN106377247BHigh speedReduce dimensionalityDiagnostic recording/measuringSensorsFeature vectorClassification methods
The invention relates to a feature selection-based arrhythmia classification method. The method comprises the following steps: preprocessing an ECG (electrocardiograph) signal; extracting a morphological feature and a time-frequency feature according to a detected position R, and constructing an original feature vector; calculating feature weights, namely calculating the weight of each feature in the original feature vector by using a Relief algorithm; instructing a population to be initialized according to the feature weights, respectively performing selection, crossover and mutation operation according to a selection probability, a crossover probability and a mutation probability according to the adaptation degree of an individual to obtain a next generation, repeating the operation until the maximum number of iteration times termination condition is met, and then outputting the individual with the highest adaptation degree as an optimal feature; and enabling a plurality of di-classifiers to form an identification classifier through a multi-classification strategy to realize identification of various arrhythmias. According to the feature selection-based arrhythmia classification method, the dimensions of the features can be reduced and the accuracy of identification of the various arrhythmias can be improved.
Owner:TIANJIN UNIV
Distributed power generation island detection improvement method based on data mining
PendingCN113945855AImprove accuracyCalm AccuracyPower supply testingTransient stateData stream mining
The invention belongs to the technical field of power grid fault diagnosis, and particularly relates to a distributed power generation island detection improvement method based on data mining, which adopts a meta-learning mode in machine learning to determine a threshold value of island detection, and the method comprises the following steps: step 1, adopting a RELIEF algorithm to identify key features of island detection before classification, wherein the key features comprise steady state quantity features and transient state quantity features; and step 2, performing a classification algorithm based on the base learner and the meta learner, and implementing distributed power generation island detection. The step 2 comprises the following steps: step 21, data stream mining: adopting an increment mode, and considering time and space efficiency of the algorithm; step 22, meta learning: utilizing complementarity of different classifiers to improve adaptability of data mining and machine learning; and step 23, online self-learning: adopting a sliding data window and a multi-classifier integration method to realize online self-learning, and keeping the classification precision at a relatively high level all the time.
Owner:STATE GRID JIBEI ELECTRIC POWER COMPANY LIMITED CHENGDE POWER SUPPLY
Power System Transient Stability Prediction Method Based on Disturbed Voltage Trajectory Cluster Characteristics
InactiveCN104578053BFast Prediction of Transient StabilityImprove applicabilityLoad forecast in ac networkDisturbance voltageVoltage amplitude
The invention discloses a power system transient stability prediction method based on disturbance voltage trajectory cluster features. According to the method, historical information collected by a WAMS in a power system is primarily screened, time domain trajectory clusters of all key power generator node voltage amplitudes after the failure are obtained to form an original data set; 27 geometrical characteristics of the trajectory clusters are calculated according to original data; a Relief algorithm is utilized for carrying weight calculation on all characteristic quantities, a plurality of wide-range fault features strongly correlated to the system transient stability are selected and used as an input data set of a transient stability prediction algorithm; on the basis of the input data set, an SVM prediction model is built. An analysis model of the power system is not built, and according to response information, obtained through the WAMS, of the power system, the transient stability of the power system is directly predicated. The system stability can be rapidly predicted, and high adaptability and robustness are achieved.
Owner:BEIJING JIAOTONG UNIV
Who we serve
- R&D Engineer
- R&D Manager
- IP Professional
Why Patsnap Eureka
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com