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63 results about "Resampling" patented technology
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In statistics, resampling is any of a variety of methods for doing one of the following...
FastSLAM algorithm based on improved resampling method and particle selection
InactiveCN101625572AImprove consistencyHigh precisionNavigation instrumentsVehicle position/course/altitude controlInput controlParticle number
The invention discloses a FastSLAM algorithm based on an improved resampling method and particle selection. The algorithm comprises the following steps: (1) a robot predicts robot poses according to input control data and updates the robot poses and road signs according to measuring data combined with a measuring model of an external sensor of the robot; (2) the robot poses are predicted by the calculation of a particle filter, and a particle resampling criterion is amended according to an effective particle number, particle weighting covariance and particle-measuring residual consistency; (3) new particles are generated by using an index grad method and a crossover operator; (4) the robot is positioned, and a map is created according to the generated new particles. The invention improves the particle resampling criterion and a new particle-generating method in the FastSLAM algorithm, thereby obviously improving the estimation consistency of the FastSLAM algorithm to the robot poses and simultaneously improving the precision of the robot positioning and map creation.
Owner:ZHEJIANG UNIV
Model-cluster-analysis-based laser-induced breakdown spectroscopy variable selection method
The invention discloses a model-cluster-analysis-thought-based variable selection method suitable for a support vector machine. According to the method, sub-datasets are acquired from a full-spectrum data matrix by carrying out Monte Carlo sampling, an SVM (support vector machine) sub-model is established for each sub-dataset and is predicted and classified, then Mann-WhitneyU examination is utilized for carrying out statistic analysis on the prediction accurate rate of all the sub-models, and available vectors remarkably acting on the model prediction capability are sorted out; a once modeling result is not taken as the basis, but data information is maximally and effectively utilized by carrying out replaceable resampling, the internal relation among the vectors in the datasets are adequately investigated, and the statistical distributions of different results are analyzed, so that the method has high universality and stability.
Owner:NORTHWEST UNIV(CN)
Fault diagnosis method for rotary machine based on angle resampling and ROC (Receiver Operating Characteristic)-SVM (Support Vector Machine
InactiveCN109186964AEliminate the change of the number of sampling points of the vibration signalImprove qualityMachine part testingTime domainSupport vector machine
The invention discloses a fault diagnosis method for a rotary machine based on angle resampling and an ROC (Receiver Operating Characteristic)-SVM (Support Vector Machine) and belongs to the field offault diagnosis of mechanical equipment. The method comprises the steps of eliminating fluctuation of a rotation speed by use of an angle resampling technology; performing characteristic value extraction from the dimensions of a time domain and a time-frequency domain; and implementing characteristic selection and fault diagnosis of the rotary machine by use of the ROC-SVM. According to the faultdiagnosis method for the rotary machine based on angle resampling and the ROC-SVM, the change of the number of sampling points of a vibration signal in unit time caused by the fluctuation of the rotation speed can be effectively eliminated by use of the angle resampling method, thereby improving the quality of the subsequent extraction characteristic value; the time domain and the time-frequency domain are combined to achieve wider characteristic extraction and obtain sufficient vibration signal information; the characteristic selection and fault diagnosis are performed by use of the ROC-SVM,the best characteristics are selected to prevent poor characteristics from reducing the effect of a fault classifier; the accuracy and effectiveness of the bearing fault diagnosis can be improved, thediagnosis speed can be accelerated, and a new concept is provided for solving the problem of the bearing fault diagnosis.
Owner:HUAZHONG UNIV OF SCI & TECH
Designing of current-statistical-model-based probability hypothesis density particle filter and filter
The invention discloses the designing of current-statistical-model-based probability hypothesis density particle filter and the current-statistical-model-based probability hypothesis density particle filter. An observed value of the filter is connected with the first input end of an updating circuit. The first input end of a prediction circuit is connected with the first output end of a state estimation circuit, and the output end of the prediction circuit is connected with the second input end of the updating circuit. The output end of the updating circuit is connected with the input end of the resampling circuit. The first output end of the resampling circuit is connected with the second input end of the prediction circuit, and the second output end of the resampling circuit is connected with the input end of the state estimation circuit. By the invention, a hardware circuit realization scheme for the current-statistical-model-based probability hypothesis density particle filter is designed based on the theory of the current-statistical-model-based probability hypothesis density particle filter, and simulation results show that the tracking performance of the designing of the current-statistical-model-based probability hypothesis density particle filter and the current-statistical-model-based probability hypothesis density particle filter is similar to that of theoretical analysis and can be used for tracking problems about maneuvering multi-target movement in a clutter environment.
Owner:ZHEJIANG UNIV
Power plant combustion process machine learning modeling method based on load resampling
InactiveCN103778324AGuaranteed generalization abilityAddressing ImbalancesSpecial data processing applicationsThermal stateSimulation
The invention provides a power plant combustion process machine learning modeling method based on load resampling. The power plant combustion process machine learning modeling method comprises the steps that (1) a steady sample is extracted from real-time operation data; (2) the sample is divided into a training subset and a testing subset based on a load; (3) a model is trained through obtained training samples, and the generalization ability of the model is verified through testing samples. According to the power plant combustion process machine learning modeling method, the sample is divided into the subsets based on the load, the defects of an existing power plant combustion process modeling method based on thermal state experimental data are overcome, the obtained model can better reflect the feature of the combustion process of a boiler, the problem of malconformation of load distribution of the steady sample of a power plant is solved, and the generalization ability of the model is guaranteed.
Owner:ZHEJIANG UNIV
AAKR model uncertainty calculation method and system based on resampling
PendingCN112100574ASimplify the analysis processImprove estimation efficiencyComplex mathematical operationsWavelet denoisingData set
The invention discloses an AAKR model uncertainty calculation method and system based on resampling, and the method comprises the steps: dividing a historical state data set of a sensor into a training data set and a testing data set, carrying out the denoising on the training data set through a wavelet denoising method, calculating a noise variance, improving the data precision, randomly selecting and replacing the historical state data of the sensor to obtain a new training data set sample so as to optimize the AAKR model architecture and the change among the plurality of model prediction values to obtain the model prediction variance of the plurality of model prediction values, and calculating the mean square error between the prediction values and the test values by utilizing Bootstrapresampling training data. Model deviation is calculated by combining prototype model variance, 95% uncertainty value is formed, modeling calculation of a noise estimation value by an empirical distribution model is not needed, the resampling process is simplified, the calculation efficiency is improved, confidence interval deviation is reduced by combining a Jackknife method, the reliability is guaranteed, and the estimation efficiency is improved on the basis of keeping convergence performance.
Owner:XI AN JIAOTONG UNIV
Automated analytic resampling process for optimally synchronizing time-series signals
ActiveUS20200125819A1Reduce computing costHigh synchronization accuracyMathematical modelsKernel methodsGround truthData mining
The system receives exemplary time-series sensor signals comprising ground truth versions of signals generated by a monitored system associated with a target use case and a synchronization objective, which specifies a desired tradeoff between synchronization compute cost and synchronization accuracy for the target use case. The system performance-tests multiple synchronization techniques by introducing randomized lag times into the exemplary time-series sensor signals to produce time-shifted time-series sensor signals, and then uses each of the multiple synchronization techniques to synchronize the time-shifted time-series sensor signals across a range of different numbers of time-series sensor signals, and a range of different numbers of observations for each time-series sensor signal. The system uses the synchronization objective to evaluate results of the performance-testing in terms of compute cost and synchronization accuracy. Finally, the system selects one of the multiple synchronization techniques for the target use case based on the evaluation.
Owner:ORACLE INT CORP
Intelligent fault diagnosis method for rotating machine at time-varying rotating speed
ActiveCN111307452AFast convergenceImprove generalization abilityMachine part testingTime domainFrequency spectrum
The invention discloses an intelligent fault diagnosis method for a rotating machine at a time-varying rotating speed. Angular domain resampling is carried out on a time-domain vibration signal of a rolling bearing and a non-stable time-domain signal is converted into a stable angular domain signal, to so that the impact on the analysis of the vibration signal from the change of the rotating speedis eliminated; gabor transformation capable of well describing transient characteristics of drastically changing signals is used for a rotating speed estimation method based on time-frequency spectrum ridge line fitting; a keyless phase order tracking method without installing a rotating speed sensor is suitable for occasions where the rotating speed sensor cannot be installed. An LSTM model capable of adaptively extracting time sequence features without expert experience and domain knowledge is adopted, and due to the existence of a BN generalization layer, convergence of the model can be accelerated, over-fitting is prevented, the generalization ability of the model is improved, and adaptive intelligent fault diagnosis and identification of the rotating machine under the variable-rotating-speed working condition are realized.
Owner:江苏天沃重工科技有限公司
Track cycle prediction method based on resampling
PendingCN113393032AAccurate predictionReduce forecast errorForecastingCharacter and pattern recognitionAlgorithmNetwork model
The invention discloses a track cycle prediction method based on resampling, and mainly solves the problems that in track prediction in the prior art, the prediction duration is short, and the prediction error is too large due to the fact that the motion state of a target changes in the future. According to the implementation scheme, the method comprises the steps of simulating a historical track and a future track of a maneuvering target; filtering, resampling and normalizing the historical track data of the target in sequence; constructing a neural network model composed of a Bi-LSTM layer, a Dropout layer, a Dense layer and an activation layer, and training the neural network model by using the preprocessed track data; generating part of historical track data by using a loop strategy, and calculating the historical track data by using the trained neural network model parameters; and carrying out smooth filtering on a calculation result to obtain a final predicted track. According to the method, the prediction error is small, the prediction time is long, when the target motion state changes in the future, the accurate prediction track can still be obtained, and the method can be used for target tracking.
Owner:XIDIAN UNIV +1
Systems and methods of using machine learning analysis to stratify risk of spontaneous preterm birth
PendingUS20210057039A1Subject is at riskReduce riskOrganic active ingredientsComponent separationPattern recognitionPreterm Births
The present disclosure relates to systems and methods of using machine learning analysis to stratify the risk of spontaneous preterm birth (SPTB). In some variations, to select informative markers that differentiate SPTB from term deliveries, a processed quantification data of the markers can be subjected to univariate receiver operating characteristic (ROC) curve analysis. A Differential Dependency Network (DDN) can then applied in order to extract co-expression patterns among the markers. In order to assess the complementary values among selected markers and the range of their relevant performance, multivariate linear models can be derived and evaluated using bootstrap resampling.
Owner:NX PRENATAL INC
Systems and methods for utilizing graph based map information as priors for localization using particle filter
ActiveUS20180328738A1Navigational calculation instrumentsParticular environment based servicesGraphicsComputational science
A computer-implemented method performed in a computerized system incorporating a central processing unit, a localization signal receiver, a plurality of sensors, separate and distinct from the localization signal receiver, and a memory, the computer-implemented method involving: using the central processing unit to initialize a plurality of particles based on an information on a map graph; using the central processing unit to repeatedly execute a particle filter loop, wherein the particle filter loop includes: using the central processing unit to perform a motion update of the plurality of particles; using the central processing unit to perform a measurement update of the plurality of particles; and using the central processing unit to perform a resampling of the plurality of particles based on particle importance weights and the map graph information. The location of the computerized system is subsequently determined based on the plurality of particles.
Owner:FUJIFILM BUSINESS INNOVATION CORP
Ensemble learning method based on heuristic sampling
InactiveCN111275206AGood classification operation efficiencyImprove discriminationEnsemble learningCharacter and pattern recognitionData setAlgorithm
An ensemble learning method for heuristic sampling is suitable for classification of an unbalanced data set, and comprises the following steps: dividing the data set into a second category according to distribution characteristics of all samples in the data set in a characteristic space; respectively setting different hardness weights according to the second category of each sample, and calculating the selection probability of each sample in combination with the unbalanced weight; and resampling the data set according to the selected probability of each sample, and performing integrated training on the resampled data set to obtain a final classification result. According to the method, emphasized resampling is carried out on the basis of the intrinsic characteristics of the sample, so thatthe sampling quality of the unbalanced data set is improved, and the classification effect of an existing ensemble learning method on the unbalanced data set is improved.
Owner:TONGJI UNIV
Semi-supervised text classification method and system based on multi-granularity modeling
ActiveCN112214597AWidening of differencesSettle the lossSpecial data processing applicationsNeural learning methodsText modelingText categorization
The invention provides a semi-supervised text classification method and system based on multi-granularity modeling, and relates to the technical field of data processing and machine learning. According to the invention, a three-channel text vector model layer is formed in a multi-granularity text modeling mode, text modeling is conducted on the same text from the character level, the word level and the sentence level; modeling of the three levels serves as three channels respectively, and input and output of the three channels are connected into three base classifier sets; therefore, divergence between samples is obtained under the condition that the samples or characteristics are not lost, and a traditional resampling and random subspace method is replaced. Meanwhile, nine base classifiers are integrated into a design of three base classifier groups, the advantages of different base classifiers are integrated, different features of the same sample are obtained by using different baseclassifiers, and divergence among the base classifiers is obtained, so that the classification result accuracy of the semi-supervised text classification method is effectively improved.
Owner:HEFEI UNIV OF TECH
Cross-version depth defect prediction method capable of relieving class overlapping problem
ActiveCN111767216AImproving the performance of defect predictionCharacter and pattern recognitionSoftware testing/debuggingData setStatistical analysis
The invention discloses a cross-version deep defect prediction method capable of relieving a class overlapping problem. The method comprises 1, a deep semantic learning-oriented overall framework in cross-version software defect prediction; 2, a semantic feature learning model based on a convolutional neural network; 3, a hybrid nearest neighbor cleaning strategy for deep semantic learning. According to the method, a hybrid nearest neighbor cleaning strategy is adopted to relieve the class overlapping problem existing in semantic features learned by deep learning. Specifically, for an abstractsyntax tree corresponding to a source code, a convolutional neural network is adopted to learn deep semantic features, and then a hybrid nearest neighbor cleaning strategy is adopted to perform resampling and data cleaning on a labeled data set. By adopting the hybrid nearest neighbor cleaning strategy, the class imbalance problem and the class overlapping problem can be processed, and the statistical analysis result of the data shows that the strategy can improve the performance of software defect prediction based on deep semantic learning.
Owner:IANGSU COLLEGE OF ENG & TECH
Synthetic ultra-narrow pulse radar detection threshold calculation method based on resampling algorithm
The invention provides a synthetic ultra-narrow pulse radar detection threshold calculation method based on a resampling algorithm. Random samples are generated through resampling, and radar detectorthresholds corresponding to a plurality of false alarm rates are determined at the same time by using a quantile interpolation algorithm so that the defect that a large number of random numbers need to be stored to occupy a memory in a traditional method is overcome, and the calculation speed of the detector thresholds is increased.
Owner:北京理工大学重庆创新中心 +1
Software defect prediction method based on generative adversarial network and ensemble learning
PendingCN114064459AAlleviate class imbalance problemSolve the existing disadvantagesCharacter and pattern recognitionSoftware testing/debuggingData imbalanceData set
The invention discloses a software defect prediction method based on a generative adversarial network and ensemble learning. The method comprises the following steps: 1, carrying out the preprocessing of a software defect data set, dividing the data set into a training set and a test set, and calculating a resampling rate; 2, constructing a generative adversarial network model; 3, inputting the training set into the generative adversarial network for training to obtain a trained generative adversarial network; 4, using the trained generative adversarial network to generate new few-sample defect data according to the resampling rate, and obtaining a resampled training set; and 5, constructing a software defect strong classifier by using an AdaBoost method, and inputting the test set into the trained software defect strong classifier to obtain a software defect prediction result. According to the software defect prediction method, the problem of software defect data imbalance is solved, and the accuracy, the correct rate, the recall rate and the F-measure performance of the software defect prediction method are improved.
Owner:XIAN UNIV OF TECH
Wind power probability prediction method based on hierarchical integration
The invention discloses a wind power probability prediction method based on hierarchical integration. According to the method, a subspace set is constructed through resampling and a partial least square method, a plurality of local areas are obtained on each subspace through GMM clustering, a corresponding local GPR model is established, and a Bayesian reasoning strategy and a finite mixing mechanism are used for fusing the local models to establish a first-layer integrated model. And a genetic algorithm is adopted to select a suitable first-layer integration model for selective adaptive integration, so that a selective hierarchical integration Gaussian process regression probability prediction model can be obtained. In order to solve the problem of performance deterioration caused by change of wind power data characteristics, an adaptive updating strategy is introduced, so that the prediction model has adaptive updating capability. According to the method, the selective hierarchical ensemble learning framework is used for ultra-short-term wind power prediction, compared with a traditional ensemble learning prediction method, the method has higher prediction precision and stability, and the generated prediction interval can provide effective reference for power dispatching.
Owner:KUNMING UNIV OF SCI & TECH
JPEG image resampling tampering identification method, device and computer equipment
ActiveCN110443804BEliminate quantization noiseImproved tamper detectionImage enhancementImage analysisPattern recognitionFrequency spectrum
Owner:数字广东网络建设有限公司
Method for evaluating service life and reliability of product based on zero-failure data
InactiveCN102207998BEfficient identificationAssessment reliabilitySpecial data processing applicationsDependabilityComputer science
The invention relates to a method for evaluating the service life and reliability of a product based on zero-failure data. The method comprises the following steps of: firstly collecting the time of zero-failure life of a product to obtain zero-failure data; carrying out equiprobable replaceable resampling processing on the zero-failure data of an individual product so as to generate a corresponding collateral constellation; forecasting the overall zero-failure data of the product according to the collateral constellation, and further acquiring the distribution function of the overall zero-failure data of the product; constructing the overall failure probability function of the product according to the number of the zero-failure data of the individual product and the distribution function of the overall zero-failure data of the product; and acquiring the overall reliability function of the product according to the overall failure probability function of the product to finally realize the evaluation on the service life and reliability of the product on the basis of the zero-failure data. According to the invention, only based on a little amount of zero-failure data, the overall failure probability function of the product can be recognized effectively, the life and the reliability of the product can be evaluated, the reliability of the product can be evaluated and forecast timely, the failure hazard can be found out, and the malignant accidents can be prevented.
Owner:HENAN UNIV OF SCI & TECH
Radar target tracking method based on dynamic resampling particle filtering
InactiveCN106056632AAchieve degradationAchieve depletionImage enhancementImage analysisRadarImproved algorithm
The invention provides a radar target tracking method based on dynamic resampling particle filtering. A dynamic resampling algorithm is adopted to dynamically determine the number of particles participating in resampling, thereby realizing effective compromise of particle degeneracy and particle impoverishment, and ensuring same distribution shown by particle sets before or after the resampling. The method is characterized in that, when resampling begins, normalized weight substitution calculation is carried out to obtain Neff(0) value; if the Neff(0) value is smaller than Nth, it is shown that particle degeneracy problem is serious, and resampling is needed; and only one particle is randomly selected for resampling each turn, and then, Neff(k) value of a current particle-weight matching set is calculated until meeting the condition that Neff(k) is equal to or larger than the Nth. In order to prevent the problem of huge computation amount due to weight update of all particles in the residual particle set after resampling of one particle in each turn, the invention adopts a dichotomy method to carry out resampling on km particles to realize quick dynamic resampling, thereby greatly improving algorithm efficiency.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Wave compensation prediction method based on random forest algorithm and Adam neural network
PendingCN111738478ALess prone to fitting problemsImprove generalization abilityForecastingCharacter and pattern recognitionAlgorithmOriginal data
The invention provides a wave compensation prediction method based on a random forest algorithm and an Adam neural network. The method comprises the following steps: S1, collecting historical data ofa ship, and carrying out the normalization of the data, and obtaining an original data set; s2, extracting samples by utilizing a Bootstrap resampling method to form a plurality of training set samples; s3, calculating feature importance according to the random forest, and performing attribute screening; s4, constructing a neural network model, and training the neural network model by using an Adam algorithm according to the screened attribute samples as input quantities and the ship prediction values as output quantities; and S5, inputting the real-time monitoring data into the Adam neural network model to obtain a predicted value.
Owner:SHANGHAI MARITIME UNIVERSITY
Internet of Things equipment working condition data analysis method and device and computer equipment
The invention provides an Internet of Things equipment working condition data analysis method and device and computer equipment, and the method comprises the steps: receiving original working condition data, carrying out the resampling of the original working condition data at a preset frequency, and generating the working condition data; splitting the working condition data in the preset time period into at least one working condition data segment with the same data change trend in the segment by utilizing a preset algorithm; performing operational analysis on the at least one working condition data segment by utilizing a preset physical model to obtain an analysis result, the preset physical model comprising an operational rule when the target equipment runs; writing the generated working condition data of the preset frequency into a cache in an offline state; and regularly executing the step of splitting the working condition data in the preset time period into at least one workingcondition data segment with the same change trend by utilizing a preset algorithm and subsequent steps. According to the invention, the accuracy of positioning the working state of the target equipment can be improved, and the accuracy of analyzing the working state of the target equipment is improved.
Owner:长沙树根互联技术有限公司
Self-service capacity expansion method based on correlation coefficient criterion
PendingCN110874453ASuitable for self-service expansionGuaranteed correctnessComplex mathematical operationsCorrelation coefficientAlgorithm
The invention discloses a self-service capacity expansion method based on a correlation coefficient criterion. The method comprises the following steps: S1, initializing parameters, and setting a correlation coefficient threshold value rho epsilon, a positive integer M, a self-service sample capacity B and an equal fraction m of a histogram function; S2, copying a new sample, generating a positiveinteger R-U (0, M) obeying uniform distribution randomly, calculating a remainder p = mod (R, n), wherein n is the sample capacity; S3, calculating the mean value of the copied samples, if the ith new sample is the ith new sample, repeating S2 to obtain a group of copied samples, calculating the mean value of the copied samples; S4, obtaining self-service resampling samples, and repeating the steps S2 and S3 to obtain the similarity rho (f (x))) of the self-service resampling samples to calculate the resampling samples and the original samples; f (x *)) if rho (f (x); and if f (x *) is greater than or equal to rho epsilon, outputting a resampling sample, otherwise, repeating the steps S1 to S4 until a similarity condition is satisfied. The resampling sample obtained by the method fully utilizes the information of the given sample, and the obtained resampling sample is closer to the real situation.
Owner:AIR FORCE UNIV PLA
Training method of garbage classification model and garbage classification method and device
The invention discloses a garbage classification model training method and a garbage classification method and device, and the method comprises the steps: carrying out the resampling of a pre-labeled garbage image sample set, and obtaining a balanced sample set; based on the junk image sample set and the balanced sample set, training a preset initial smooth perception model until the initial smooth perception model reaches a first preset convergence condition, and obtaining a target smooth perception model; performing iterative training on a preset first garbage classification model based on the garbage image sample set to obtain a first loss value, and performing smoothing processing on the first loss value by using the target smoothing perception model to obtain a second loss value; and finally, according to the second loss value, model parameters of the first garbage classification model are updated until the first garbage classification model reaches a second preset convergence condition, a target garbage classification model is obtained, and the accuracy of a classification result of the model is improved.
Owner:中原动力智能机器人有限公司
Unbalanced data resampling method for ecological environment evaluation
ActiveCN113298148AQuality improvementReduce quantity varianceCharacter and pattern recognitionEcological environmentData set
The invention relates to an unbalanced data resampling method for ecological environment evaluation, which comprises the following steps of: 1, acquiring ecological environment data, and standardizing the ecological environment data of each instance; 2, calculating the data density of each instance and the quantity difference d between the majority class and the minority class; 3, calculating the distribution unbalance degree DI and the number unbalance degree IR of the minority class and the majority class; 4, performing oversampling on the minority class; and 5, undersampling the majority classes. The method has the advantages that the data density of each instance in the data set is used for measuring the uniformity degree of distribution, and oversampling and undersampling are carried out according to the unbalance degree of data distribution, so that the purpose of balancing data is achieved; the ecological environment data is further balanced, the quality of the data set is improved, and when the ecological environment quality evaluation is carried out, the accuracy is higher, and the true positive rate is increased.
Owner:NANJING UNIV OF POSTS & TELECOMM
A Bayesian Resampling Method for Solving Structural Failure Probability Function
InactiveCN107704428BImprove solution efficiencyEffective design toolsComplex mathematical operationsAlgorithmFailure probability
Owner:XIAMEN UNIV
Joint Estimation Method of Direction of Arrival and Polarization Parameters of Cold Array Based on Resampling
ActiveCN108414993BImprove estimation accuracyAvoid searchingWave based measurement systemsRadarEngineering
The invention belongs to the technical field of radar array signal processing, and discloses a method for jointly estimating direction-of-arrival and polarization parameters of a COLD array based on resampling. The method mainly solves the problems of huge amount of computation in the prior art and a large decrease in parameter estimation accuracy under low signal-to-noise ratio. The main idea is: pre-estimate the range of the incoming wave direction and polarization parameters, and combine it with resampling, so that the estimated values of the parameters after resampling are normal values, and there are no abnormal values. Each group of resampling selects the optimal pseudo-noise weight. The present invention can avoid spectral peak search, reduce polynomial order of root-finding, reduce calculation amount and hardware complexity, and greatly improve DOA and polarization parameter estimation performance under low signal-to-noise ratio. The disadvantages of spectral peak search are the large amount of computation and the performance of parameter estimation is severely degraded under low signal-to-noise ratio.
Owner:XIDIAN UNIV
Rock-soil sampling layout system and method based on artificial intelligence analysis
ActiveCN112160306AChange inefficiencyExecution for user interfacesIn situ soil foundationPattern recognitionSoil science
The invention provides a rock-soil sampling layout system based on artificial intelligence analysis. The system comprises a rapid pre-sampling engineering vehicle and a re-sampling static penetrometer, wherein the rapid pre-sampling engineering vehicle comprises a power cone penetrometer, a data processing engine and a visual interface; the power cone penetrometer carries out penetration samplingon a predetermined rock-soil target area, and an optical fiber sensor sends data obtained by each time of penetration sampling to the data processing engine; after the data processing engine analyzesthe sampling data through an AI data extension engine, recommended resampling layout points in the predetermined rock-soil target area range are displayed on the visual interface; and the re-samplingstatic penetrometer performs rock-soil sampling on the recommended resampling layout points. The invention also discloses a rock-soil sampling layout method realized based on the system and a computerreadable storage medium for realizing the corresponding method. By means of the technical scheme, the rock-soil sampling data can be more representative.
Owner:湖南省水工环地质工程勘察院有限公司
Self-adaptive resampling method based on window function design
PendingCN111327297AIncrease flexibilitySmall amount of calculationAdaptive networkDigital technique networkFractional delay filterSelf adaptive
The invention discloses an adaptive resampling method based on window function design. The adaptive resampling method comprises the following steps: firstly, establishing a universal resampling modelbased on a Farrow structure; establishing a fractional delay filter design model based on a window function method, and obtaining a plurality of groups of fractional delay filters by utilizing the model; constructing a fractional delay filter matrix on the basis of the obtained multiple groups of fractional delay filters; solving a coefficient matrix C of the Farrow structure sub-filter by using aleast square method; and substituting the obtained Farrow structure sub-filter coefficient matrix C into the resampling model established in the step 1 to complete the establishment of a universal Farrow structure resampling model. The method is better in flexibility and smaller in calculated amount, a proper model can be obtained by adjusting parameters for multiple times, sampling at any pointis achieved, and the function of arbitrary conversion between sampling rates is achieved.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING) +1
Hybrid framework-based unbalanced classification method, system and equipment and storage medium
PendingCN113378963AReduce in quantityProcessing speedCharacter and pattern recognitionData classData set
The invention relates to a hybrid framework-based unbalanced classification method, system and equipment and storage medium. According to the method, an unbalanced network anomaly detection data set is used for verifying a hybrid resampling integrated model. The number of majority classes is reduced by providing the combination of resampling methods, so that the processing speed is increased. The unbalanced dataset is processed at the data level, and the dataset is converted into equilibrium distribution using a resampling technique. An integrated model comprising 12 different classifiers is established, and compared with 5 classifiers in previous work, more choices are provided. The slightly balanced data obtained after the processing is classified by using an integrated model, so that a novel combination of undersampling and oversampling is provided to balance the imbalance among different data categories, and the processing speed is increased with less memory overhead.
Owner:NAT UNIV OF DEFENSE TECH
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