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37 results about "Bootstrap aggregating" patented technology
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Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.
Grounding grid corrosion rate level prediction method
InactiveCN103136539AEasy to handleNovel and reasonable designCharacter and pattern recognitionGrounding gridData set
The invention discloses a grounding grid corrosion rate level prediction method which comprises the following steps: (1) inputting training sample data; (2) randomly sampling training samples according to a bootstrap sampling principle in a Bagging algorithm, forming training sample bootstrap subsets with the number of M, and constituting training sample bootstrap subset data sets; (3) structuring a weak classifier model according to a k-nearest neighbor (KNN) algorithm, sequentially training the training sample bootstrap subsets with the number of M, and obtaining weak classifiers with the number of M; (4) structuring a strong classifier model according to an Adaboost algorithm; (5) inputting to-be-tested sample data, predicting a grounding grid corrosion rate level, obtaining a predicting result, and displaying the predicting result through a displayer. The grounding grid corrosion rate level prediction method has the advantages of being novel and reasonable in design, convenient and fast to use and operate, high in predicting precision, capable of achieving an accurate prediction to the grounding grid corrosion rate level by means of a small amount of data samples which are measured in the prior art, low in implementation cost, strong in practicability and high in value of popularization and application.
Owner:XIAN UNIV OF SCI & TECH
Selective integrated learning-based rotating machinery fault diagnosis method
ActiveCN106980822AImprove performanceCharacter and pattern recognitionNeural learning methodsLearning machineState of art
The present invention discloses a selective integrated learning-based rotating machinery fault diagnosis method. The method comprises the steps of pre-processing a known input signal to obtain a training sequence; based on the Bagging algorithm, processing the training sequence to generate a series of differentiation-based learning machines; through the selective integrated learning process, giving a preference on the series of differentiation-based learning machines to obtain a well trained PSOSEN model; by utilizing the trained PSOSEN model, diagnosing a rotating machinery fault. The method solves the problem in the prior art that a classifier with better performances is difficult to be selected out of a series of classifiers can be solved.
Owner:BEIHANG UNIV
Lithium ion battery service life forecasting method based on integrated model
ActiveCN103293487AImprove applicabilityImprove stabilityElectrical testingEcho state networkEngineering
The invention discloses a lithium ion battery service life forecasting method based on an integrated model and relates to a lithium ion battery cycle life forecasting method. The lithium ion battery service life forecasting method is used for solving the problem that the existing lithium ion battery is low in service life forecasting adaptability and poor in stability. The lithium ion battery service life forecasting method includes: performing preprocessing on battery cycle charging and discharging test testing data; adopting a Bagging algorithm to perform secondary resampling on a Train database; building a monotonous echo state network model; initializing inner connection weights of a monotonous echo state network, and repeating for T times to obtain T untrained monotonous echo state network sub-models; setting a first free parameter set and a second free parameter set of the monotonous echo state network model; integrating output RULi of the monotonous echo state network model, adopting the Test database to drive the integrated monotonous echo state network model, and obtaining remaining service life of a lithium ion battery. The lithium ion battery service life forecasting method based on the integrated model is suitable for lithium ion battery service life forecasting.
Owner:HARBIN INST OF TECH
Fatigue detection method based on multi-source information fusion
The invention discloses a fatigue detection method based on multi-source information fusion. Electroencephalogram signals, twinkling information and electrocardiosignals of a testee are synchronously collected by means of an electroencephalogram collecting device and an electrocardiogram collecting device respectively; electroencephalogram signal features including the relative energy of electroencephalogram rhythm waves alpha, beta, theta and delta, electro-oculogram information including twinkling frequency E and twinkling intensity F, and electroencephalogram features including heart rate values HR, LF and HF are extracted; by means of the logistic regression algorithm, the fatigue degrees are primarily divided into three classes, namely, the non-fatigue degree, the mild fatigue degree and the deep fatigue degree, and meanwhile features with large weights are screened according to logistic regression weights for feature fusion; fused feature vectors are classified again by means of the bagging algorithm based on a support vector machine, the processed feature vectors serve as input of the bagging algorithm, and the current fatigue degree of the testee is determined; different fatigue relieving methods are used according to classification results of the fatigue degree of the testee. The method has the advantages of being high in applicability, high in fatigue detection precision, good in improvement effect and the like.
Owner:YANSHAN UNIV
Lake-reservoir cyanobacteria water bloom recognition method based on remote sensing monitoring and evidence fusion technology improvement
ActiveCN103439472AImprove fitting accuracyResolve indicatorsTesting waterPhylum CyanobacteriaRegression analysis
The present invention discloses a lake-reservoir cyanobacteria water bloom recognition method based on remote sensing monitoring and evidence fusion technology improvement, and belongs to the technical field of environmental projects. The method comprises: establishing a remote sensing inversion model, determining cyanobacteria water bloom breaking-out degree recognition indexes, pre-treating monitoring data, assigning belief function values, and recognizing monitoring area cyanobacteria water bloom. According to the present invention, an ensemble learning Bagging algorithm is fused into regression analysis of the remote sensing inversion model (ie., the normalization vegetation index model) to correct parameters of the model to obtain the corrected remote sensing inversion model so as to improve fitting accuracy; the problem of selection of indexes for lake-reservoir cyanobacteria water bloom recognition and value ranges thereof is solved; the problems of selection of the monitoring area and pretreatment of the monitoring data are solved; the problem of assignment of the belief function values is solved; and effective cyanobacterial water bloom recognition is achieved.
Owner:BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
A Composite Intrusion Detection Method Based on Bagging Algorithm
ActiveCN102291392AReduce false alarm rateReduce false negative rateDigital computer detailsTransmissionLearning machineData set
The invention relates to a hybrid intrusion detection method based on a bagging algorithm, which comprises the following steps of creating an initial history data sample set S; constructing the sample set S into a sample set S<sample> which can be read by a weak learning algorithm in the bagging algorithm and selecting a ball vector machine as the weak learning algorithm; cyclically calling the weak learning algorithm to complete the training of the data sample S<sample> to obtain a strong learning machine H; inputting current data samples to be detected into the strong learning machine H which is used as a hybrid intrusion detection model, the strong learning machine H using all generations of weak learning machines hi to conduct preliminary intrusion detection and judging the intrusion detection results of the current data samples to be detected through a voting method, and the intrusion detection result which gets the most votes being taken as the final intrusion detection result of the strong learning machine H. By adopting the method disclosed by the invention to conduct the intrusion detection to a target network, the defects of low detection accuracy, poor generalization ability and the like commonly existing in the original intrusion detection technique can be overcome, and the rate of false alarms and the rate of missed alarms can be greatly reduced.
Owner:CHINA ELECTRIC POWER RES INST +2
Method for planning paths on basis of ocean current prediction models
ActiveCN108803313AFast convergenceReduce complexityPosition/course control in three dimensionsAdaptive controlLongitudeQ-learning
Owner:HARBIN ENG UNIV
Method of using deep learning for movie recommendation based on emotion analysis
InactiveCN108710680AQuality improvementHigh precisionNeural architecturesSpecial data processing applicationsPattern recognitionAnalysis data
The invention discloses a method of using deep learning for movie recommendation based on emotion analysis. Movie online-comments of a user and movie status information are acquired, and cleaning operations and pre-processing are carried out on movies and comment information thereof; structure and feature information extraction of data is carried out, and emotion analysis is carried out on featureinformation through deep learning; and then a user interest model is established on the basis of emotion analysis data, then interest degrees of the user on movies are calculated, and movie recommendation is carried out. According to the method, a Bagging algorithm and a bi-directional long short-term memory (Bi-directional LSTM) network are used as a method of emotion inclination analysis, subjective emotion expressed by the user in the text information is mined, the effective user interest model is constructed, accuracy of a recommendation system is improved, and quality of movie recommendation is optimized.
Owner:HARBIN UNIV OF SCI & TECH
A river water quality prediction and an evaluation method of water quality influencing factors
InactiveCN109242203AAccurate predictionGuide governanceGeneral water supply conservationForecastingRelational modelDecision taking
The invention discloses a river water quality prediction and an evaluation method of water quality influencing factors. The method comprises the following steps: firstly, extracting historical data of river water quality and watershed characteristics of corresponding sampling points to form an original training set; Secondly, through the bagging algorithm, randomly selecting samples from the original training set, and constructing several sub-training sets . third, generating a decision tree by selecting split attribute according to that characteristics of different watersheds, and constructa random forest model according to the decision tree; Fourth, evaluating the simulation effect of the model; 5, acquiring watershed characteristic data of a point to be predicted, putting that data into a stochastic forest model, and obtaining corresponding water quality prediction data; Sixth, assessing the impact of different basin characteristics on river water quality. The relationship modelbetween watershed characteristics including watershed hydrology, climate, geographical characteristics, seasonal factors, human influence and the like and river water quality is built, river water quality index data is accurately predicted according to watershed characteristics of a target point and importance of the influence of a watershed characteristic on water quality is evaluated.
Owner:HUATIAN NANJING ENG & TECH CORP MCC
Electricity fee sensitive user analysis method based on stacking and bagging algorithms
ActiveCN107507038AQuick identificationAccurate identificationMarket data gatheringElectricityOriginal data
The invention discloses an electricity fee sensitive user analysis method based on stacking and bagging algorithms. The electricity fee sensitive user analysis method comprises steps of using an optimal zone algorithm to solve problems of nonequilibrium and intolerance and construct a training set and a testing set according to electricity fee sensitive classification targets and on the basis of multiple core business indexes of an electricity fee sensitive original data table, further subdividing related core business structuralized characteristics based on electricity fee sensitivity, using a stacking method to construct an electricity sensitive primary model on the basis of nonstructural text characteristics of particles and word frequency statistics, generating extended stacking characteristics for each sample, combining characteristics of the two as an integral input, using a bagging algorithm and a vote algorithm to construct an electricity fee sensitivity secondary model on the training set and the testing set, using a trained model to perform prediction on a verification set and verifying during practical business. The Electricity fee sensitive user analysis method based on stacking and bagging algorithms can improve mastery degree of an electric power company about electricity fee sensitivity of the user, and is beneficial for providing differential and specific quality power supply service.
Owner:MERIT DATA CO LTD
Software reliability forecasting method based on selective dynamic weight neural network integration
InactiveCN103294601ASolve blindnessSolve selected problemsBiological neural network modelsSoftware testing/debuggingCluster algorithmDynamic models
The invention belongs to the field of software reliability forecasting, and particularly relates to a software reliability forecasting method based on selective dynamic weight neural network integration. The software reliability forecasting method mainly includes the steps: A, generating neural network individuals: selecting Elman neural networks as network individuals and generating n neural network individuals by a Bagging algorithm; B, optimizing the individuals: firstly, determining the cluster number of the generated neural network individuals by a K value optimization algorithm, secondly, clustering the neural network individuals according to a K-mean clustering algorithm to increase individual difference, and finally, integrating the clustered individuals; C, building a dynamic model: building a dynamic weight model based on a fuzzy neural network by the aid of errors of fitting data of the optimized individuals; and D, performing integrated output: combining forecasting results of the optimized individuals with weights generated by the dynamic weight model to generate final forecasting results. A neural network integration algorithm is applied to software reliability forecasting, and the software reliability forecasting method has the advantages of high precision and fine stability.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)
Unbalanced data classification method based on boundary upsampling
InactiveCN105975993AReduce the impactReduce the variance of classification errorsCharacter and pattern recognitionPositive sampleData set
The invention relates to an unbalanced data classification method based on boundary upsampling. The unbalanced data classification method includes that Euclidean distance is used as the distance metric for each positive sample in the unbalanced dataset, K sample points nearest to the positive samples in the unbalanced dataset are taken, the class labels of the samples are compared, the number ki of negative samples in the K sample points is given, if ki >=K / 2, the positive sample point is near to the real decision boundary of positive and negative samples, conversely, the positive sample point is far from the boundary, one positive sample is randomly selected from the K nearest positive samples, and a new positive sample is generated between the selected positive sample and the positive sample selected from the K nearest positive samples, this process is subjected to iterative execution for many times until the positive and negative samples are same in number, and thus a balanced dataset is constructed, and the generated balanced dataset is trained by a Bagging algorithm to obtain a final classification model. According to the invention, a better classification effect can be achieved in the unbalanced dataset.
Owner:TIANJIN UNIV
Soft measurement modeling method based on semi-supervised ensemble learning
ActiveCN108764295AAccurate predictionThe predicted output is accurateCharacter and pattern recognitionData setSupervised learning
The invention discloses a soft measurement modeling method based on semi-supervised ensemble learning, belongs to the field of modeling and soft measurement in a complex industrial process, and is used for a chemical process with a small quantity of label samples. The method is an online prediction strategy based on semi-supervision. A Bagging algorithm is adopted to divide a no-label sample set into three sub sample sets, and the label sample is adopted to train three regression models; then, on the basis of a confidence coefficient index, the corresponding index value of the no-label sampleis calculated, the no-label sample which meets a confidence coefficient requirement is selected for carrying out labeling, and the labeled sample is added into the corresponding label sub sample set;and finally, a Gaussian process regression model is independently established for three enlarged label datasets, and a weighting method is adopted for carrying out result fusion. By use of the method,the no-label sample information in the chemical process can be effectively utilized to accurately predict a key variable so as to improve product quality and lower production cost.
Owner:合肥名龙电子科技有限公司
Arrhythmia identification and classification method based on sparse representation and neural network
ActiveCN108647584AMaintain morphological characteristicsImprove denoising effectCharacter and pattern recognitionEcg signalNerve network
The invention discloses an arrhythmia identification and classification method based on sparse representation and neural network. The method comprises the following steps: preprocessing electrocardiosignals by utilizing a sparse representation frame to acquire a low frequency part of QRS (Quality Rating System) wave; subsequently, realizing feature extraction by utilizing discrete cosine transform; carrying out analysis of main components to acquire transform coefficients as feature attributes after dimension reduction; and finally, carrying out automatic classification on heat beats of the six types including normal cardiac rate (N), left bundle branch block (LBBBB), right bundle branch block (RBBBB), auricular premature beat (APB), premature ventricular contraction (PVC) and pacemaker hear beat (PB) in arrhythmia by utilizing a Bagging algorithm using BP (Back Propagation) neural network as a base learner. According to the arrhythmia identification and classification method disclosed by the invention, feature extraction is carried out from a low frequency range of the QRS wave, so that the dimensionality of the feature attributes is reduced; and the problem of unbalance classification is solved by utilizing the Bagging algorithm in ensemble learning, so that the classification accuracy is improved.
Owner:XI AN JIAOTONG UNIV
Lithium ion battery remaining service life prediction method based on AR ensemble learning model
ActiveCN104090238AHigh precisionImprove forecast accuracyElectrical testingDensity curveEnsemble learning
The invention provides a lithium ion battery remaining service life prediction method based on an AR ensemble learning model, relates to a lithium ion battery remaining service life prediction method and aims at solving the problem that an existing single AR model is limited in accuracy and low in stability during nonlinear time series prediction. The method performs prediction on the lithium ion battery remaining service life based on the AR ensemble learning model, vectors composed by input data are randomly selected to form a group of sub-vector sets by adopting a Bagging (Bootstrap Aggregating) integration method, one AR model is input into each vector set to perform parameter computation and capacity prediction, finally prediction results are integrated and output, a capacity degeneration curve and a probability density curve are drawn, and finally a final prediction output is obtained. The lithium ion battery remaining service life prediction method can improve the stability and accuracy of the lithium ion battery remaining service life prediction and is suitable for lithium ion battery remaining service life prediction.
Owner:HARBIN INST OF TECH
Identification system and method for determining the geographic origin of a fresh commodity
The detection method includes generating a plurality of neural network models. Each model has as a training set a data set from a plurality of samples of a commodity of known origins. Each sample has been analyzed for a plurality of elemental concentrations. Each neural network model is presented for classification a test data set from a plurality of samples of a commodity of unknown origins. As with the training set, the samples have been analyzed for the same plurality of elemental concentrations. Next a bootstrap aggregating strategy is employed to combine the results of the classifications for each sample in the test data set made by each neural network model. Finally, a determination is made from the bootstrap aggregating strategy as to a final classification of each sample in the test data set. This final classification is indicative of the geographical origin of the commodity. The system includes software for generating the neural network models and a software routine for performing the bootstrap aggregating strategy.
Owner:IDAHO POTATO COMMISSION
Hierarchical Bagging method for sentiment analysis based on electroencephalogram signals
PendingCN110414548AImprove stabilityImprove robustnessCharacter and pattern recognitionSignal classificationAlgorithm
The invention belongs to the technical field of electroencephalogram signal processing. The invention discloses a hierarchical Bagging method for sentiment analysis based on electroencephalogram signals. The hierarchical Bagging method comprises the steps of electroencephalogram sample data preprocessing, feature extraction and feature selection, replacement sampling of a training set, training ofa plurality of data subsets through different base classification algorithms and voting of a plurality of classifiers to obtain a classification result. Different from a traditional Bagging algorithmin which a single training subset corresponds to a single classification algorithm, hierarchical Bagging enables a plurality of training subsets to correspond to a single classification algorithm, and the risk that a classification algorithm with good single performance is deleted due to the fact that the classification algorithm does not adapt to individual data is reduced. According to the method, the accuracy of electroencephalogram signal classification can be effectively improved, the problem of poor stability of a single classification algorithm is solved, and the method can also be popularized to other similar types of data processing. The method is of great significance to emotion monitoring, risk prediction and supervised learning classification.
Owner:XIDIAN UNIV
data-driven SPI defect type intelligent identification method on an SMT production line
ActiveCN109657718AImprove classification accuracyReduce setting errorsCharacter and pattern recognitionManufacturing computing systemsData setIntegration rule
The invention discloses a data-driven SPI defect type intelligent identification method on an SMT production line. The method comprises the following steps of in a first stage, carrying out clusteringprocessing on SPI historical quality detection data sets, independently sampling the obtained K types of training data sets for 20 times by adopting a Bagging algorithm, and respectively training independent defect classifiers for K types of K * 20 groups of training sets by utilizing a BP neural network model to obtain K * 20 independent defect classifiers to form a classifier set; in A second stage, detecting 6 solder paste printing quality parameters online; comparing with a historical training data set to classify the detection records T; determining which category of the real-time detection point belongs to the K categories of training data sets, and when T is just located on the boundary of two or more categories of training data sets, simultaneously selecting twenty independent defect classifiers from the multiple categories of K categories of training data sets according to approximately equal quantity to carry out category judgment of detection records T; And inputting T intoeach independent defect classifier, carrying out integrated prediction on an output result according to an integration rule, and judging a defect type. According to the invention, the effect of people in automatic detection is reduced, and the online real-time detection efficiency and accuracy are improved.
Owner:GUANGDONG INTELLIGENT ROBOTICS INST
Improved algorithm for missing value interpolation
The invention discloses an improved algorithm for missing value interpolation. The algorithm comprises the steps that hierarchical clustering is performed on all data; for a class containing missing values, according to the judgment of whether a record of the missing values is available, the record is divided into a complete data set m1 and a missing data set m2; the data in m1 is randomly dividedinto a training set and a test set, k types of interpolation methods are used to predict the test set, and the method with the highest accuracy is obtained; whether the obtained method is a weak method is judged, if yes, a function for missing value interpolation of the class is obtained in combination with a bagging algorithm, and if not, the algorithm is a final algorithm; the final interpolation function is adopted to perform interpolation on the missing values of the class; and whether another class containing missing values exists is judged. According to the improved algorithm for missing value interpolation, the method suitable for the data set can be selected from numerous missing value interpolation schemes according to specific properties of the data, interpolation effects of themissing values through various methods are compared according to the principle of the bagging algorithm, and therefore the practical method for missing value interpolation of the data is obtained.
Owner:GUANGDONG KINGPOINT DATA SCI & TECH CO LTD
Electroencephalogram (EEG) recognition method
ActiveCN107174203AMake up for interferenceImprove generalization abilityDiagnostic recording/measuringSensorsSupport vector machineEnsemble learning
The invention relates to an electroencephalogram (EEG) recognition method. The method comprises the following steps: carrying out a character experiment to extract an EEG including 300 signals so as to serve as a training set; integrating a plurality of support vector machines with mixed kernels to serve as a learner by utilizing a bagging algorithm, and adaptively adjusting parameters of the learner based on an immune algorithm by adopting the training set so as to obtain optimum parameters; and finally, recognizing a P300 signal in the EEG by utilizing the learner with the optimum parameters, wherein the optimum parameters refer to parameters capable of enabling the learner to accurately recognize the P300 signal, and accurate recognition is that the accuracy of repeated experiments of more than 12 times is 96-98%. According to the EEG recognition method disclosed by the invention, the parameters can be intelligently selected according to optimized contents, the defects that the traditional learner needs to be continuously adjusted and optimized and needs a cross validation process are overcome, the intelligence of the integrated learner is improved, and the EEG recognition method disclosed by the invention is excellent in recognition performance, high in accuracy rate and high in overall generalization ability and has excellent popularization and application values.
Owner:DONGHUA UNIV
Improved LR-Bagging algorithm based on characteristic selection
The invention discloses an improved LR-Bagging algorithm based on feature selection, which comprises the following steps: first, determine the initial data set from the original data, and require that the degree of correlation between the independent variable and the dependent variable cannot be too low; secondly, the initial data set Discrete independent variables are encoded by WEO; then random sampling is used to obtain a certain number of records and feature fields to form training examples, and the training examples are trained with LR ((LogisticRegression) model and the normal significance test of the coefficient is performed. If not significant, then Eliminate, on the contrary, add the combination model. Carry out cyclic iterations until the combination model is better. Finally, you can use the better combination model for prediction and grouping. This algorithm can improve the diversity of classification results, the degree of extraction of variable information and the prediction results It can also effectively reduce the possibility of multicollinearity and "overfitting" caused by too many variables in the base LR model.
Owner:GUIZHOU POWER GRID INFORMATION & TELECOMM
Image-based re-lighting method
InactiveCN106570928AHigh PSNR valueReduce training timeNeural architecturesNeural learning methodsEnsemble learningComputer graphics
The present invention discloses an image-based re-lighting method and belongs to the computer graphics field. In order to achieve re-lighting as accurately as possible with as few samples as possible, quantitative random sampling is performed repeatedly in the spaces of image samples and image pixels, and training is performed through using an artificial neural network until the training accuracy of all pixels reaches a set threshold value; and provided that the artificial neural network has a requirement for minimum samples in training, the Bagging algorithm of ensemble learning is utilized to perform averaging processing on pixel training samples when the pixel training samples are insufficient. The method of the present invention is tested in a simulated three-dimensional scene, and a test result indicates that the image-based re-lighting method has the advantages of less training time and high robustness as well as fewer image samples, high speed, excellent real-time performance and high PSNR (peak signal to noise ratio) of a reconstructed scene image under the same relative error accuracy compared with the prior art.
Owner:HOHAI UNIV
An electric vehicle charging facility fault prediction method and system
InactiveCN109886328AAccurate failure prediction resultsRealization of failure prediction resultsCharacter and pattern recognitionData setPredictive methods
The invention provides an electric vehicle charging facility fault prediction method and system, and the method comprises the steps of reading a charging data set, and carrying out the type division of the data set; setting the data range of each hyper-parameter; selecting a group of hyper-parameters with the highest accuracy as hyper-parameters of the fault prediction model to reinitialize the model; generating different sub-data sets by using a Bagging algorithm; respectively submitting different sub-data sets to the corresponding decision tree models for regression analysis; and according to different output weights of each decision tree during training, outputting a result obtained after all outputs are unified as distance fault prediction time of the model. According to the method, the fault prediction of the charging facility is realized, the prevention measures can be taken before faults really occur, the faults are avoided, the part damage, service interruption and the like caused by the faults are reduced, and the facilities are prevented from entering an unsafe or uncertain state caused by the faults, so that the operation and maintenance cost is reduced, the equipment operation efficiency is improved, and the safety is ensured.
Owner:ELECTRIC POWER RES INST OF STATE GRID ZHEJIANG ELECTRIC POWER COMAPNY
Fatigue driving detection system and use method thereof
InactiveCN108921135ATimely judgment of driving statusAvoid dangerous drivingCharacter and pattern recognitionSignalling/lighting devicesDriver/operatorComputer module
The invention discloses a fatigue driving detection system and a use method thereof. The fatigue driving detection system comprises a 3D structured light module, a central processing unit module and an alarm system; the 3D structured light module is installed in the front of a vehicle driver seat; the 3D structured light module is used for shooting face characteristics of a driver during driving,and transmitting data shot by the 3D structured light module to the central processing unit module; the central processor module is used for detecting, analyzing and judging whether the driver is in afatigue driving state or not by adopting a bagging algorithm, and sending a signal to the alarm system if the driver is detected to be in fatigue driving; and the alarm system sends out a warning signal to inform the driver not to be in fatigue driving. According to the system, the identification precision is high; the error is small; the driving state of the driver can be judged effectively andtimely; measures are correspondingly adopted when the driver is in fatigue driving; and dangerous driving of the driver is avoided by means of reminding the driver and the like.
Owner:HEFEI ZHIXIN AUTOMOTIVE TECH CO LTD
EV charging station position generation method and device based on random particle swarm algorithm
The invention relates to an EV charging station position generation method and device based on a random particle swarm algorithm. The method comprises the steps of S1, collecting geographic information data; S2, based on the geographic information data, constructing an objective function and constraint conditions of an optimization problem according to the power distribution network factors, the traffic factors and the social factors; and S3, solving an optimization problem by combining a PSO algorithm and a Bagging algorithm, and generating an optimal charging station position. Compared withthe prior art, the method has the advantages that the charging station planning model taking cost and the planning effect as targets is adopted, and the PSO and Bagging algorithms are combined, so that the optimal site selection scheme of the charging station is realized, the user experience of the obtained charging station can be improved, and the energy consumption of idle driving is reduced.
Owner:SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
Full-waveform LiDAR point cloud classification method based on multi-wavelet support vector machine WSVM integration
PendingCN110503132AEnsure heterogeneityEnsure homogeneityCharacter and pattern recognitionParticle swarm optimization pso algorithmSupport vector machine
The invention discloses a full-waveform LiDAR point cloud classification method based on multi-wavelet support vector machine WSVM integration. The method comprises the following steps: firstly, decomposing full-waveform LiDAR data, extracting geometric and waveform characteristics of point cloud, constructing a plurality of wavelet support vector machine (WSVM) classifiers by utilizing differentwavelet kernel functions, and constructing an integrated system by taking the WSVM classifiers as base classifiers; performing parameter optimization in each WSVM classifier by adopting a particle swarm optimization PSO algorithm, and integrating the WSVM classifiers by adopting a bagging algorithm; and finally, adopting a wavelet support vector machine (WSVM) classifier integrated system to classify point clouds decomposed from the original full-waveform LiDAR data, and outputting a result. According to the invention, a good point cloud classification result can be obtained.
Owner:WUHAN UNIV
Transformer fault diagnosis method based on integrated learning Bagging algorithm
InactiveCN102289682BAvoid defectsImprove accuracyMaterial analysis by electric/magnetic meansElectrical testingLearning machineTransformer
The invention discloses a transformer fault diagnosis method based on integrated learning Bagging algorithm in the technical field of transformer fault diagnosis. In the method, with a ball vector machine (BVM) as a weak learning algorithm of the Bagging algorithm, the dissolved gas analysis (DGA) technology as a data acquiring method, a sample set suitable for the BVM is obtained through data processing methods such as data normalization, category numeralization and the like; the weak learning algorithm is repeatedly invoked to train the sample set in the integrated learning Bagging algorithm comprises so as to obtain a strong learning machine H; the strong learning machine H is used as a transformer fault diagnostic model to judge the fault of a to-be-diagnosed record. The transformer fault diagnosis method has good adaptability and low diagnosis error in terms of improving the transformer fault diagnosis precision.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)
Heart disease predicting method based on Bagging-Fuzzy-GBDT algorithm
ActiveCN110265146AReduce complexityIncrease diversityHealth-index calculationMedical automated diagnosisDiseasePrediction algorithms
The invention discloses a heart disease predicting method based on a Bagging-Fuzzy-GBDT algorithm. The hart disease predicting method comprises the steps of according to characteristics of patient heart disease data, extracting attributes with a large value range change in the data, and fuzzifying the data by means of a fuzzy logic; combining the fuzzified data with a GBDT algorithm, and forming a Fuzzy-GBDT heat disease predicting algorithm; and finally improving data diversity by means of a Bagging algorithm through m times of sampling with replacement, and combining the Bagging algorithm with the Fuzzy-GBDT algorithm for presenting the heart disease predicting algorithm based on the Bagging-Fuzzy-GBDT algorithm. The heart disease predicting method has advantages of reducing variance of the Fuzzy-GBDT predicting algorithm, improving data diversity, preventing over-fitting of a single point, realizing high generalization of the predicting algorithm, and improving accuracy of the predicting algorithm. (4) The heart disease predicting method realizes performance evaluation through an experiment. A result proves a fact that the heart disease predicting algorithm based on the Bagging-Fuzzy-GBDT algorithm has relatively high accuracy and high generalization.
Owner:东北大学秦皇岛分校
Lithium-ion battery life prediction method based on integrated model
ActiveCN103293487BImprove applicabilityImprove stabilityElectrical testingData setEcho state network
The invention discloses a lithium ion battery service life forecasting method based on an integrated model and relates to a lithium ion battery cycle life forecasting method. The lithium ion battery service life forecasting method is used for solving the problem that the existing lithium ion battery is low in service life forecasting adaptability and poor in stability. The lithium ion battery service life forecasting method includes: performing preprocessing on battery cycle charging and discharging test testing data; adopting a Bagging algorithm to perform secondary resampling on a Train database; building a monotonous echo state network model; initializing inner connection weights of a monotonous echo state network, and repeating for T times to obtain T untrained monotonous echo state network sub-models; setting a first free parameter set and a second free parameter set of the monotonous echo state network model; integrating output RULi of the monotonous echo state network model, adopting the Test database to drive the integrated monotonous echo state network model, and obtaining remaining service life of a lithium ion battery. The lithium ion battery service life forecasting method based on the integrated model is suitable for lithium ion battery service life forecasting.
Owner:HARBIN INST OF TECH
A Software Reliability Prediction Method Based on Selective Dynamic Weight Neural Network Ensemble
InactiveCN103294601BSolve blindnessSolve selected problemsBiological neural network modelsSoftware testing/debuggingCluster algorithmNerve network
The invention belongs to the field of software reliability forecasting, and particularly relates to a software reliability forecasting method based on selective dynamic weight neural network integration. The software reliability forecasting method mainly includes the steps: A, generating neural network individuals: selecting Elman neural networks as network individuals and generating n neural network individuals by a Bagging algorithm; B, optimizing the individuals: firstly, determining the cluster number of the generated neural network individuals by a K value optimization algorithm, secondly, clustering the neural network individuals according to a K-mean clustering algorithm to increase individual difference, and finally, integrating the clustered individuals; C, building a dynamic model: building a dynamic weight model based on a fuzzy neural network by the aid of errors of fitting data of the optimized individuals; and D, performing integrated output: combining forecasting results of the optimized individuals with weights generated by the dynamic weight model to generate final forecasting results. A neural network integration algorithm is applied to software reliability forecasting, and the software reliability forecasting method has the advantages of high precision and fine stability.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)
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