36 results about "Adaboost algorithm" patented technology

A multi-model fused avionic product health assessment method includes the following steps: collecting relevant data of an avionic product; performing data pre-processing on the relevant data to obtain first data and second data; training a plurality of base models on the basis of the first data; performing quantitative measurement and fusion on the plurality of base models to obtain an integrated model; and inputting into the integrated model the second data which serves as a test sample to obtain a health assessment result of the avionic product. A plurality of base models are integrated by using an AdaBoost algorithm, and a reference can be provided for a method based on data driving in terms of application in the health assessment, prediction and management of an avionic product.

The invention relates to the technical field of air temperature risk assessment, in particular to an AdaBoost-based urban air temperature inference and thermal exposure risk assessment method. The method comprises the following steps: acquiring original city form data and city meteorological data of a city; performing data preprocessing on the original city form and the city meteorological data of the city to generate processed original city form data and city meteorological data; integrating the processed original city form data and city meteorological data into a model training set and a model test set; and designing an artificial neural network architecture by adopting an AdaBoost algorithm, and performing model training on the model training set by utilizing the artificial neural network architecture to generate an urban air temperature inference pre-model. According to the method, by fusing multi-source data, applying an advanced machine learning algorithm and enhancing sensitivity analysis and Monte Carlo simulation, the accuracy and reliability of urban air temperature inference and thermal exposure risk assessment are improved.

The invention belongs to the technical field of circuit breaker fault diagnosis, and particularly relates to a high-voltage circuit breaker and a mechanical characteristic health diagnosis method and system thereof. The method comprises the following steps: S1, acquiring operation state information of the high-voltage circuit breaker in a switching-off / switching-on process; s2, inputting the running state information into a pre-trained fault diagnosis model to obtain a fault state type of the mechanical characteristics of the high-voltage circuit breaker; the fault diagnosis model comprises a strong classifier, and the strong classifier is a classifier obtained by combining at least two weak classifiers through an AdaBoost algorithm. According to the method, the classification model is constructed by adopting a multi-algorithm fusion technology through the AdaBoost algorithm, and the AdaBoost algorithm enables the model to pay more attention to samples which are difficult to classify by adjusting the weight of data points, so that the classification effect of the model is enhanced. The technical problem of low fault diagnosis precision caused by low classification precision of fault data difficult to classify in the prior art is solved.

The invention relates to the technical field of information, in particular to a black land plough layer thickness analysis method based on machine learning. The scheme has the advantages that double-frequency radar signals (200MHz low frequency and 2GHz high frequency) are combined with power amplification and a broadband antenna, a double-layer feature comparison model is constructed by utilizing the frequency sensitivity inversion characteristic of dielectric constant difference between a plough layer and a plough pan, interface details are captured in cooperation with a 1GS / s super-Nyquist sampling rate, a strong regression model is constructed in combination with an AdaBoost algorithm, and a high-precision feature comparison model is constructed. The method achieves the high-precision, non-destructive and large-area rapid detection of the thickness of the plough layer of the black land, and solves the problems that a conventional method is time-consuming and labor-consuming, is low in precision, and cannot give consideration to the penetration depth and resolution of a single-frequency radar.

The present invention discloses an anti-interference target acquisition method based on machine vision technology measurement, the steps of which include: S1, original image multi-stage filtering preprocessing; S2, target feature extraction; S3, random forest construction: calling a historical data sample library to obtain an anti-interference target acquisition library, randomly selecting M data points from it, creating a decision tree for the selected data points, each decision tree will produce a result, comprehensively analyze the results and output a majority vote or average; S4, through random forest, obtain a target feature reference set; S5, select the first N frames of the on-site detection picture as a training set, obtain appropriate counting weights through continuous training, and then start from the N+1th frame image, perform voting analysis by determining the weight and using the Adaboost algorithm to determine the final target. The present invention can effectively solve the interference problem in the target capture process under complex scenes, and can effectively improve the accuracy and robustness of target detection.

The application discloses a GIS partial discharge pattern recognition method based on polar coordinate distribution entropy optimization, and belongs to the field of large power grid on-site power equipment defect diagnosis and recognition. Firstly, aiming at the signal missing phenomenon of photoelectric partial discharge signals, a non-subsampled contourlet transform is used to fuse and process photoelectric PRPD graphs, so that photoelectric fusion graphs are obtained. Then, feature points and scale vectors of the fusion PRPD graphs are extracted based on a KAZE algorithm. Next, the feature points are dispersed to polar coordinate expressions according to coordinates and scale vectors. Distribution entropy of each region divided on the polar coordinates is calculated to form a feature vector. Finally, the feature vector is loaded into a long short-term memory network optimized by an Adaboost algorithm to verify the accuracy of partial discharge pattern recognition. Compared with a statistical parameter method and a KAZE feature extraction method, the algorithm can better extract a PRPD graph feature vector, and the accuracy of partial discharge defect pattern recognition is significantly improved.

The present invention discloses a credit risk rating assessment method, apparatus, device, and storage medium, belonging to the field of artificial intelligence. The method comprises: obtaining credit data to be assessed of a target object; inputting the credit data to be assessed into a target assessment model to obtain a credit risk rating corresponding to the target object; wherein the target assessment model is obtained by training a credit risk rating assessment model based on an Adaboost algorithm based on a sample credit data set. The present invention improves the accuracy of user credit risk assessment and reduces credit risk in the credit business of financial institutions such as banks. At the same time, there is no need for credit approval personnel to review the borrower's loan application materials one by one, thereby improving the loan approval efficiency of financial institutions such as banks and shortening the business processing time of borrowers.

The application provides a multi-LLM collaborative sentiment analysis method and device based on Adaboost dynamic weight adjustment and a medium. The method uses the Adaboost algorithm to dynamically update the weights of multiple large language model analyzers, and obtains target weight parameters associated with each large language model analyzer. Then, sentiment orientation recognition is performed on the preprocessed text by using multiple large language model analyzers to obtain an initial prediction result. When the initial prediction result does not satisfy a preset consistency condition, a large language model arbitrator is introduced, and multiple rounds of collaborative interaction verification are performed by the analyzer and the arbitrator combination to obtain multiple interaction analysis results. The multiple interaction analysis results are weighted and fused, and the final sentiment analysis result is determined according to the statistical score after the weighted fusion. Through the dynamic weight calibration and deep interaction arbitration mechanism, the accuracy, robustness and decision reliability of sentiment analysis are significantly improved.

The application discloses an equipment part fault diagnosis method and device based on mixed noise suppression of sensing data and equipment, and relates to the field of fault diagnosis.The diagnosis method comprises the following steps: acquiring vibration signals of equipment parts under different fault types; performing data preprocessing on the vibration signals to obtain samples, taking the fault types corresponding to the vibration signals as real labels, and constructing a training set and a verification set; using the training set and the verification set, using a transferable Adaboost algorithm and an adaptive cost-sensitive learning method to train a plurality of deep learning models, and obtaining a plurality of trained deep learning models; and using the plurality of trained deep learning models to perform fault diagnosis on the to-be-tested parts of the equipment.The application improves the accuracy of equipment part fault diagnosis in a mixed noise environment.

The invention relates to a circuit breaker health state evaluation method and system, and belongs to the field of circuit breaker state evaluation. When the health state of the circuit breaker is evaluated, the used model is jointly constructed based on a PSO-AdaBoost-RF algorithm, that is, hyper-parameters in the RF algorithm are determined by using the PSO algorithm; parameters of each decision tree are adjusted through the training set, the decision trees are used as weak classifiers of an AdaBoost algorithm in the training process, and the weights of the weak classifiers are adjusted based on the AdaBoost algorithm; after training is completed, weighted summation is carried out on the weak classifiers with the adjusted weights to obtain a strong classifier of the AdaBoost algorithm, and the strong classifier serves as a classification model. After the corresponding classification model is obtained, online deployment is carried out on the classification model, then a mechanical characteristic curve when the circuit breaker acts is collected in real time, then the state quantity obtained according to the mechanical characteristic curve is input into the classification model, and evaluation of the health state of the circuit breaker can be achieved. According to the invention, faults can be identified in a short time, and the fault identification accuracy is high.

The application provides a kind of multi-model fusion inverse calculation pumping unit well dynamic liquid level method, system, electronic equipment and storage medium, the method comprises: based on oilfield data lake and oil and gas production internet of things database, obtains dynamic liquid level inverse calculation parameter data and is stored in dynamic liquid level inverse calculation system standard library;With the data in the dynamic liquid level inverse calculation system standard library, respectively through mechanism analysis method model, AdaBoost algorithm and MLP&RNN algorithm, inverse calculation obtains dynamic liquid level depth H1, H2 and H3;Adopt multivariate linear regression method, combine the three inverse calculation results, construct dynamic liquid level fusion refined calculation model, to realize the accurate prediction of actual dynamic liquid level depth H.The application can accurately obtain the dynamic liquid level of pumping unit well, improve the accuracy and adaptability of dynamic liquid level parameter, provide accurate data basis for dynamic analysis to take measures and determine reasonable working system.

The invention discloses a network data maintenance method and system based on big data and artificial intelligence. The method comprises the following steps: constructing a network data multi-dimensional feature space, extracting time sequence features by using an optimized long and short-term memory network, dividing feature subsets according to data traffic fluctuation, and integrating key features by improving an Adaboost algorithm; abnormal data are recognized through anomaly detection analysis, repairing or reconstruction operation is executed according to priorities, and finally the data are stored and an associated index is established. The system comprises seven units including a network data multi-dimensional feature space construction unit and a long-short-term memory network feature extraction unit, and all the units are sequentially connected to cooperatively work. According to the scheme, efficient analysis, precise maintenance and ordered management of the network data are realized based on the multi-dimensional parameters of the network data by fusing an innovative algorithm, and the integrity and availability of the network data are effectively guaranteed.

The application discloses a short-term photovoltaic power prediction method suitable for multi-county geographical heterogeneity, and relates to the technical field of photovoltaic power prediction; the method comprises the following steps: a data preprocessing step, collecting multi-county meteorological and power generation data, performing time alignment, missing value filling, abnormal value elimination and feature engineering processing; a county heterogeneity quantification step, performing K-means++ clustering based on geographical features and meteorological statistical features, and quantifying the power contribution degree of each factor by adopting a SHAP value analysis method; an adaptive feature screening step, screening core features based on a factor weight vector, and generating a county exclusive feature subset; a mixed model integration step, constructing an LSTM, XGBoost and GPR basic model library, and integrating by dynamically distributing model weights by adopting an AdaBoost algorithm; and a summer and autumn scene optimization step, applying special correction to different county scenes. The application fully considers the influence of geographical heterogeneity on photovoltaic power generation, realizes accurate prediction of multi-county photovoltaic power, and improves the prediction accuracy.

The application discloses a driver road rage emotion detection method based on machine learning, aiming to realize accurate detection of driver road rage emotion by collecting the face image, sound and force state of the driver through various sensors, and improve driving safety. The method comprises a sampling module, a face recognition module, a feature point positioning module, a road rage emotion detection module and a warning module, wherein an AdaBoost algorithm, a Haar-Cascade algorithm, a constrained local neural field model and a support vector machine model are adopted, and a particle swarm optimization algorithm is combined to optimize parameters, so that the accuracy and generalization ability of road rage emotion detection are improved; the method can be applied to various vehicle models and drivers, and real-time detection and warning can be realized.

The application discloses a kind of classification method and system for PolSAR data combining deep learning model and traditional classifier, wherein the method adopts random stratified sampling method to select training sample, then trains CNN using training sample, and then divides entire PolSAR data into general pixel and key pixel, then uses AdaBoost algorithm to combine SVM classifier, wishart classifier and decision tree classifier into a strong classifier to classify key pixel again.Finally, the class of important pixel and the class of general pixel are combined as the final result.On the one hand, multiple traditional classifiers are fused, and on the other hand, the deep learning model is combined with the fused traditional classifier to classify the key pixel again, which can greatly improve the classification accuracy.

The invention particularly relates to a radar target detection method based on an Adaboost algorithm and constant false alarm detection, and the method comprises the steps: firstly carrying out the preliminary screening of a distance Doppler image through a sorting constant false alarm detection algorithm, and extracting a candidate target unit, so as to reduce the data processing scale; and then, extracting amplitude features, statistical features and other auxiliary features from the candidate target units, and inputting the features into a pre-constructed strong classifier model for accurate classification. Through a grading detection framework of coarse screening of a sorting constant false alarm detection algorithm and fine classification of a strong classifier model, the detection probability of weak and small targets can be effectively improved, the false alarm rate is reduced, the algorithm calculation amount can be controlled by adjusting model parameters, and the method is suitable for radar target detection application scenes.

The application discloses a dynamic local sensitive discriminant analysis fault diagnosis method based on Mahalanobis distance, which is used to establish an accurate process fault diagnosis model in a complex chemical field. First, the original data is expanded by using a dynamic data expansion technology, and then a local sensitive discriminant analysis method combined with Mahalanobis distance is used to extract features of fault data, so that the behavior of the system is accurately described. The data after feature extraction is used to train a weak classifier, and the AdaBoost algorithm is used to integrate the weak classifier into a strong classifier, so as to improve the accuracy of the fault diagnosis model. The method is used to establish a fault diagnosis model for a Tennessee-Eastman process fault case, and the accuracy of the model is significantly improved, so that the method has certain superiority and application prospect.

This invention discloses a ventilation hole identification and localization method based on the AdaBoost algorithm, comprising: deploying a detection robot with visual detection capabilities into the generator chamber, and acquiring video images of elliptical ventilation holes, single-row circular ventilation holes, and double-row circular ventilation holes through two cameras on its abdomen; processing the acquired images; performing Brenner gradient calculation, Tenengrad gradient calculation, Laplacian gradient calculation, and variance calculation on each frame of the acquired video images, and performing normalization processing; performing image recognition; image labeling; for images where the ventilation hole is about to enter or exit the field of view or where the image edge is only 0.3-0.7mm, the camera continuously acquires five images, and then locates the ventilation hole according to the label; establishing an SVM-AdaBoost model, importing the training set data into the model for training, testing the generated model using the test set data, and visualizing the training accuracy of the model; calculating the training error of the weak classifier to obtain the final classification judgment criterion.

The present application discloses a method for early warning brandish of a transmission wire based on an improved Bayes-Adaboost algorithm, including: forming a classifier by training a historical brandish fault training set, and by using an Adaboost ensemble learning method, and obtaining an early warning result of the brandish of the transmission wire via the classifier according to real-time forecast meteorological information and information of different parameters of the transmission wire. The present invention can realize calculation and processing of forecast information of meteorological characteristic factors of the brandish of the transmission wire, structural parameters of the transmission wire and other related data, and obtain an early warning analysis result of a brandish disaster of the transmission wire in a region.

The application relates to the technical field of underground pipeline detection, and discloses an underground pipeline target identification method and device based on a ground penetrating radar, which collects the multi-polarization scattering characteristics of underground pipelines based on a multi-polarization radar or a single-stage radar, obtains original image data of the underground pipelines, fuses the multi-polarization scattering characteristics by adopting a Laplacian pyramid algorithm to obtain fused image data, solves a plurality of polarization attributes of the fused image data, and identifies target pipelines in the underground pipelines based on a particle swarm AdaBoost algorithm and the polarization attributes. The application adopts a multi-polarization technology, combines a deep learning method to realize the identification and classification of underground pipeline targets, solves the problem that single-polarization ground penetrating radar information is not comprehensive, which leads to low target classification accuracy, improves target classification and identification speed, and provides technical support for further improving urban infrastructure.

The application discloses a kind of safety state evaluation model construction and evaluation method, device, medium and equipment, the construction method includes: based on state monitoring index system extraction multiple electric power mobile terminal in each shop mobile terminal in normal operating state monitoring index data and abnormal operating state monitoring index data;Respectively quantifying monitoring index data, obtain positive sample set and negative sample set;Based on positive sample set and negative sample set training AdaBoost algorithm corresponding classification framework obtains electric power mobile terminal safety state evaluation model.Through implementation of the application, while considering physical class, system class, data class, application class, network class and other general mobile terminal safety indexes, environmental class and historical reliability index are added, and on this basis, electric power mobile terminal safety state evaluation model is established by combining AdaBoost algorithm, and the online safety state evaluation of electric power mobile terminal is realized.

The present application relates to a kind of litchi identification and yield estimation method, equipment and medium based on image vision, method includes obtaining orchard litchi fruit tree contour, litchi fruit etc. Image data as training data, pre-processing is carried out;Litchi fruit identification is carried out to the Haar-like feature in matrix feature after pre-processing training data;The data of the image after identification are input into litchi volume prediction model, are deduced using multilayer perceptron, obtain connection weight;Using the litchi volume prediction model after training to carry out volume prediction to the data to be predicted;The yield of orchard litchi is obtained by adding the predicted volume.This application obtains orchard litchi fruit tree contour, litchi fruit etc. Image data as prediction input value, uses AdaBoost algorithm to matrix feature as weak classifier, constructs strong classifier, obtains accurate prediction volume of litchi using error back propagation algorithm, lays foundation for the relationship between weather, wind, fertilization and yield.

The invention belongs to the technical field of medical information prediction, and particularly relates to an EEG epileptic seizure prediction method based on an adversarial auto-encoder. The method comprises the following steps of: segmenting an EEG signal by using a time window sliding for 1s, and performing synchronous compression wavelet transform on each segment of EEG signal to obtain a high-resolution time-frequency graph; then, learning the characteristics of the data through a confrontation auto-encoder model, and constraining an unsupervised training process in combination with least square loss, so as to form a characteristic extractor; and finally, the feature extractor after unsupervised training is connected with a Bi-LSTM classification network combined with an AdaBoost algorithm, and an epilepsy classification (prediction) device is generated. The method shows performance superior to that of other traditional methods in the aspect of epilepsy prediction.

A kind of gas-liquid two-phase flow gas volume fraction measurement method, local gas rate signal at each position is collected by array optical fiber probe sensor, corresponding standard gas volume fraction is measured by flowmeter simultaneously, and sample data set is constructed;BP neural network is constructed, the weight and threshold of BP neural network are optimized using particle swarm optimization algorithm, and T weak predictors are obtained;T weak predictors are combined according to the following formula to obtain strong predictor, which is used to obtain the final gas volume fraction prediction value.The optical fiber probe sensor used in the application can accurately detect the gas phase and liquid phase medium in gas-liquid two-phase flow, provide basic data for accurate measurement, the weight and threshold of BP neural network are optimized using particle swarm optimization algorithm, help the network to jump out of local optimum, improve the global search ability of model, thereby improve the measurement accuracy, AdaBoost algorithm combines multiple BP neural network weak predictors optimized by PSO into a strong predictor, greatly improves the prediction accuracy and stability of model.

The invention relates to the technical field of power systems, and discloses a multi-energy-consumption carbon emission prediction method, system, device, medium and product, and the method comprises the steps: obtaining power operation sample data and carbon emission sample data of a plurality of energy consumption types of a power enterprise, and carrying out the feature extraction of the power operation sample data; using the operation sample feature data and the carbon emission sample data as mapping samples, constructing a training data set, constructing an extreme learning machine based on an AdaBoost algorithm, and training the extreme learning machine by using the training data set to obtain a trained carbon emission prediction model; and the carbon emission prediction value of the current calculation time period is predicted through the trained carbon emission prediction model, so that the accuracy and reliability of carbon emission prediction are effectively improved.

The invention provides a method for correcting a leakage channel size prediction model based on machine learning. The method comprises the following steps: acquiring drilling basic parameters related to crack width; inputting the drilling basic parameters into an obtained leakage channel size prediction model to obtain a basic prediction crack width; calculating and analyzing the correlation among the drilling basic parameters by using a Spearman correlation analysis method to obtain an analysis result; parameters with correlation coefficients larger than a preset threshold value are screened out according to the analysis result, and a data set is constructed; performing model training based on the data set and an Adaboost algorithm to obtain a crack width correction model; and correcting the basic predicted crack width through the crack width correction model to obtain a corrected crack width. According to the method, the accuracy and the reliability of leakage layer depth prediction are improved.

The invention discloses a die bonder thermal error modeling method based on a GCRA-BP-AdaBoost algorithm, and relates to the technical field of numerical control machine tool semiconductors, and the method comprises the following steps: 1, taking a die bonder as an experiment object, and obtaining a plurality of groups of temperature data and corresponding thermal error data; 2, selecting temperature sensitive points for the multiple groups of temperature data by adopting density peak clustering; step 3, establishing a GCRA-BP-AdaBoost model of the thermal error of the die bonder; and 4, obtaining a thermal error of the GCRA-BP-AdaBoost model and the prediction performance of the GCRA-BP-AdaBoost model. According to the GCRA-BP-AdaBoost prediction model, the GCRA algorithm is used for optimizing the weight and bias of the BP neural network, the weight and bias are used as weak learners of the AdaBoost algorithm, multiple groups of weak learners are constructed, the strategy is used for integrating the weak learners into a model of a strong learner, and the model can meet the requirement of thermal error compensation and has the advantages of being high in adaptability and robustness and the like.

The present invention discloses a method, system, electronic device, and medium for identifying equipment surface defects, and relates to the field of defect identification technology. The method comprises constructing an original data set; augmenting and histogram equalizing the original data set to obtain an enhanced data set; processing the enhanced data set using the SURF algorithm to obtain a feature vector set for each image; performing K-means clustering on all feature vectors in the feature vector set to obtain clustering results; obtaining a reduced-dimensional feature vector using a feature bag model based on the clustering results; reconstructing the reduced-dimensional feature vector based on the number of feature points in each image to obtain a reconstructed feature vector; and training an SVM model using the Adaboost algorithm based on the reconstructed feature vector and the defect category to obtain a typical equipment surface defect identification model, thereby identifying equipment surface defects. The present invention can improve the accuracy and efficiency of the results of identifying small defects on the equipment surface.

The application provides a method and device for automatically auditing certification materials, which can automatically analyze and judge whether the certification materials uploaded by a user are standard, greatly improves the overall auditing efficiency, and has high accuracy. The application utilizes a template configuration function to configure templates of different certification materials and a similarity threshold value; according to a similarity algorithm, the similarity between the certification materials uploaded by the user and the templates is calculated according to different templates uploaded by the user, and the picture data that does not meet the requirements can be pre-filtered. The application trains the data of the certification materials of different modules by collecting different template training data by using a tool, then trains and improves an adaboost algorithm, obtains a picture auditing algorithm, and uses the data set obtained by the manual review module as training samples again, recursively trains the auditing algorithm, and improves the algorithm accuracy.

A real-time prediction method for an engine emission is provided, including: acquiring multiple known historical test data samples for engine emission, dividing the samples into a training set and a test set to train multiple neural network (NN), calculating mean square errors (MSEs) output by each of the NNs with the different numbers of hidden layer nodes to determine a topological structure of the NNs, optimizing initial weights and initial thresholds for each of the NNs with a mind evolutionary algorithm (MEA), and establishing a real-time engine emission prediction system with an Adaboost algorithm.