34 results about "Support vector method" patented technology

The invention discloses a SVM (Support Vector Machine)-based multi-sensor target tracking data fusion algorithm. According to target information acquired by sensors, a compact combination mode is adopted, the SVM serves as a middle layer, an environmental variable and a measurement variance normalized vector serve as input of the SVM, output of the SVM serves as trust of the sensor, a known training sample is used for offline training, real-time filter information is used for online estimation, and a fusion knowledge base performs track fusion through real-time weighting according to the obtained trust of the sensor. According to the SVM-based multi-sensor target tracking data fusion algorithm and the system thereof, the SVM principle is adopted, the algorithm complexity is low, the environmental variable and the measurement variance normalized vector are introduced, the biological robustness and the fault tolerance are strong, extension is easy, and the algorithm and the system thereof are applicable to the field of multi-sensor tracking.

Disclosed is an improved technique for training a support vector machine using a distributed architecture. A training data set is divided into subsets, and the subsets are optimized in a first level of optimizations, with each optimization generating a support vector set. The support vector sets output from the first level optimizations are then combined and used as input to a second level of optimizations. This hierarchical processing continues for multiple levels, with the output of each prior level being fed into the next level of optimizations. In order to guarantee a global optimal solution, a final set of support vectors from a final level of optimization processing may be fed back into the first level of the optimization cascade so that the results may be processed along with each of the training data subsets. This feedback may continue in multiple iterations until the same final support vector set is generated during two sequential iterations through the cascade, thereby guaranteeing that the solution has converged to the global optimal solution. In various embodiments, various combinations of inputs may be used by the various optimizations. The individual optimizations may be processed in parallel.

The invention relates to an aero-engine reliability monitoring method based on mixed weibull distribution, and the method comprises the following steps of extracting state monitoring information and performance degradation information of an aero-engine which is already replaced and repaired; extracting a relation between each monitoring parameter and the performance degradation of the aero-engine by utilizing a support vector method, and realizing the monitoring of the performance degradation value of a wing engine; utilizing a degradation model to describe the accumulated degradation volume of the aero-engine on the basis of the monitoring result of the real-time degradation value of the wing aero-engine; estimating a random parameter in a linear degradation model of the aero-engine, and determining the variation of the random parameter and the upper limit and the lower limit of the accumulated performance degradation volume of the aero-engine; establishing an aero-engine reliability monitoring model based on the dual-parameter mixed weibull distribution; utilizing a maximal likelihood method to give an expression of each parameter in the aero-engine reliability monitoring module; estimating hyper-parameters of the aero-engine reliability monitoring module based on the dual-parameter mixed weibull distribution; and calculating the reliability monitoring value of the aero-engine, and realizing the real-time and precise reliability monitoring of the aero-engine.

The invention discloses a least square method support vector machine-based generalized prediction method in a lysozyme fermentation process. The prediction method comprises the following steps of establishing a non-linear prediction model, and training a least square method support vector machine by using production data with higher yield screened from tank fermentation; performing real-time linearization on the input and output non-linear prediction model, setting a reference trajectory, rolling-optimizing controller design, and intelligently embedding an LS-SVM (least square-support vector machine)-based generalized prediction control algorithm in the lysozyme fermentation process into an upper computer. According to the method, the least square method support vector machine and the generalized prediction control are combined, so the QP problem of time consumption of solving in the solving process with the model is avoided, the operation is simple, the convergence speed is speed, and the precision is high. A genetic algorithm and the rolling optimizing in the generalized prediction control are combined, so the robustness of a system is enhanced, and the lag and disturbance of the system are effectively overcome.

The invention discloses a magnetic resonance parallel imaging method of a multi-support vector model, which belongs to the field of magnetic resonance parallel imaging. The method comprises completely sampling an intermediate region in a K space, dividing into a training set and a testing set, performing accelerated sampling on the other regions to obtain samples as a prediction set, and performing normalization processing on the data in the sets; dividing the training set into a plurality of training subsets, and selecting different parameters to train each training subset by using a support vector to obtain different combined weighting function models; testing the combined weighting functions on the testing set, and selecting a plurality of optimal submodels; and predicting the prediction set by using the optimal submodels, taking an average value as a value of uncollected points, performing reverse normalization processing, and converting K space data into an image. The parallel imaging method has good generalization and small overall reconstruction error by using weighting functions fitted by the support vector.

The invention discloses an intelligent online quality detection method for an automobile transmission, belonging to the field of automobile noise and vibration control. Specific to the important and complexity of online delivery testing, synchronous acquisition of vibrating voltage signals and rotating speed pulse signals is realized on the aspect of signal acquisition. The vibrating voltage signals acquired in the delivery testing process of the automobile transmission are unstable signals, so that the phenomenon of 'frequency alias' occurs when the conventional signal processing method is adopted. Processing of unstable signals of the transmission is performed by combining an order method with a cepstrum method for extracting characteristic signals, so that the problem of 'frequency alias' is solved, sidebands of power spectra are eliminated, and characteristic signals are convenient to observe and extract. A support vector method is used on the aspect of quality detection, and a support vector model is continuously enriched and updated by using a standard extremal curve method, so that the model comprises more information, and the aim of realizing high detection speed and high accuracy is fulfilled.

To provide a method capable of reducing support vectors without decreasing the performance of an SVM. The method includes: a step of learning an SVM by using a set of training samples for initial learning which have known labels; a step of evaluating a training sample for initial learning corresponding to an outlier (value greater than 0 and equal to or less than C) based on a parameter a value obtained by learning the SVM; and a step of removing the training sample for initial learning corresponding to the outlier from a set of the original training samples for initial learning.

The invention relates to a reciprocating compressor intelligent diagnosis method based on EMD-PCA. Intelligent diagnosis of faults of a reciprocating compressor is realized by comprehensively using an empirical mode decomposition (EMD) method, a principal component analysis (PCA) method and a multi-class classification support vector method; the method is characterized in that common faults of the reciprocating compressor are subjected to feature extraction, the fault alarm and diagnosis are performed according to different sensitive features of different faults, acceleration signals of a reciprocating compressor cylinder are subjected to feature extraction by using empirical mode decomposition, and each group of acceleration signals have 30 features; then dimension reduction is performed by using principal component analysis to obtain a three-dimensional feature space; finally, five working conditions obtained by experiments are diagnosed by using multi-class classification support vector, and the intelligent diagnosis process framework of the reciprocating compressor is summarized and proposed. Through the adoption of the method, fault diagnosis accuracy under the five working conditions is high.

The invention discloses a finite-element analysis, monitoring and support method of soft-rock slope stability. The method includes the following steps: step one, obtaining rock slope failure types, slope stability influence factors and slope reinforcement support measures by induction, establishing a mechanical model of engineering geology simulation analysis under the action of the different influence factors, and analyzing a dominant factor controlling the soft-rock slope stability and a potential mode thereof; and step two, using an approximate equivalent physical model, which is formed bya plurality of mutually associated unit bodies, by a finite-element analysis method of the slope stability to replace actual structures or continua, and establishing equations, which characterize force and displacement relationships, through basic principles of structure and continuum mechanics and physical characteristics of units. The finite-element analysis, monitoring and support method of thesoft-rock slope stability uses the finite-element method to analyze the stability thereof, carries out support and treatment on places where danger may further occur, and has certain guiding significance for solving slope stability problems.

The present invention discloses an electric power system load prediction method and system. The method comprises the steps of: obtaining historical load data of an electric power system; performing preprocessing of the obtained historical load data; employing a grey theory to perform de-trending processing of the data after preprocessing, and obtaining a trend item; performing spectrum analysis todetermine the number of decomposition layers, and employing variation modal decomposition to perform decomposition of data after the de-trending processing; and employing a support vector machine optimized by an improved NSGA-II to perform classification summation reconstruction of the decomposed data; obtaining a final prediction result according to a reconstructed result and the trend item; andperforming superposition of the prediction value of each load component to determine an actual prediction result. The electric power system load prediction method and system can effective respond todefects that the load prediction fluctuation is large, the accuracy is not high and it is easy to be caught in local optimum based on the grey theory-variation modal decomposition and the support vector machine method optimized by the NSGA-II.

The invention discloses a degradation data missing interpolation method based on a support vector machine and an RBF neural network. The method includes the following steps that firstly, a degeneration data trend model is established by means of the support vector machine; secondly, residual error sequences of observed degradation data are calculated; thirdly, the RBF neural network is set up, and the network is trained by means of the residual error sequences of the observed degradation data; fourthly, residual error sequences of missing data are estimated through the trained RBF neural network; fifthly, trend terms of the missing data and estimation results of the residual error sequences are merged, so that a degradation data interpolation result is obtained. A support vector machine method and an RBF neural network method are combined to obtain the degradation data missing interpolation method, and the problem of interpolation of performance missing degradation data in an accelerated degradation test is solved.

The invention discloses a chromatic aberration histogram and DAG-SVMs-based photovoltaic battery piece color classification algorithm. According to the invention, after an acquired original image is preprocessed, a target image is obtained; the target image is subjected to color space conversion, and then the color information of the image of a photovoltaic battery piece is accurately extracted; meanwhile, the calculation amount is simplified and the image dimension quantization is carried out; the feature vector of the image is obtained through the calculation of a color difference histogram,so that an image feature information library is built. In this way, the training and the learning are carried out, and the DAG-SVMs classification is carried out by means of a support vector machineclassifier based on six types of sample classification. As a result, the color classification of photovoltaic battery pieces is realized. The algorithm improves the traditional support vector method which can only solve the dichotomy problem in the prior art, wherein the provided algorithm can solve the DAG-SVMs algorithm of the k classification problem. Therefore, the classification of six typesof colors of photovoltaic battery pieces can be realized. The algorithm is strong in application performance and high in classification accuracy.

The invention provides an MSER (Maximally Stable Extremal Region) and genetic optimization SVM (Support Vector Machine)-based TSR (Traffic Sign Recognition) method, and belongs to the technical fieldof image processing. By adopting a method for carrying out edge detection and image segmentation on a to-be-recognized region through a feature vector of a blocking HOG (Histogram of Oriented Gradients), influence brought by translation and rotation can be inhibited to a certain degree, and the interference to an image due to variation of illumination intensity can be reduced; meanwhile, comparedwith a traditional HOG, the blocking HOG has the advantages that the dimensionality is greatly reduced, and the computing efficiency is increased; during a classification and recognition phase, an optimal SVM classifier parameter is obtained by computation by applying an adaptive crossover and mutation-based improved genetic optimization optimal parameter searching algorithm, fallibility of manualmarking and large-amount time consuming of machine training can be avoided, the requirements on accuracy and instantaneity are well balanced by combining the advantages of all methods, and automaticdetection and recognition of traffic signs can be realized; according to the MSER and genetic optimization SVM-based TSR method provided by the invention, testing images in a Germany traffic sign detection standard database are recognized, and a better effect is obtained.

The invention relates to an energy demand condition density prediction method. The method comprises the following steps of establishing a support vector quantile regression module; establishing a support vector weighing quantile regression module for energy demand; estimating the parameters of the models; predicting the condition density, and the like. The method has the beneficial effects that by combining the advantages of non-linear processing capability of a support vector machine and complete description capability of quantile regression on the condition distribution feature, the support vector quantile regression module for predicting the energy demand is established; on one hand, the non-linear structure of an energy system in a low-dimension space is mapped into a high-dimension space by the support vector machine, and is converted into a linear structure, so the complexity of modeling is reduced; on the other hand, the change rule of the whole condition distribution of energy demand is depicted by the quantile regression, and more available information is provided; a non-parameter kernel density estimation technology is adopted to establish the energy demand condition density prediction method, and the complete prediction of whole condition distribution feature of energy demand is realized.

The invention relates to a data stream prediction method and a data stream device. The data stream prediction method comprises the steps of: updating an integration model index according to sample data, wherein the integration model index is used for storing the mapping relationship between a keyword and a support vector set, the support vector in the support vector set is one of an SVM (support vector machine) classifier in the integration model; performing word segmentation to an enter text to obtain the key word of the enter text, wherein the enter text is the data stream for predicting; according to the updated integration model index, searching the support vector set containing the key word and the information of the SVM classifier in which the support vector in the support vector set is arranged; and predicting the enter text by all support vectors in the searched support vector set. The data stream prediction method and the data stream device provided by the invention perform sublinear on-line prediction based on the integration model index; and because of aggregating the support vectors according to the key word by an inverted list, the prediction is obviously accelerated.

The present invention relates to a modeling method for a support vector machine based on data compression. The modeling method has the technical characteristics that the method comprises the following steps: sampling modeling data through an equidistant sampling method; compressing the modeling data; calculating the boundary of each cluster of data under leaf nodes of a clustering feature tree, and choosing a boundary point most possibly becoming a support vector as the modeling data of the support vector machine; and establishing a model of the support vector machine: establishing a model of the support vector machine according to the modeling data through a support vector machine method. In the modeling method of the present invention, the modeling sample quantity of the support vector machine is greatly reduced under the condition of ensuring the accuracy rate of the algorithm to the greatest extent through a pre-sampling strategy, a data compression technology, an increment sampling strategy and the like, so as to greatly improve the modeling speed of the support vector machine and lower the memory consumption, so that the support vector machine technology can be applied to a big data analysis scene, thereby remedying the defect that a neural network method, a Bayes method and the like in the big data analysis have low prediction accuracy.

The invention provides an island detection method based on binary tree complex wavelet transformation, and belongs to the field of electric power systems. The island detection method comprises the following steps of acquiring control variables of a three-phase voltage type inverter, and establishing a PQ decoupling control mathematical model of a grid-connected inverter based on the acquired control variables; acquiring electrical change characteristics of a public connection point of a microgrid island under a power matching condition according to the established PQ decoupling control mathematical model; extracting voltage and current harmonic signals at the common connection point by means of a wave trap, decomposing the voltage and current harmonic signals based on binary tree complex wavelet transformation to construct a characteristic vector corresponding to the micro-grid; and improving a support vector machine on the basis of a support vector selection and genetic algorithm, andadopting the improved support vector machine to identify the island state of the microgrid. By virtue of improvement of the support vector selection and genetic algorithm, damage of the active detection method to the electric energy quality can be eliminated, and the problem of detection blind areas existing in the traditional passive detection method also can be solved.

The invention discloses an eye movement signal recognition method based on an EEG signal. The method includes the following steps of step (1) obtaining an EEG signal when eyes move and performing datapreprocessing on the EEG signal; step (2) for the processing result of the step (1), by use of a modified SK algorithm, determining irrelevant optimal vectors, selecting a small number of support vectors, and introducing a kernel technique into the algorithm for mapping the vectors to a high-dimensional space so as to achieve the purpose of enabling classification; and step (3) by use of an MDM algorithm, solving the optimal hyperplane for the support vectors obtained in the step (2). The method is very reliably by use of the EEG signal for recognition and classification of an eye movement way; furthermore, the SK algorithm and the MDM algorithm (SVM) used in the method minimize the structural risk through maximum marginalization in the case of fixed-experience risk, and can make a classifier have satisfactory learning accuracy and stronger promotion ability.

Disclosed is an improved technique for training a support vector machine using a distributed architecture. A training data set is divided into subsets, and the subsets are optimized in a first level of optimizations, with each optimization generating a support vector set. The support vector sets output from the first level optimizations are then combined and used as input to a second level of optimizations. This hierarchical processing continues for multiple levels, with the output of each prior level being fed into the next level of optimizations. In order to guarantee a global optimal solution, a final set of support vectors from a final level of optimization processing may be fed back into the first level of the optimization cascade so that the results may be processed along with each of the training data subsets. This feedback may continue in multiple iterations until the same final support vector set is generated during two sequential iterations through the cascade, thereby guaranteeing that the solution has converged to the global optimal solution. In various embodiments, various combinations of inputs may be used by the various optimizations. The individual optimizations may be processed in parallel.

The invention discloses a text classification method based on sample scaling. The method comprises the following steps: calculating the distance from a data sample to a classification hyperplane, finding a sample far away from the classification surface of the support vector machine, deleting the sample, endowing corresponding weights to the remaining samples according to the distance, and training the support vector machine by using the weighted data samples. According to the classification method provided by the invention, the sample data is firstly reduced, and then the data is correspondingly weighted so as to be used for text classification in a support vector machine. The influence of noise data on support vector machine classification can be reduced, the noise immunity of the modelis improved, the number of support vectors is reduced, and better text classification accuracy is obtained.

The invention relates to a point cloud compression encoder key parameter optimization method based on a support vector machine, and the method comprises the steps: carrying out the preprocessing of the geometric information and color information of a point cloud; extracting a feature vector of the point cloud; for a given target code rate, finding an optimal parameter pair which minimizes distortion by using a full search method; extracting an optimal tag under a given target code rate from all point clouds in the training set; and writing the optimal label information and the feature vector information into a training set, performing training by using a support vector machine and the training set information to obtain a model, testing the feature vector information in the test set by using the model, and predicting an optimal test label on a continuous domain to obtain an optimal parameter pair of the test set. According to the method, the distribution characteristics of the point cloud are utilized, a support vector machine method is used for training to obtain the optimal coding parameter pair of the test point cloud, and the time cost is greatly reduced while the coding performance of the encoder under the condition of a given coding bit rate is ensured.

The invention discloses a distributed support vector clustering method which comprises the following steps: processing the input data set according to a preset processing rule, and initializing global parameters and tasks; distributing a preset data set or a specific calculation result to each computational node; initializing a weight vector of the preset data set when the preset data set is distributed to the computational node; performing iterative operation according to a preset formula, and calculating a weight coefficient value of each sample in the preset data set; finding a sample of which the weight coefficient value is higher than a preset minimal value to serve as a support vector, and numbering the support vector; and building a support function by utilizing the support vector and the weight coefficient of each support vector, performing cluster division to obtain a cluster label of the support vector, and calibrating a non-support vector sample as a clustering analysis result. According to the method, the support vector clustering efficiency can be effectively improved.

The invention relates to a hearth outlet NOx prediction system based on numerical simulation. The hearth outlet NOx prediction system comprises a prediction model building part and a real-time prediction part. The invention also relates to a prediction method which comprises the following steps: processing DCS data, obtaining boundary conditions required by numerical simulation calculation, establishing a three-dimensional geometric model of a hearth, calculating NOx concentration data of a hearth entrance under different working conditions, and determining a database; according to the database, establishing a hearth outlet NOx prediction model by adopting a support vector method; and finally, predicting the NOx concentration at the outlet of the hearth in real time according to the real-time inlet parameters of the hearth under the actual working condition. The method can predict the change of NOx at the outlet of the hearth and the distribution of NOx in the furnace when the combustion condition changes, namely the operation parameters of air / powder and the like are adjusted, can guide SCR ammonia injection in advance, and has important significance for improving the denitration efficiency of a coal-fired power plant, the operation safety of an air pre-heater and the economical efficiency.

The invention relates to the technical field of aviation generator testing, in particular provides a generator life prediction algorithm based on support vector base. According to the algorithm, structural risk minimization principle is adopted to replace the empirical risk minimization principle for generator life prediction, the problem of sample learning under limited data can be solved better,the idea of kernel function is adopted, the problem of nonlinear space is transformed into that of high dimensional feature space, the original problem can be solved by linear classification method in high dimensional feature space, the algorithm complexity is reduced, the maximal spacing principle is adopted, so that the effect of processing of classification samples is better, the effect of life prediction is good, and it is achieved that the maintenance of the aviation generator changes from the ex post maintenance or regular maintenance to the on-condition maintenance, so that the maintenance cost is lowed.

The present invention provides a voltage sag waveform point detection method based on the space vector method, which specifically includes the following steps: Step 1. Obtain the three-phase voltage instantaneous value waveform of the voltage sag to be detected. Step 2. Extract the basic features of known waveforms. Step 3. Construct a space vector through the three-phase voltage vector, and calculate the rate of change of the radius of the space vector. Step 4. Through the rate of change of the radius of the space vector, find the position of the sampling point where the waveform point is located. Step 5. Calculate the waveform point by combining the angle between the space vector x(t) and the real axis and the position of the sampling point obtained. The invention synthesizes space vectors by three-phase voltages, detects waveform points according to different characteristics of the space vectors when no sag occurs and when sag occurs, and solves errors in existing algorithms in principle.

The invention discloses a magnetic resonance parallel imaging method of a multi-support vector model, which belongs to the field of magnetic resonance parallel imaging. The method comprises completely sampling an intermediate region in a K space, dividing into a training set and a testing set, performing accelerated sampling on the other regions to obtain samples as a prediction set, and performing normalization processing on the data in the sets; dividing the training set into a plurality of training subsets, and selecting different parameters to train each training subset by using a support vector to obtain different combined weighting function models; testing the combined weighting functions on the testing set, and selecting a plurality of optimal submodels; and predicting the prediction set by using the optimal submodels, taking an average value as a value of uncollected points, performing reverse normalization processing, and converting K space data into an image. The parallel imaging method has good generalization and small overall reconstruction error by using weighting functions fitted by the support vector.

The invention discloses an SVM-based ultra-supercritical boiler heating surface pollution monitoring method, and belongs to the field of monitoring systems. Traditional calculation results based on heat balance and heat transfer principles often have large access to actual working conditions. An SVM-based ultra-supercritical boiler heating surface pollution monitoring method applies a support vector method to a regression prediction process, minimizes a convex function, and obtains a sparse solution: defining a loss function which can ignore a real value error, i.e., an epsilon insensitive loss function; an epsilon-SVM regression machine is established on the basis of the epsilon insensitive loss function; based on the epsilon-SVM regression machine, an upsilon-SVM regression machine is developed. The mean square error of the detection result is very small, and the curve can well track the actual process.

The invention provides a method for detecting waveform points of the voltage sag based on a space vector method. The method specifically comprises the following steps of: S1, obtaining an instantaneous value waveform of a three-phase voltage of a voltage sag to be detected; S2, extracting basic features of known waveforms; S3, constructing a space vector through the three-phase voltage vector, andcalculating a radius change rate of the space vector; S4, finding the position where the waveform point is located of a sampling point, through the change rate of the space vector radius; and S5, calculating waveform points based on the position of the sampling point, which is obtained by combining the angle of the space vector x (t) with the real axis. According to the method for detecting waveform points of voltage sag based on space vector method, the method combines the space vector by the three-phase voltage, detects waveform points according to the different characteristics of the spacevector when the sag does not occurs and the sag occurs, and solves the errors existing in the principle of the existing algorithm.

The invention provides a single-bit spatial spectrum estimation method based on a support vector machine and relates to the field of support vectors in spatial spectrum estimation field and artificial intelligence in array signal processing. Problems of complicated calculation and quite low precision in conditions of single-bit extreme quantization and super-large scale antenna arrays are solved. The single-bit spatial spectrum estimation in a large-scale antenna array is modeled into a classification problem in artificial intelligence and the spatial spectrum of wave signals is solved by use of the support vector machine method. Compared with the traditional algorithm, the provided estimation method is advantageous in that estimation precision of the spatial spectrum is improved; the structure of a receiving machine is simplified; and angles of multiple signal resources can be simultaneously estimated. The estimation method is used for estimation of the spatial spectrums.

The invention discloses a degradation data missing interpolation method based on a support vector machine and an RBF neural network. The method includes the following steps that firstly, a degeneration data trend model is established by means of the support vector machine; secondly, residual error sequences of observed degradation data are calculated; thirdly, the RBF neural network is set up, and the network is trained by means of the residual error sequences of the observed degradation data; fourthly, residual error sequences of missing data are estimated through the trained RBF neural network; fifthly, trend terms of the missing data and estimation results of the residual error sequences are merged, so that a degradation data interpolation result is obtained. A support vector machine method and an RBF neural network method are combined to obtain the degradation data missing interpolation method, and the problem of interpolation of performance missing degradation data in an accelerated degradation test is solved.