2122 results about "Support vector machines svms" patented technology

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.

The invention provides a method for three-dimensional reconstruction of laser speckle structured light and depth information. The three-dimensional reconstruction technology is an important subject for machine vision research and refers to the content that a three-dimensional space geometrical shape of a three-dimensional body is restored through images of the three-dimensional body. Generally, three-dimensional reconstruction is conducted through the binocular parallax principle of a binocular camera or through a triangulation method or space codes are obtained through the structured light and the depth information is obtained through the triangulation method. The method aims at obtaining the depth information through the laser speckle structured light, a similar invention such as the kinect of the Microsoft Corporation also obtains the depth information (namely different depths are matched through a cross-correlation function of laser speckles) of an object through the method, and the difference is an algorithm for obtaining the depth information through the speckles. According to the method, parallel code number sorting is conducted on each pixel block one by one by a thinning window through multiple support vector machines, so that the depth of each pixel window is obtained, coordinates under a world coordinate system of the object are obtained by inversely solving a camera model through the depth information, and therefore the depth information with the higher accuracy can be obtained.

The invention discloses a rolling bearing fault diagnosis method based on composite multi-scale permutation entropies, and belongs to the technical field of fault diagnosis. The method comprises the following steps: measuring a vibration signal of a faulty object; extracting composite multi-scale permutation entropies from the vibration signal; reducing the dimension of the composite multi-scale permutation entropies with use of a Laplacian score; taking the first multiple composite multi-scale permutation entropies with low scores after dimension reduction as fault feature vectors and dividing the fault feature vectors into multiple training samples and multiple test samples; inputting the multiple training samples into a multi-fault classifier established based on a support vector machine to perform learning so as to classify the test samples; and identifying the working mode and the fault type of the faulty object according to the classifying result. According to the fault diagnosis method disclosed by the invention, feature extraction is highly innovative, and the degree of identification is high in the process of fault mode identification.

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

The invention discloses an indoor passive positioning method based on channel state information and a support vector machine. The method comprises the following steps: firstly preprocessing the acquired channel state information data, performing de-noising and smoothness through the adoption of a density-based spatial clustering of applications with noise and a weight-based moving average algorithm, and then using the principal component analysis algorithm to extract the features. The data after the preprocessing and feature-extracting can reflect the signal change more accurately and the dimension is greatly reduced. The passive positioning adopts two-stage positioning. In the training stage, the large positioning space is divided into sub-regions, the support vector machine classification and regression model is established for each sub-region so as to acquire a statistic model for accurately representing the nonlinear relationship between the position and the signal. The two-stage positioning firstly determines the sub-regions through the classification of the support vector machine, and the precision position is determined in the sub-region through the regression of the support vector machine. The method disclosed by the invention has the beneficial effects that the passive positioning can be performed in the absence of the active participation of the target, and the indoor positioning precision is improved to sub-meter level.

System and method for training a support vector machine (SVM) with process constraints. A model (primal or dual formulation) implemented with an SVM and representing a plant or process with one or more known attributes is provided. One or more process constraints that correspond to the one or more known attributes are specified, and the model trained subject to the one or more process constraints. The model includes one or more inputs and one or more outputs, as well as one or more gains, each a respective partial derivative of an output with respect to a respective input. The process constraints may include any of: one or more gain constraints, each corresponding to a respective gain; one or more Nth order gain constraints; one or more input constraints; and/or one or more output constraints. The trained model may then be used to control or manage the plant or process.

The invention relates to a vehicle type identification method based on a support vector machine and used for an earth inductor. The vehicle type identification method includes the following steps: vehicle type waveform data which require to be identified are collected by the earth inductor; a plurality of numeralization features are extracted from waveforms, effective data are screened out, and the features are normalized; multilayer feature selection is performed according to the extracted features, and an optimal feature combination is picked out; a vehicle type classification algorithm based on the clustering support vector machine is built, and parameters in a classification function are optimized by adopting a particle swarm optimization algorithm; a binary tree classification mode is built, classifiers on all classification nodes are trained, and a complete classification decision tree is built; and earth induction waveforms of a vehicle type to be identified are input to obtain identification results of the vehicle type. The vehicle type identification method builds a waveform feature extraction and selection mode, simultaneously adopts the classification algorithm based on the support vector machine and the particle swarm optimization algorithm, greatly improves machine learning efficiency, and enables a machine to identify vehicle types rapidly and accurately.

The invention relates to a workpiece recognition method based on a geometric shape feature and a device thereof. The method comprises the steps of (1) taking a two-dimensional image of a workpiece on a conveyor belt, carrying out difference operation on the two-dimensional image of the workpiece on the conveyor belt and a conveyor belt image, and obtaining a foreground area comprising the workpiece and the shadow of the workpiece, (2) using a shadow detection method to remove the shadow in the foreground area comprising the workpiece and the shadow of the workpiece to obtain the accurate area contour of the workpiece, (3) extracting the geometric feature for the accurate area contour of the workpiece so as to obtain the feature vector of the workpiece area contour, wherein the feature vector comprises a Hu moment and a Fourier operator, and (4) using a support vector machine SVM to train and classify the feature vector of the workpiece area contour. Through employing the above steps, the method and the device can be widely applied to the fields of workpiece grasp and transportation in a factory production line, circumferential welding manipulator, industrial painting and equipment assembly.

The invention relates to a method for distinguishing working conditions and managing and controlling the energy of a plug-in hybrid electric vehicle. The method disclosed by the invention mainly comprises two parts of a method for distinguishing the working condition and a method for managing and controlling the energy. For the working condition distinguishing part, a support vector machine (SVM) model is adopted for training and studying the characteristic parameters of the working conditions so as to distinguish real-time working conditions; for the energy managing and controlling part, a fuzzy rule is formulated. Through the method for distinguishing the working conditions and managing and controlling the energy, and on the premise of ensuring power performance, the fuel economy of the vehicle can be markedly improved, and the energy conservation and emission reduction can be realized.

The invention provides a countercheck method for automatically identifying a speaker aiming to voice deception, which is a voice anti-spoofing technology based on a method combining various features and a plurality of sub-systems. According to the invention, the serial features of the posterior probability of a phoneme in the phonological level and the MFCC features of voice level or MFDCC features of phase level are combined, thus the performance of the system is significantly enhanced. By combining the provided i-vector sub-system and OpenSMILE (open Speech and Music Interpretation by Large Space Extraction criterion containing voice and rhythmic information, the final presentation of the system is further enhanced. To a back-end model, the development datum are used; and under the situation of knowing deceptive attacks, a two-level support vector machine has better performance compared with one-level cosine similarity or PLDA evaluations, while the one-level evaluation approach has better robustness under the situation without seeing the test datum and knowing the deceptive conditions.

The invention discloses a thunderstorm strong wind grade prediction classification method based on a multi-source convolutional neural network. According to the method, a multi-source convolutional neural network model is adopted to carry out feature extraction on various data images obtained by the Doppler meteorological mine, more meteorological data information can be fused, and the extractionof difference features is improved. Meanwhile, the method is combined with a classification method in a support vector machine, and a model obtained on a meteorological data training set of small andmedium samples has a very good thunderstorm strong wind level prediction classification effect.

A software defect priority prediction method based on an improved support vector machine is mainly characterized in that an improved support vector machine model is used for modeling defect priority prediction and judging and predicting defect report processing priority. The software defect priority prediction method includes the steps: firstly, selecting solved, closed and determined error report as training data; secondly, extracting needed characteristics; thirdly, giving a sampling weight to each sample and training a classifier to classify the samples by the aid of the support vector machine on the samples; fourthly, redistributing weight vectors by the aid of obtained error rate in the manner of distributing larger weights to mistakenly classified samples and distributing smaller weights to correctly classified samples; and fifthly, sequentially iterating in the manner to finally obtain a strong classifier equal to the weighted sum of a plurality of weak classifiers. The classifiers are trained by means of machine learning, so that defect priority is automatically determined, and consumption of staff and cost is reduced.

The invention relates to a wind electric power prediction method and a device thereof. The method comprises the following steps of: step one: extracting data from SCADA (Supervisory Control and Data Acquisition) relative to a numerical weather prediciton system or a power system, and carrying out smoothing processing on the extracted data; step two: determining input and output of training samples of a least squares support vector machine according to the processed data; step three: initializing relevant parameters of a smallest squares support vector machine and an improved self-adaptive particle swarm algorithm; step four: optimizing model parameters according to an optimization process; step five: acquiring a model of the smallest squares support vector machine according to the optimized parameters; and step six: carrying out prediction according to the model of the smallest squares support vector machine. According to the wind electric power prediction method disclosed by the invention, a modelling process is simple and practical, wind electric power can be rapidly and effectively predicted, and the wind electric power prediction method has an important significance on safety and stability, and scheduling and running of the electric power system, and therefore, the wind electric power prediction method has wide popularization and application values.

The invention discloses a wind power prediction method based on continuous time slice clustering and support vector machine (SVM) modeling. The method comprises the following steps of: (1) performing annual similar day unsupervised clustering according to the wind characteristic; (2) partitioning an entire year into n continuous time slices according to a similar day clustering result obtained in the step (1), and clustering and classifying every time slice according to the frequency of each type of date within every time slice and the wind characteristic in the continuous time slices; and (3) modeling the time slices of the same type in the step (2) by using an SVM for predicting the same time of future years. An annual continuous time slice clustering method is adopted on the basis of day similarity, so that day similarity and time continuity are considered simultaneously, and the similarity of a training sample in a prediction model and the accuracy of wind power prediction are increased greatly. Compared with the conventional method, the wind power prediction method has the advantages that: the relative power prediction error is decreased by 7.2 percent, and the prediction accuracy of the wind power is up to 83.96 percent.

The invention relates to a Fourier descriptor and gait energy image fusion feature-based gait identification method. The method comprises the steps of performing graying preprocessing on a single frame of image, updating a background in real time by using a Gaussian mixture model, and obtaining a foreground through a background subtraction method; performing binarization and morphological processing on each frame, obtaining a minimum enclosing rectangle of a moving human body, performing normalization to a same height, and obtaining a gait cycle and key 5 frames according to cyclic variation of a height-width ratio of the minimum enclosing rectangle; extracting low-frequency parts of Fourier descriptors of the key 5 frames to serve as features I; centralizing all frames in the cycle to obtain a gait energy image, and performing dimension reduction through principal component analysis to serve as features II; and fusing the features I and II and performing identification by adopting a support vector machine. According to the method, the judgment whether a current human behavior is abnormal or not can be realized; the background is accurately modeled by using the Gaussian mixture model, and relatively good real-time property is achieved; and the used fused feature has strong representability and robustness, so that the abnormal gait identification rate can be effectively increased.

The invention relates to a tide predicting method for the tide is influenced by various factors, including cyclical factors, such as tidal generation force, and non-cyclical factors, such as wind power, atmospheric pressure, coast characteristics, rainfall, dip angles of the lunar orbit and the like. The predicting accuracy of the traditional harmonic analysis method is influenced by partial tide number, and the traditional harmonic analysis method cannot analyze the influence of non-cyclical factors; the artificial neural network method developed recent years overcomes the defect that the non-cyclical factors cannot be predicted by the harmonic analysis method to a certain extent, but has great data volume required by study training samples and wide involve range, can cover various possible conditions, but has less station historical data of non-cyclical factors. The invention provides a predict model, wherein factors which influence tide non-cyclically, such as wind directions, rainfall, storm surge, coast characteristics and the like, can be fused into the model, and small sample data can receive more accurate results. In the method, a support vector machine (SVM)-based predict model is established, wherein, an SVM toolbox is imported into MATLAB 7.8; training sample data is trained by utilizing svmtrain function; the formed model is tested by using a test sample svmpredict function; and the trained and tested data can predict the tide in the same tide test station.

The invention provides a combined modulation recognition method based on clustering and a support vector machine in order to overcome the shortcoming of low modulation recognition rate of a clustering algorithm with a low signal to noise ratio. According to the method, a characteristic parameter of a modulation signal is extracted by using the clustering algorithm according to a phase shift keying/quadrature amplitude modulation (PSK/QAM) mode based on a constellation diagram; and a modulation mode for a signal is recognized through the support vector machine, so that the modulation recognition rate of a system is increased. The method comprises the following steps of: aiming at the PSK/QAM mode based on the constellation diagram, reconstructing the constellation diagram of a receiving signal by using the clustering algorithm; and obtaining an effective function value, which can reflect an outstanding difference of modulation types under different clustering central numbers, as the characteristic parameter input into the support vector machine by constructing an effectiveness evaluation function. In order to overcome the shortcoming that two common algorithms of one to multiple and one to one have high calculation complexity when the support vector machine recognizes multiple types, the support vector machine is trained by adopting a hierarchical algorithm.