2180 results about "Support vector machines svms" patented technology

Methods for training machines to categorize data, and / or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples. Also provided are tools, systems, and devices, such as support vector machines (SVMs) and other methods and features, software implementing the methods and features, and computers or other processing devices incorporating and / or running the software, where the methods and features, software, and processors utilize specialized methods to analyze data.

Systems and methods for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. Optimal solutions based on distinct input data sets may be combined to form a new input data set to be input into one or more additional support vector machine. The methods, systems and devices of the present invention comprise use of Support Vector Machines for the identification of patterns that are important for medical diagnosis, prognosis and treatment. Such patterns may be found in many different datasets. The present invention also comprises methods and compositions for the treatment and diagnosis of medical conditions.

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.

Kernels (206) for use in learning machines, such as support vector machines, and methods are provided for selection and construction of such kernels are controlled by the nature of the data to be analyzed (203). In particular, data which may possess characteristics such as structure, for example DNA sequences, documents; graphs, signals, such as ECG signals and microarray expression profiles; spectra; images; spatio-temporal data; and relational data, and which may possess invariances or noise components that can interfere with the ability to accurately extract the desired information. Where structured datasets are analyzed, locational kernels are defined to provide measures of similarity among data points (210). The locational kernels are then combined to generate the decision function, or kernel. Where invariance transformations or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points (222). A covariance matrix is formed using the tangent vectors, then used in generation of the kernel.

The invention discloses a vivo-face detection method based on HSV (hue, saturation, value) color space statistical characteristics. The method includes firstly, converting a face image captured by a camera from RGB (red green blue) color space to YCrCb color space; secondly, subjecting the face image to skin color segmentation, denoising, mathematical morphological treatment and connected region boundary calibration so as to obtain coordinates of a face matrix region; thirdly, acquiring a facial image to be detected from the face image according to the coordinates of the face matrix region; fourthly, segmenting the facial image to be detected into image blocks, and acquiring characteristic values of three color components of each image block in the facial image to be detected; and fifthly, using normalized characteristic values as a sample to be detected, and sending the sample to a trained support vector machine for detecting whether the face image is a vivo real facial image or not. The vivo-face detection method based on HSV color space statistical characteristics has the advantages that delay of a face recognition system is reduced, computational complexity is lowered, and detection accuracy is improved.

Subsurface reservoir properties are predicted despite limited availability of well log and multiple seismic attribute data. The prediction is achieved by computer modeling with least square regression based on a support vector machine methodology. The computer modeling includes supervised computerized data training, cross-validation and kernel selection and parameter optimization of the support vector machine. An attributes selection technique based on cross-correlation is adopted to select most appropriate attributes used for the computerized training and prediction in the support vector machine

A system for performing face registration and authentication using face information, and a method thereof. A set of readily distinguishable features for each user is selected at a registration step and only the set of features selected at the registration step is used at a face authentication step, whereby memory use according to unnecessary information and amount of data calculation for face authentication can be reduced. Thus, the present system has an advantage in that identity authentication through face authentication can be performed even under restricted environments of a USB token or smart card with limited resources. The present system further has advantages in that authentication performance is improved, as readily distinguishable feature information is used, and the time for face authentication is reduced, as face authentication is performed using the SVM built by using the optimal set of readily distinguishable features at a training step.

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 hyperspectral remote sensing image SVM classification method by combining spectrum and texture features and a hyperspectral remote sensing image SVM classification system thereof. The method comprises the following steps that S1, original hyperspectral images to be classified and a ground survey data sample set are inputted; S2, the image elements of the corresponding coordinate positions in the original hyperspectral images are extracted so as to form a reference data sample set; S3, a training sample set is randomly selected for each ground feature class; S4, principal component transformation is performed, and first principal component images are extracted; S5, a region segmentation image is acquired; S6, filtering images are acquired; S7, statistics of spectrum feature information and texture feature information of each segmentation region are performed; S8, a support vector machine model is solved; S9, the original hyperspectral images are classified so that the classified hyperspectral images are obtained; and S10, the classified images are outputted. The new strategy for combining the spectrum and texture features is provided so that the hyperspectral image classification precision can be effectively enhanced.

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.

The invention belongs to the technical field of computer-aided diagnosis, and specifically relates to a brain glioma molecular marker nondestructive prediction method and a prediction system based on radiomics. The method comprises the following steps: adopting a three-dimensional magnetic resonance image automatic segmentation method based on a convolution neural network; registering a tumor obtained from segmentation to a standard brain atlas, and acquiring 116 position features of tumor distribution; getting 21 gray features, 15 shape features and 39 texture features through calculation; carrying out three-dimensional wavelet decomposition on the gray features and the texture features to get 480 wavelet features of eight sub-bands; acquiring 671 high-throughput features from the three-dimensional T2-Flair magnetic resonance image of each case; using a feature screening strategy combining p-value screening and a genetic algorithm to get 110 features highly associated with IDH1; and using a support vector machine and an AdaBoost classifier to get a classification of which the IDH1 prediction accuracy is 80%. As a novel method of radiomics, the method provides a nondestructive prediction scheme of important molecular markers for clinical diagnosis of gliomas.

The invention discloses target detection system and method based on covariance and a binary-tree support vector machine. The system comprises a video data collecting unit, an image preprocessing unit and a background modeling vehicle partitioning and displaying unit. The video data collecting unit is used for acquiring information in real time and carrying out digitization and system conversion on analog videos. The image preprocessing unit comprises an FPGA (Field Programmable Gate Array) and a DSP (Digital Signal Processor), wherein the FPGA is used as a coprocessor; and the DSP is used as a main processor to accomplish the background modeling of video images, the partitioning and the extraction of vehicle targets and the realization of a model identification algorithm. Utilizing the combination of the FPGA and the DSP, the invention can realize multi-video real-time model identification through combining with the model identification algorithm based on the covariance features of the images and the support vector machine. The invention can be widely used for a plurality of fields of intelligent traffic management, intelligent video monitoring and the like.

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.

The invention provides a multiple-target operation optimizing and coordinating control method and a device of a garbage power generator. The multiple-target operation optimizing and coordinating control method includes the following steps. Operational parameters are downloaded from a data communication system (DCS), data judged as reasonable based on a threshold value are transmitted to a database. In terms of environmental protection, economy and safety of the power generator, three models are respectively set up by means of a support vector machine and a fuzzy neural network. A modified strength PARETO genetic algorithm is used for comprehensively optimizing multiple targets and then optimum operation parameters under the present working condition are worked out. Operational staff can adjust operation of corresponding parts based on the optimum operation parameters. The device comprises a data collecting module, a data filtering module, a database module, a data modeling module, an optimizing module, a forecasting module, a remote monitoring module, a monitor, an alarming module and a manual alarming module. The multiple-target operation optimizing and coordinating control method and the device of the garbage power generator achieve multiple functions of real-time forecasting, offline simulation, dynamic optimizing and the like and have the advantages of being strong in adaptability, good in self-learning ability, high in fitting precision, obvious in optimizing effect and the like.

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 belongs to the field of image processing, and discloses a video monitoring method of an electric transmission line. The video monitoring method of the electric transmission line comprises the steps of greasy weather grade recognition, ice covering / snow covering recognition, conductor galloping recognition and periphery alarm. In the step of greasy weather grade recognition, vector included angle characteristics are extracted, and a support vector machine is selected and used for classification. In the step of ice covering / snow covering recognition, the ice covering / snow covering thickness is measured through image processing technologies such as image segmentation, edge detection and improved Hough conversion. In the step of conductor galloping recognition, multiple wave trough points and wave crest points in a conductor are detected, the lowest point and the highest point of conductor galloping are obtained by comparing all the wave trough points and all the wave crest points, and then amplitude and frequency of conductor galloping are computed. In the step of periphery alarm, a method of updating backgrounds automatically is adopted, influence of noise is greatly reduced in the process of judging foreign matter, and whether the foreign matter enters a monitoring area or not is judged in a percentage mode. The video monitoring method achieves video monitoring on the electric transmission line under the atrocious weather environment.

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 discloses an RMB sequence number identification method which comprises the following steps of: S1, performing pretreatment on a paper money image, including improvement of serious exposure, extraction of paper money image and registration of paper money image; S2, positioning the sequence number by a two-step method, namely the first step of approximate positioning using priori knowledge and the second step of accurate positioning of the sequence number; and performing character segmentation of the sequence number by use of a vertical projection method; and S3, performing multiple extraction for the characteristic value according to the characteristics of the confusable character by a new 13-point characteristic extraction method, and performing identification by use of a support vector machine according to the position-type relationship of the character to obtain relatively high identification accuracy. The method disclosed by the invention can improve the robustness of a sequence number identification system on the input paper money images different in angle, illumination, background and resolution, and improves the positioning and identification speed and the identification accuracy.

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 provides a method for diagnosing and predicating a rolling bearing based on a grey support vector machine. The method is characterized in that the rolling bearing is used as a key part of a mechanical device, and the advantages and disadvantages of the operation state influence the operation performances of the whole device. The method is the method for diagnosing and predicating the rolling bearing based on GM (1, 1)-SVM. The method comprises the steps of extracting a vibration signal time domain and frequency domain feature values of the rolling bearing under various fault and normal states; selecting important feature parameters to build a predicating model, namely, grey model; predicating the feature value; training a binary tree supporting vector machine according to various fault feature values and normal state feature values of the bearing; creating a rolling bearing decision making tree for determining the fault as well as classifying the fault type to diagnosis the fault of the bearing; then predicating the fault according to the predicating value and the trained supporting vector machine.