31 results about "Statistical learning theory" patented technology

A method for activating at least one safety device that has a first step of acquiring at least two features from at least one signal of a crash sensor system in order to form a feature vector from the features acquired. In a second method step, the formed feature vector is subsequently classified with the aid of a classifier based on the statistical learning theory in order to classify the feature vector in one of at least three possible feature classes. As a third method step, the safety devices are activated in accordance with an activation instruction for the feature class in which the feature vector was classified.

A method is proposed for activating at least one security means (FDR, PS1), which comprises a first step of acquiring at least two features (M1, M2, M3) from at least one signal of an accident sensor system, in order to calculate a feature vector from the acquired features (M1, M2, M3). Subsequently, in a second method step, a classification of the calculated feature vector is performed with the aid of a classifier (MSVM) on the basis of the statistical learning theory, in order to classify the feature vector in one of at least three possible feature classes (K1, K2, K3). An activation of the security means (PS1, FDR1) according to an activation guidelines (T1, FLIC1, T2, T2, FLIC2) for the feature class (K1, K2, K3) in which the feature vector was classified is provided as a third method step.

The invention discloses a structural risk minimization based weighted least squares power system state estimation method. According to the characteristic that the measurement number of power system state estimation is limited, a structural risk minimization based weighted least squares estimation model is provided through a statistical learning theory, so that the norm of a residue can be minimized, and meanwhile the confidence interval of a state variable can be minimized. Besides, a detailed solving process of the method is provided. The method accords with the structural risk minimization thought in the statistical learning theory, can obtain an estimation result more approximate to the true value of the state variable under limited measurement conditions, and has good engineering application prospects.

The invention discloses a structure damage identification method based on independent components and a support vector machine. The method includes: firstly, adopting an ICA to extract an independent component and a hybrid matrix which represent structural characteristics; calculating the front four-order statistical characteristics of each independent component, deforming the hybrid matrix into asingle column vector, taking the combination of the front four-order statistical characteristics and the column vector after the hybrid matrix is deformed as a structural damage characteristic index,and finally inputting the structural damage characteristic index into a support vector machine (SVM) classifier based on a statistical learning theory to carry out damage identification. The method provided by the invention has more effective identifiability and repeatability.

The invention discloses a compressed sampling sensing matrix disturbance optimization model construction method based on statistical learning. The method comprises the following steps: setting a measurement matrix mathematical model phi with disturbance and a sparse matrix mathematical model psi with disturbance; obtaining a disturbance mathematical model A of the sensing matrix according to phi and psi, wherein the formula is A=[phi]*[psi]; obtaining a compressed sensing sparse signal model according to A, n being additive white Gaussian noise, theta being a sparse vector, and y being an observation data vector; and establishing a robust compressed sensing optimization function according to the sparse vector theta, and enabling f(theta) to be equal to ||theta||<1>, converting f(theta) into G (y0, y), and obtaining a sensing matrix disturbance optimization model. The statistical learning theory is applied to compressed sensing, and a robust compressed sensing reconstruction optimization mathematical model for resisting disturbance uncertainty in measurement is established, so that the influence of external environment and internal interference on the measurement uncertainty in thesampling process is reduced, and the accuracy of signal acquisition is improved.

The invention discloses a leaf wetting time monitoring method and system, and the method and system can calculate the wetting time of a leaf more accurately. Moreover, the method and system does not need to carry out the adjustment of equipment in the whole process according to the growth condition of plants, and are simper in calculation process. The method comprises the steps: S1, collecting a fluorescence image of the leaf; S2, carrying out the clustering segmentation of the fluorescence image through employing K-means clustering, carrying out the binarization of a clustering segmentation result, and obtaining a binary image; S3, carrying out the correction of the binary image in a mode of on / off alternate filtering; S4, enabling the preset water drop shape features and size to serve as the judgment standard for judging whether the leaf is wetted or not, employing a support vector machine based on the statistical learning theory to serve as a classifier for distinguishing whether the fluorescence image of the leaf is wetted or not, carrying out the recognition of the corrected image, and obtaining the number of images of the wetted leaves; S5, calculating the wetting time of the leaf according to the number of images of the wetted leaves and the photographing time interval.

The invention discloses a machine learning method for reducing the inconsistency between traditional Chinese medicine subjective questionnaires. The machine learning method comprises the following steps that 1), subjective questionnaire data is vectorized, wherein a subjective questionnaire consists of questions, weight and point values, and the vectorization allows a structure of the questionnaire to be converted into a vector; 2), a consistency target of questionnaire groups is defined and expressed; a contradiction function C(x) is defined to express the consistency between the questionnaire groups; the contradiction function takes a value obtained by converting a point value of the questionnaire group as an input, wherein negative correlation serves as the contradiction function in the process, and accords with a statistical learning theory; 3), main subjective questionnaires such as an NPQ (Nonverbal Personality Questionnaire), an MPQ (Mcgill Pain Questionnaire) and an SF-36 (Short Form-36) used by traditional Chinese medicine are subjected to consistency analysis, wherein each of the NPQ and the MPQ has a sub-questionnaire, and the SF-36 has eight sub-questionnaires; the following objective function of the consistency is defined according to the contradiction function in Step 2); and 4), the objective function is solved. The machine learning method reduces the inconsistency between results of different traditional Chinese medicine treatment effect evaluation questionnaires, and improves the accuracy of the evaluation on a traditional Chinese medicine treatment effect.

The invention discloses a medical automatic image processing system. The image data mining theoretical research is tightly combined with the practical application, and the mainstream technology of current medical image data mining is discussed; the method is based on improvement of a statistical learning theory, a decision tree classification algorithm, an association rule algorithm and a rough set decision tree algorithm. The invention further discloses an association rule classification method, so problems existing in association rule classification are solved; the format of the multi-sourcemedical equipment data is packaged, and a unified processing interface is adopted to form a normalized data directory, so the classifying efficiency is improved and the processing time is reduced; meanwhile, the key technology can be applied to computer-aided diagnosis through the construction of the medical knowledge graph, and the medical automatic image processing system based on the image data mining technology is constructed.

The invention discloses a method and system for monitoring the wetting time of leaves, which can calculate the wetting time of leaves more accurately, and does not need to adjust equipment according to the growth and development of plants in the whole process, and the calculation process is relatively simple. The method includes: S1, collecting fluorescence images of leaves; S2, clustering and segmenting the fluorescence images using K-means clustering, and binarizing the results of the clustering and segmentation to obtain binarized images; S3, The binarized image is corrected by means of open and close alternate filtering; S4, the preset water droplet shape feature and area size are used as the criterion for judging whether the leaf is wet or not, and the support vector machine based on statistical learning theory is used to distinguish the leaf A classifier for whether the fluorescence image is wet or not, identifies the corrected image, and obtains the number of images of leaves being wet; S5, calculates the wet time of the leaves according to the number of images of the leaves being wet and the time interval of taking pictures.

The invention relates to a method for analyzing the service state of numerical control equipment, belonging to the technology for monitoring the service state of major equipment and forecasting the service life of the major equipment. The method comprises the following steps: firstly, recognizing the service states of a plurality of the major parts of the numerical control equipment through the characteristic physical quantity acquired by a multisensor; then, forecasting the service state of the complete machine through a classification model of a support vector machine established by the statistical learning theory; and calculating the residual service lives of the major parts and the complete machine through a 'hidden semi-Markov' random process model. The method of the invention not only can be used for recognizing the current running states of the parts but also can be used for forecasting the residual service life of the parts. The current running state and the residual service life of the complete machine are obtained by the classification forecasting method of the support vector machine according to the operation result of each part. The invention provides a new method for decision support of preventive maintenance.

The invention discloses a spatial information change detection and classification method based on spatial information and statistical learning. By comprehensively collecting spatial information, analyzing and determining the changing characteristics and classification rules of various spatial elements in a vector layer, combined with statistical learning theory, Use the support vector machine model for sample training, and then detect and classify spatial information changes; through the above method, the present invention can realize comprehensive and rapid detection and classification of spatial information changes, improve detection accuracy, and the method is simple and applicable to When the number of samples is small, the scope of application is wider.

The present invention relates to the wind speed prediction method and device based on G-L mixed noise characteristic nuclear ridge regression technology, the method comprises the following steps: 1) obtain wind speed data set D1, utilize Bayesian principle, obtain the loss function of Gauss-Laplace mixed noise characteristic; 2) Utilize statistical learning theory and optimization theory, combined with the loss function in step 1), establish the original problem of the kernel ridge regression model based on Gauss-Laplace mixed noise characteristics, derive and solve the dual problem of the kernel ridge regression model; 3 ) Determine the optimal parameters of the dual problem of the kernel ridge regression model, select the kernel function, and construct the decision function of the kernel ridge regression model; 4) Construct the wind speed forecast model of the kernel ridge regression model, and use the forecast model to forecast and analyze the wind speed value. The device includes a loss function acquisition module, a dual problem solving module, a decision function construction module and a wind speed forecast module. The invention can meet the requirements of wind speed forecast accuracy in practical applications, such as wind power generation, agricultural production and the like.

The present invention relates to the characteristic of mixed noise based on G-L v ‑Support vector regression machine wind speed forecasting method, the method includes the following steps: 1) Acquire wind speed data set D l , using the Bayesian principle to obtain the empirical risk loss function of Gauss-Laplace mixed noise characteristics; 2) Using statistical learning theory and convex optimization technology, combined with the loss function in step 1), to establish a Gauss-Laplace mixed noise characteristic based on v ‑The original problem of the support vector regression model, using the Lagrange multiplier method to derive and solve the v ‑Support Vector Regression Model Dual Problem; 3) Determine the v ‑Support vector regression model optimal parameters for the dual problem, select the kernel function, and construct the v ‑Decision function of support vector regression model; 4) Construct the v ‑Support vector regression model for wind speed forecasting model, using this forecasting model to predict and analyze wind speed values. The method includes an empirical risk loss function acquisition module, a dual problem solving module, a decision function construction module and a wind speed forecasting module. The invention can meet the requirements of wind speed forecast accuracy in practical applications, such as wind power generation, agricultural production and the like.

The invention discloses a method for measuring and calculating water requirement of regional crops, comprising the following steps of: selecting the measuring and calculating area, invoking DEM, crop planting structure, soil type, weather factors and water requirement data of the crops about the measuring and calculating from a system database; interpolating the terrain factor data and the weather factor data to obtain a grid database set of regional impact factors; extracting comprehensive factor date having an impact on the crop water requirement from each terrain factor data, soil type data and weather factor data; establishing nonlinear mapping relationship between the comprehensive factor date and the crop water requirement to acquire the water requirement data of the different crops; the invention can use diverse space processing methods aiming at diverse impact factors, sufficiently takes account of the space variability of each impact factor, and through the change process of original data and the application of statistic theories, the invention can efficiently reduce the operation workload, improve the data analysis efficiency, and enhance the measuring and calculating precision for the water requirement of regional crops on condition of complicated terrain and sparse site.

The invention discloses a structural risk minimization based weighted least squares power system state estimation method. According to the characteristic that the measurement number of power system state estimation is limited, a structural risk minimization based weighted least squares estimation model is provided through a statistical learning theory, so that the norm of a residue can be minimized, and meanwhile the confidence interval of a state variable can be minimized. Besides, a detailed solving process of the method is provided. The method accords with the structural risk minimization thought in the statistical learning theory, can obtain an estimation result more approximate to the true value of the state variable under limited measurement conditions, and has good engineering application prospects.