229 results about "One step prediction" patented technology

The present invention leverages analysis methods, such as expected value of information techniques, rate-based techniques, and random selection technique, to provide a fusion of low-level streams of input data (e.g., raw data) from multiple sources to facilitate in inferring human-centric notions of context while reducing computational resource burdens. In one instance of the present invention, the method utilizes real-time computations of expected value of information in a greedy, one-step look ahead approach to compute a next best set of observations to make at each step, producing “EVI based-perception.” By utilizing dynamically determined input data, the present invention provides utility-directed information gathering to enable a significant reduction in system resources. Thus, of the possible input combinations, the EVI-based system can automatically determine which sources are required for real-time computation relating to a particular context.

An ECU in an automatic engine control device predicts a maximum discharging current to be supplied from a battery to a starter during a next restart of the engine based on a present voltage, an internal resistance value of the battery, and a starter total resistance value during automatic engine stop. The ECU further predicts a minimum voltage of the battery during a period until the next restart of the engine based on the present voltage, the present internal resistance value of the battery, and the predicted maximum discharging current. The ECU judges whether or not execution of the next restart of the engine during the automatic engine stop based on the predicted minimum voltage of the battery.

The invention relates to a power abnormal failure data analyzing device and a diagnosing method, and belongs to the field of equipment failure detection. The analyzing device comprises a signal acquisition unit, a rotary capacitor filter circuit, a signal processor, a power supply module, a communication module and a host computer. The method for diagnosing abnormal failure data by using the device comprises: 1, by using a method of phase space reconstruction, evaluating embedding delay t and optimal embedding dimension d; 2, modeling the acquired data to obtain a neural network predicting initial model; 3, predicting a next output x[i+1]<->; 4, updating the weight of the next predicted BP network; and 5, judging the error. The device and the method have the advantages that the BP neural network is skillfully combined with the phase space reconstruction method, and the device and the method are suitable for various acquisition signal types, have accurate and rapid processing, are applicable to various complex storage working stations, and can effectively analyze and process signal failure, in particular fine failure data. A rotary filter capacitor has strong noise inhibiting capability.

The present invention discloses an in-service life evaluation based spare part demand forecasting method. The present invention uses the historical record of changing parts and components during equipment running process to establish a statistic model and evaluate the life of a spare part under the current in-service condition; spare part demand function is defined according to the evaluation value of in-service life and practical service life, and the spare parts demand total quantity of a plurality of sets of equipment within certain rang of time is forecasted further. The method combines the spare part life evaluation with practical service condition closely, improves the accuracy for forecasting the spare part demand, is applicable to the maintenance instruction of frequently changed parts and components at the equipment and has important meaning for the enterprise to ensure the supply of the spare part and reduce the spare part stock.

The invention discloses a shale gas multi-stage fracturing horizontal well post-fracturing crack parameter evaluation method and system. The method comprises the following steps: 1) aiming at a targetshale gas field, collecting static geological parameters, dynamic data and pipe column parameters of a plurality of shale gas multi-stage fractured horizontal wells; 2) obtaining values of fracturingtransformation stratum coefficients of the plurality of wells; 3) obtaining a dynamic total reserve value of each well; 4) establishing an empirical relationship between the fracturing transformationstratum coefficient and the total geological reserve of the volume fracturing transformation area according to data obtained in the steps 2 and 3; 5) obtaining the total geological reserve of the volume fracturing reconstruction area; 6) calculating the half length of the effective support hydraulic fracture; and 7) performing gas well production history fitting and/or shut-in pressure recovery well test chart fitting based on the crack half-length constraint to obtain a fracturing transformation parameter evaluation result. According to the method, the problem of high historical fitting multiplicity can be solved, a more reliable fracturing transformation parameter interpretation result is obtained, and a basis is provided for predicting the gas well productivity and making a reasonabledevelopment technical policy in the next step.

A method, system and computer-readable medium are presented for computer-based supervised classification of digital documents that can exclusively identify an optimal category for the single class model by dividing a calculated score of each category into groups (thresholds can be automatically decided from the knowledge base) and can further predict whether it will be subjected to human examination and whether feedback learning should be performed.

The invention discloses a small-size unmanned rotary wing aircraft dynamic model identification method based on an adaptive genetic algorithm, which relates to flight status data acquisition and optimization, dynamic model building and parameter identification, and parameter optimization validation. Firstly, status data and control data when a small-size unmanned rotary wing aircraft executes standard actions are acquired through a data acquisition system, and smoothing and filtration are conducted to eliminate wild values; then aiming at the operating characteristics of the small-size unmanned rotary wing aircraft at autonomous takeoff and landing stages, a small-size unmanned rotary wing aircraft dynamic model is built through a balance point linearization method and model parameters are identified through the adaptive genetic algorithm; and finally intelligent parameter evaluation indexes are built and the effectiveness of the model parameters is evaluated and judged through a one-step predication method. The small-size unmanned rotary wing aircraft dynamic model identification method based on the adaptive genetic algorithm solves the problem in the dynamic model identificationof the small-size unmanned rotary wing aircraft, can realize the high-accuracy control of the small-size unmanned rotary wing aircraft, and has the advantages of low testing cost, short cycle, simplecalculation, high dynamic model accuracy and weak dependence on initial values.

The invention discloses a bayesian fitering-based general data assimilation method. The method comprises the steps of: inputting an initial value set into an analysis model in a prediction step so as to obtain a prediction set value; calculating prediction error covariance matrix by using set kalman filtering in an updating step, and updating each prediction set according to the observation value and kalman gain matrix; or, calculating importance weight of each set sample by adopting particle filtering through set prediction value, calculating the number of effective particles by utilizing normalization importance, resampling the set according to the weight to obtain updated analysis value and analysis set; or, calculating prediction error covariance matrix by adopting unscented kalman filtering, and updating each prediction set according to the observation value and kalman gain matrix; conducting next prediction and assimilation by taking the updated analysis set as the initial values of the analysis model, and repeating the prediction step and the updating step. The method can enable Earth remote-sensing observation information and land surface process model information to be effectively integrated, thus forming a land surface process information prediction system with small errors.

Low-rate denial of service LDoS attack is novel DoS attack. The low-rate denial of service LDos attack transmits periodic pulse attack to a sufferer by utilizing a TCP protocol retransmission time outmechanism. The average attack rate of the LDoS is lower, so the LDoS can evade a traditional detection method. Aiming at the LDoS attack, the invention provides a detection method based on Kalman filtering. The method comprises the following steps: firstly, setting a detection period to sample the flow rate of a suffered terminal; then, carrying out wavelet transformation to sample data and utilizing a wavelet coefficient to extract a waveform trend; afterwards, adopting a Kalman filtering algorithm to take an error value of one-step forecasting and maximum likelihood estimation as an evidence for detecting mutation; and finally, utilizing t hypothesis detection to judge the initiation and the termination of the attack. A test result shows that the method provided by the invention can effectively detect the LDoS attack.

The invention relates to a method for controlling and compensating an ink dropping point in an inkjet printer. The method at least comprises: a step 101, in which according to the moving speed of a word car, the jetting speed of a nozzle in the self-motion of ink drops and the distance between the nozzle and a printing medium, a graph 2 is referred and Newton's laws of kinematics is used as basis to list an equation set of horizontal projectile motion; the time ts needed for early injection is calculated; a step 102, in which according to ts, sampling time T of Kalman filtering wave is determined; according to the moving speed of a uniform speed section of the word car and the ts in the step 101, a speed curve of the word car is divided so that the sampling time of the Kalman filtering wave is more than the time of early injection; a step 103, in which according to the determined initial value of the Kalman filtering wave and the observed displacement value of the word car, one-step prediction is carried out to obtain the state variable of next time; and a step 104, in which the predicted speed value in the step 103 is utilized to carry out calculation of relevant controlled quantity; and the step returned to the step 103 for iterative prediction. The method can compensate for errors of the ink dropping point, has good precision of prediction, broadens the printing area and improves printing efficiency.

The invention discloses a double-base MIMO radar tracking, positioning and deception jamming recognition method and belongs to the technical field of radar communication technology. The method comprises the steps that firstly, a residual observation vector is worked out, a measurement predicted value of a target is obtained with a one-step prediction method, and a residual equation set coefficient matrix is obtained through the predicted value; secondly, the signal to noise ratio of an echo signal is worked out, and a least square weighting matrix is worked out; thirdly, target coordinate residuals are worked out according to the observation vector, the residual equation set coefficient matrix and the least square weighting matrix, and target coordinates are worked out according to the target coordinate residuals and the target coordinate predicated values; a fitting error vector is worked out according to the least square weighting matrix and the residual observation vector, then a detection statistical magnitude is worked out, the detection statistical magnitude is substituted into a detector, an obtained value is compared with the detection threshold lambda <0>, if the obtained value is larger than the detection threshold lambda <0>, the target is deception jamming, and if the obtained value is smaller than the detection threshold lambda <0>, the target is a real target. In this way, the double-base MIMO radar tracking, positioning and deception jamming recognition method has the advantages of being high in practicability, high in precision, capable of recognizing deception jamming, small in calculation amount and high in reliability in the double-base MIMO radar tracking process.

The invention discloses a centroid tracking framework based particle filter and mean shift cell tracking method, which mainly solves the problem of low accuracy rate of the traditional cell tracking method. The cell tracking method comprises the following steps of: performing binary segmentation to a video image, and extracting the central position of each cell; tracking the centroid of the cell, and recording the tracking trace of the cell; respectively recording the starting coordinates and the terminating coordinates of the trace into a starting coordinate set and a terminating coordinate set, and selecting a cell to be tracked; further predicting the trace of the cell to be tracked by using particle filter to obtain a predicted coordinate point in the next frame of image; selecting the subsequent tracking trace of the cell to be tracked by using the mean shift method in good time according to the predicted coordinate point; and circulating the steps of prediction and selection till the last frame of image, and completing the tracking of all cells. Compared with other traditional tracking methods, the cell tracking method has improvement in the aspects of tracking effect and accuracy rate and can be used for analyzing motor cells in a medical microscope video image.

The invention relates to a multi-sensor quantitative fusion target tracking method based on a variational Bayesian method and a strong tracking information filtering method. According to the multi-sensor quantitative fusion target tracking method, a structure including a primary processor and a secondary processor are provided. In the primary processor, an enhanced measurement matrix H(k) and enhanced global information z (upsilon, k) are constructed; one-step prediction (k|k)-1) and corresponding covariance P(k|k-1) are calculated, global information predication z (k|k-1) is calculated, and a z (upsilon, k, 1), the (k|k)-1 and the P(k/k-1) are sent into the secondary processor. In the secondary processor, information noise variance is calculated, and (upsilon, k, 1) is sent to the primary processor, and in the primary processor, fusion estimation and corresponding covariance can be obtained through calculation, wherein please see the instruction for the formula of fusion estimation and corresponding covariance. Due to the fact that the variational Bayesian method and the self-adaptive strong tracking information filtering method are adopted in the multi-sensor quantitative fusion target tracking method, the high tracking capacity is achieved, unknown variance of noise can be estimated and measured, and the self-adaptive function can be achieved. Meanwhile, an attenuation coefficient can be estimated through an iterative method without calculating a jacobian matrix.

An one-step interpolation-predication method for quantifying the vectors of 1 kbps LSF parameters can be used in middle- or low-speed speech encoding and parametric speech synthesizing system. Its featureis that a vector quantifier of 1 kbps LSF parameters is designed by using one-step predication in frame and one-step interpolation between frames.

The invention discloses a pseudo-measurement-based asynchronous track fusion algorithm with a feedback maneuvering target. Firstly, input interaction is carried out on a model set, and the filtering initial value of each model is calculated according to the model probability and the model transfer probability; secondly, a fusion center calculates one-step prediction values on the basis of the Kalman filtering algorithm, after new sensor measurement information in the filtering period is obtained, the one-step prediction values are distributed in a time shaft sequence, recurrence is conducted on a fusion moment, information such as sensor observation matrixes, noise and model prediction are added, and asynchronous track fusion is conducted; thirdly, secondary filtering is carried out for calculating model output, output interaction is performed in the fusion center to obtain a fusion center estimated value and an estimation error matrix, and the fusion center estimated value and the estimation error matrix are fed back to a sensor according with feedback conditions. The overall precision of the algorithm is improved by introducing a fusion structure with feedback so that a better effect can be achieved in multi-sensor maneuvering target tracking.

The invention discloses a method for searching and sequencing personalized web pages based on user retention time analysis. The method comprises the following steps of: firstly, obtaining document-level user retention time through a custom web browser; accordingly, predicting concept word-level user retention time; then, according to the predicted concept word-level user retention time, further predicting personalized reading interests of a user to each web page in any web page searching result; and finally, according to the personalized reading interests of the user, generating a personalized web page searching result facing to the user. In the method disclosed by the invention, by using an artificial intelligent related technology and methods for searching web pages, processing texts and the like, reading interests of users to different concepts can be estimated; therefore, personal reading habits and requirements are considered in the process for searching and sequencing the web pages; and the sequencing of the web page searching results is closer to the user personalized prediction result, therefore, better network search and browser support are provided for users.

The invention belongs to the technical field of carrier navigation of ships, aircraft, vehicles and the like, in particular to a target tracking method with colored measurement noise and variational Bayesian adaptive Kalman filter. Including: 1. Establishing the state equation and measurement equation of target tracking. 2. converting that color measurement noisento white measurement noise by themeasurement difference method. 3. SelectingThe prior distribution of the one-step prediction covariance matrix and the measurement covariance matrix of the state spread vectors the inverse Wishart distribution. 4. Performing Variational approximation of joint posterior probability density function. 5. Estimating The extended state vector and its one-step prediction covariance matrix and measurement covariance matrix by variational Bayesian method. The method of the invention completes the state estimation task in the target tracking process under the condition of imprecise noise covariance matrix and colored measurement noise, and the tracking accuracy is higher than the existing target tracking method based on other filters.

There is provided a mobile blood-sugar level measuring system enabling a diabetic patient to accurately and easily perform management of blood-sugar level measurement timing, management of changes in blood-sugar level, and further prediction of a future blood-sugar level. The blood-sugar level measuring system is formed by using a blood-sugar level processing device including a data-arithmetic-processing unit for arithmetically processing the measurement-data, an external data importing unit for entering and accumulating external data on blood-sugar level, a display device for displaying the result of the arithmetic-processing, and a display device control unit for controlling the display device, in combination with a conventional, mobile blood-sugar level monitoring device.

The invention relates to a no-gyro satellite gesture determination method based on tensor product multi-cell robust heavy hydrogen (H2) filtering, and belongs to the technical field of aircrafts. As for the nonlinear characteristics of satellite gesture dynamic equations and kinematical equations, the no-gyro satellite gesture determination method based on the tensor product multi-cell robust H2 filtering provides the multi-cell robust H2 filtering based on tensor product conversion to transform a nonlinear filtering problem into a linear filtering problem. The no-gyro satellite gesture determination method based on the tensor product multi-cell robust H2 filtering includes the steps of first establishing a state equation and a star sensor measurement equation of a gesture determination system and transforming a nonlinear system into a linear variable parameter error system by utilizing Jacobian linearization, then establishing linear parameter varying (LPV) system multi-cell model description according to tensor product model transformation and acquiring state estimation correction amount of the gesture determination system through combining with the robust H2 filtering, and last correcting gesture single-step pre-measurement acquired through a extended Kalman filter (EKF) method by utilizing the estimation correction amount to obtain a gesture estimation value. Accordingly, real-time updated filtering gain in the EKF method is avoid, and filtering calculated amount is reduced greatly.

The invention provides an underwater terrain-aided navigation method based on an adaptive sampling particle filter. The method comprises the steps that a state space model based on an inertial navigation position error and a measurement model based multi-beam depth-sounding sonar are built; one-step predicted particle updating is conducted through initial state distribution and the state space model, the number of particles is adjusted according to predictive state distribution by adopting a KLD sampling technology, and a predicted particle set is obtained; when a multi-beam measured value isreached, the depth, the inertial navigation guiding position and an underwater reference digital map interpolation function are measured in combination with a pressure-depth meter, and particle measurement updating is conducted through the measurement model; finally, by means of the particle set and weight which are obtained after measurement updating is conducted, aircraft position error estimation is conducted by adopting mean squared error minimization rules, and the estimated error is used for correcting the inertial navigation guiding position. Accordingly, the navigation real-time property can be improved while the underwater terrain-aided navigation precision is guaranteed.

The invention discloses a maneuvering target state prediction optimization method based on Kalman filter, which mainly solves the problem of large error of the existing Kalman filter to the target state prediction result. The realization scheme is as follows: setting the parameters of discrete-time linear stochastic dynamic system; According to the set system parameters, the one-step prediction value and gain matrix of Kalman filter system state are calculated. According to the set system parameters, the system state error prediction value and the system measurement error prediction value arecalculated. According to the predicted value of system error, the gain matrix of system error is calculated. According to the gain matrix of system error, the estimation of system state prediction error is calculated, and the system state prediction value is corrected. The invention improves the accuracy of prediction, and enables the prediction result to be closer to the real value of the movingstate of the target, and can be used for predicting the states of the maneuvering targets such as the aircraft, the ship and the automobile.

The present invention provides a system and method for monitoring vibration of a power transformer. The system comprises at least one vibration sensor operably mounted to the outer case of a transformer for sensing vibration of the power transformer; a spectrum analyzer for processing the vibration signal from the vibration sensor, the spectrum analyzer generating a frequency spectrum of the vibration signal and calculating a velocity rating from the frequency spectrum; a diagnosing means for evaluating the velocity rating of the vibration signal, the diagnosing means assigning a vibration grade for each velocity rating, the diagnosing means finding the maximum velocity rating; and a means for dispatching a control message to an operator when the maximum velocity rating reaches a threshold value. It can be appreciated that the mechanical health of a large-sized transformer can be managed in a systematic and efficient manner during its operation. This system can prevent accidents due to the mechanical failure of the transformer or diagnosing failure of the transformer itself during its operation. This system also can provide useful information to determine when the transformer has to be replaced and further to predict what the expected lifespan of the transformer will be.

The invention discloses a time series forecasting method and system based on SVR (Support Vector Regression). The time series forecasting method based on SVR comprises the following steps of: selecting historical data from an existing time series data set and obtaining a plurality of training data sets; determining regular parameters and Gaussian nuclear parameters of SVR modules to be constructed, and constructing an SVR module corresponding to each training data set; selecting T historical data between a moment of t-T+1 to a current moment t; and under the condition that a first difference of a moment to be forecasted and the current moment is less than or equal to the number of SVR modules, selecting the SVR module corresponding to the first difference, and obtaining a forecast value of the moment to be forecasted from the T historic data by directly utilizing the SVR module. Compared with a method for obtaining the forecast value of the moment to be forecasted through multi-step forecasting in the prior art, in the method for obtaining the forecast value through a one-step forecasting disclosed by the invention, the accumulation of forecast errors is reduced, and thus the accuracy of obtaining the forecast value is improved.

The invention discloses an automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation. The method comprises the following stepsthat: firstly, setting a system initial value, and calculating a cubature point value in a time update stage; spreading the cubature point; estimating a one-step prediction state and an error covariance square root factor; in a measurement update stage, importing a Gauss-Newton nonlinear iteration method to carry out iteration update, and calculating the cubature point during each-time iteration;spreading the cubature point; calculating measurement estimation; calcauting the square root factor of an innovation covariance and a cross covariance matrix; calculating Kalman gain; updating a current iteration state, and estimating the square root factor of an error covariance; judging whether an iteration termination condition is achieved or not; updating a current state, and estimating the error covariance square root; and in the measurement update process, regulating the noise compensation factor to optimize state estimation. By use of the method, accuracy and stability in an automotiveradar target tracking process can be effectively improved.

The invention relates to a nonlinear filtering method based on polytope differential inclusion, which belongs to the technical field of system filtering and control. The method comprises the steps of describing a nonlinear filtering error system with a PLDI (Programming Language Design And Implementation) model, converting a nonlinear filtering algorithm design problem to a linear uncertain system robust filtering algorithm design problem, designing a dynamic equation for estimated error correction solving by a hybrid robust H2/H-to-infinity filtering method, then designing a discrete nonlinear filtering equation by combining an EKF (Extended Kalman Filter) one-step prediction equation, and applying the discrete nonlinear filtering equation to a nonlinear discrete system to obtain the state estimation of the nonlinear discrete system in real time. According to the method, the nonlinear filtering design is simplified, the filtering gain is not required to be updated in real time, and the jacobian matrix is not required to be computed in real time in the implementation process, so that the computation amount is reduced greatly, the real-time performance of the nonlinear filtering is improved effectively, and the method is applicable to the design of a nonlinear filter.