99 results about "Linear prediction model" patented technology

An efficient coding scheme with higher audio bandwidth and / or better audio quality at lower bitrates, wherein the scheme eliminates long-term and short-term frequency domain correlation in a signal via frequency domain predictors. The coding scheme compresses information consisting of coded low frequency components as well as a parametric representation for the high frequency components based on a non-linear model. Additionally, by working on the frequency domain representations of the signal (such as the MDCT representation which is naturally available to a PAC encoder and decoder), low pass and high pass signal components are easily obtained by windowing the appropriate ranges of frequencies in the signal. Furthermore, the power functions of the signal are replaced by corresponding convolution functions of the same order.

The invention relates to a channel prediction method based on particle filtration correction, which comprises the following steps: (a) obtaining an AR linear prediction model by training sequence of the historical information of a channel, then carrying out prediction by an LRP channel prediction algorithm, and outputting the prediction value; (b) carrying out error calculation on the output prediction value and an actual value; if the error e between the prediction value and the actual value is smaller than a set value E, using the prediction value of the LRP channel prediction algorithm as the channel estimation value; if the error e between the prediction value and the actual value is larger than the set value E, and the system is disturbed by nonlinear non-Gaussian noise, entering a particle filter in the next period of time to carry out particle filtration correction, and using the prediction value of the particle filtration as the channel estimation value under the prior probability; and (c) updating the coefficient of the AR linear prediction model, and carrying out channel prediction for the next period of time. The channel prediction method based on particle filtration correction has the characteristics of stable channel estimation performance, strong robustness and strong anti-noise ability, and can be realized easily.

The invention discloses a nondestructive detection method of the total number of bacteria in livestock meat, which comprises the following steps: using a high spectral imaging system to obtain a high spectral scattering image of a livestock meat sample to be detected; using the Lorentz function to fit the scattering features to obtain Lorentz parameters, and using the arithmetic product of the parameters as spectral data; using a stepwise regression method for selecting the optimum wavelength combination; and using the Lorentz parameters at the optimum wavelength part to establish a multivariant linear predict model which can be used for judging the total number of bacteria in the livestock meat. The invention has the advantages of high speed and nondestructive effect, light within the visible-near infrared spectral wavelength range (400 to 1100 nm) is used as a light source for irradiating the livestock meat sample, the scattering spectral information on the surface of the livestock meat sample is analyzed, and the method can be used as the nondestructive detection method of the total number of the bacteria in the livestock meat. After necessary modification, the method of the invention can also be used as a novel method to be applied to the nondestructive detection fields of internal components such as moisture content, protein content, fat content and the like in the livestock meat.

The invention discloses a parameter optimization control method of semiconductor advance process control (APC). In semiconductor technological process, a traditional method uses a linear prediction model for the optimization control method of batch process. The parameter optimization control method of the semiconductor advance process control uses an optimized back propagation (BP) neural network prediction model based on genetic algorithm, optimizes the initial weight values and threshold values of the neural network through the genetic algorithm, uses selecting operation, probability crossover and mutation operation and the like according to the fitness function F corresponding to each chromosome, and outputs the optimum solution finally to determine the optimum initial weight value and the threshold value of the BP neural network. The performance of the BP neural network is improved with an additional momentum method and variable learning rate learning algorithm being used, so that the BP neural network after being trained can predict the non-linear model well. The genetic algorithm in the method has good global searching ability, a global optimal solution or a second-best solution with good performance is easy to obtain, and the genetic algorithm well promotes the improvement of modeling ability of the neural network.

The invention discloses a soft measurement research method for the industrial production process under the condition that the number of available training samples is small, which is applied to carrying out soft measurement modeling under the condition that modeling data is small in amount and realizing prediction for product information. According to the invention, an effective linear prediction model is established by using a cooperative training based partial least squares learning method, a problem of low model precision under the condition that sampling data of the industrial production process is small in amount, and the predication accuracy and the performance of the model established in allusion to the process are improved, thereby enabling the industrial production process to be more reliable, and enabling the product quality to be more stable.

Systems, methods and articles of manufacture for rendering an image. Embodiments include selecting a plurality of positions within the image and constructing a respective linear prediction model for each selected position. A respective prediction window is determined for each constructed linear prediction model. Additionally, embodiments render the image using the linear prediction models using the constructed linear prediction models, where at least one of the constructed linear prediction models is used to predict values for two or more of a plurality of pixels of the image, and where a value for at least one of the plurality of pixels is determined based on two or more of the constructed linear prediction models.

The invention discloses a vehicle lane-changing path tracking control method based on model prediction, and belongs to the technical field of intelligent vehicle control. The vehicle lane-changing path tracking control method is applied to an advanced driver-assistance system of a vehicle and comprises the steps of establishing an expected lane-changing path model based on obverse and reverse trapezoid yaw angle acceleration, performing force analysis on a lane-changing vehicle, establishing a 3-DoF vehicle dynamic model, converting the nonlinear 3-DoF vehicle dynamic model into a discrete linear prediction model, designing an objective function and constraint conditions of a model prediction controller, and calculating and outputting a physical quantity for controlling the motion of the vehicle according to an expected path. The expected lane-changing path planned according to the vehicle lane-changing path tracking control method improves the comfort of a driver. Control quantities comprise a driving force and a front wheel steering angle of vehicle driving so as to ensure high-precision vehicle speed control while satisfying the lateral lane-changing requirements. The robustnessof control tracking is high, and the control precision is high, so that lateral tracking errors can be effectively reduced.

The invention discloses a control method of a penicillin production process of cooperative training and Local Weighted Partial Least Squares (LWPLS), and the control method is used for soft measurement modeling under the condition that the quantity modeling data is relatively small and realizing prediction of product information of a penicillin production process. According to the control method, an effective linear prediction model is established by using a cooperative training-based local weighted partial least squares learning method, the problem of low model precision under the condition that the quantity of sampling data of the penicillin production process is too small is overcome, and the predication accuracy and the performance of the model established directing at the process are improved, thereby enabling the penicillin production process to be more reliable, and enabling the product quality to be more stable.

An auditory event boundary detector employs down-sampling of the input digital audio signal without an anti-aliasing filter, resulting in a narrower bandwidth intermediate signal with aliasing. Spectral changes of that intermediate signal, indicating event boundaries, may be detected using an adaptive filter to track a linear predictive model of the samples of the intermediate signal. Changes in the magnitude or power of the filter error correspond to changes in the spectrum of the input audio signal. The adaptive filter converges at a rate consistent with the duration of auditory events, so filter error magnitude or power changes indicate event boundaries. The detector is much less complex than methods employing time-to-frequency transforms for the full bandwidth of the audio signal.

The invention discloses a respiratory movement predicting method, which comprises the following steps: (1) inputting a status characteristic set; (2) acquiring a real-time respiratory signal f(t) anda status characteristic R(t) thereof; (3) setting up a similarity constraint condition of the status characteristic R(t) and a status characteristic R(k) through a probability likelihood model, and setting up a continuity constraint condition of adjacent status characteristics R(ti) and R(ti+1) through a probability prior model; (4) screening characteristic elements meeting the conditions in the step (3) from a candidate set through a maximum posteriori model, and predicating a respiratory signal; and (5) outputting a respiratory signal f(t+ Deltat) delayed for Deltat. The method adopts the maximum posteriori algorithm to construct the models and makes full use of local and global respiratory characteristics to set up the probability model. Compared with the conventional linear predictionmodel, the model can better predict the real respiratory movement condition, has small average prediction error and improves the respiratory movement predicting accuracy so as to improve radiotherapyeffect.

The method comprises: in the first, respectively according to the frame layer rate controlling policy, head information predication model, and complexity summation linear predication model, calculating the target bits number of all macro blocks in current frame, head information bit number, and predicating the complexity summation; in the second , calculating the target bit number of encoded macro blocks in current frame, head information bit number and complexity summation; according to the code transformation rate-distortion model, calculating the quantization step length of current macro blocks, and making the rate-distortion optimal encode for the current macro blocks; according to the actual encode data of current macro block, updating the target bit number, head information bit number and complexity summation of the encoded macro block; deciding if the current macro block is the last one of current frame; if not, then updating the coefficient of the encoded signal source rate-distortion model; repeating above step, until last macro block; if yes, then updating the complexity summation and coefficient of the linear predication model.

The invention discloses a novel method for predicting the gas emission quantity of a mine, which is characterized in that the prediction is carried out by the following steps: constructing a gas geomathematics model algorithm on the basis of the quantification theory I; carrying out analysis of gas geological conditions on factors which influence the gas emission quantity; carrying out statistic unit division and variable obtaining and establishing a multiple multi-attribute linear prediction model in which the gas emission quantity of a mined region is used as a dependent variable and the factors which influence the gas emission quantity are used as independent variables; carrying out repeated theory and practice examination on the established prediction model and finally, determining a gas emission quantity prediction model according with the actual condition; and carrying out value obtaining on a prediction region according to the statistic unit division and the value obtaining principle of a model reservation variable in the model establishing process and substituting the obtained value into the prediction model to carry out prediction on the gas emission quantity of an unmined region.

The invention discloses a method for realizing displacement self detection of a three-DOF (Degree of Freedom) AC / DC (Alternating Current / Direct Current) hybrid magnetic bearing by utilizing a displacement prediction model of a hybrid kernel function SVM. The method is characterized in that magnetic bearing control current is used as an input sample, radial and axial displacements are used as output samples, sample data are collected, a hybrid kernel function is selected, performance parameters of an SVM are optimized through a PSO (Particle Swarm Optimization), an LS (Least Squares) SVM is trained by utilizing a training sample and the performance parameters, a non-linear prediction model is established, the prediction model is connected with a linear closed-loop controller before being connected to the three-DOF AC / DC hybrid magnetic bearing in series, magnetic bearing displacement closed-loop control is formed with an extended current hysteresis loop three-phase power inverter and a switch power amplifier, and self detection of a three-DOF AC / DC hybrid magnetic bearing displacement-free sensor is realized.

An image correction method employable in a pattern inspection method for emitting light falling onto a workpiece with a pattern formed thereon and for inspecting a pattern image resulting from the pickup of an optical image of the workpiece by comparing it to a corresponding fiducial pattern image is disclosed. The method includes the step of generating a system of equations describing therein an input / output relation using a 2D linear prediction model(s) with respect to the pattern image being tested and the fiducial pattern image. Then, estimate the equation system by least-squares methods to thereby obtain a parameter of the equation system. Next obtain a centroid of the parameter. Then perform interpolation using the value of the centroid, thereby to generate a corrected image. A pattern defect inspection method using the image correction method is also disclosed.

The invention provides a Japanese squid winter-spawning group resource abundance prediction method. The method comprises steps: 1, the sea surface temperature (SST) of a marine environmental factor ina Japanese squid winter-spawning group spawning ground sea area is acquired; 2, SST time sequence values of sample points in the spawning ground in a spawning month and corresponding Japanese squid CPUE values are calculated for correlation analysis, and sea areas with high correlation are selected; 3, six sea areas S1 to S6 with a high correlation coefficient in continuous three months are selected; 4, a multivariate linear prediction model is built for the SSTs from S1 to S6 and the CPUE; 5, the correlation coefficients for six SSTs and the CPUE are ranked from large to small, and input factors are sequentially added to build four BP neural network forecasting models; and 6, the multivariate linear prediction model and the four BP neural network forecasting models are compared, and a BPneural network forecasting model with a 6-4-1 structure is selected as the Japanese squid winter-spawning group resource abundance prediction model.

The invention provides a resonance peak-based ultrasonic cavitation state identification method. The method comprises the following steps: acquiring signal data of an ultrasonic cavitation field in a set time period, framing the signal data, and using the signal subjected to framing as a signal to be processed; modeling the signal to be processed by using an all-pole linear prediction method to acquire a linear prediction model, acquiring the peak value of each resonance peak and the frequency value of the peak value through the linear prediction model, and acquiring the average value of fundamental frequency and the average value of relatively high frequency resonance peak value according to the frequency value; and judging the cavitation state according to the relationship between the average value of the fundamental frequency and the ultrasonic frequency of excitation cavitation and the average value of relatively high frequency resonance peak value. By the method, the ultrasonic cavitation intensity is described briefly and intuitively by using the ultrasonic cavitation signal.

The invention discloses a method for quickly classifying bacterial colonies on a culture medium on the basis of a hyperspectral imaging technology. The method comprises the steps that a hyperspectral imaging system is utilized to collect a reflecting image of the bacterial colonies on the culture medium, wherein the image comprises spectral information and image information of the bacterial colonies; the hyperspectral reflecting image is corrected through a black-white file, and a corrected image is obtained; the corrected image is processed through an image processing technology, and a mask image of the original hyperspectral image is obtained; spectral data information of each bacterial colony is extracted according to the positions where the bacterial colonies in the mask image are located; a full-wavelength linear prediction model based on bacterium categories and the spectral data information is built, and category prediction on an unknown bacterium sample is achieved through the model. In addition, multiple wavelength selection methods are utilized to optimize characteristic wavelengths, a corresponding simplified model is built, and the simplified model can also predict the category of the unknown bacterium sample. By means of the method for quickly classifying the bacterial colonies on the culture medium on the basis of the hyperspectral imaging technology, high-precision, quick and lossless identifying detecting and classifying of the bacterial colonies on the culture medium are achieved.

The present invention discloses a method for recognizing whispering voice tones in Chinese language on the basis of sound track parameters, which comprises: performing digital sampling for the recorded whispering voice, and analyzing the sample data to recognize the whispering voice tone; the method is characterized in: the analysis for the sample data is: performing framing and windowing for the sample data of whispering voice, at a window length no greater than 20ms; calculating linear prediction model parameters of each frame of voice, calculating the gain parameter of each frame of voice signal, and thereby obtaining a gain curve of the voice signal; comparing the gain curve with the reference voice tune curve, to determine the tone of the whispering voice. The present invention employs a sound track gain parameter analyzing method on the basis of sound track parameters to implement recognition of whispering voice tone in Chinese language. The recognition method is applicable to Chinese voice recognition systems, and is can achieve very high recognition ratio and has outstanding advantages.

The invention discloses a tracking focusing method and device, equipment and a medium, which are used for solving the problem of untimely focusing in the tracking process. The method comprises the following steps: receiving an object distance between image acquisition equipment and a measured object, which is returned by a radar and is measured for a current frame, and determining an object distance difference between every two adjacent image frames in the current frame and a preset number of image frames before the current frame; predicting the object distance of the next frame according to the object distance difference of every two adjacent frames, each currently stored parameter value corresponding to each object distance difference and a linear prediction model corresponding to each parameter value; and adjusting the focal length of the image acquisition equipment according to the predicted object distance of the next frame. According to the object distance difference between every two adjacent image frames in the current frame and the preset number of image frames before the current frame, the object distance of the next frame is predicted by using the linear prediction model, so that the algorithm is simple, the calculation amount is not too large, the focal length can be adjusted in advance according to the predicted object distance, the focusing speed meets the real-time requirement, and the focusing effect is ensured.

The invention provides a blind detection method for median filter in a digital image. The method analyzes excellent characteristics of the median filter in preserving edges of the image, and uses the statistics characteristics of a fringe area for detecting that whether an image is subjected to medium filtering with the combination of the influences of medium filtering treatment on adjacent pixel relevance and the inhabitation of the medium filtering treatment on noises. According to the blind detection method, the image is divided into subblocks which are not mutually overlapped, the subblocks are then divided into different types according to the gradient characteristics of the subblocks, neighbourhood linear prediction model treatment is conducted on the subblocks to extract prediction coefficients of the subblocks to form an edge based predication matrix (EBPM), then the EBPM characteristics are used as input of a support vector machine for training so as to obtain a medium filter detector, so that the detector can detect that whether the image is subjected to medium filtering. By the control method, the image subjected to the medium filtering can be accurately detected, the method has excellent robustness, can effectively resist JPEG compression treatment and belongs to the field of image authentication.

The invention discloses a method refining a parameter of a narrow band vocoder on a decoding end. Based on the coherence of an excitation parameter and a sound track parameter, all parameters are more fine reconstructed on the decoding end, thus the quantization precision is improved, and the quality of synthetic speech sound is further improved. More particularly, for encoding and decoding parameters in a narrow band low-rate speed coding based on a hybrid excitation linear prediction model, a method based on mapping and refining among different parameters is adopted. Various encoding and decoding parameters are quantized by adopting an independent vector through an original technology. According to the invention, the coherence between the excitation parameter and the sound track parameter is considered, and various inverse-quantized encoding and decoding parameters are refined by adopting a nonlinear mapping method, thus the quantization efficiency of various parameters is increased, and the quality of the synthetic speech sound is improved. According to the method, the natural degree of the synthetic speech sound can be improved.

The invention discloses a coal seam gas content prediction method based on a PSO-BP model and seismic attribute parameters. The coal seam gas content prediction method comprises the specific technological process of: extracting pre-stack seismic attributes and post-stack seismic attributes, calculating and primarily selecting correlation coefficients of the seismic attributes, performing clustering analysis and optimization on the seismic attributes, constructing the PSO-BP prediction model, and finally predicting the coal seam gas content by means of the PSO-BP prediction model trained by using well data. The coal seam gas content prediction method is different from a single seismic attribute prediction technology, and strives to mine seismic attribute response information of the coal seam gas content from multiple angles; meanwhile, since the coal seam gas content is influenced and controlled by various geological conditions and geological factors, the PSO-BP prediction model can effectively represent the nonlinear mapping relation compared with a traditional linear prediction model, the technical process is more advanced, the prediction precision and reliability can be guaranteed, and the prediction speed is greatly accelerated. Therefore, compared with a traditional coalbed methane gas content prediction process, the coal seam gas content prediction method has more advantages in information mining, technical process and prediction precision.

The invention discloses a code rate control method in video coding. The method comprises the following steps: dividing an input video signal into serial video frame images; updating the number of remaining encoding bits of the current encoding frame; calculating an MAD value of a current basic unit according to a time domain linear prediction model; if MADcb is greater than TH, performing airspacecorrection; if MADcb is less than TH, not performing the airspace correction; calculating the number of coding bits used by a current macro block; calculating and correcting a quantization parameterQP value of the current macro block; executing rate distortion optimization to obtain an actual MAD value of the current macro block; performing circular execution until the macro blocks in the current basic unit are completely processed; and calculating the next basic unit, and performing circular execution until the basic units in the video frames are completely processed. The movement conditions of the current processing unit on time and space can be well predicted to accurately predict and process the video coding quantization parameter.

The invention relates to a joint estimation method for the angle of an array antenna and the number of signal sources in a complex noise environment, and belongs to the technical field of radio positioning. The joint estimation method comprises the steps of 1) solving a cycle correlation entropy matrix V<alpha><y>(tau) of array signals under a condition that the cycle frequency is known; 2) building a cycle correlation entropy array linear prediction model V=[Phi]A which is applicable to broadband signals and narrow-band signals; 3) estimating the number K of interested signal sources and an error variance [sigma]<2>; 4) estimating a flow pattern matrix of the array model; and 5) performing DOA estimation by using spectrum peak searching. According to the invention, a cycle correlation entropy theory is organically applied to array signal processing, correlation characteristics of the cycle correlation entropy are innovatively proposed, and the array linear prediction model is built based on the characteristics. The algorithm is put forward according to actual requirements, and has the characteristics of high anti-noise performance, low computation complexity, small number of required snapshots, high angular resolution and the like.