88 results about "Residual matrix" patented technology

The present invention relates to a quick scanning method for a residual error matrix in video coding process, which belongs to the field of video coding in signal processing. The method comprises: storing elements in a two dimension residual error matrix to a one-dimension serial according to a preset mapping way; extracting the lots of residual error elements gradually and orderly from the one-dimension serial, computing a level value, a map value and a run value of each residual error element, and storing them respectively. In the process of scanning the residual error matrix, the invention can parallelizably and effectively read the residual error element, accomplish parallelizably data computing and information extracting, and can quickly obtain the required scanning results, thereby, the invention greatly reduces the time cost in whole scanning process, enhances the coding rate of a coder, and facilitates the realization of real-time encoding under high distinguishability.

The invention relates to an AVS fast intra-frame predicting method and a device thereof. The method is characterized by comprising the following steps: a decoding module recodes the current sub-blockframe code stream; an intra-frame predicting mode judging module obtains a current sub-block intra-frame code predicting mode according to decoded data operation; an intra-frame predicting computing module interpolates according to an intra-frame predicting mode and memorized adjoining one-row and one-line sample points; an IDCT / IQ module computes residual data; and a residual overlapping reconstructed module overlaps an interpolation matrix obtained by the interpolation and a residual matrix obtained by the IDCT / IQ module to obtain the final current frame image data and control the one-row and one-line sample points need for returning to the next interpolation. By adopting the design idea such as module multiplexing, the maximum parallel processing among the modules, and the like, the invention greatly improves computing speed and is very applicable to being realized by FPGA.

The present invention discloses a method, a computer system, a computer readable medium and a sever. The method of the present invention comprises steps of; creating said document matrix from said documents using at least one attribute; creating a scaled residual matrix based on said document matrix using a predetermined function; executing singular value decomposition to obtain a basis vector corresponding to the largest singular value; re-constructing said residual matrix and scaling dynamically said re-constructed residual matrix to obtain another basis vector; repeating said singular value decomposition step to said re-constructing step to create a predetermined set of basis vector; and executing reduction of said document matrix to perform detection, retrieval and identification of said documents in said database.

The invention provides an optimized video data coding method based on boundary adaptive transform. The method mainly comprises steps: according to the existing DCT (Discrete Cosine Transform) matrix for video coding, a new transform matrix is generated, the new transform matrix and the existing DST (Discrete Sine Transform) / DCT matrix are combined into a new transform matrix set; according to a prediction mode, the size of a transform unit, and the relationship between the boundary information of a transform unit and the boundary information of a prediction unit, a group of transform matrixes is selected from the new transform matrix set; and by using the group of new transform matrixes, inputted video data are transformed, and a coding stream is generated. The method of the invention makes full use of distribution characteristics of residual matrixes after inter-frame prediction, and based on the boundary information of the prediction unit and the transform unit, a different transform matrix is adopted for a different boundary characteristic to carry out transform. The optimized method can select a more efficient transform matrix for transform in a targeted mode according to the characteristics of residuals after inter-frame prediction, and the coding efficiency is thus improved.

The invention relates to a tail-biting staircase code coding method. The tail-biting staircase code coding method comprises the following steps: 1) selecting an (n, k) linear block code as a component code, wherein n represents a code length, n belongs to (500, 5000), and k represents an information length before coding; and 2) grounding received information according to a mode in staircase codes, constructing a matrix B<1>, B<2>, B<3>, ..., B<n>, adding a full-zero matrix participating the coding to a lower part of B<1>, coding the residual matrix using the component code then, generating a check bit matrix while the coding, completing end-to-end connection of codes of a final matrix and a first matrix in a group then, finally replacing the full-zero matrix complemented in the B<1> with the check bit matrix, and completing the coding. The coding method is advantageous in that, through adoption of the novel coding scheme, the novel tail-biting staircase codes have a characteristic of the block code, coding processes between groups are mutually independent, information of each matrix in the groups is still contained in the two component codes, and error correction performance is guaranteed.

The invention provides an abnormity detection and fault diagnosis algorithm of a gas turbine based on sensor associated network and relates to the technical field of gas turbine fault diagnosis. The diagnosis algorithm comprises following steps: step 1. signal processing; step 2: evaluating the correlation between any two sensor test points in a pretreated test point set; step 3. hierarchically clustering an obtained gas turbine sensor associated network model; step 4. evaluating the structure distribution of the residual matrix corresponding to the smallest granularity sub-class by using the information entropy index; step 5. calculating information entropy of all sub-classes in all layers and calculating super-class information entropy index of sub-classes whose information entropy indexes are transfinite. The abnormity detection and fault diagnosis algorithm of gas turbine based on sensor associated network is used achieving abnormity detection on the complete operation status of large gas turbines, especially suitable for the case where sensor test point information is very rich.

The invention discloses an undersampled spectrum sensing method based on detection of a residual correlation matrix, and solves the problem the prior art must depends on prior information that is notprone to sense. The scheme of the invention comprises the steps of sampling an input signal; acquiring a measurement matrix A and a residual matrix V; initializing a signal recovery parameter; computing a column index value I<tau> most correlated to the current residual matrix; acquiring a middle residual matrix FORMULA; acquiring a corrected residual matrix V<tau>; acquiring a detection statisticFORMULA; computing a judgment threshold FORMULA; and comparing the detection statistic FORMULA with the judgment threshold FORMULA, to acquire a signal support set. According to the method provided by the invention, the undersampled spectrum sensing is converted into binary judgment, the iterations are controlled via residual detection, the judgment threshold is not affected by noise, the excellent detection performance still can be acquired without the prior knowledge and with the extremely low noise to signal ratio and the relatively low false alarm probability, the number of sampling timesis increased and the detection performance can be significantly improved. The method is practical, low in missing and false alarm probability and low in complexity; and the sensing performance is less affected by the noise power, so as to be relatively stable.

The invention discloses an anti-interference realization method of a self-adaptive array antenna, and aims at providing an anti-interference method with small calculated amount, strong real-time and better anti-interference effect. The method is realized through the following technical schemes: a data pre-processing module performs N-order time domain tapping on M path digital signals to obtain continuous space-time two-dimension snapshot data in a uniform circular array antenna with antenna array element number of M, and a space-time two-dimension guiding vector is obtained according to a desired signal direction provided by an upper computer; a guiding vector residual matrix is computed by using the space-time two-dimensional guiding vector through a minimum variance distortionless response MVDR algorithm simplification realization module, and the space-time two-dimension beam synthesis and space-time two-dimension weight iteration are synchronously performed according to a built-inbeam synthesis module and a weight iteration module by combining the space-time two-dimension snapshot data; and an array output module converts the digital signal after the beam synthesis into an analog signal through a digital-to-analog DA converter, and the analog signal is sent to a back-end processor after being output in a up-conversion way through an up-conversion module.

The invention provides a preparation method for a standard sample of chloramphenicol residual matrix lyophiled powder in the muscle of a carp, which belongs to the technical field of animal food matrix standard substances in the residual detection of veterinary medicines. The problem that the bigger difference exists between the result of treatments such as extraction, purification and the like and a daily analytical sample as the condition of combining a target object with a matrix and a really detected sample are not consistent completely is solved. The method comprises the following steps of: carrying out cultivation and addition on a live carp by adopting chloramphenicol, so that the chloramphenicol is contained in carp in vivo, culturing the carp in water which does not contain a medicine until the medicine concentration in vivo reaches a stable metabolic state, fishing the carp, quickly freezing the carp to a temperature below -18 DEG C, unfreezing the frozen carp at a room temperature, taking a muscle part to homogenate to make into minced fillets, freezing, drying, screening and bottling the minced fillets, and encapsulating the minced fillets in a vacuum way, so as to prepare and obtain the standard sample of the chloramphenicol residual lyophiled powder in the muscle of the carp. The chloramphenicol concentration in the prepared standard sample is 1.0-10.0 [mu]g / kg.

The present invention discloses a method, a computer system, a computer readable medium and a sever. The method of the present invention comprises steps of; creating said document matrix from said documents using at least one attribute; creating a scaled residual matrix based on said document matrix using a predetermined function; executing singular value decomposition to obtain a basis vector corresponding to the largest singular value; re-constructing said residual matrix and scaling dynamically said re-constructed residual matrix to obtain another basis vector; repeating said singular value decomposition step to said re-constructing step to create a predetermined set of basis vector; and executing reduction of said document matrix to perform detection, retrieval and identification of said documents in said database.

The invention provides a method for compressing the space of a finite automaton, comprising the steps: executing each status in the finite automaton in a clustering way to obtain a plurality of clusters for expressing a status set; classifying transfer edges of the each status in the finite automaton according to the clusters so as to obtain a plurality of cluster matrixes, bitmaps corresponding to the cluster matrixes and remaining matrixes, wherein the cluster matrixes comprise transfer edges for pointing to the same cluster, the bitmaps are used for describing the effectiveness of correlative elements in the cluster matrixes corresponding thereto, and the remaining matrixes comprise remaining transfer edges which are not contained in the cluster matrixes in the finite automaton; and extracting a basic value for each row in the cluster matrixes, converting the cluster matrixes into an offset matrix, merging the each row in the offset matrix, and adding with a index data set for marking a status which can be merged, to obtain compression matrixes of the cluster matrixes. The method improves not only compression effect but also matching speed when realizing regular expression match.

The invention provides a video decoding method which includes the following steps: 1) according to a preset time percentage K which is required for reducing, a processing level of loop wave filtering is determined; 2) a residuals matrix of a video macroblock is calculated; 3) according to the video macroblock after decoding, a predicted value matrix is calculated; 4) according to the processing level of the loop wave filtering, the loop wave filtering is carried out on the sum of the residuals matrix and the predicted value matrix. By adopting the invention, a percentage K is given and the video decoding method can ensure that the calculating time is reduced by K of the former time and image quality which is maintained can be accepted.

The invention relates to a method of estimating a noise and interference covariance matrix and to a receiver. The method comprises: estimating an initial noise and interference covariance matrix on the basis of a received signal; reducing the impact of the background noise of the receiver from the initial noise and interference covariance matrix for obtaining a residual matrix; accepting the obtained residual matrix when the residual matrix is at least approximately positive semidefinite; modifying the obtained residual matrix such that the positive semidefinity of the residual matrix is achieved when the residual matrix is not at least approximately positive semidefinite; and adding the impact of background noise back to the residual matrix for estimating a final noise and interference covariance matrix.

A robust width learning system collects training data and performs linear conversion processing on the training data; solving an extended input matrix by using the input data matrix and the enhanced node matrix, solving an iterative initial connection weight matrix by using a ridge regression algorithm, and solving a residual matrix by using a residual formula; obtaining a residual probability density function by using a kernel density estimation algorithm, and calculating a weight matrix formed by all training data; and solving the connection weight matrix of the kth iteration, and if the maximum value of the absolute value of the difference between the output weights of two adjacent steps is not greater than a set threshold value or the number of iterations reaches a preset maximum number of iterations, ending the iteration, stopping the training of the model by the robust width learning system, and establishing a robust width learning system model. According to the system, the robustness of a width learning system can be improved, the problem of adverse effects on modeling precision caused by outliers can be effectively suppressed, and a robust width system model can be conveniently established so as to be suitable for prediction of related indexes in a complex industrial process.

The invention discloses a vectorization realization method for a deconvolution matrix of a GPDSP. A CPU core of the GPDSP distributes corresponding scalar storage space and vector storage space for a weight matrix generated at a forward propagation stage and a residual matrix at a reverse calculation stage in a convolutional neural network. The method comprises the steps that S1, a residual matrix A(m,m), a weight matrix B(n,n) and a deconvolution result matrix C(m+n-1,m+n-1) are set, wherein m is greater than n; S2, by controlling the number of cycles, elements in the first (n-1) rows of the deconvolution result matrix C are calculated first; S3, the number of cycles is fixed, and elements from the n(th) row to the m(th) row of the deconvolution result matrix C are calculated; and S4, by controlling the number of cycles, elements from the reciprocal (n-1)(th) row to the reciprocal first row of the deconvolution result matrix C are calculated. The method has the advantages that the principle is simple, operation is convenient, a vector processor can be fully utilized to complete special data calculation, total algorithm running time is shortened, and algorithm execution efficiency is improved.

The invention provides a method and a device for intra-frame mode prediction based on two-dimensional Hadamard transformation. The method includes: S1, presetting an optimal coding cost Jbest; S2, selecting a candidate mode Mk corresponding to a prediction block, and computing a predication result under the candidate mode Mk; S3, computing a residual matrix of an original image corresponding to the predication result and the predication block, performing two-dimensional transformation for the residual matrix and quantifying a transformation result; S4, computing the distortion degree and the distortion rate under the candidate mode Mk after quantification, and computing the coding cost Jk under the candidate mode Mk; S5, comparing the Jk with the Jbest, if the Jk< the Jbest, the Jbest= the Jk, and the Mk serves as the optimal candidate mode, and if the Jk>=the Jbest, the Jbest and the optimal candidate mode corresponding to the Jbest are unchanged; and S6, repeating the steps from S2 to S5 until candidate modes of the predication block are traversed, so that the final optimal coding cost and the optimal candidate mode corresponding to the optimal coding cost are obtained. By the method, power consumption expenses and hardware area are reduced.

The invention relates to a method of estimating a noise and interference covariance matrix and to a receiver. The method comprises: estimating an initial noise and interference covariance matrix on the basis of a received signal; reducing the impact of the background noise of the receiver from the initial noise and interference covariance matrix for obtaining a residual matrix; accepting the obtained residual matrix when the residual matrix is at least approximately positive semidefinite; modifying the obtained residual matrix such that the positive semidefinity of the residual matrix is achieved when the residual matrix is not at least approximately positive semidefinite; and adding the impact of background noise back to the residual matrix for estimating a final noise and interference covariance matrix.

The invention discloses a multiple-input-multiple-output radar near-field positioning algorithm which includes the steps: firstly, performing estimation of a first weighted least square method by the aid of thinking of a chan algorithm; secondly, defining a new residual matrix, and performing estimation of a second weighted least square method; finally, substituting a result initial value of second estimation into a Taylor algorithm, performing iterative processing until an outputted positioning result is smaller than a minimum set threshold value. According to the positioning algorithm, near-field targets can be accurately positioned, and the positioning algorithm is applicable to multiple-input-multiple-output radar near-field positioning systems.

The invention relates to a coal mill fault early warning method based on DPC-MND and multivariate state estimation, and the method comprises the following steps: 1), building a training matrix K through operation data, corresponding to observation parameters related to a coal mill fault, in historical operation data of a coal mill, enabling the training matrix K to cover all dynamic change conditions of each observation parameter during normal operation of the coal mill; 2) adopting a density peak clustering algorithm based on mutual proximity to extract and construct a memory matrix D in thetraining matrix K, and constructing an MSET early warning model based on the memory matrix D; 3) obtaining an observation matrix Xobs in a to-be-estimated time period, inputting the observation matrixXobs into the MSET early warning model, and adopting multivariate state estimation to obtain an estimation matrix Xest; and 4) obtaining a residual matrix RL = Xobs-Xest between the estimation matrixXest and the observation matrix Xobs, setting a fault early warning threshold, and performing fault early warning through a sliding window method. Compared with the prior art, the method has the advantages of accurate early warning, real-time monitoring, accordance with actual operation, high fault early warning sensitivity and the like.

The invention relates to an efficient video coding zero-coefficient early detection method. The method includes the steps that firstly, a quantization parameter Qp, a two-dimensional DCT transformation matrix C, a residual matrix Re and the size N of a TU block are set; secondly, a zero block detection threshold value is worked out according to the Qp, the C, the Re and the N; thirdly, a detection parameter SAD corresponding to the TU block with the size of N is worked out; fourthly, for the TU block with the size of N, whether the SAD meets the inequation SAD<Th0 or not is judged, if yes, it is determined that the TU block is an all-zero block and the third step is executed again, and otherwise the fifth step is executed; fifthly, whether the SAD can meet the inequation SAD<Thj, j=1,2,...,N-1 or not is judged, if yes, a final quantization block from Z(0,0) to Z(j-1,j-1) with the size of j*j is subjected to DCT, quantization, inverse quantization and inverse DCT operation and the third step is executed again, and otherwise the total TU block is subjected to DCT, quantization, inverse quantization and inverse DCT operation and the third step is executed again. Compared with the prior art, the efficient video coding zero-coefficient early detection method has the advantages of being capable of improving video coding speed and reducing coding calculation complexity, high in detection efficiency and the like.

The invention discloses a water supply pipe network leakage monitoring and early warning method based on time sequence analysis. According to the method, relevance among data coming from pressure sensors distributed in different spaces and received at different times is analyzed in real time, and leakage warning is conducted after monitoring indexes exceed threshold values. The water supply pipe network leakage monitoring and early warning method includes the following steps that firstly, a leakage pressure residual matrix and real-time pressure residual vectors are established; secondly, a space-time relevance matrix is established; and thirdly, leakage monitoring and early warning are conducted. By means of the method, under the given scanning time window length (STWL), influences of model uncertainty and measurement uncertainty can be reduced, and therefore the real-time leakage recognition rate is increased, the false alarm rate (misinformation rate) is decreased, and the water supply pipe network leakage monitoring and early warning method has important significance in improving the comprehensive performance of a leakage monitoring system.

The invention discloses a decoding method, a decoding device and a decoder, relating to the field of video treatment, saving system hardware resources, reducing the decoding complexity by a hardware circuit and obviously enhancing the system performance. The decoding method provided by the embodiment comprises the following steps: receiving the data of an input residual matrix; decoding; and obtaining the data of an output residual matrix by two-time inverse transformation of the same type of the data of the input residual matrix and an inverse transformation matrix. The invention is suitablefor any decoding occasion by inverse integer cosine transformation.

A method of detecting anomalies from a bipartite graph includes analyzing the graph to determine a row-cluster membership, a column-cluster membership and a non-negative residual matrix, and in a processor, detecting the anomalies from the non-negative residual matrix.

The invention discloses a water supply network leakage alarm threshold selection method based on time sequence monitoring data analysis. According to the invention, alarm threshold value is updated inreal time by using the correlation among time sequence data from a plurality of pressure sensors, and leakage alarm is carried out after the real-time monitoring index exceeds the alarm threshold value. The method comprises the following steps: (1) establishing a leakage pressure residual matrix and a non-leakage pressure residual matrix; (2) constructing a space-time maximum correlation matrix;and (3) acquiring the cumulative probability distribution of the maximum correlation coefficient, and selecting a threshold value. According to the method, under the condition that the false alarm rate (false alarm rate) is given, the alarm threshold value can be minimized, the real-time leakage identification rate is further improved, and the method has important significance for improving the comprehensive performance of a leakage alarm system.

The invention discloses a welding quality real-time detection method and system based on LSTM and residual distribution. The method comprises the steps of building a residual matrix for an extremely short time sequence window based on a pre-trained LSTM model, measuring the distribution similarity of the residual matrix in a single time window and an overall residual matrix, and achieving welding quality defect detection in a short time according the judgement about whether the residual matrix exceeds a threshold value or not. According to the method and the system, on the basis of high-frequency time sequence signal data collected by non-invasive equipment, the residual matrix of real data and predicted data is constructed by utilizing LSTM, the distribution similarity of the residual matrix is measured, and an abnormal time sequence threshold value is determined to identify welding quality defects, so that the technical problems that welding images, spectrums, sounds and other data are difficult to collect, defect data are difficult to label, and model robustness is insufficient are solved, and real-time detection of the welding quality defects can be achieved.

The invention discloses a bus net load prediction method based on ARIMA and phase-space reconstruction (SVR). The method comprises the following steps: 1, determining the parameters of an ARIMA modeland establishing the ARIMA model; 2, subjecting the residual sequence of the ARIMA model to SVR based on a CC method; 3, based on a reconstructed residual matrix, creating a net load nonlinear part prediction model by using SVR; and 4, obtaining a final result of net load prediction. The prediction model provided by the invention makes full use of the good tracking ability of the ARIMA model to linear changes and the good capture ability of the SVR to nonlinear changes. The SVR via CC method is based on a theory that the embedding space dimension and time delay are interdependent, has low calculated quantity, high reliability and good anti-noise performance. The SVR model has good tracking and catching ability for nonlinear changes, and can effectively analyze the nonlinear components of the net load.

The invention relates to a label query and change method based on active learning, belonging to the active learning field. The method comprises the following steps: firstly, training a plurality of classification models by using a 10-fold cross-validation method and a strong-weak classification algorithm; Then calculating the residual matrix and confidence matrix; Secondly, splicing the two new matrices into one matrix horizontally as the eigenmatrix. using the K-Means algorithm to cluster the generated feature matrices, and obtaining four types of clusters A, B, C and D according to the threshold; Finally, relabeling some samples by active learning method for the suspected noise clusters (type A and type C), and obtaining the final noise clusters according to the labeling results. The sample labels of noise clusters are modified to correct labels, and the modified A, C samples and B, D samples are merged into the final training sample set. The invention has excellent effect of screening out label noise, can reduce the cost of manual labeling, and can obtain higher classification accuracy rate with very small active learning cost.

The invention discloses a priori knowledge-based method for monitoring a fault of a magnesite electric melting furnace by kernel flexible manifold embedding, and belongs to the technical field of fault detection and diagnosis. The method comprises the steps of performing fault type marking on partial data in a historical data set of a running process of the magnesite electric melting furnace; mapping historical data to a high-dimensional space; building an optimization model capable of obtaining a fault prediction matrix, and calculating the fault prediction matrix by utilizing the historical data set comprising marking data; and obtaining monitoring data of the magnesite electric melting furnace in real time, calculating a fault monitoring matrix by utilizing the historical data and the fault prediction matrix, and obtaining a fault type of the monitoring data. According to the method, priori knowledge is converted into knowledge identifiable for a machine, so that the magnesite electric melting furnace can safely and robustly run even if old workers are not on site; a regression residual matrix is added to the fault prediction matrix, so that a prediction matrix model is more flexible and better in generalization ability; and the kernel flexible manifold embedding method is used, so that data disasters caused by excessively high data dimension are avoided, and processing and fault diagnosis of a nonlinear system are realized.

The invention discloses an on-line gas sensor drifting correction method based on an internet of things and a mobile robot, and belongs to the field of the drifting correction of a sensor. The method comprises the following steps: step I, distributing and networking sensor nodes; step II, determining a route for the mobile robot to traverse all the nodes, and implementing the route; step III, setting a plurality of time windows, so as to obtain a residual matrix and a drifting component matrix; step IV, correcting two matrixes of a previous time window to data of the current time window according to a given weight; and V, completing the online correction for the drifting of the sensor. By adopting the correction method, the on-line drifting compensation of the gas sensor can be realized, the reliability of a wireless sensor network can be improved, and the service life of a system can be prolonged.

The invention discloses an image transformation method for coding and decoding. The method comprises the following steps of: acquiring a residual matrix between an original image and a predictive image; performing horizontal integer transformation and vertical integer transformation on the acquired residual matrix to acquire integer transformation data; compensating error between the acquired integer transformation data and discrete cosine transform (DCT) of the residual matrix by using a compensation matrix, wherein each element of the compensation matrix is a rational number which is close to 1; and multiplying the compensated integer transformation by a scale factor for controlling compensation accuracy to acquire image-transformed data. The scheme of the invention makes frequency domain characteristics of a core matrix of the integer transformation closer to the DCT on the basis of increasing partial computation complexity, so that the compression effect on useful information in a transformation and quantification process can be improved to a certain extent.