97 results about "Matrix sequence" patented technology

The invention relates to the technical filed of wireless communication, which discloses a transmission method, network side equipment and a terminal for signaling control, wherein, the method comprises the steps that: a linear pre-coding matrix sequence number corresponding to a resource block allocated to users by a base station is obtained; the resource block used for the transmission of a special control channel is determined, and the information of the pre-coding matrix sequence number of the resource block in which the special control channel is positioned is loaded to a physical downlink control channel; a linear pre-coding matrix which has the same data with the resource block in which the special control channel is positioned is adopted to carry out pre-coding processing to the information loaded by the special control channel. The method, the network equipment and the terminal of the invention cause PMI information corresponding to most RB to obtain performance gain by the pre-coding processing during transmission, thereby promoting the reliability of PMI information transmission.

The invention discloses a short-term traffic flow prediction method based on a generative confrontation network. The prediction method can be applied in an urban traffic road network, wherein historical traffic data is modeled into a matrix sequence, a depth model is used for learning spatial-temporal correlativity in the data, a generator and discriminator in the network are iteratively trained,the trained model can predict the traffic conditions of all road segments in the road network in a short time in the future. The short-term traffic flow prediction method based on the generative confrontation network fully utilizes the spatial-temporal characteristics of the traffic data, the predicted object is extended from a single road segment to the entire traffic network, and the accuracy ofthe prediction is significantly improved.

The invention discloses a large-scale MIMO time-varying channel state information compression feedback and reconstruction method. The method comprises the steps that a channel matrix sequence is acquired, T channel matrixes are subjected to DFT separately, and a channel matrix sequence which is sparse in the angle delay domain is obtained; a channel feedback and reconstruction model CsiNet-LSTM isconstructed, the channel matrix sequence is input to a coder, and codewords are output; the codewords are sequentially input to a decoder, and a reconstructed channel matrix sequence is output; the channel feedback and reconstruction model is trained to gradually approximate to the channel matrix sequence to obtain model parameters; each channel matrix in the output reconstructed channel matrix sequence is subjected to two-dimensional inverse DFT, and a reconstruction value of an original space-frequency domain matrix sequence is obtained through recovery; and channel state information to befed back and reconstructed is input to the model, and a reconstruction value is output. According to the method, the feedback overhead of large-scale MIMO channel information can be reduced, the reconstruction precision is improved, and the excellent robustness is particularly achieved on decrease of the compression ratio.

A construction method of a code book is applicable to a multi-input multi-output system of 2 sending antenna. The method comprises the following steps: dividing a theta k and a phi k to i, j angle values which have same rank and are quantified; respectively substituting the quantified angle values to a V for figuring out a matrix, and the V is indicated in the right; after using the matrix to divide the root of the number N of a data stream which waits for sending, and taking out front N rows of the matrix to get a codebook. After configuring the code book on a sending terminal and a receiving terminal, a pre-coding method which is based the code book manner executes the following steps: the receiving terminal conducts the analysis of singular values on a channel matrix which is acquired through the estimation of a channel so as to get a pre-coding matrix V of the sending terminal; the receiving terminal finds out a code book matrix which is close to the V matrix on the code book which is configured on the receiving terminal, and sends the sequence number of the code book matrix to the sending terminal; the sending terminal obtains a pre-coding matrix from the code book which is configured on the sending terminal according to the received code book matrix sequence number, and uses the pre-coding matrix to conduct the pre-coding on the data stream. The invention has the advantages of enabling the 2 sending antenna to get better performance under the condition of costing a little.

A method and an apparatus for predicting inter-area travel demand are disclosed. The method comprises the steps of: step S1, constructing a deep model of multi-context information extraction, utilizing a traffic demand matrix sequence of a plurality of historical time periods as an input and an actual traffic demand matrix of a corresponding time period as a target output, and utilizing a back propagation algorithm of a neural network to train the deep model; step S2, constructing a traffic travel demand matrix sequence rich in context information; step S3, taking the deep model parameters andthe deep model trained in step S1 as the final predictor together, inputting the continuous traffic trip demand matrix sequence, and predicting the unknown traffic trip demand matrix of the next timeperiod. The invention can improve the accuracy of the inter-area trip demand prediction.

The invention relates to the technical field of matrix sequence grey correlational assessment of system efficiency of equipment, and discloses a matrix sequence grey correlational assessment method for system efficiency of equipment. According to the method, system efficiency assessment data which covers the whole task process of the equipment is described by adopting a matrix sequence, a common grey relational analysis model for the efficiency assessment matrix sequence is constructed, and the basic problems such as a basic thought of the efficiency assessment of the matrix sequence as well as the acquisition and quantification of indexes are analyzed. According to the method, a whole task process-oriented system efficiency matrix sequence model is provided, and the matrix sequence is constructed by utilizing the data which covers the whole task process of the equipment so as to carry out system efficiency assessment, so that the problem of information omission or uncertainty during the process of carrying out efficiency assessment by selecting a representative section of the task is avoided to a certain extent, and the credibility and stability of the assessment result are ensured. Compared with an analytical analysis method and an analogue simulation analysis method, the method disclosed in the invention is a most real and reliable assessment method.

The invention discloses a differential encoding space-time-frequency modulation method, and belongs to the technical field of wireless communication. The method comprises that: a mobile terminal of a communication system performs space-time encoding on an information bit sequence required to be transmitted to generate a unitary matrix code word sequence; the unitary matrix code word sequence is subjected to differential modulation to generate a matrix sequence to be transmitted; symbols in the matrix sequence to be transmitted are respectively mapped to a space domain, a time domain and a frequency domain so as to acquire a group of space-time-frequency three-dimensional signals; a plurality of groups of transmission signals are acquired through OFDM modulation, and each group of transmission signals is transmitted through a transmitter antenna; and the OFDM modulation and differential demodulation are directly carried out a receiving end, and required information is acquired. Through differential space-time-frequency block code mapping or differential space-time-frequency cyclic code mapping, the encoding and the differential modulation are respectively carried out at different dimensionalities, so that a requirement of a channel on relevance of the time domain and the frequency domain is reduced, and a requirement of reliable signal transmission in high-speed mobile environment in a wideband communication system can be met.

The invention discloses an abnormal double-person interaction behavior recognition method based on a vision co-occurrence matrix sequence, and the method comprises the steps: 1, carrying out the motion detection and segmentation of a transaction behavior in a video collected by a camera; 2, respectively carrying out the regional HOG feature extraction of left and right action performers in the video; 3, constructing a vision word through employing the HOG features extracted at step 2 and a K-means algorithm, generating a vision word bag, coding the words in the vision word bag, carrying out the vision word coding of region features through employing a similarity measuring function, carrying out the statistics of vision co-occurrence relation among the interaction individuals in a time dimension, and obtaining the vision co-occurrence matrix sequence so as to represent the abnormal double-person interaction behaviors in the video; 4, carrying out the training and recognition of an HMM algorithm. The method is simple and efficient, and is higher in recognition accuracy. Aiming at the recognition of abnormal double-person interaction behaviors in an intelligent monitoring system, the method is better in recognition performances.

A method for constructing a simplified trellis diagram for BCH-encoded information is disclosed. BCH-encoded information is received, having a corresponding parity check matrix H. The parity check matrix H is expressed as an ordered sequence of columns of matrices. A sequence of sub-code words is provided, corresponding to one or more code words, each satisfying a given condition. A matrix Hcp, having columns that are generated as a selected permutation of the columns of the matrix H through a column-permutation-for-binary-matching process, is provided, and a sequence of sub-matrices and a corresponding sequence of permuted sub-code words is provided. A trellis diagram, representing an ordered sequence of code word transitions in the received information and symmetric about a central location, is provided for each code word c, connecting n+1 stages, numbered i=0, 1, . . . , n, in an ordered sequence.

The present invention discloses a device for reminding the pedestrian to cross a street safely at night. The device for reminding the pedestrian to cross the street safely at night is characterized in that a speed bump 1, a speed bump 2 and a speed bump 3 are arranged in the lanes, pedestrian induction devices are arranged in a pavement, and the three speed bumps are all induction type light reminding speed bumps; each pedestrian induction device is divided into a pedestrian waiting induction area and a pedestrian direction determination area which are both sensing coils, and the sending coils are connected with a junction box and then are connected with a control chassis. Compared with the prior art, the pedestrian induction devices determine the pedestrian crossing directions by a method of analyzing a matrix sequence, thereby improving the accuracy of the induction devices of identifying the pedestrian crossing; the deceleration reminding devices realize a simple and visual reminding effect by combining the pedestrian crossing identifiers with the vehicle speed-limiting prompt marks, by a segmented reminding mode and under the premise of not occupying the excessive road space.

The invention provides a method for detecting fMRI brain network dynamic covariations. The method comprises the following steps of: obtaining a sample; Firstly, constructing a brain functional networkby utilizing functional magnetic resonance data; according to a certain basis (factors such as age, disease course length, education degree and the like), different network matrixes are arranged in sequence;obtaining a cross-tested network matrix sequence; then, a sliding window method is used for researching a covariant relationship between ROI brain regions which cross a tested object; and obtaining a series of covariant matrixes, and depicting the difference of the brain interval collaborative activity modes under the trans-tested scale is by calculating mathematical indexes for describing covariant relationship variability or through statistical inspection, so that the interactive relationship between brain regions in the network can be better understood.

The invention relates to an interlacing method used for a WLAN frequency hopping system, and belongs to the communication signal processing technology field. An information sequence is converted into an m*n matrix, according to a matrix size of a signal sequence, a prime code matrix of an appropriate dimension is generated, according to a prime code matrix sequence the signal sequence is subjected to interlacing, and after prime code interlacing the signal sequence is converted into a signal sequence and is sent. After receiving the signal sequence, a receiving terminal converts the signal sequence into an m*n matrix with a same dimension of the sending terminal, the signal sequence is subjected to deinterlacing by using a prime code matrix which is synchronous with the sending terminal, and the signal sequence after deinterlacing is converted into a signal sequence used for post processing. The method is flexible in realization, and when a series of errors appear, error code resistance performance which exceeds error correction capability of an error correction code can be improved; the method has a good inhibition effect on single-tone interference; situations of high time-delay and large memory space in the prior art are improved, and anti-interference performance of frequency hopping communication is improved.

Discloses are a method and a device of proceeding geometric transformation in a video reappearance, wherein the method comprises entropy decoding, reordering, quantizing in reverse, inverse transformation, inter-frame / intra-frame reconstruction, compensating reconstructed frame by using matrix sequence which is obtained after the step of inverse transformation, and proceeding geometric transformation to the matrix sequence after the reordering step and before the compensating step. The step of the inter-frame / intra-frame reconstruction is configured to geometric transformation which leads the forecasting direction and the reference pixel location adapts the step of geometric transformation.

The invention discloses a long-term network traffic prediction method based on deep learning, and the method comprises the steps: obtaining a regional network traffic sequence, and carrying out the statistics of a traffic value used at each moment; preprocessing the traffic matrix sequence, and obtaining the input data of a Transform model; establishing a Transform model, and carrying out adaptive extraction of temporal correlation and spatial correlation on the two-dimensional matrix data by adopting the Transform model; and finally, carrying out model training by adopting a self-adaptive training mechanism. According to the method, the accuracy of multi-step long-term prediction of the network flow is improved, so that an operator can plan future network resources in advance, and the reasonable allocation of wireless resources is facilitated.

This invention relates to utilising image compression and reconstruction algorithms for transmission and reception of data that can be represented as a numerical matrix or a sequence of numerical matrices in bandwidth limited channels. The method and algorithms described herein may reduce data size to such an extent that data that was considered too large to be practically transmitted in a useful time frame can be transmitted in real or near real time. The algorithms can be applied to data communications for a variety of sensor, electronic and information systems.

The invention discloses an electroencephalogram spatial-temporal feature learning and emotion classification method based on a hybrid neural network. The method comprises the steps of collecting electroencephalogram signals of multiple channels; extracting PSD (power spectral density) features from the electroencephalogram signals of the multiple channels; converting the features into a two-dimensional mesh matrix sequence; dividing the sequence into a plurality of fragments Pj; establishing a CASC_CNN_LSTM model and a CASC_CNN_CNN model, jointly extracting deep spatial features and temporal features of the electroencephalogram signals from each fragment Pj through the CASC_CNN_LSTM model, and inputting the deep spatial features and temporal features extracted by the CASC_CNN_LSTM model into a softmax layer corresponding to the CASC_CNN_LSTM model to perform emotion category prediction; jointly extracting deeper spatial features of the electroencephalogram signals from each fragment Pjthrough the CASC_CNN_CNN model; and inputting the deeper spatial features extracted by the CASC_CNN_CNN model into a softmax layer corresponding to the CASC_CNN_CNN model to perform emotion categoryprediction. According to the invention, the emotion classification is more accurate.

The invention provides a text recognition method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a to-be-recognized target image, and extracting afeature matrix of the target image based on a preset convolution layer; determining a plurality of sub-feature matrixes according to the feature matrix, and generating a matrix sequence containing allthe sub-feature matrixes; and taking each sub-feature matrix in the matrix sequence as input of a recurrent neural network in sequence, determining a text corresponding to each sub-feature matrix, and determining a text sequence in the target image according to all the determined texts. According to the text recognition method and device, the storage medium and the electronic equipment provided by the embodiment of the invention, the text or the character corresponding to each position is comprehensively determined based on the glyph features and the semantic features in the target image, thecharacters with similar glyph can be effectively distinguished, the text recognition accuracy is higher, and the recognition rate is greatly improved.

The invention relates to a multi-STATCOM (Static Synchronous Compensator) damping controller design method. The method comprises the following steps: (1), setting a power system which has N STATCOMs under the same oscillation mode, and sequentially setting the parameters of lead-lag links of the N STATCOM damping controllers; (2), taking each STATCOM critical gain matrix as a basic value to unify operation gain matrices so as to obtain a gain coefficient matrix; (3), calculating a gain matrix sequence of an operation gain micro-increment test; (4), obtaining a gain matrix sequence extreme value and an operation gain through a synchronous gain micro-increment test for the N STATCOM damping controllers; (5), determining the operation gain of the N STATCOMs based on gain constraint conditions; and (6), verifying the damping effect, regulating the gain, and finally determining a coordination operation gain of the N STATCOM damping controllers. Due to the adoption of the method, the potential hazards in the safety and stability of a system are eliminated so as to realize the coordinated and stable operation of the multi-STATCOM damping controllers and fully play the better damping control performance of the multiple STATCOMs, therefore, the system is improved in safety and stability.

The invention discloses a digital video interframe tampering detection method and system. The method comprises the steps of obtaining a to-be-detected digital video; converting the to-be-detected digital video into a video frame sequence sorted according to time; obtaining a difference matrix sequence sorted according to time; calculating the similarity between every two adjacent or spaced difference matrixes in the difference matrix sequence, obtaining an inter-frame similarity / time scatter diagram of the to-be-detected digital video according to all the similarities, and judging whether thedigital video has inter-frame tampering or not. The motion target action in the digital video which is not tampered is continuous; therefore, the inter-frame similarity / time scatter diagram between the adjacent or spaced difference matrixes should be smooth, whether the digital video has inter-frame tampering or not can be directly judged according to the inter-frame similarity / time scatter diagram, and a more efficient and accurate feasible scheme is provided for digital video tampering inter-frame detection.

The invention discloses a big data secure transmission method based on a matrix two-dimensional code. The method comprises the following steps: extracting to-be-exchanged data from a database for encryption; encoding the encrypted data to generate a two-dimensional code matrix sequence to obtain a two-dimensional code image; wherein special equipment authentication information and time authentication information are stored in the two-dimensional code matrix sequence; acquiring a two-dimensional code image through an acquisition module and then decoding to obtain final exchange data; wherein the step of generating the two-dimensional code matrix sequence comprises the sub-steps of segmenting and encrypting data information to be exchanged, and adding check information to form a plurality ofsegments of data; mapping the plurality of pieces of data into a plurality of two-dimensional code pictures; analyzing the two-dimensional code picture to obtain fragmented data, and assembling the fragmented data into complete text information. Authentication information is stored through the matrix two-dimensional code technology, the technical problems that in the prior art, the data transmission capacity is small, and data insecurity is caused are solved, and the data transmission capacity and the data transmission security are greatly improved.

The invention discloses a construction method of a multi-time scale time series collaborative prediction model, which comprises the following steps: 1) inputting recording data generated by the powerconsumption condition of a user with the change of time, and constructing a relevant time sequence representation according to the recording data; 2) based on the time sequence representation obtainedin the step 1), analyzing all the power consumption data, capturing different characteristics and analyzing the corresponding variation rules, and thereby constructing a time scale matrix sequence; 3) according to the output of the step 1) and the step 2), constructing a prediction model of the time series of the power consumption data, wherein the prediction model of the time series is a multi-scale RNN model; 4), according to the output of step 1), step 2) and step 3), and the output of external factors, the weighted fusion solution is carried out to obtain a multi-time scale power consumption sequential data cooperative prediction model. The method of the invention enables the accuracy of the power consumption prediction of the user to be improved.

The invention discloses a semi-parameter number estimation method for coherent and incoherent mixed signals. The method comprises the following steps: firstly, determining ranks of a cross covariance matrix and a combined matrix, and estimating the number of incoherent signals and the number of coherent signal groups; secondly, estimating elevations of the incoherent signals by utilizing a linear operator and the cross covariance matrix of two uniform linear arrays, and further calculating an oblique projection operator according to the estimated elevations of the incoherent signals; finally, constructing a telescopic matrix sequence only containing coherent signal information by utilizing the oblique projection operator, and determining the number of signals in each coherent signal group according to the rank sequence of vector product matrixes of telescopic matrixes. According to the method, the number of the incoherent signals and the number of the signals in each coherent signal group are estimated respectively, the influence of superposition noise is reduced, and complicated characteristic decomposition is avoided. Under the condition of small snapshot number and / or low signal to noise ratio, the method is lower in calculation complexity and higher in result accuracy for the number of the incoherent and coherent mixed signals with similar detection distance.

The invention provides an action recognition method of a self-adaptive mode, and the method comprises the steps: 1, collecting a human body motion video, extracting the position information of each frame of human body skeleton point, and forming a whole body skeleton point motion sequence; 2, reading a half-body skeleton point action sequence from the whole-body skeleton point action sequence, andperforming data processing to obtain a half-body action posture matrix sequence Sp and half-body action conformity s1, and the whole-body action posture matrix sequence Sh and the whole-body action conformity s2; step 3, if s1 is not greater than s2, taking Sh as input data; and otherwise, taking the Sp as input data, and performing action recognition on the input data by adopting an SVM supportvector machine to obtain a recognition result. According to the method, different action modes are adopted for recognition, the optimal skeleton point set is selected to represent the action posture characteristics, the influence of irrelevant joint points on action quality evaluation is effectively avoided, and the accuracy of action recognition is improved.

The invention discloses a regional network flow prediction method based on deep learning, and the method comprises the steps: 1, obtaining a regional network flow sequence, and carrying out the statistics of a flow value of the regional network flow sequence at each moment; 2, extracting a flow matrix sequence with a corresponding characteristic as the input of a deep learning prediction model according to the spatial correlation and time correlation of the regional flow sequence, wherein the time correlation comprises compactness, periodicity and tendency; 3, for the three input flow matrix sequences obtained in the step 2, extracting time and space correlation by using a 3D convolutional neural network and ConvLSTM respectively; and 4, fusing the features, extracted from 3D convolution and ConvLSTM, of the three flow matrix sequences, and carrying out the final flow prediction based on an attention mechanism. According to the method, the periodic change characteristic of the flow sequence is covered under the limited input length through the time sequence extraction method, the regional network flow value at the next moment is predicted with high accuracy, reasonable distributionof wireless resources is facilitated, and the resource utilization rate is increased.

The invention discloses a matrix multiplication operation method and device and a computer readable storage medium, and the method comprises the steps: obtaining a first scalar parameter of a to-be-operated first matrix and a second scalar parameter of a to-be-operated second matrix when a matrix multiplication operation instruction is received; determining a first matrix and a second matrix basedon the first scalar parameter and the second scalar parameter, and determining a first segmentation parameter, a first filling parameter, a second segmentation parameter and a second filling parameter; performing sub-matrix segmentation filling operation on the first matrix based on the first segmentation parameter and the first filling parameter to obtain a first sub-matrix sequence, and performing sub-matrix segmentation filling operation on the second matrix based on the second segmentation parameter and the second filling parameter to obtain a second sub-matrix sequence; and sequentiallyselecting a sub-matrix in the first sub-matrix sequence and a sub-matrix in the second sub-matrix sequence to perform multiplication to obtain a plurality of products, and accumulating the plurality of products to obtain an operation result. The matrix operation speed can be increased, and memory consumption is reduced.

The invention discloses a method and system for improving floating point matrix operation efficiency. The method comprises the following steps of: extracting sample matrixes in a floating point matrix operation to obtain a sample matrix sequence; determining an optimum calibration parameter sequence of the sample matrix sequence; and transforming calibration parameters in the optimum calibration parameter sequence and applying the transformed calibration parameter sequence into all the matrix sequence operations. By utilizing the method and system disclosed by the invention, the floating point matrix sequence operation response time can be shortened and the operation precision can be ensured.

The invention discloses a three-dimensional magnetic code based train positioning identifying method. The method comprises the following steps: combining the magnetic N pole, S pole and 0 vacancy of apermanent magnet, which represent numerical values 2, 1 and 0 respectively, to form three-dimensional magnetic codes of train positioning information; pasting the three-dimensional magnetic codes along a track according to certain interval; and performing information reading and decoding by an electromagnetic induction type identifying and reading device mounted on the train, so as to obtain coordinate positioning as well as the speed and acceleration of the train. In the method, the preset positions of the train are subjected to coded combination by using three code elements according to a matrix sequence way, and the preset sequences of different code elements represent different positional information; and the method relates to the technologies such as coding principle, inductive identification and fast decoding identification. The most remarkable characteristics of the patent are strong environmental suitability and high reliability, and the method can be used normally in varioussevere environments such as heavy dust, poor light, low temperature and rainy and snowy weather.

The invention discloses a method for visually displaying high-frequency financial time sequence correlation. The method comprises the following steps: obtaining n groups of high-frequency financial data sequences, wherein n is greater than and equal to two; and processing the high-frequency financial data sequences to obtain n*n-dimensional matrix sequence; and processing the matrix sequence by use of a slide window mode and an investigation width of w to form a window including n*n*w items, combining any items among the n*n*w items to form item set sequences, recording the frequency of the item set sequences occurring in the window in the slide window mode, and screening a frequent sequence formed by an item set sequence whose support number is greater than and equal to a preset support number threshold from the item set sequences, wherein the frequency is the support number of the item set sequences. The invention provides a device for visually displaying high-frequency financial time sequence correlation. According to the technical demand for financial time sequence analysis, correlation between multiple financial time sequence data can be visually and rapidly determined and the existence mode of a correlation status is discovered.

The invention provides a user behavior sequence anomaly detection method, a terminal and a storage medium. The method comprises the steps of obtaining user behavior information in a preset time period; aggregating the characteristic attributes according to a sequence; configuring behaviors of the user in a preset time period into row vectors, and then forming a behavior row vector time sequence; extracting behavior row vector time sequences of any two users, calculating correlation coefficients, and judging vector similarity; searching the optimal distance between the behavior row vector timesequences of the two users by adopting a dynamic warping algorithm; calculating a distance average value and a standard deviation among all users; and if the distance between the user and other usersis greater than +3 times of the standard deviation of the average value, judging that the user is an abnormal user. User behavior details can be analyzed, the problems that a feature matrix cannot begenerated and sequence lengths are inconsistent due to the fact that a user does not have continuous behaviors are solved, and the false alarm rate of anomaly detection is reduced. Therefore, abnormalbehaviors hidden in the group can be identified, and the security of data information is ensured.

The invention relates to the technical field of special matrix sequences and discloses a special matrix sequence display method for weaponry effectiveness evaluation data. The advantages of describing the weaponry effectiveness evaluation data by the concept are analyzed by employing the concept of a special matrix sequence and the descriptive connotation thereof. The method comprises the following steps: 1) setting and connotation of the special matrix sequence; and 2) special matrix sequence setting of the weaponry effectiveness evaluation data. The invention provides the special matrix sequence description of the weaponry effectiveness evaluation data, a series of continuous mission profile data in an entire mission process is selected by the behaviors of the special matrix sequence description equipment in the entire process of completing regulated missions, and the operational effectiveness is evaluated by constructing the special matrix sequence in an entire fitting process, so the confidence and the reliability of the evaluation result are enhanced.