51 results about "Sequence reconstruction" patented technology

The invention discloses a training sequence reconstruction-based channel estimation method and a training sequence reconstruction-based channel estimation system. The method comprises the following steps: acquiring a known channel estimation result; according to the known channel estimation result, constructing linear convolution of former frame transmitted data and a channel and the linear convolution of a transmitted training sequence and a channel impulse response; eliminating inter-block interference of data on a training sequence; obtaining a cyclic convolution, of the training sequence and the channel impulse response, serving as a reconstruction item; according to the reconstruction item, reconstructing the training sequence; and performing channel estimation by utilizing the reconstructed training sequence, and updating a channel estimation result. The method and the system of the invention can ensure that a TDS-OFDM system can also obtain relatively accurate channel estimation when maximum delay extension of the channel exceeds the guard space length of the training sequence, simultaneously improves the accuracy of the channel estimation and improves the spectrum utilization ratio and the mobility performance of the system.

The invention discloses a method for synchronously reconstructing a dynamic PET image and a tracer kinetic parameter on the basis of TV (total variation) and sparse constraint. The method introduces a dictionary based on sparse constraint and a TV operator to carry out a whole reconstruction process by establishing a mathematic model for synchronously estimating the dynamic PET image and the kinetic parameter, wherein a dynamic PET sequence reconstruction subproblem provided with the total variation operator is subjected to iterative optimization solution by an ADMM algorithm, and a PET kinetic parameter estimation subproblem in combination with the dictionary based on sparse constraint is solved by a soft threshold iterative algorithm. The method solves a problem for simultaneously estimating the dynamic PET image sequence and the kinetic parameter, introduces the TV operator to improve the low result resolution and noise interference proneness in the PET image reconstruction process, and may obtain a better reconstruction result compared with other algorithms which individually reconstructs the PET image or estimates the kinetic parameter.

A system and method for generating separations between jobs in a batch of scanned pages are provided. The method includes, for each of a set of jobs, capturing an image of a representative page of the job. Thereafter, the method includes scanning the set of jobs as a batch to generate a set of scanned pages. The captured images are compared with scanned pages in the set of scanned pages to identify, for at least one captured image, a respective scanned page which matches the captured image. At least one separator is generated for separating the set of scanned pages based on a location of the matching scanned page(s).

The invention relates to a degradable PLA film material and a preparation method thereof. The PLA film material is prepared by adopting the following steps: (1) putting polylactic acid into a screw extruder 1 to form a hot melt 1; (2) putting a molecular chain modifier and a degradation agent into a screw extruder 2, and performing a shear melting reaction to form a hot melt 2 with fluidity; (3) pumping the hot melt 1 and the hot melt 2 into a screw reactor, adding a chain extender, and performing a reaction; and (4) pumping a reaction melt into a screw extruder 3, adding a crosslinking agent,performing mixing homogenization, and performing extrusion to form the film. Compared with the prior art, the method provided by the invention performs a process of ''polymer functional fragmentation-> functional fragment sequence reconstruction'' by simulating DNA sequence recombination to generate an ultra-high molecular structure with a main chain containing a functional fragment and auxiliary chains having multiple functions, so that operation time and a temperature window of film forming are optimized, and the physical performance enhancement and continuous, controllable and stable filmforming of the PLA film material are realized.

The invention relates to an enterprise carbon emission prediction method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining to-be-analyzed data, wherein the to-be-analyzed data comprises at least one of a per capita domestic total production value, energy intensity, an added value of an industry to which a target enterprise belongs, total energy consumption of the industry to which the target enterprise belongs, main business income of the target enterprise, revenue profit of the target enterprise and power consumption of the target enterprise; performing empirical mode decomposition on the to-be-analyzed data to obtain a plurality of subsequences with different frequencies and a residual component sequence; inputting a plurality of sequences formed by the subsequences and the residual component sequence into a carbon emission prediction model to obtain sequence prediction results corresponding to the sequences; and performing sequence reconstruction on each obtained sequence prediction result to obtain a reconstruction result, and determining an enterprise carbon emission prediction result corresponding to the target enterprise based on the reconstruction result. By adopting the method, the carbon emission prediction precision can be improved.

The invention relates to the technical field of radio long wave communication technology, and specifically relates to an extremely-ultra low frequency channel atmosphere noise suppression method based on variance median filtering. The extremely-ultra low frequency channel atmosphere noise suppression method based on variance median filtering includes the steps: constructing a multi-reference magnetic vector sensing system, synchronously receiving extremely-ultra low frequency signals through multi channels, and obtaining a magnetic induction output sampling point matrix X; according to the channel sampling point sequence, grouping X, estimating the variance of each group, and obtaining the variance matrix D; based on a large number decision criterion, performing pulse detection and two-stage adaptive median filtering on D; according to output of the second stage median filtering and input of the first stage median filtering, performing sequence reconstruction, and obtaining an output matrix Y; and performing power frequency harmonic interference and remaining atmosphere noise suppression on Y. As the extremely-ultra low frequency channel atmosphere noise suppression method based on variance median filtering taking the variance of the sampling point group as input of the adaptive median filter, the impulsiveness is improved. As the extremely-ultra low frequency channel atmosphere noise suppression method based on variance median filtering determines the pulse state of each element of the variance matrix based on the large number decision criterion, the pulse detection accuracy is improved and the defect that the performance is low when the median filtering is in the local high density pulse noise condition in the traditional method.

The invention discloses a speech emotion recognition method based on time slicing. The method comprises a first step of speech equal length complementing: setting the maximum time length of a sound and performing time complementing of a read-in speech signal by white noise; a second step of speech signal slicing: slicing the speech signal obtained in the first step, that is, performing the time equal length slicing and speech envelope unit slicing, and the like; a third step of speech segment time-frequency analysis by using a time-frequency analysis tool; a fourth step of sequence reconstruction; a fifth step of emotion marking; a sixth step of image feature extraction; a seventh step of emotion training; an eighth step of speech emotion recognition: obtaining an emotion feature set by using the first to sixth steps for the newly input speech signal, inputting the emotion feature set into an emotion classification model obtained in the seventh step, and finally obtaining the emotion type of the current speech. The method adopts the time slice convolution and deep learning for the speech emotion classification, and well improves the recognition efficiency.

The invention discloses a PN sequence reconstruction-based frequency offset estimation method, which comprises the following steps: reconstructing the received PN sequence with length of N, forming a new PN sequence; utilizing frequency offset information in the formed new PN sequence to obtain a frequency offset rough estimation value, finishing the rough estimation process of frequency offset; compensating the frequency offset rough estimation value to the originally received PN sequence with length of N to calculate a fine frequency offset estimation value, and obtaining the final frequency offset estimation value. By reconstructing the PN sequence, the estimation precision of the frequency offset estimation method is improved by obtaining average value of a plurality of estimation values and performing rough and fine estimation processes.

The invention discloses an adaptive channel modeling method and system for enhancing a conditional generative adversarial network, and the method comprises the following steps: S1, initializing a generative adversarial network model, and obtaining a channel sample sequence; s2, a potential variable method is set through priori knowledge, and a potential variable sequence is obtained; s3, obtaining a reconstructed sample sequence meeting neural network processing requirements through a complex sequence reconstruction method; s4, obtaining a penalty sample sequence through a penalty sample construction method; s5, obtaining an input sequence and a generative adversarial network objective function through an enhancement condition generative adversarial network error function optimization method; and S6, performing network adversarial training, and outputting a generator model capable of simulating real channel data distribution. According to the method, the improved generative adversarial network is selected to accurately capture random channel behaviors, and self-adaptive channel modeling without manually assuming a physical model is realized.

The invention discloses a three-dimensional facial expression migration method and system, and the method comprises the steps: obtaining a current three-dimensional facial model of a controller, and obtaining a current three-dimensional facial model of a controlled person; performing feature extraction on the current three-dimensional face model of the controller to obtain a source basic expression model sequence; performing feature extraction on the current three-dimensional face model of the controlled person to obtain a target basic expression model sequence and a target model detail feature vector; mapping the source basic expression model sequence to a low dimension by using a source sequence mapping function to obtain a low-dimension source basic expression model sequence; transmitting the low-dimensional source basic expression model sequence to a target basic expression model sequence by utilizing a target sequence reconstruction function to obtain a target model with a sourceexpression; and performing detail recovery on the target model with the source expression by utilizing the detail feature vector of the target model.

The present invention discloses a method for illegal invasion identification base on wireless network signal CO complexity, and relates to the information safety protection field. The method comprises the following steps: firstly, performing signal direct current subtracting processing of an original wireless network signal sequence, obtaining a direct current subtracting signal sequence, performing signal Fourier transform processing, and obtaining a Fourier transform sequence; secondary, according to a threshold energy value, performing sequence reconstruction of the Fourier transform sequence, and solving the estimation value of the CO complexity; and finally, employing a nearest neighbor method to perform identification of the illegal invasion. Besides, the present invention further discloses a system for illegal invasion identification base on the wireless network signal CO complexity. The method and system for illegal invasion identification base on the wireless network signal CO complexity do not depend on traditional wireless network safety protection means such as passwords, MAC address verification, digital watermarking and the like to thoroughly solve the safety threats such as password leakage, MAC address falsification or digital watermarking and the like; and moreover, the calculation efficiency is high, and processing delay is little.

The invention provides a vegetation index time sequence reconstruction method in combination with matrix completion and trend filtering, which comprises the following steps of: firstly, determining a missing position in time sequence data through quality marker data in a vegetation index product; the method comprises the following steps of: converting a one-dimensional vector into a two-dimensional matrix through matrix change, establishing an optimal energy equation for low-rank matrix completion for a matrix after each pixel is converted, and realizing matrix completion through an imprecise augmented Lagrange algorithm to obtain a time sequence completion matrix which does not contain data missing preliminarily; and finally, vectorizing the completion matrix, establishing an energy optimization equation of weighted trend filtering on the basis of a one-dimensional vector, and solving the model through an alternating direction multiplier method so as to further filter residual noise and obtain smooth and clean high-quality vegetation index time sequence data. And high-precision reconstruction of the long-time remote sensing vegetation index sequence is realized.

The invention discloses an omega-transaminase mutant based on ancestor sequence reconstruction, and relates to the technical field of molecular biology. The mutant is obtained by mutation of omega-transaminase from Aspergillus terreus, and the amino acid sequence of the omega-transaminase mutant is as shown in SEQ ID NO. 4 or SEQ ID NO. 6. The invention further discloses a preparation method of the mutant. Compared with a wild type enzyme, the half-life period of the omega-transaminase mutant is more than 24 hours, the half-life period of the wild type enzyme is only 6.90 minutes, the half-inactivation temperatures of the mutant are 49.00 DEG C and 49.03 DEG C respectively and are about 11 DEG C higher than that of the wild type enzyme (37.89 DEG C), and the thermal stability is remarkably improved.

The invention provides a three-dimensional magnetic resonance image super-resolution reconstruction method, electronic equipment and a storage medium. The three-dimensional magnetic resonance image super-resolution reconstruction method comprises the steps of determining a three-dimensional magnetic resonance image; determining a first slice sequence of the three-dimensional magnetic resonance image in the first slice direction, performing two-dimensional super-resolution reconstruction on the first slice sequence to obtain a first reconstruction sequence, and reconstructing a first three-dimensional image based on the first reconstruction sequence; and determining a second slice sequence of the first three-dimensional image in the second slice direction, performing two-dimensional super-resolution reconstruction on the second slice sequence to obtain a second reconstruction sequence, and reconstructing the high-resolution three-dimensional image based on the second reconstruction sequence. According to the method, the electronic equipment and the storage medium provided by the invention, two-dimensional super-resolution reconstruction is alternately carried out in two directions, so that the continuity of a super-resolution reconstruction result is ensured. In the process, the realization of two-dimensional super-resolution reconstruction does not need to provide and apply a high-resolution three-dimensional image, so that the acquisition difficulty and the acquisition cost of the training sample are greatly reduced, and the practicability of the method is improved.

The invention relates to a long-chain molecule sequence mathematical reconstruction method, and specifically relates to a mathematical sequence reconstruction method for long-chain molecules, for geneDNA sequencing, protein amino acid sequencing or other long-chain structural chemical detection. The mathematical sequence reconstruction method for long-chain molecules includes the steps: 1) providing at least two DNA molecular chains, 2) breaking the at least two DNA molecular chains into fragment sequences to form X gene libraries, 3) sequencing gene fragments of the gene libraries to obtainlibrary fragment information; 4) performing full arrangement and splicing on the fragments of the library fragments, and obtaining a set of possibilities; 5) solving an intersection; and 6) judging the number of elements in the intersection to get a correct gene sequence map. The mathematical sequence reconstruction method for long-chain molecules belongs to a mathematical algorithm technology, and can realize sequencing and reconstruction of gene sequences, and sequencing and reconstruction of proteins, polysaccharides or other polymers having a single structure, thereby improving the accuracy in current gene sequencing, and the mathematical sequence reconstruction method for long-chain molecules is not probability speculation, but is based on rigorous mathematical algorithms, thus beinghigh in accuracy of sequencing structure.

The invention discloses an abnormal driving behavior online identification method based on an Encoder-Decoder attention network and an LSTM (Long Short Term Memory). The method is composed of three main modules, namely an encoder-decoder based on LSTM, an attention mechanism and a classifier based on SVM, and comprises the steps of input encoding, attention learning, feature decoding, sequence reconstruction, residual calculation and driving behavior classification. According to the method, on the basis of mobile phone multi-sensor fusion data, on the basis of driving behavior data characteristics and behavior pattern analysis, an Encoder-Decoder deep learning model, an Attention attention mechanism and an SVM classification model are fused to recognize abnormal driving behaviors. The method has the advantages of being easy in data acquisition, non-intrusive, low in cost and the like, not only considers the time correlation of the driving behaviors, but also considers the difference of different moments, can perform online identification on abnormal driving behaviors in an end-to-end mode, can provide a method basis for driving behavior evaluation and safety early warning, and has a good application prospect. The method has significant meaning for intelligent driving system design and traffic safety decision making.