33 results about "Linear coding" patented technology

The invention provides a multi-keyword private information retrieval method and system based on inverted indexes, and belongs to the field of information security and privacy computing. The method comprises the following steps that: a server splits an original database into a reverse index part of a keyword-index set and a key value pair part of an index-data value; mapping the index set into an integer by utilizing Godel coding; processing the data by adopting probability batch coding and binary random linear coding and publishing a hash function; the client maps a multi-keyword query into a bucket by using a hash function to generate a query vector, and the query vector is sent to the server after being subjected to fully homomorphic encryption; the server performs homomorphic calculation on a matching result in a ciphertext state and returns the result; and the client obtains a matching index set after decryption verification, and then initiates a second round of query to obtain final data. According to the method, symmetric privacy protection of multi-keyword non-primary key query in a single-server environment is realized, leakage of query content and database information is effectively prevented, and private information retrieval security is improved.

The invention discloses a wide-range absolute type linear displacement sensor based on photoelectric linear coding and a measuring method. The upper surface of a sizing base body is provided with a row of excitation electrodes and covered with a light absorption insulating layer covering all the excitation electrodes, and the light absorption insulating layer is provided with two columns of reflective patterns; and the two columns of reflective patterns are different and coprime in quantity. The area corresponding to the unit length of each reflective pattern changes in a function relation mode in the length direction. The lower surface of the movable ruler base body is provided with an induction electrode, a light-emitting module and two rows of photoelectric sensors, the two rows of photoelectric sensors directly face the two rows of reflective patterns, and the number of each row of photoelectric sensors is two. The light emitting module sends light beams to the two columns of reflective patterns, and the photoelectric sensor collects light intensity reflected by the reflective patterns and outputs obtained photoelectric signals to the signal processing circuit. The photoelectric sensing principle is adopted for rough measurement, the transverse size of the sensor can be greatly reduced, fine measurement and rough measurement do not interfere with each other, the measurement precision is high, and implementation is easy.

The present invention relates to a signal design technique resistant to phase distortion and may provide a method of a transmitter, the aspect comprising the steps of: modulating a sequence of source bits to be transmitted to a receiver to generate a first block of symbols; inserting at least one pilot into the first symbol block to generate a second symbol block; performing cumulative linear coding on the second symbol block to generate a third symbol block; performing real phase conversion on the third symbol block to generate a first signal converted to a phase value; adding a cyclic prefix (CP) to the first signal to generate a second signal; and wirelessly transmitting the second signal to the receiver.

This application provides a natural language processing method, apparatus, device, and computer-readable storage medium; relating to the field of artificial intelligence technology, the method includes: obtaining a hyperbolic word vector sequence based on a preset word vector table; performing attention encoding on the current hyperbolic word vector to obtain attention encoding features corresponding to the current hyperbolic word vector; performing a linear transformation on the attention encoding features through a preset Lorentz transformation function to obtain linear encoding features corresponding to the attention encoding features; the preset Lorentz transformation function is obtained by training a linear transformation matrix model that satisfies preset constraints; the preset constraints are used to ensure that the output feature vector obtained by the linear transformation matrix model after processing the input feature vector in the hyperbolic space is located in the hyperbolic space; and obtaining the target processing result corresponding to the sentence to be processed based on the linear encoding features. This application can improve the stability and efficiency of natural language processing using hyperbolic neural networks.

The application discloses an image semantic segmentation method applied to an electronic device comprising an image semantic segmentation model, wherein the image semantic segmentation model comprises a down-sampling module and a sparse multi-layer perception module; the image semantic segmentation method comprises the following steps: acquiring an original image, extracting a down-sampled feature map of the original image by using the down-sampling module; performing linear coding on the down-sampled feature map to obtain a linear coding feature map; performing feature extraction on the linear coding feature map by using the sparse multi-layer perception module to obtain an output feature; training the image semantic segmentation model by using the output feature, and performing an image semantic segmentation task by using the trained image semantic segmentation model; and the application can balance the speed and accuracy of image semantic segmentation, so that the image semantic segmentation operation has a faster speed and higher accuracy. The application also discloses an image semantic segmentation device, an electronic device and a storage medium, which have the above beneficial effects.

The embodiment of the invention relates to the field of artificial intelligence, and provides an epilepsy electroencephalogram signal detection method and device based on frequency attention and a pulse recurrent neural network and a storage medium, and the method comprises the steps: obtaining an epilepsy electroencephalogram signal to be detected; performing datum line following coding on the epilepsy electroencephalogram signal based on a grid search algorithm to obtain a coded epilepsy electroencephalogram signal; constructing a bidirectional pulse recurrent neural network model based on a preset adaptive pulse neuron model; the bidirectional pulse recurrent neural network model is used as a reference model, the reference model is adjusted based on a frequency attention mechanism and a pulse full-connection linear coding mechanism, and an improved model is constructed; and inputting the epilepsy electroencephalogram signal into the trained improved model to output a detection result associated with the epilepsy electroencephalogram signal. By adopting the method, the model calculation performance can be improved on the basis of reducing the energy consumption, and the detection effect on the epilepsy electroencephalogram signals is improved.

Disclosed in the present application is a trend analysis and assessment method for industrial time-series data. The method comprises: collecting industrial data over a period of time, and verifying and pre-processing the industrial data, so as to obtain an industrial time-series data set; performing feature extraction on the industrial time-series data set, so as to acquire feature data of the industrial time-series data set; forming a plurality of data intervals in view of the number of intervals preset by a target user; assigning approximate values to industrial time-series data, wherein each approximate value is the upper limit value or lower limit value of a data interval within which the industrial time-series data falls, so as to form an approximate data set; and in view of the magnitude relationship between consecutive pieces of data in the approximate data set, acquiring linear encoded data sets, and determining a data trend on the basis of adjacent data in adjacent linear encoded data sets. When using the method, a target user only needs to preset the number of intervals, and the precision of trend analysis and assessment can be controlled by means of the number of intervals, thereby reducing the impact of data noise on a trend analysis and assessment result, and improving the stability of the method with regard to trend analysis and assessment.

This application discloses a verifiable computation method based on polynomial commitments, relating to the field of information verification technology. The method includes linearly encoding the coefficient vector of a target polynomial and generating a corresponding binding commitment based on hierarchical hash commitments, sending the binding commitment to a verification end; in response to a random challenge initiated by the verification end, generating an intermediate vector based on the random challenge and the coefficient vector; in response to multiple random numbers sent by the verification end, obtaining a first verification value if the first proof from the proof end is confirmed to be successful through the binding commitment; obtaining a second verification value if the second proof from the proof end is confirmed to be successful through the binding commitment; and confirming the correctness of the evaluation result of the target polynomial at a specified point by evaluating the basic polynomial commitment if the first and second verification values ​​are equal. This application effectively reduces the overall complexity and communication overhead of information verification.

The invention discloses a data synchronization method and system based on medical consumables, and relates to the technical field of data synchronization, and the method comprises the steps: collecting monitoring target list data of medical equipment, carrying out the arrangement and structuring of the sampled data, forming an independent data group, extracting the collected data of different sensor data items, and forming an organization matrix; and carrying out linear coding on the organization matrix by using a random linear network coding (RLNC) method, generating a redundant coding packet data matrix by using matrix multiplication, and carrying out homomorphic encryption on coding packet data. According to the method, data protection and calculation operation are seamlessly combined through homomorphic encryption, encryption monitoring data can be directly processed, and encryption potential features are generated through combination of time random features and mapping hidden features, so that the hidden space features can express time dependence features and global features of random coding at the same time; and the multi-dimensional characteristic fusion capability in a medical consumable monitoring scene is enhanced.

The invention discloses a bearing device residual life prediction model and method based on a TCN and a decay diagram, and belongs to the technical field of industrial equipment state monitoring and fault prediction diagnosis. Comprising an input processing unit, a local time period coding module, a TCN module based on a difference graph, a decay graph module and a prediction output module which are connected in sequence. The input processing unit receives information collected by the sensor and splices the information to obtain input data; the local time period coding module is used for performing time period segmentation and linear coding and outputting a local time period set coding result; outputting decay characteristics in local time periods through a TCN module based on a difference graph; outputting an adjacent matrix of a final time period through a decay diagram module; and a non-negative integer bearing residual life is output through the prediction output module. By adopting the bearing device residual life prediction model and method based on the TCN and the decay diagram, an integrated dynamic modeling framework from signal input to life output is formed, and meanwhile, working condition changes are adapted to strengthen early decay recognition.

The embodiment of the present application provides a touch chip and an electronic device, the touch chip comprises at least one analog front-end processing unit, each analog front-end processing unit comprises: an encoding module, configured to perform linear encoding on detection signals from a plurality of sensing electrodes based on a preset encoding matrix, to generate a plurality of encoded detection signals of the plurality of sensing electrodes; a plurality of analog front-end circuits, respectively configured to process the plurality of encoded detection signals of the plurality of sensing electrodes, to generate a plurality of encoded digital signals of the plurality of sensing electrodes; and a decoding module, configured to perform linear decoding on the encoded digital signals of the plurality of sensing electrodes based on a preset decoding matrix, to generate a digital detection signal of each sensing electrode in the plurality of sensing electrodes; wherein the product of the preset encoding matrix and the preset decoding matrix is a unit matrix, and the maximum value of diagonal elements in the covariance matrix of the preset decoding matrix is less than 1.

The application discloses a method, system, terminal and storage medium for predicting a metabolic pathway high-yield gene mutation combination, and relates to the technical field of bioinformatics. The method comprises the following steps: determining a unique non-zero integer identifier of each state type of each gene in a metabolic pathway through a preset non-zero coding mechanism; determining a mutation combination to be predicted according to each gene and state type in the metabolic pathway; converting the mutation combination to be predicted into a non-zero integer representation vector based on the unique non-zero integer identifier, and performing linear coding and position coding to generate a combination feature matrix; inputting the combination feature matrix into a trained yield prediction model to obtain a predicted yield; or inputting the combination feature matrices of a plurality of mutation combinations to be predicted into a plurality of trained yield prediction models to obtain a predicted yield ranking result output by each yield prediction model, and determining a plurality of candidate verified mutation combinations from the mutation combinations to be predicted according to the predicted yield ranking result.

The invention discloses a method and a device used in a node of Internet of Things communication in wireless communication. The node receives the first information block; a first PRDCH is sent, and the first information block configures the first PRDCH; the first PRDCH adopts an OOK (On-Off Keying); the first PRDCH carries a data information block and a physical layer control information block, the first bit sequence is a bit sequence obtained by performing linear coding and bit repetition on information bits included in the physical layer control information block, and the second bit sequence is a bit sequence obtained by performing linear coding on information bits included in the data information block; the target number of repetition times is the number of repetition times of bits subjected to bit repetition, and the target number of repetition times is equal to the ratio of the code rate of the data information block transmitted on the first PRDCH to the code rate of the physical layer control information block transmitted on the first PRDCH; the first bit sequence and the second bit sequence are commonly used for generating the first PRDCH. The resource utilization rate is increased.

One or more methods, devices, and / or systems can provide techniques for linear encoding channel state information (CSI) compression for multi-transmission / reception point (mTRP). This can provide improvements to wireless communication systems, for example, by improving resilience against transmission errors and mismatches, joint or separate compression of channel matrices from different TRPs, and / or fallback to legacy CSI reporting operations in mTRP CSI compression.

The application relates to a nonlinear encoding imaging method and system, relates to the field of nuclear magnetic resonance, and solves the problem that an image cannot be directly obtained through Fourier transform for image reconstruction under nonlinear encoding. The method comprises the following steps: acquiring a uniform ring-distributed sub-encoding field of coil units around a Z axis, so that the current amplitude of all the coil units is distributed on a sine curve in a period, and a static encoding field is generated through superposition; performing sine modulation on the current amplitude of the coil units, so that the current amplitude of all the coil units changes in the sine period, a dynamic encoding field is generated; a nuclear magnetic resonance signal generated by the dynamic encoding field and excitation is received through a radio frequency receiving array coil group, additional spatial encoding information provided by the sensitivity distribution of the radio frequency receiving array coil group is analyzed, a target image is acquired, and reconstruction is performed. The application has the following effects: when the encoding field is a nonlinear encoding field, the rapid imaging of a nuclear magnetic resonance image can be effectively ensured.

The invention discloses a customs commodity classification method and system based on multiple features of a table, and relates to the technical field of customs commodity classification. Comprising the steps that tabular customs commodity declaration data are acquired, and the data comprise numerical features, category features and text features; piecewise linear coding PLE is adopted for numerical value features, integer mapping is adopted for category features to be combined with an embedded layer, and RoBERTa pre-training model is adopted for text features to be combined with a linear transformation layer to be processed; stacking the three types of feature embedding vectors into an embedding matrix, and inputting the embedding matrix into a model based on a Transform architecture to complete multi-feature deep fusion; generating an adversarial sample, constructing a positive and negative sample pair, and combining cross entropy loss and contrast loss to train a model; and inputting to-be-classified tabular customs commodity declaration data into the trained model, and outputting a coordination system code corresponding to the commodity. According to the invention, the time cost and labor cost of manual auditing can be reduced, the error rate of manual classification can be effectively reduced, and the fairness and rationality of tax collection are ensured.

The invention discloses a verifiable calculation method based on a polynomial commitment, and relates to the technical field of information verification, and the method comprises the steps: carrying out the linear coding of a coefficient vector of a target polynomial, generating a corresponding binding commitment based on a hierarchical hash commitment, and transmitting the binding commitment to a verification end; in response to a random challenge initiated by the verification end, generating an intermediate vector based on the random challenge and the coefficient vector; in response to the plurality of random numbers sent by the verification end, obtaining a first verification value under the condition that the first certification of the certification end is determined to pass through the binding commitment; obtaining a second verification value under the condition that the second certification of the certification end is determined to pass through the binding commitment; and under the condition that the first verification value is equal to the second verification value, determining the correctness of the evaluation result of the target polynomial on the specified point through evaluation verification committed by the basic polynomial. The method and the device have the effect of reducing the overall complexity of information verification and the communication overhead.

The application discloses a bearing device residual life prediction model and method based on TCN and decay graph, and belongs to the technical field of industrial equipment state monitoring and fault prediction and diagnosis. The model comprises sequentially connected input processing units, local time period coding modules, TCN modules based on differential graphs, decay graph modules and prediction output modules. The input processing units receive information collected by sensors and splice input data. The local time period coding modules are used for time period division and linear coding and output local time period set coding results. Decay characteristics between local time periods are output through the TCN modules based on differential graphs. Adjacency matrices of final time periods are output through the decay graph modules. Non-negative integer bearing residual life is output through the prediction output modules. The bearing device residual life prediction model and method based on TCN and decay graph are used to form an integrated dynamic modeling framework from signal input to life output, adapt to working condition changes and strengthen early decay identification.

The application provides a concrete crack leakage damage evaluation method and system, relates to the technical field of damage evaluation, and the method comprises the following steps: detecting and scanning the whole domain of concrete to determine a detection spectrum; deploying a classification encoder at a front-end interface, reconstructing a frame to determine a crack frame set, introducing a second-order classification based on a crack mode-leakage mode, and determining a detection data packet; introducing environmental erosion-fatigue load for secondary linear coding, deploying an evaluation encoder, returning the detection data packet, triggering the evolution decision of the evaluation encoder under code symbol collision, and determining damage coding; decoding processing is performed, which is taken as a damage evaluation result and is displayed and warned at a terminal. The application solves the technical problem that, in the prior art, the crack damage evaluation process is complicated due to the coupling of multiple factors causing the generation of concrete cracks, and the damage evaluation efficiency is poor. Through the whole domain scanning and the damage coding of the evaluation encoder, the accuracy and efficiency of damage evaluation are improved.

The invention provides a robust SVD (Singular Value Decomposition) approximation method based on two-way Transformer and dynamic course learning, which comprises the following steps: acquiring a channel matrix of an MIMO (Multiple Input Multiple Output) system, inputting the channel matrix into a trained SVD approximation network, decomposing the channel matrix into a column view angle sequence and a row view angle sequence, and linear coding, decoding and guiding processing are respectively carried out on the column view angle sequence and the row view angle sequence in parallel, so that a transmitting end precoding matrix, a receiving end processing matrix and a singular value matrix are decomposed. According to the invention, through a double-path parallel encoder architecture and an internal linear attention mechanism, and in combination with an efficient MLP decoder, the decomposition efficiency and accuracy can be greatly improved, and the requirements of low time delay, real-time processing and high accuracy of wireless communication are met. Besides, the adaptive F norm singular value scaling strategy effectively overcomes the problem of unstable training, so that the model has good prediction stability for different channel qualities.

The application provides a film parameter optimization model training method, a film preparation parameter determination method, an electronic device and a system. The training comprises: linearly encoding sample bacterial cellulose composite film sample preparation parameters to obtain sample encoding data; obtaining sample film performance evaluation parameters according to updated latent state parameters obtained by performing a latent state update operation using state space parameters; and training a deep learning model according to the sample film performance evaluation parameters and sample film performance evaluation labels to obtain a film parameter optimization model.

The application discloses a high coding sensitivity neuromorphic visual sensor based on complementary super-sublinear coding and a preparation method thereof. The visual sensor realizes complementary super-sublinear pulse coding through a pixel structure containing two 1T1R in a single pixel unit. The 1T1R structure is connected with a plasmonic volatile Ag / hBN / Au photoelectric memristor through a MoS2 photoelectric transistor. The surface plasmon photo-thermal effect of Ag can regulate the resistance change characteristics of the memristor to realize a negative photoconductive effect, and the super-linear coding behavior with high coding sensitivity under strong light is achieved. The sub-linear coding regulates the persistent photoconductive effect of MoS2 photo-generated electrons through defect capture and de-capture of a gate dielectric, and the sub-linear coding behavior with high coding sensitivity under weak light is realized with the help of a volatile pure electric memristor. One 1T1R sub-pixel realizes sub-linear coding, and the other 1T1R sub-pixel realizes super-linear coding, so that high coding sensitivity imaging is realized through complementary coding in a single exposure.

The invention discloses an electrocardiosignal classification method based on a dual-threshold neuromorphic reservoir, which comprises the following steps of: 1, receiving an electrocardiogram time sequence signal, and carrying out adjacent time sequence nonlinear coding operation on the electrocardiogram time sequence signal to generate an enhanced feature vector containing an original signal and a second-order interaction feature; step 2, inputting the enhanced feature vector into a dual-threshold neuromorphic reservoir, and generating a dual-channel pulse response sequence by using a dual-threshold pulse neuron to distinguish signal strength states; and step 3, constructing a joint feature vector according to the two-channel pulse response sequence, and inputting the joint feature vector into a classifier to obtain a classification result of the electrocardiosignals. According to the method, through cooperation of feature enhancement and a double-threshold mechanism, the problems that a traditional liquid state machine is poor in linear separability and neurons are prone to saturation are effectively solved, the number of the neurons is greatly reduced, and meanwhile the accuracy and the energy efficiency ratio of arrhythmia classification are improved.

This application discloses a method and system for archival data disaster recovery based on distributed technology, relating to the field of data disaster recovery technology. The method includes: encoding the original archival data object to obtain K data fragments and M verification fragments, which are then distributed and stored in a cluster; real-time monitoring of the storage node status, and when a fault is detected, determining the set of fragments lost due to the fault, denoted as the set of fragments to be repaired; calculating the heat value of each fragment to be repaired, and predicting the availability of repair resources for multiple future time intervals, generating a repair task queue based on the heat value and the availability of repair resources; selecting a subset of surviving fragments for repair by calculating their network pressure index, and generating repair fragments through linear encoding operations; performing hash verification, and storing the verified fragments in healthy storage nodes. This application aims to solve the problems of large impact on business performance, high latency in critical data recovery, and severe network traffic redundancy during data repair, effectively suppressing repair storms.

The application discloses a kind of fusion physical priori and multi-space alignment small sample light weight fault diagnosis method, belong to mechanical equipment intelligent fault diagnosis technical field.First, construct time-frequency joint input, using data normalization and fast fourier transform realizes preliminary alignment of distribution.Second, design light weight multi-scale frequency attention network.In which, time domain branch uses multi-scale depth separable convolution to extract impact characteristics;Frequency domain branch introduces sparse frequency attention mechanism guided by physical priori, adaptively filters key frequency band, while using linear Transformer coding method to reduce computational complexity.Further, to eliminate the difference of working condition, establish adaptive alignment mechanism consisting of adversarial training, frequency distribution alignment and embedding space center alignment, ensure the domain invariance of feature.Simulation verification result shows that the application has higher diagnostic accuracy under small sample and cross-condition condition.Model only needs a small amount of labeled samples to complete cross-condition rapid adaptation, and output physical interpretability.

The application discloses a no-reference image quality evaluation method based on visual clue correlation analysis, and steps are as follows: a local non-equal interaction transpose attention subnetwork is constructed; a visual clue correlation analysis module is constructed; a no-reference image quality evaluation network is generated; all images in a generated training set are linearly coded; the no-reference image quality evaluation network is trained; and quality evaluation is performed on a no-reference image to be evaluated. The local non-equal interaction transpose attention subnetwork can imitate the perception process of a human visual system on an image, and the visual clue correlation analysis module can fuse the correlation between semantic information of different granularity levels. The application improves the prediction precision of distorted image quality, and has the advantages of higher consistency with human eye visual perception and stronger generalization performance when evaluating no-reference image quality.

The invention provides a near-field dynamics neighborhood construction method and device. The neighborhood construction efficiency of particles can be improved. The method comprises the following steps: determining space coordinates of all particles in a preset simulation model; dividing a computational domain into a grid unit set according to the space coordinates; performing linearization processing on each grid unit in the grid unit set to obtain a unique linear code corresponding to each grid unit in the grid unit set; counting and recording the number of particles and the number of particles in each grid unit, and respectively establishing a corresponding relationship between the number of particles and the number of particles in each grid unit and the unique linear code; determining potential neighborhood particles of each first particle in all the particles according to the corresponding relation; and constructing a neighborhood particle set of each first particle according to the potential neighborhood particles of each first particle.

The invention belongs to the technical field of wireless communication, and particularly relates to an OFDM (Orthogonal Frequency Division Multiplexing) transmission method based on space-time linear coding index modulation. According to the method, space-time linear coding is carried out on activated index information, and the safety performance of the system is improved while the characteristics of index modulation and space-time linear codes are reserved. Since space-time linear coding is carried out by using the activation index information, after receiving information is linearly combined during detection, sending information can be directly demodulated without channel information, and meanwhile, full diversity gain is ensured.

The invention discloses an archive data disaster recovery method and system based on a distributed technology, and relates to the technical field of data disaster recovery, and the method comprises the steps: encoding an original archive data object, obtaining K data fragments and M verification fragments, and storing the K data fragments and the M verification fragments in a cluster in a distributed manner; the state of the storage node is monitored in real time, and when a fault is detected, a fragment set lost due to the fault is determined and recorded as a fragment set to be repaired; calculating the thermodynamic value of each to-be-repaired fragment, predicting the available amount of repair resources in a plurality of time intervals in the future, and generating a repair task queue according to the thermodynamic value and the available amount of the repair resources; calculating a network pressure index to select a survival fragment subset for restoration, and generating restoration fragments through linear coding operation; and after the verification is passed, storing the data into a healthy storage node. The invention aims at solving the problems of large service performance impact, high key data recovery delay and serious network flow redundancy during data recovery, and effectively inhibiting the recovery storm.