20 results about "Linear coding" patented technology

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.

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 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.

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 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 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 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 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 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.

The application provides a no-reference image quality evaluation method based on causal pairing intervention. The steps are as follows: a training set and a test set are generated; the samples in the training set and the test set are linearly coded; a feature extraction subnetwork is constructed; a causal inference subnetwork is constructed; a no-reference image quality evaluation network based on causal pairing intervention is constructed; the image quality evaluation network is trained; an image to be evaluated is input to obtain an image quality evaluation result; the application constructs a no-reference image quality evaluation network based on causal pairing intervention, learns the mapping relationship between image distortion and content and image quality by using the trained network, and evaluates the no-reference image quality according to the learned mapping relationship, so that the problem of low accuracy of no-reference image quality evaluation can be effectively solved, and the application has the advantages of strong generalization ability and high image evaluation accuracy.

The invention provides a multi-energy supply and demand balance method and system, and the method comprises the steps: carrying out the data preprocessing of collected electric energy data, thermal energy data, hydrogen energy data and environment parameters, so as to generate a standardized data matrix; sequentially carrying out grey correlation analysis and clustering division based on the standardized data matrix, and quantifying an energy complementary relationship and piecewise linear coding to screen out key contradictory features; constructing an electricity-heat-hydrogen dynamic equation based on the key contradictory features, and performing space-time parameter configuration based on the electricity-heat-hydrogen dynamic equation to generate a digital twin multi-energy coupling model; extracting model constraint parameters based on the digital twin multi-energy coupling model; and inputting the model constraint parameters into a deep reinforcement learning network, updating the reward function weight in combination with a real-time state, generating a dynamic scheduling strategy, and encoding the dynamic scheduling strategy into an equipment executable scheduling instruction. The method has the advantages of being accurate in optimal scheduling strategy, efficient in supply and demand balance, high in system self-adaptive capacity and capable of improving the energy utilization rate.