82 results about "Linear network" patented technology

The invention discloses a multi-modal news recommendation method and system in combination with clock interests, and relates to the technical field of data mining and recommendation methods.The method comprises the steps that a user behavior sequence, a candidate news set and a related timestamp set are extracted; obtaining the multi-modal coding representation of the historical interaction news through the time perception multi-modal feature coding; performing clock interest modeling based on multi-modal coding representation of historical interactive news, and fusing long and short-term interests through Gaussian weighted aggregation and long-term interest enhancement operation to obtain a user interest vector; and calculating the matching degree of the candidate news and the current user interest vector, applying time period sensitive suppression through time sequence gating, performing click probability prediction on the output of the time sequence gating by adopting a linear network, and performing a dynamic recommendation decision. According to the method, by means of the feature fusion technology of time context perception, the interest continuity of the hour granularity is captured, and the recommended content is more accurately matched with the requirements of the user in different time periods.

In a blockchain, transacting a record comprises performing linear network coding on the record or on a data file corresponding to the record, to produce a plurality of coded data parts; and storing at least one of the plurality of coded data parts on the blockchain or storing a proof of knowledge on the blockchain, the proof of knowledge derived from the coded data parts. Linear network coding coefficients might be derived from cryptographic hashes of the plurality of coded data parts. An all-or-nothing transform can use the cryptographic hashes to provide the proof of knowledge.

The invention discloses an electromagnetic spectrum prediction method and system based on fractional Fourier transform. The method comprises the following steps: constructing historical spectrum observation data in a target area; mapping the collected spectrum observation data to a corresponding fractional order Fourier domain by using adaptive fractional order Fourier transform, and adaptively adjusting fractional order parameters of a fractional order Fourier transform layer to output a fractional order Fourier domain containing predictable spectrum components; in the mapped fractional order Fourier domain, non-predictive noise in the converted data is filtered out; performing feature extraction and prediction on the frequency spectrum data of the fractional order Fourier domain after noise filtering by using a complex valued linear network to obtain a prediction result of the fractional order Fourier domain; and mapping the prediction result of the fractional Fourier domain back to the original time domain by adopting inverse fractional Fourier transform, and generating final frequency spectrum prediction output. According to the method, the accuracy of weak periodic spectrum prediction is effectively improved through fractional Fourier transform and a filtering strategy.

The invention provides an accelerated simulation solving method and system for a controllable self-recovery energy dissipation device, and the method comprises the steps: S1, enabling an inductance element between a nonlinear network and a linear network in a hybrid cascade DC power transmission system to be equivalent to a short transmission line, and building circuit models for the linear network and the nonlinear network respectively; s2, obtaining the value of the current controlled voltage source based on the node voltage of the previous simulation time step of the linear and nonlinear networks, the value of the controlled voltage source and the information interaction format; s3, node voltage solving is carried out based on the value of the controlled voltage source of the current simulation time step to obtain the node voltage of the current simulation time step, if the number of iterations is met, the operation is stopped, and otherwise, the step S2 is executed; the nonlinear network comprises a controllable self-recovery energy dissipation device; according to the invention, the energy dissipation device and the linear main circuit in the circuit system are decoupled in a sub-network manner, the calculation amount is reduced, and the iterative algorithm is used for solving, so that the high-efficiency and high-precision simulation of a nonlinear network and a linear network is ensured.

The invention discloses a gas sensor multi-mode adaptive preprocessing method based on a Mama state space model, and relates to the field of gas detection, and the method comprises the following steps: synchronously collecting original signals, environmental parameters and historical time sequence data of a sensor; performing feature extraction on the three types of data to generate a sensor feature vector, an environment feature vector and a historical feature vector; the method comprises the following steps of: obtaining a final fusion feature through processing, mapping the final fusion feature to a 128-dimensional embedding space through a contrast learning encoder, inputting a three-layer residual error connection Mamba time sequence prediction network to output a zero offset, dynamically predicting process noise and observation noise through a neural network, executing adaptive Kalman filtering to output a smooth signal, and outputting a final fusion signal. Adopting a learnable piecewise linear network to carry out nonlinear correction on the filtering signal; the gas concentration value is calculated by integrating the correction signal and the zero offset, and finally a confidence score and a data quality mark are provided; according to the method, high-precision, real-time and robust pretreatment of various gas sensors in a complex environment is realized.

The invention discloses a cross-subject electroencephalogram emotion recognition method and system based on dynamic domain invariant representation decoupling and recombination, and belongs to the technical field of artificial intelligence. The invention provides a non-personalized decoupling and recombination framework for cross-subject electroencephalogram emotion recognition, and aims to separate emotion-related individual invariant features from cross-subject individual invariant features through complex electroencephalogram signal characterization obtained through dynamic decoupling, so that individual differences are eliminated while emotion classification performance is guaranteed. Specifically, the method comprises the following steps: carrying out original EEG data analysis and preprocessing by using MATLAB and Python MNE libraries; frequency spectrum and space features of EEG signals are extracted through a multi-channel frequency spectrum space self-attention mechanism module, and capture of emotional features is enhanced in combination with a self-attention mechanism and a cross-attention mechanism; a joint distribution alignment method based on a category prototype is adopted, and decoupled feature distribution is optimized, so that invariant features in subjects and invariant features among subjects have higher distinction degree in emotion classification; the optimized decoupling features are recombined through a linear network, the two features are coordinated to perform more sufficient emotion representation extraction, and emotion classification is performed through a multi-layer perceptron. According to the method, excellent cross-subject emotion recognition performance is obtained on multiple data sets, and the emotion recognition rate of the electroencephalogram signals in a cross-subject scene can be effectively improved.

The invention discloses a multi-modal dialogue emotion recognition method and device, and relates to the technical field of multi-modal emotion recognition. More accurate emotion prediction is realized through the steps of extracting multi-modal features, inferring an implicit relationship, constructing a mixed relationship graph, performing multi-modal semantic alignment and the like. The method comprises the steps of performing single feature vector extraction and fusion on an input multi-modal dialogue sequence needing emotion recognition to obtain an initial multi-modal feature vector, determining an implicit association relationship between sentences included in each modal according to a constructed similarity function, and performing emotion recognition on the multi-modal dialogue sequence according to the implicit association relationship. And mapping the representation subjected to weighted fusion with the visual and audio modal information to a word list through a linear network to obtain a predicted word generation probability of the target sentence, and determining a difference loss value between the predicted word probability and an actual word probability based on a cross entropy loss function to obtain a loss function.

The invention discloses a near-field channel state information feedback method under a super-large scale array based on Transform, which comprises the following steps: in a downlink XL-MIMO system in a near-field area, a user side performs two-dimensional matrix processing on acquired CSI data and transmits the processed CSI data to a coding end; two-channel coding compression is carried out on the CSI matrix, a first channel uses an encoder to extract channel related characteristics, and a second channel enables an original CSI matrix to pass through a linear network so as to reserve original information; compressing the output of the two channels, and splicing to generate a low-dimensional code word to be fed back; the base station side decoding end splits the low-dimensional code word and decompresses the low-dimensional code word of the corresponding channel; decompressed dual-channel code words are respectively input to two ends of a decoder; and carrying out size change on the output of the decoder to obtain the estimated original CSI. According to the method, angle and distance information of a near-field channel is fully utilized, and efficient CSI compression and high-precision reconstruction are realized.

The invention discloses an ultra-short-term wind power prediction method and device based on improved Hilbert-Huang transform, and relates to the field of wind power prediction.The method comprises the steps that collected wind power data and wind speed data are processed, and initial wind power data and initial wind speed data are obtained; adding the initial wind speed data into white noise, generating new wind speed data, and performing iterative empirical mode decomposition to obtain a plurality of mode components; sequentially subtracting the modal component from the initial wind speed data to obtain a residual component; converting the components through a Hilbert transform algorithm to obtain new sequence data; inputting the new sequence data into a linear network for processing to obtain corrected wind speed data; constructing an ultra-short-term wind power prediction model based on the sequential sequence lightweight adaptive network; and inputting the corrected wind speed data and the initial wind power data into a prediction model for prediction, and outputting ultra-short-term wind power prediction data. According to the invention, the predicted wind speed is closer to the real wind speed, and the power is predicted more accurately.

Vector network coding, such as linear network coding, is used to compute a plurality of coded data parts from original data, wherein each coded data part is computed from a cryptographic hash function of a previous coded data part. A recipient of the coded data parts can compute the cryptographic hash functions of the coded data to reproduce a system of linear equations, which can be solved to recover the original data. A cryptographic key that employs a polynomial over a finite field can have polynomial coefficients that comprise a function of vector network coding coefficients.

According to disclosed embodiments, methods and systems of data transmission are provided. An aspect of the present disclosure is a method comprising receiving an audio stream, parsing the audio stream into packets, encoding each packet using Alphabet Linear Network Coding (ALNC), and transmitting the encoded packets.

Methods and systems for generating trim-pass metadata for high dynamic range (HDR) video are described. The trim-pass prediction pipeline includes a feature extraction network followed by a fully connected network which maps extracted features to trim-pass values. In a first architecture, the feature extraction network is based on four cascaded convolutional networks. In a second architecture, the feature extraction network is based on a modified MobileNetV3 neural network. In both architectures, the fully connected network is formed by a set of three linear networks, each set customized to best match its corresponding feature extraction network.

The invention discloses a light quantum chip which is combined with high-dimensional coding, is provided with two data coding layers and two trainable entanglement layers, is compatible with a data recoding technology, and can be used for variable component subtasks such as a quantum neural network. The chip structure comprises a cascaded Mach-Zehnder interferometer binary tree array, d groups of photon pair sources, d groups of controlled unitary gate modules, a multi-quantum bit control Z gate, four d-dimensional adjustable linear networks and two state tomography linear networks. The cascaded MZI binary tree array and the controlled unitary group jointly form a first coding layer and a parameter-containing entanglement layer, and the adjustable optical linear network and the multi-quantum bit control Z gate jointly form a second coding layer and a parameter-containing entanglement layer. The two state chromatography linear networks can further improve the computing power of the chip in a mode of changing a measurement basis or executing quantum state chromatography. According to the method, the expression ability of the light quantum neural network is improved, and the development of a light quantum computing chip and the application of the light quantum computing chip in actual tasks are facilitated.

A method for autoconfiguration of a plurality of nodes in a linear network allows extracting the address and position of each node. The method includes applying an identifier field for transmitting to the bus the bit sequence of the identifier of a chosen node. Then for at least for the first node to the last but one node, a field comprising a predetermined bit sequence is applied. The field comprises dominant bits, so a current is transmitted. Then, a further field is applied for transmitting any stored direction bit associated to that node and obtained in any previous iteration. The iteration continues by choosing a node different from a node chosen in any previous cycle, starting the communication, until all nodes are identified.

The invention discloses a time sequence prediction method based on a frequency shift linear network. The method comprises the following steps: S1, converting time sequence data from a time domain to a frequency domain; s2, performing segmentation processing on the frequency domain data in the S1 to obtain a plurality of continuous frequency band data; s3, performing frequency shift operation on the segmented frequency band data in the non-low-frequency region, and moving to a low-frequency region; s4, converting all frequency band data in the low-frequency region to a time domain; s5, respectively inputting the time domain data in the S4 into a plurality of trained linear networks to carry out time relation modeling; s6, converting a modeling result in S5 into a frequency domain; s7, restoring the frequency domain data in the step S6 to the original frequency band position; and S8, converting the frequency domain data restored in the S7 to a time domain to obtain a corresponding prediction result in the time domain. According to the method, the problem of low prediction accuracy caused by different learning capabilities for different frequency components in data in an existing time sequence prediction method based on deep learning is solved.

The present invention provides a method for calculating nodal electricity prices considering non-linear network losses and distributed balancing nodes, belonging to the field of electricity markets. The method of the present invention includes: establishing an improved DC optimal power flow model considering non-linear network losses; using a correction method for distributed balancing nodes of the load weight type to correct the nodal electricity price model; then, using a relaxation method based on the second-order cone to process the corrected nodal voltage model, solving the model and calculating the nodal electricity price to ensure the optimality of the electricity price result; finally, substituting the nodal electricity price and system parameters into the proposed sufficient conditions for exact relaxation to verify the rationality of the electricity price calculation result. The method of the present invention scientifically measures nodal electricity prices, adapts to the actual needs of rapid clearing and accurate solution in the spot market, helps to improve the enthusiasm of electricity users to participate in the spot market, helps to improve the resource allocation efficiency of the electricity spot market, and has practical value for the pricing mechanism of the electricity spot market in China.

The invention discloses a high-precision hybrid expert large model and a fine tuning method thereof. The hybrid expert large model is composed of a domain expert group and a shared expert group. A high-precision data transmission fine tuning module based on each linear network LN in the shared expert, wherein the fine tuning module comprises an extended linear network, a numerical value alignment network, a blocking controller and a precision converter; the extended linear network is of a replicated linear network (LN) structure and converts an input vector into a first data flow vector; the numerical value alignment network performs numerical value coding on the output data flow vector of the extended linear network to generate a second data flow vector; the blocking controller controls whether the extended linear network and the numerical alignment network participate in domain expert group reasoning or not according to binary data signals; the precision converter adjusts the precision of the data stream output by the numerical alignment network and the precision of the data of the shared expert linear network to be consistent; according to the invention, the flexibility and intelligent control capability of the high-precision hybrid expert large model can be enhanced.

A method for encoding data includes: selecting a sequence of pivot candidate positions from a sequence of g encoded vectors to encode blocks of g data symbols in a round of encoding by the following steps: providing a set of g pivot candidate positions; selecting pivot candidate positions for the sequence from the set of pivot candidate positions; removing the selected pivot candidate positions from the set of pivot candidate positions; and repeating until the set of pivot candidate positions is empty and the sequence of selected pivot candidate positions in the round is non-linear. A set of encoded vectors is generated based on the sequence of selected pivot candidate positions, each encoded vector including zero-valued coefficients at positions within the encoded vector preceding the pivot candidate positions and non-zero-valued coefficients at least at the pivot candidate positions.

The invention discloses a tuning method for a radio frequency matcher, and relates to the technical field of radio frequency matchers. The method comprises the steps that an a coefficient value is written into a matcher program, and a matcher runs; the method comprises the following steps: reading a # imgabs0 # value, a # imgabs1 # value and a Y0 value in a linear network through a sensor; # imgabs2 #, # imgabs3 # and Y0 are substituted into a substitution formula to obtain an estimated value X0 of the current load impedance, and target capacitance # imgabs4 # tar and target capacitance # imgabs5 # tar are calculated according to the estimated value X0 in order to enable the impedance of the power supply end to reach a set value Ytar; the matcher controls a motor of the executing mechanism to enable the capacitors # imgabs6 # and # imgabs7 # to move to the calculated target positions # imgabs8 # tar and # imgabs9 # tar, the executing mechanism works according to a calibration curve between a pre-stored capacitor impedance value and a mechanical position, and the method can be suitable for any linear matching network such as an L shape, a T shape, a Pi shape and the like.

The present invention relates to a linear network coding method based on BLS. The source node divides the file to be signed into several file blocks and represents them as a prime domain #imgabs0# k dimensional vectors, which are then augmented to become a set of linearly independent vectors; a subspace generated based on this set of vectors is signed, and a label for the subspace and a signature for each basis vector are generated. The signatures are loaded into a data packet and sent to a downstream node; after receiving the data packet, the intermediate router or the destination node verifies the signature of the data packet from the same source node; the intermediate router linearly combines all verified data packets to form a new data packet and sends it to the downstream node; if the destination node receives a predetermined number of verified data packets, it can parse the original data sent by the source node. The present invention can not only resist pollution attacks in network coding and ensure data integrity and authenticity, but also reduce user computing complexity and ensure secure and efficient data transmission.

This invention discloses a multimodal news recommendation method and system that incorporates clock-based interests, belonging to the field of data mining and recommendation methods. The method includes extracting user behavior sequences, candidate news sets, and related timestamp sets; obtaining multimodal encoded representations of historical interactive news through time-aware multimodal feature encoding; performing clock-based interest modeling based on these representations, and fusing long-term and short-term interests through Gaussian weighted aggregation and long-term interest enhancement operations to obtain user interest vectors; calculating the matching degree between candidate news and the current user interest vector, and applying time-sensitive suppression through time-series gating; using a linear network to predict click probabilities for dynamic recommendation decisions. This invention leverages time-context-aware feature fusion technology to capture hourly-level interest continuity, enabling more accurate matching of recommended content with user needs at different times.

This invention relates to a data-driven switching gain state estimation method for connected vehicles. The method includes: collecting system data from various subsystems within the connected vehicle to construct a nonlinear network system as a state-space model; collecting process data and establishing a data-driven switching gain state observer; defining error data and constructing an error system; setting stable operating conditions for the nonlinear network system based on constant quantities and the gain matrix; and operating the nonlinear network system under stable conditions to achieve data-driven switching gain state estimation for the connected vehicle. By collecting a large amount of system data from various subsystems within the connected vehicle for extraction, insight, and prediction, the method emphasizes both data quantity and quality, enabling the connected vehicle to exhibit adaptive and flexible characteristics. Because a data-driven switching gain state observer is established, real-time monitoring and switching gain control of the network system can be performed to improve the efficiency and quality of network data transmission and ensure the stability of the connected vehicle's operation.

This invention discloses a method and system for optimizing data interaction between intelligent terminals based on link state awareness, relating to the field of wireless communication data transmission technology. The method includes: constructing a directed acyclic graph topology; performing cooperative topology state discovery to generate a real-time link state communication topology; performing hierarchical packetization for transmission quality constraints; performing random linear network coding to obtain multiple linearly independent coded packets; performing distributed congestion-aware scheduling decisions to obtain multiple primary transmission links; performing differential coding and obfuscation transformation to obtain multiple obfuscated coded packets; and concurrently transmitting multiple obfuscated coded packets to a second terminal for Gaussian elimination decoding and reception. This invention solves the technical problems of weak topology adaptability, unreasonable congestion scheduling, and insufficient security protection in existing technologies, achieving efficient and secure data interaction between terminals and improving the real-time performance and transmission security of local area network data transmission.

The invention relates to the technical field of data processing, in particular to a text generation sequence length prediction model and a training method thereof, which introduces a target network structure in the process of generating a token by an open source large language model, is grafted behind the open source large language model, and is used for predicting the length of a text generation sequence while the token is generated by the open source large language model. And predicting the number of tokens which need to be continuously generated for completing the current dialogue. Wherein the target network structure can comprise a trainable request network structure, a Transform network structure and a Linear network structure, and the sequence length can be predicted while the text is generated by combining the open source large language model and the target network structure.

The invention discloses a novel fault diagnosis method and system for an oil-electricity hybrid power offshore operation platform, and the method is based on a deep residual convolutional neural network of enhanced discriminant feature learning, forms a residual network through introducing a residual learning unit, can avoid the problem of low accuracy in a conventional convolutional neural network, and improves the fault diagnosis accuracy of the oil-electricity hybrid power offshore operation platform. The method has strong learning and analysis capabilities. According to the novel fault diagnosis system of the oil-electricity hybrid power offshore operation platform, potential features of samples are learned through multi-layer nonlinear network training, so that the classification or prediction capability is improved, and the fault diagnosis system has advantages in the aspect of system-level complex fault diagnosis. Besides, the oil-electricity hybrid power system provided by the invention comprises a multi-task convolutional neural network, can process multiple tasks at the same time, allows different tasks to share certain layers, and reserves a specific layer for each task, thereby effectively improving the efficiency and accuracy of fault diagnosis of the oil-electricity hybrid power system of the offshore operation platform.

This application discloses a method, device, terminal, and medium for assessing the synchronous stability of grid-connected wind power. The solution obtains the topology information of the grid-connected wind power combined transmission system and constructs an equivalent circuit. Then, combining the preset grid-connected wind power control logic and the principle of linear network superposition, it constructs an output active power relationship under different control conditions, including the coupling relationship between the output active power of the grid-connected wind power and the terminal voltage and output phase angle of the grid-connected wind power. Based on this output active power relationship, it generates the output phase angle characteristic curve of the grid-connected wind power and calculates the output phase angle acceleration area and the maximum deceleration area. Finally, based on the area comparison results, it determines the synchronous stability assessment result of the grid-connected wind power, thereby accurately assessing the synchronous stability of the grid-connected wind power in the grid-connected wind power combined transmission system, improving the reliability of the assessment results, and ensuring the safe and stable operation of the new energy transmission system.

The invention provides a linear network and a relay dynamic adjustment method thereof, the method is applied to a communication front-end processor of the linear network, the linear network further comprises a plurality of acquisition stations connected in sequence, and at least one relay acquisition station is arranged in the plurality of acquisition stations; the communication front-end processor obtains signal quality margins of the relay acquisition stations, and the signal quality margins are matched with peak-to-peak value amplitude margins and signal-to-noise ratio margins of the relay acquisition stations; and performing relay dynamic adjustment according to the signal quality margin of the relay acquisition station and a preset safety threshold. Through obtaining the signal quality margin of the relay acquisition station, the change of the industrial environment is quantified, and then the relay dynamic adjustment is adaptively performed, so that the relay layout is optimized, the communication delay and energy consumption are reduced as much as possible, and the data transmission efficiency is ensured under the condition that the communication signal quality of the acquisition station in the linear network is ensured.

The invention discloses a wireless communication method and a wireless communication system. In the wireless communication method, each of N slave unmanned aerial vehicles passing an authentication program confirms that the slave unmanned aerial vehicles have the same time scale area as ground processing equipment according to a synchronization timestamp received from the ground processing equipment, selecting a corresponding key in a random key group permutation sequence as a current key according to a time point of receiving the random key group permutation sequence from the ground processing equipment, and encrypting k public advancing tracks received from the ground processing equipment by using the current key based on a random perturbation algorithm to obtain perturbation encryption data, transmitting the disturbance encrypted data to the main unmanned aerial vehicle according to the received serial number and coordinate of the main unmanned aerial vehicle from the ground processing equipment; and the master unmanned aerial vehicle executes random linear network coding on the received N disturbance encrypted data transmitted by the N slave unmanned aerial vehicles to generate a coding code block, and transmits the coding code block to ground processing equipment.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an action in response to each context in a sequence of contexts. One of these methods includes maintaining data specifying a respective gated linear network corresponding to each of a plurality of actions; for each context in a sequence of contexts: for each action, processing the context using the gated linear network corresponding to the action to generate a predicted probability; for each action, generating an action score for the action based on at least the predicted probability; and selecting an action to perform in response to the context based on the action scores.

The invention provides a procambarus clarkii individual identification method and system based on biological characteristics, and relates to the technical field of deep learning. Extracting local digital biological characteristics of each slice image and the target image and global digital biological characteristics of the target image, and fusing the local digital biological characteristics and the global digital biological characteristics to obtain a digital biological characteristic matrix; inputting the digital biological characteristic matrix into an improved graph neural network model, respectively generating a query and key through an independent multi-layer perceptron, calculating an edge weight through a zoom dot product of the query and key, and carrying out multi-head attention splicing to obtain a plurality of node characteristics; splicing different node features, generating an attention score for each node based on an aggregation module of a gating mechanism, and outputting a global representation vector of each group of point sets after global pooling operation; and mapping the global representation vector into a fixed dimension through a linear network, embedding the fixed dimension as a final point set, and finally outputting to obtain a procambarus clarkii individual identification result.