35 results about "Linear network" patented technology

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.

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 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.

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.

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.

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 present disclosure provides a method and system for identifying individual procambarus clarkii of biological characteristics, relating to the technical field of deep learning, comprising: extracting local and global digital biological characteristics of each slice image and a target image, and fusing the local and global digital biological characteristics to obtain a digital biological characteristic matrix; inputting the digital biological characteristic matrix into an improved graph neural network model, generating a query and a key through an independent multi-layer perceptron, calculating edge weights through a scaling dot product of the query-key, and obtaining multiple node features after multi-head attention splicing; splicing different node features, generating an attention score for each node based on a gating mechanism aggregation module, and outputting a global representation vector of each group of point sets after global pooling operation; mapping the global representation vector to a fixed dimension through a linear network, and taking it as a final point set embedding, and finally outputting a procambarus clarkii individual identification result.

The application discloses a train network architecture based on Ethernet deep fusion, which comprises a red network switch topology, a blue network switch topology and train subsystems connected to the blue network switch topology and the red network switch topology; the train subsystems are redundantly designed and arranged on a head car, a middle car or a tail car of the train; the backbone network topologies of the red network and the blue network are not limited and are suitable for ring network topology, linear network topology or link aggregation network topology; the application saves switch hardware devices and communication cable materials; the whole train network data ports are centrally managed, so that traffic monitoring and fault positioning are facilitated; the application builds an intensive integrated network architecture and fully utilizes the data bearing capacity of a gigabit backbone network; and the application uniformly plans a train-ground safety transmission boundary, which is beneficial to the deployment and drilling of diversified safety strategies.

The application belongs to the technical field of power grid power supply, and particularly relates to a measurement method and system for tower grounding resistance, which comprises the following steps: constructing a chain linear network model, setting an engineering typical value of the grounding impedance of each tower, constructing a prior admittance matrix, inverting the prior admittance matrix to obtain a prior impedance matrix, injecting a mixed excitation current signal with multiple different frequencies into the grounding lead of the measured tower, synchronously collecting the measured voltage of all nodes, separating the measured voltage under each frequency, calculating an admittance deviation matrix and an impedance deviation matrix, establishing an approximate linear relationship between the impedance deviation matrix and the admittance deviation matrix and a voltage difference vector, solving the approximate linear relationship, obtaining the impedance deviation of the grounding impedance under each frequency relative to the engineering typical value of the grounding impedance, calculating the actual grounding impedance, and separating the actual grounding impedance to obtain the grounding resistance. The application realizes the inversion of the grounding resistance from the external port measurement value without disassembling any grounding lead.

The application discloses a preset performance synchronization control method of a nonlinear networked time-delay system based on state translation transformation, which transforms communication time delay of the system by using state translation transformation technology, designs a preset time synchronization control scheme through the system after translation transformation, analyzes the synchronization of the transformed system, indirectly obtains effectiveness of the original system under the preset time controller, and finally realizes preset time synchronization control of the nonlinear networked time-delay system. Based on the constructed Lyapunov function, sufficient conditions of preset time synchronization are obtained, under the conditions, even if the system is affected by communication time delay or preset performance index changes, the synchronization control of the nonlinear networked time-delay system can still be realized under the preset time requirement. Finally, the effectiveness of the synchronization control scheme is verified through numerical simulation.

This invention discloses an intelligent detection method for disinformation on social networks based on deep learning. The method includes: collecting text information of all messages on a social network over a given time period, processing reply text information and topological information, and performing text preprocessing; inputting the message text information into a local information encoder to output message text feature vectors; inputting the topological information of the social network topology graph formed by the messages into a global information encoder to output message topological information feature vectors; concatenating the message text feature vectors and topological information feature vectors to obtain a fused feature vector; and inputting the fused feature vector into a linear network to output the final prediction vector. This invention, using fixed-length samples while ensuring training efficiency, significantly enhances the model's feature extraction capability by fusing network message content, reply message content, and message topological information in the propagation graph, overcoming the problem of insufficient model feature extraction ability.

The application provides a linear network and a relay dynamic adjustment method thereof. The method is applied to a communication front end machine 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 machine acquires signal quality margins of each relay acquisition station. The signal quality margin is matched with a peak-to-peak amplitude margin and a signal-to-noise ratio margin of the relay acquisition station. According to the signal quality margin of the relay acquisition station and a preset safety threshold, relay dynamic adjustment is performed. The signal quality margin of the relay acquisition station is acquired to quantify the change of an industrial environment, and then the relay dynamic adjustment is adaptively performed. In the case of guaranteeing the communication signal quality of the acquisition stations in the linear network, the relay layout is optimized, the communication delay and the energy consumption are reduced as much as possible, and the data transmission efficiency is guaranteed.

The utility model discloses a novel multi-aperture linear network switch, and relates to the technical field of network switches, the periphery of the bottom of a network switch main body is provided with support rods, the bottom of each support rod is provided with a cushion block, and the rear end face of the network switch main body is provided with a wiring port. Heat dissipation grooves are formed in the left side and the right side of the top of the network switch main body; clamping grooves matched with the cushion blocks are formed in the periphery of the top of the network switch main body; when the indoor temperature is too high in summer, the cover plate can be opened through the hinge, the contact area between the inner cavity of the network switch main body and the outside is increased, and the working heat dissipation fan can increase the air flowing speed at the top of the network switch main body, so that the heat dissipation processing effect of the heat dissipation fan can be improved, and the heat dissipation effect is improved; when a plurality of groups of network switch main bodies are stacked together, gaps can be formed between the adjacent network switch main bodies through the arranged supporting rods, so that heat accumulation can be avoided, and heat dissipation is facilitated.

This invention belongs to the fields of medical image processing, neural engineering, and artificial intelligence-assisted diagnosis. It provides a Parkinson's disease identification system, medium, and product based on brain network control energy characteristics. Based on the time series of brain region functional activities, it extracts brain states at rest and calculates the temporal dynamic characteristics of each brain state. Using the whole-brain structural connectivity matrix as the network topology, it constructs a linear network control model to calculate the optimal control energy characteristics for brain state transitions and the energy regulation capacity characteristics of whole-brain regions. Based on brain dynamics feature sets, brain network energy feature sets, and brain network graph theory feature sets, it extracts a feature set significantly related to Parkinson's disease and constructs a multi-dimensional fusion feature matrix. Using this multi-dimensional fusion feature matrix as input, it trains the Parkinson's disease identification model. Based on the multimodal brain imaging data of the subject and the trained Parkinson's disease identification model, it outputs the Parkinson's disease identification result. This achieves non-invasive, accurate, and early identification of Parkinson's disease.

The application discloses a new energy output risk early warning method and system based on power mutation feature recognition, comprising: the application collects output measurement values, meteorological forecast data and grid-connected operation state identifiers and generates operation observation records, reduces misjudgment caused by state switching, and improves early warning input reliability; the mutation feature vector is extracted by sliding window variable point detection and compared with the mutation threshold to generate a mutation trigger identifier, sudden climbing, sudden drop and other abnormalities are positioned in time, and the trigger condition is provided for subsequent risk inference; the mutation feature vector and the meteorological forecast data are input into the Bayesian linear network after triggering to output the future 60-minute output prediction interval and the out-of-limit probability, the quantitative expression of the mutation subsequent evolution uncertainty is realized; the risk intensity is calculated from the out-of-limit probability and the interval width, the risk grade is mapped, the early warning result and the disposal strategy are output, and the operation risk under high proportion of new energy is reduced.

The invention discloses a wind power grid-connected system transient overvoltage evaluation method considering unit dynamic characteristics, and relates to the technical field of new energy grid connection. The method comprises the following steps: firstly, establishing a wind turbine generator model considering power grid following control and low voltage ride through dynamics, and deducing a dynamic constraint condition of a reactive current support coefficient; secondly, establishing equivalent admittance of the unit during the fault period and embedding the equivalent admittance into an original admittance matrix of the system to obtain a corrected system impedance matrix; correcting a reactive current support coefficient value based on a dynamic constraint condition, updating an impedance matrix, and calculating a transient voltage increment caused by a unit injection current during a fault period; and finally traversing a fault scene and outputting an evaluation result. According to the method, unit nonlinear dynamic is converted into equivalent admittance, decoupling solving with a linear network is achieved, the calculation efficiency is remarkably improved, meanwhile, dynamic mechanisms such as direct-current bus voltage constraint are effectively described, and the accuracy of transient overvoltage evaluation is guaranteed.

A named entity recognition method for intelligent recommendation systems of financial products is proposed. Based on existing datasets, a BERT pre-trained model is fine-tuned to predict other unlabeled data within the domain, and a confidence screening mechanism is used to select reliable samples. More data is then fed into subsequent training. Word vector features are generated for each sample using the BERT pre-trained model. Based on the word vector features, multi-scale convolution is used to obtain the sample sentence features. Furthermore, a FLAT-BERT model is used to generate word features for each sample. The sentence features and word features are concatenated with equal weights to obtain the overall features. A linear network is then used to identify named entities in the text based on the overall features. This invention can fully utilize product information text to accurately extract named entities from product information. It achieves excellent results on publicly available named entity recognition data and has good practicality in intelligent recommendation systems with high accuracy requirements.

The invention relates to a network traffic anomaly detection method and system based on deep feature extraction. The method comprises the following steps: collecting original network flow data; performing standardization processing on the original network traffic data to generate a primary traffic tensor sequence; performing depth feature extraction processing on the primary flow tensor sequence through a trained encoder to generate a spatial-temporal feature vector; performing data reconstruction processing on the spatial-temporal feature vector to obtain a reconstructed traffic tensor sequence; calculating a reconstruction error between the reconstructed flow tensor sequence and the primary flow tensor sequence; and based on the reconstruction error and a preset error threshold value, a flow anomaly detection result is generated. By adopting the method, the problems that a traditional method depends on expert knowledge, complex space-time dependence is difficult to capture and generalization ability is weak can be effectively solved, and automatic and high-precision anomaly detection of high-dimensional and nonlinear network flow data is realized.

The invention relates to the technical field of network security, and discloses an isolated node optimized graph neural network vulnerability chain construction method and system, and the method comprises the steps: obtaining vulnerability association graph data and real vulnerability chain information, employing a linear network to extract isolated node features, fusing the node association features extracted by a graph neural network (optimized GCN / GAT, etc. And predicting isolated node vulnerability chains based on fusion features, quantifying a search space reduction effect through VCPR, and screening effective vulnerability chains by taking VCPR > = 1.1 and true establishment proportion > = 70% as standards. According to the method, the problem that a traditional graph neural network is insufficient in isolated node feature extraction is solved, the vulnerability chain search space is reduced, and the construction efficiency and the real utilization rate are improved.

This application belongs to the field of power grid supply technology, specifically a method and system for measuring the grounding resistance of power poles. The method includes constructing a chain-like linear network model, setting typical engineering values ​​for the grounding impedance of each power pole, constructing a priori admittance matrices, inverting the priori admittance matrix to obtain a priori impedance matrix, injecting multiple mixed excitation current signals of different frequencies into the grounding leads of the power pole under test, simultaneously acquiring the measured voltages of all nodes, separating the measured voltages at each frequency, calculating the admittance deviation matrix and impedance deviation matrix, establishing an approximate linear relationship between the impedance deviation matrix, admittance deviation matrix, and voltage difference vector, solving the approximate linear relationship to obtain the impedance deviation of the grounding impedance relative to the typical engineering value of the grounding impedance at each frequency, calculating the actual grounding impedance, and separating the actual grounding impedance to obtain the grounding resistance. This application enables the direct inversion of grounding resistance from external port measurements without disassembling any grounding leads.

The invention discloses a power system state estimation method based on a multi-scale near-end linear network and uncertainty quantization, and belongs to the field of power system state estimation, and the method comprises the steps: firstly, carrying out the dynamic weighting of noise-containing measurement through a self-adaptive attention mechanism, and suppressing the abnormal data; then a coarse-fine double-branch residual near-end linear network is constructed, global and local features are extracted in parallel with different synchronization lengths, and high-precision state estimation is rapidly obtained through near-end gradient expansion and soft threshold iteration; a lightweight uncertainty branch is designed, state probability distribution is output, and real-time quantification of a confidence interval is realized; further introducing a discriminator to carry out adversarial training, and enhancing the distribution consistency of an estimated value and a real state; and finally, optimizing the whole network end to end by using the comprehensive loss. According to the method, high robustness and low time delay are still kept in non-Gaussian noise, parameter time varying and measurement missing scenes, and a reliable and explainable state result is provided for power grid online monitoring.

A badminton swing action recognition method based on surface electromyogram signal, comprising: dividing badminton swing action; collecting surface electromyogram signal using MYO; obtaining active section by pre-processing electromyogram signal; transforming active window electromyogram signal into wavelet time-frequency graph; sending time-frequency graph into CNN network of fusion space and channel attention mechanism, completing extraction of spatial features, sending spatial features restored into time sequence into ConvLSTM network, completing extraction of time sequence features, sending features into linear network for integration, and finally obtaining badminton swing action recognition result using Softmax function. The application effectively reduces decoding time and improves recognition accuracy.

The application discloses a kind of wind power grid-connected system transient overvoltage evaluation methods considering unit dynamic characteristics, it is related to new energy grid-connected technology field.The method first establishes the wind turbine model considering grid following control and low voltage ride through dynamics, deduces the dynamic constraint condition of reactive current support coefficient;Second, construct the equivalent admittance of unit during fault and embed system original admittance matrix, obtain the modified system impedance matrix;Further based on dynamic constraint condition, the reactive current support coefficient value is revised and impedance matrix is updated, the transient voltage increment caused by unit injection current during fault is calculated;Finally, traverse fault scene output evaluation result.The application realizes decoupling solution with linear network by converting the nonlinear dynamics of unit into equivalent admittance, while significantly improving the calculation efficiency, effectively describes the dynamic mechanism such as direct current bus voltage constraint, and guarantees the accuracy of transient overvoltage evaluation.

An extension element (600) for a device (604), suitable for being connected in a linear network having a bus topology, comprising: - a first connection point (401) and a second connection point (402); - a switch (405) connected to the first connection point (401) and to the second connection point (402), so that in a closed state of the switch (405) there is a direct connection between the first connection point (401) and the second connection point (402); in an open state of the switch (405) the direct connection is interrupted; - a read-write connection (605) between a read port of a first interface (407) and a write port of a second interface (502), - a processing unit (406) configured to modify the state of the switch (405) and to change, via the processing unit (406), the impedance in said read-write connection or in a connection between the first interface (407) and the second interface (502), in order to change the state of the extension element (600) from an idle state to a forwarding state.

The application relates to the technical field of energy storage power station equipment updating, and discloses a batch firmware updating method and system for energy storage power station equipment, which comprises the following steps: using a random linear network coding technology to fragment and encode and combine a firmware updating burning file, so as to obtain an encoding package corresponding to the firmware updating burning file; dynamically adjusting the priority of executing a firmware updating task in combination with the task emergency degree, system idleness, firmware updating emergency and firmware updating deadline in the current energy storage power station; and performing batch firmware updating through the encoding package according to the priority of the firmware updating task. The application can improve the efficiency of firmware updating, is simple and safe, and has high practicability.

This invention discloses a spatiotemporal traffic flow prediction method based on dual-flow decoupling. The method first decomposes the temporal data of road network traffic flow into trend and periodic components using the exponential moving average method. Then, features are extracted and fused using a deep linear network and a local feature hybrid network with temporal block embedding to obtain the global temporal prediction component. After the traffic flow temporal data is encoded in the temporal domain by gated dilated convolution, multi-view spatial aggregation is performed using forward, backward, and normalized graph convolutions with adaptive adjacency matrices to obtain the local spatiotemporal prediction component. Finally, dynamic weights are generated by a gated network, and the two types of components are weighted and fused to obtain the final prediction result. This invention accurately separates long-term and short-term traffic flow features, adaptively balances global patterns and local details, improves the accuracy and robustness of long-term temporal prediction, and reduces computational complexity, making it suitable for intelligent traffic flow management scenarios.

The invention discloses a data-driven switching type dynamic event trigger control method and device, and the method comprises the steps: constructing a convex optimization problem based on the measurable input state data of an offline open-loop system for an unknown linear networked control system under a denial of service attack; solving an optimal control gain matrix of the system based on the optimal solution of the convex optimization problem; deriving an expression of unknown parameters based on the optimal solution and the optimal control gain matrix; designing a switching type dynamic event triggering mechanism, and obtaining a preliminary constraint condition of a to-be-designed parameter in the switching type dynamic event triggering mechanism in combination with the expression of the unknown parameter under the condition of no denial of service attack; and aiming at the condition that the denial of service attack exists, on the basis of the initial constraint condition of the to-be-designed parameter, obtaining a sufficient condition for the index stability of the unknown linear networked control system, so that the closed-loop system model is exponentially stable at the balance point. According to the method, the problem of effectively reducing the communication load of the networked control system with unknown parameters under the denial of service attack is solved.