35 results about "Transition matrices" patented technology

This invention belongs to the field of electronic digital data processing technology, specifically relating to a low-altitude communication data anomaly detection system and method based on edge intelligence. The system parses protocol fields from the raw bitstream of the low-altitude communication link layer, extracts type identifiers and payload lengths to construct a protocol state machine transition matrix, and utilizes a Long Short-Term Memory (LSTM) network to extract state transition probability sequence features. Geographically adjacent edge nodes are set as micro-federated learning groups. Each node, after training using local features, only uploads network gating weight parameters to its neighboring nodes for weighted aggregation to update its local model. An alarm is triggered when the real-time state transition probability sequence deviates from the normal transition matrix and exceeds a preset topology threshold. This invention can identify protocol state machine logic errors and transition anomalies, reducing the amount of communication data required for collaborative model updates between edge nodes.

This invention relates to the field of biosensor technology, specifically to a sleep staging method and system based on the fusion of label calibration and inference calibration. The method includes the following steps: constructing a sleep stage transition matrix M driven by both data statistics and domain knowledge; performing a light calibration on the original sleep staging labels of the training set based on the transition matrix M to obtain calibration labels; extracting the time-frequency features of the electroencephalogram (EEG) signal, and training an LSTM model using the calibration labels to obtain a base model; and using the LSTM model to perform preliminary staging on a fixed-length EEG signal sequence to obtain an initial staging sequence. This invention employs a fusion scheme of a dual-driven transition matrix, light label calibration, dynamic programming global calibration, and iterative feedback.

The application discloses an omnidirectional guiding interaction multi-model positioning method and system for a motorized mother ship to recover an underwater robot and a storage medium, the method obtains the state information and distance data of the mother ship by integrating a water acoustic ranging and a water acoustic communication device, constructs an interaction multi-model singular value decomposition unscented Kalman filtering algorithm fusing a uniform straight line and a uniform bow turning model, and realizes real-time estimation of the position of the mother ship under a mouth-shaped maneuvering track. An event-triggered communication mechanism is designed, information is broadcast only when the turning state of the mother ship changes, so as to reduce the communication load and maintain real-time performance; meanwhile, a Markov transition matrix is updated, the turning model weight is enhanced, and the estimation accuracy and model matching are improved. According to the model probability and the estimation result, a LOS guiding law with an adaptive look-ahead distance is constructed, and the autonomous guiding recovery of the underwater robot to the motorized mother ship is realized. The method provided by the application can realize dynamic estimation and guiding based on the measurement data of a single water acoustic device under the conditions of the mother ship maneuvering and communication limitation.

A method of training, verification, and advanced notice for an autonomous system, comprising: by a machine learning classifier, wherein an adversary vehicle, and a primary vehicle are on the same path; creating a path position probability transition matrix, wherein the path position probability transition matrix comprises an acceleration parameters array of one or more acceleration parameters for a potential position, sorting a sample path, and using Ne elite path; creating a samples array, wherein the sample array is one or more paths; evaluating a sample path; sorting the sample path, based at least in part a custom score of a custom score function and selecting a subset of the sample paths as Ne elite paths; using the Ne elite paths to update the path probability transition matrix and the acceleration parameters array; and repeating until the Ne elite paths stabilizes for a predetermined number of iterations.

A method of quantum probability estimation of an event in a stochastic process is provided. The stochastic process is modelled based on a finite state-machine model. A classical predictive model for the event is modelled based on the stochastic process and includes a set of states and a set of state transition matrices associated with the finite state-machine model. The method includes: generating a circuit description of a quantum state-preparation operator based on a past observation sequence of the stochastic process and the set of state transition matrices of the classical predictive model; generating a circuit description of a quantum search operator based on the circuit description of the quantum state-preparation operator and an event indication function, whereby the event indication function is configured to indicate whether the event is to occur with respect to the future sequence of the stochastic process; generating a plurality of circuit descriptions for a plurality of measurement quantum circuits, respectively, for compiling the plurality of measurement quantum circuits on a quantum computer, whereby each circuit description of the measurement quantum circuit is generated based on the circuit description of the quantum state-preparation operator and the circuit description of the quantum search operator; for each of the plurality of circuit descriptions of the plurality of measurement quantum circuits: compiling the circuit description of the measurement quantum circuit on the quantum computer to form the measurement quantum circuit thereon; and executing the measurement quantum circuit formed on the quantum computer to obtain a measurement result associated with the measurement quantum circuit that indicates a number of times that the measurement quantum circuit produces an indication that the event is to occur; and estimating a probability of the event based on the measurement results obtained. There is also provided a corresponding system for quantum probability estimation of an event.

This invention discloses a flexible control method for digital twin manufacturing systems addressing uncertainties in the production process, belonging to the field of intelligent manufacturing system technology. The method includes: statistically analyzing the time of the four stages of perception, analysis, decision-making, and execution; determining the advance prediction time by combining system complexity and simulation speed; constructing an scalable system state transition matrix; performing accelerated simulation based on real-time states and injecting uncertain events according to trigger probabilities to obtain multiple future states; selecting the top n high-probability states in descending order of occurrence probability for parallel decision-making; binding decision instructions with corresponding states and issuing them in advance; and executing the corresponding instructions through state matching when the physical system reaches the predicted time, achieving flexible control. This invention can improve the consistency and robustness of "virtual control of the real" in digital twin systems under uncertain conditions and is applicable to multi-level digital twin systems such as machine tools, robots, production lines, and workshops.

This invention discloses a time-series segmentation method for hydraulic support action states that integrates physical priors, belonging to the field of intelligent coal mining technology. First, pressure monitoring data of the hydraulic support columns is obtained from the hydraulic support pressure monitoring system database to construct a multi-dimensional feature vector containing poor directionality, and a sample-level undersampling strategy based on the majority class proportion is used to balance the data distribution. Second, a temporal convolutional network model optimized with rare stage recall rate is constructed to output a frame-level probability distribution. Finally, the aggregated action stages are input into a conditional random field model with a frozen physical transition matrix for global logical correction. This invention effectively solves the problems of missed detection and fragmented prediction of key actions under long-tail distribution, and significantly improves the recognition accuracy and logical completeness of rare actions such as initial support and support shifting by eliminating illegal state transitions through physical hard constraints.

This invention discloses a prediction residual-driven multi-target tracking method, belonging to the field of computer vision technology. First, target detection is performed on the current frame. Based on the updated model transition matrix and model probabilities from the previous frame, multiple motion models are used to predict existing target trajectories in parallel within a unified state space, and the prediction results are weighted and fused to obtain the target prediction state. Motion constraints are constructed using the motion uncertainty index from the previous frame, and together with geometric overlap and motion direction consistency, they form an association cost, realizing data association between the trajectory prediction state and the detection boxes. Kalman filtering is performed on the successfully associated detection boxes to update the data, and the prediction residual is calculated. Based on the prediction residual, the model transition matrix is ​​adaptively adjusted, and an equivalent prediction residual is constructed to quantify the target motion uncertainty index for data association in subsequent frames. This method improves the robustness and stability of multi-target tracking in complex scenes without relying on appearance features.

Methods, systems, and apparatus, including medium-encoded computer program products, for electrical power grid simulation. One of the methods includes obtaining a state-transition matrix model of an electrical power grid and executing a simulation of electric power grid behaviors by executing the state-transition matrix model using a parallel processing device that includes multiple cores.

This invention discloses a personalized location trajectory privacy protection method based on mutual information, belonging to the field of crowdsourced sensing technology. It includes: Step 1: Data preprocessing; Step 2: After generating the Markov matrix in Step 1, the matrix is ​​normalized, and a location privacy protection mechanism is established by setting a distortion threshold and regularization parameters; Step 3: Measuring the effectiveness of location privacy protection. This method generates a Markov transition matrix by merging user location trajectory data, analyzes implicit privacy information and adds distortion perturbations, and uses mutual information to measure the degree of privacy leakage, achieving a trade-off between privacy and usability. It solves the problem that existing technologies cannot effectively overcome the risk of location information privacy leakage.

This invention discloses an improved pathfinding method and chip design system, comprising the following steps: generating multiple routing rule matrices with the same grid size; adding various routing constraints to the corresponding routing rule matrices; obtaining a total routing rule matrix; forming an outer polygon P1; calculating the movement cost of all possible moving positions within the outer polygon P1; generating corresponding mask matrices and transition matrices according to the constraint type; starting pathfinding from the starting point, and extracting a submatrix centered at the new position and with the same size as the mask matrix from the total routing rule matrix at each new position; obtaining the constraint cost; selecting the adjacent position with the lowest constraint cost as the next moving point, and repeating the above steps until the destination is reached. The pathfinding speed of this invention is 45.4% faster than traditional algorithms, and when the number of lines increases from 1 to 5, the pathfinding speed is 75.6% faster than traditional algorithms, making it particularly suitable for high-density, multi-constraint chip routing environments.

A method for monitoring the state of health of a planetary gearset equipping a rotating machine and adapted to perform power transmission on a shaft line of the rotating machine, the method including acquiring a vibratory signal of the rotating machine by a vibration sensor, the vibratory signal including vibrations generated during power transmission by the planetary gearset; constructing a measurement vector and a transition matrix from a phenomenological vibratory model; estimating a possible fault vibratory signature from the measurement vector, the transition matrix and the vibratory signal acquired, the possible fault vibratory signature taking account of a modulation overlap effect; determining a distance by comparing the possible fault vibratory signature with a reference signature.

This disclosure relates to the field of logistics and transportation technology, and proposes a freight volume prediction method, device, electronic device, and readable storage medium. The method includes: acquiring historical waybill data, and generating a multi-dimensional initial probability transition matrix based on the historical waybill data, the initial probability transition matrix including historical route resource information and route delivery probability information; acquiring real-time waybill barcode scanner operation data, and determining the planned route resource information, real-time vehicle status, and real-time freight volume summary data for the current day based on the real-time waybill barcode scanner operation data; adjusting the route delivery probability information of the initial probability transition matrix based on the planned route resource information for the current day, historical route resource information, and real-time vehicle status to obtain a target probability transition matrix; and determining the expected freight volume allocated to each target route based on the real-time freight volume summary data and the target probability transition matrix. The technical solutions provided by one or more embodiments of this disclosure can accurately predict the allocated freight volume of each route.

This disclosure relates to the field of text processing technology, and particularly to a keyword extraction method, apparatus, device, storage medium, and computer program. The method includes: inputting text data to be processed into a pre-trained word vector model for vector mapping processing to obtain word vectors; inputting the word vectors into a bidirectional long short-term memory network for global feature extraction to obtain global features, wherein the global features represent the global features corresponding to the sentence; inputting the word vectors into a text convolutional neural network for local feature extraction to obtain local features, wherein the local features represent the local features corresponding to the word; constructing a probability transition matrix based on the global features and the local features, wherein the probability transition matrix is ​​used to calculate the importance score corresponding to the word; and selecting keywords from the words based on the importance score. This method can efficiently and accurately extract keywords from text data.

The application relates to the technical field of intelligent traffic systems, and discloses a sparse perception traffic scene blind area information interpolation estimation method. Traffic data collected by sensors at traffic network nodes is acquired, and a plurality of characteristic information is adaptively fused; dynamic time characteristic extraction is carried out according to the multi-modal fusion characteristics; the extraction result of the dynamic time characteristics is added to the multi-modal fusion characteristics and then input into a space attention module and a bidirectional time attention module to obtain a new adjacency matrix and a dynamic transfer matrix; the new adjacency matrix and the dynamic transfer matrix are input into an S-fusion layer module to obtain a road network adjacency matrix with forward and backward long and short term space-time dependence; the road network adjacency matrix and the multi-modal fusion characteristics are input into a graph attention gate recurrent module and a decoding output module to obtain interpolation estimation of nodes with missing values. The application can accurately capture the complex dynamic space-time correlation characteristics between scene nodes, and guarantee the accuracy and robustness of scene space-time data completion.

This invention discloses a dynamic intention control method based on time-varying transition matrices and multi-source evidence fusion, comprising the following steps: 1) constructing a transition matrix with temporal decay; 2) fusing multi-source observational evidence; and 3) recursive belief updating. This invention's dynamic intention control method achieves human-like rhythm control and a closed-loop experience of real-time interaction, improving human-computer collaboration efficiency.

The present invention is applicable to the technical field of wireless communications, and particularly relates to a microwave network connection method and system, and a related device. The method of the present invention comprises: on the basis of whether port attributes are equipotential, dividing a first sparse block diagonal matrix into four sub-matrices, so as to obtain a second sparse block diagonal matrix comprising the four sub-matrices; acquiring a transfer matrix used for representing power distribution of the equipotential ports of microwave networks to be connected; and, on the basis of a preset rule, performing computation on the transfer matrix and the second sparse block diagonal matrix, so as to obtain a scattering parameter matrix; and, by means of the scattering parameter matrix, connecting the plurality of microwave networks to be connected. Compared with the prior art, the present invention involves dividing a sparse block diagonal matrix into four smaller sub-matrices, which have lower computation time complexity, thus significantly reducing computation time and improving computational efficiency during computation, thereby improving the efficiency of microwave network connection.

This application discloses an interactive command recommendation method, device, and medium for CAD software, belonging to the field of computer-aided design software technology, and solves the problem of low accuracy in existing operation command recommendation results. The method includes: based on the user's design state and selected elements, fusing the probability values ​​of the next-stage operation commands corresponding to a preset general probability transition matrix and a preset personal probability transition matrix, respectively, to obtain the final probability value of the predicted next-stage operation commands; constructing a recommended command list suitable for the current user based on the final probability value; and dynamically updating the preset personal probability transition matrix based on the probability value of the operation commands actually selected by the current user and the historical selection probability values ​​corresponding to the selected operation commands, so as to predict the next recommended command list based on the updated preset personal probability transition matrix, thereby achieving dynamic optimization of interactive command recommendation.

This invention discloses a patrol path decision-making method for unmanned systems in adversarial environments, belonging to the field of autonomous unmanned systems and intelligent decision-making technology. The method includes: acquiring an undirected patrol topology graph of the physical environment to be patrolled; calculating the steady-state distribution of the target based on the value parameters, attack time parameters, and degree of each node; and defining a patrol strategy unpredictability index weighted by intra-strategy entropy and inter-strategy entropy. PSUI To address the uncertainty of strategies in terms of microscopic randomness and macroscopic diversity, a simulated annealing dual-entropy balance optimization algorithm is employed to maximize... PSUI To achieve the goal, a set of state transition matrix strategies is generated under the constraints of topological adjacency, row normalization, and target steady-state distribution. The unmanned system is then controlled to perform spatiotemporally decoupled patrols through random matrix switching and probabilistic transitions within the matrix. This invention unifies patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol paths, and enhances the protection capabilities for critical infrastructure.

This invention discloses an adaptive robustness method for IGGⅢ parameters suitable for GNSS positioning in complex environments, belonging to the field of satellite navigation and positioning technology. The method first establishes double-difference observation equations and state equations based on satellite observations and the state transition matrix; it then estimates parameters using extended Kalman filtering to obtain post-hoc residuals and Mahalanobis distance statistics; it standardizes the post-hoc residuals to construct a test metric for the IGGⅢ model; it performs a chi-square test on the Mahalanobis distance statistics of the current epoch to obtain adjusted IGGⅢ model tuning parameters, and performs outlier satellite deletion and observation weight reduction processing; when the preset maximum number of iterations is met or no outliers are found in the current test, a floating-point solution is output, and ambiguity is fixed using the LAMBDA algorithm to obtain a fixed solution or a floating-point solution. The method provided by this invention can effectively reduce the adverse effects of NLOS and multipath errors in complex environments on precise GNSS positioning, improving GNSS positioning performance and reliability.

This invention relates to the field of land planning technology, specifically to a land use planning optimization method and system based on spatiotemporal big data. A land use planning optimization system based on spatiotemporal big data includes: a historical land use data sequence acquisition module, a change characteristic calculation module, and an iterative optimization module. This invention acquires multi-temporal land use remote sensing raster map sequences of the land to be planned, constructs a pixel-level land use change dataset, comprehensively characterizes the land use evolution trends and change sensitivity of each location at different historical stages, and ensures that the model can identify the spatial distribution characteristics of stable areas and high-frequency change areas. Based on land type change statistics, a global transition matrix and trend probability vector are constructed to achieve quantitative modeling of land use evolution patterns, and this is used for trend-guided construction of the initial population of a genetic algorithm, improving the directionality and convergence efficiency of the optimization.

The application discloses an entity relation extraction method, device and equipment and a storage medium. The method comprises the following steps: identifying a text to be identified, obtaining at least two entities in the text to be identified and types to which the two entities belong; obtaining an entity relation transition matrix used for predicting the relation between entities according to an ontology graph corresponding to the text to be identified, querying the entity relation transition matrix based on the types to which the two entities belong, and obtaining a first relation classification result of the two entities; and performing fusion processing on the first relation classification result and a second relation classification result to obtain a target relation classification result of the two entities, wherein the second relation classification result is obtained by identifying the text to be identified through an entity relation classification model.

The application discloses a kind of unmanned system patrol path decision-making method under hostile environment, belong to autonomous unmanned system and intelligent decision-making technical field.The method includes: obtaining the undirected patrol topology graph of the physical environment to be patrolled, target steady-state distribution is calculated based on the value parameter of each node, attack time parameter and degree;Define the patrol strategy unpredictability index formed by the weight of strategy internal entropy and strategy entropy PSUI , to quantify the uncertainty of strategy in micro-randomness and macro-diversity;Using simulated annealing double-entropy balance optimization algorithm, to maximize PSUI As target, generate state transition matrix strategy set under the constraints of meeting topology adjacency, line normalization and target steady-state distribution;Control unmanned system to carry out space-time decoupling patrol by random switching matrix and probabilistic transition in matrix.The application realizes the unity of patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol path, and enhances the protection ability to key infrastructure.

A diagnostic method for lesions in ultrasound video based on alternating similarity and temporal proximity attention, relating to the field of intelligent medical diagnostic technology, includes: S1, preprocessing the decoded ultrasound video to obtain a standardized ultrasound image video frame sequence; S2, acquiring candidate regions of lesions in the standardized ultrasound image video frame sequence and constructing a candidate region set and feature vector matrix; S3, using each candidate region as a node with feature similarity weights and temporal proximity weights with other nodes to obtain a probability transition matrix; S4, initializing an initial random walk probability distribution based on the queried candidate regions, and performing alternating random walks on the probability transition matrix to obtain a final random walk probability distribution; S5, weighting and fusing the feature vectors based on the final random walk probability distribution, performing temporal dimension smoothing fusion, and then averaging to obtain the ultrasound video lesion diagnosis result; This method solves the misjudgment problem caused by jitter, strong noise, and strong texture interference, and improves the accuracy of the diagnostic results.

This invention relates to a time-varying frequency offset dynamic tracking method in a VDES satellite link, comprising: acquiring the VDES satellite link baseband digital signal and performing coarse frequency offset estimation to obtain a coarse frequency offset estimate; setting a processing window that slides along the sampling time, calculating the frequency offset change rate based on the coarse frequency offset estimate within the window, and adaptively adjusting the window length according to the frequency offset change rate; constructing a Kalman filter with the frequency offset change rate and the optimal frequency offset estimate as the state vector, and establishing a frequency offset estimation state transition matrix based on a uniformly accelerated motion model; performing iterative calculation using the Kalman filter, outputting the posterior frequency offset estimate as the optimal frequency offset estimate at the corresponding time to achieve time-varying frequency offset dynamic tracking, and introducing a fine frequency offset estimate during iteration to optimize the prior frequency offset estimate at the current time. This invention can achieve fast and high-precision tracking of time-varying frequency offset in VDE-SAT links, improving the communication stability of VDES satellite links.

This invention discloses a network threat perception system based on full traffic mirroring analysis, belonging to the field of network traffic security technology. Specifically, it includes: receiving full traffic mirroring data, completing network layer fragmentation and transport layer TCP stream reassembly, establishing a bidirectional network session stream, extracting payload features and retaining protocol control messages; generating interactive behavior feature vectors based on the message arrival time interval transition matrix; extracting the initial session interaction message field and constructing a cryptographic feature set by combining the payload byte sequence information entropy; splicing and fusing the two types of features to generate a global multidimensional feature tensor; constructing a communication attribute graph with the addresses of both communicating parties as nodes and the feature tensor as edge attributes; introducing a graph attention neural network to aggregate node representations, fusing features to generate a prediction vector, and outputting classification labels to complete threat determination, effectively identifying encrypted, obfuscated, and variant unknown attack traffic.

The application discloses a kind of based on data fusion's slope displacement estimation method, comprising: based on multiple sensors data acquisition and pre-processing, the data time and space alignment of acquisition;Based on the data after alignment, discrete observation point extraction and reference sampling section generation are carried out, and high-quality data section is screened by sliding window method;Based on neural differential equation, dynamic modeling is carried out to the data after screening, and nonlinear modeling is carried out to the deformation process of slope by neural network;Based on the prediction-update process of Kalman filter enhanced model, state transition matrix and observation matrix are dynamically adjusted;Based on multiple sensor data, fusion estimation and adaptive error correction are carried out;Through the combination of distributed computing and edge computing, real-time calculation of slope displacement estimation is carried out.The application also discloses a kind of based on data fusion's slope displacement estimation device, equipment and storage medium.The application can realize high-precision, high-reliability slope displacement monitoring by integrating data fusion enhancement technology.

This invention discloses an energy balance control method for a self-powered pole-mounted vacuum circuit breaker, relating to the field of distributed control optimization technology. The method includes: constructing initial feature vectors for energy storage nodes and performing temperature compensation; using particle swarm optimization analysis to adjust amplitude and direction to generate a first distributed control vector set; combining real-time temperature adaptive correction to obtain a second distributed control vector set; issuing commands in parallel; each energy storage node independently recursively updating and generating a corrected feature vector; then using extended Kalman filtering to construct a low-temperature state transition matrix; recursively correcting the corrected feature vectors to obtain a corrected data matrix; and finally generating a distributed feature vector control matrix. This method is applied to energy balance control, solving the problem that distributed battery balance control technology in cold regions is prone to over-response due to increased differences in battery state, and that abnormal state change rates lead to insufficient control synchronization.