302 results about "Probability estimation" patented technology

The invention discloses a domain speech recognition method and system based on RAG. The method comprises the steps that an original domain document set is acquired, a hierarchical document structure is constructed through subject classification, paragraph segmentation and sentence segmentation, vectorization processing is executed to generate a multi-granularity vector matrix set, and a hierarchical knowledge vector index structure is established; performing multi-scale time-frequency analysis on the real-time audio stream, extracting and enhancing acoustic features, and generating a multi-scale feature tensor; performing adaptive blocking and progressive attention processing on the feature tensor, constructing a probability estimation model, and generating a candidate text sequence; executing multi-layer retrieval in the knowledge index based on the multi-dimensional query vector to generate related knowledge fragments; and carrying out space mapping and dynamic attention processing on the features, and executing probability optimization decoding to obtain a final recognition text. According to the invention, through knowledge enhancement and multi-level feature processing, the accuracy and robustness of voice recognition in the professional field are improved.

The invention discloses a marine water environment abnormal state secondary and derivation probability modeling and estimating method, which belongs to the field of state modeling estimation, and comprises the following steps: acquiring real-time data flow of marine environment parameters, performing segmentation processing by adopting a sliding window mechanism, and reducing data dimension by utilizing principal component analysis. Then, constructing a graph structure model based on a time sequence, and capturing correlation and a space-time dependency relationship among parameters; and performing nonlinear mapping on the graph structure model by adopting a radial basis function core, and capturing complex nonlinear characteristics among the marine environment parameters. And finally, classifying the abnormal states by using a multi-class support vector machine algorithm, and outputting secondary and derivative probability estimation results of the abnormal states in real time. According to the method, abnormal events in the marine environment can be found in time, dynamic modeling support can be provided for marine environment monitoring, and the accuracy and real-time performance of marine environment monitoring are remarkably improved.

The invention belongs to the technical field of 4D millimeter-wave radar perception, and particularly relates to a 4D millimeter-wave radar space occupation probability estimation method based on laser radar supervision, which comprises the following steps: constructing an environment occupation probability graph as a supervision signal of a laser radar; feature extraction is carried out on the sparse point cloud data of the 4D millimeter wave radar; performing cross-modal feature alignment on the supervision signal of the laser radar and the radar point cloud feature, and constructing a first-stage occupancy probability model; and obtaining a reference occupancy probability model through laser radar point cloud self-supervision by adopting the same network and supervision mode, performing supervision and fine tuning on the first-stage occupancy probability model by using the reference occupancy probability model, and deploying the first-stage occupancy probability model to a downstream task. According to the invention, the geometric priori knowledge of the laser radar is utilized to constrain 4D millimeter wave radar data learning, the problem of insufficient modeling precision caused by point cloud sparsity, noise and penetrability of the 4D millimeter wave radar is solved, and low-cost and high-robustness environmental geometric perception is realized.

The invention discloses a sparse array antenna optimization method based on an improved zebra algorithm, is used for reducing the peak side lobe level of a sparse array antenna, and belongs to the technical field of radio frequency phased array antennas. The implementation process of the method comprises the following steps: firstly, adding a probability estimation model on the basis of an original zebra optimization algorithm, and converting a real number variable into a binary code; secondly, an adaptive T distribution disturbance strategy and a dynamic selection strategy are introduced, the global search capability of the algorithm is enhanced, the algorithm is prevented from falling into local optimum, and the convergence speed is effectively improved; and finally, an optimal solution is obtained through iterative optimization for a certain number of times. In order to verify the performance of the method, three groups of simulation experiments are carried out, and the result shows that compared with other optimization algorithms, the method can obtain a lower array antenna peak sidelobe level within shorter optimization time, so that the performance of the antenna is improved.

The invention relates to the technical field of power distribution cabinet detection, and discloses an intelligent power distribution cabinet state dynamic monitoring method and system, and the method comprises the following steps: collecting multi-source data generated in the operation process of a power distribution cabinet; preprocessing the multi-source data to generate a standardized data set; a random field model is constructed and used for describing the spatial relevance between units in the power distribution cabinet and the relation between the state of each unit and multi-source data, and model parameters are estimated through the maximum logarithmic posterior probability. An intelligent power distribution simulation model and a multi-source data acquisition technology are adopted, multi-dimensional parameters are acquired in real time through a high-precision sensor, monitoring efficiency and decision accuracy are remarkably improved, data analysis integrity and model sensitivity are ensured through dynamic time modeling and capturing of space and time relevance of the power distribution cabinet, and the power distribution cabinet can be monitored more accurately. And the state distribution visualization and maintenance priority strategy generation module optimizes the maintenance plan, reduces the operation and maintenance cost, and guarantees the stability of the power distribution system.

The invention belongs to the technical field of secure communication, and discloses a residual error probability estimation method for secure communication under a guess decoding condition, which comprises the following steps: generating a noise error pattern by using a GRAND algorithm and a receiving end SPDU, performing guess decoding on the receiving end SPDU, and calculating a guess SPDU sequence; calculating a syndrome of CRC coding, judging a guess decoding result according to the value of the syndrome, and calculating REP performance after guess decoding; and searching the minimum CRC signature length under the condition of the given maximum error correction capability M, so that the REP performance of CRC error detection is optimal. According to the method, on the basis of an IEC61784-3 international standard heavy residual error probability calculation mode, closed mathematical expressions of residual error probabilities corresponding to different maximum error correction capabilities M under the condition of guessing random additive noise decoding are deduced, and the residual error probability performance is improved at the cost of increasing the CRC signature length.

The invention provides a failure probability estimation method and device for an automatic driving system, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the sampling of a uniform distribution space, obtaining an initial sampling result, and constructing a plurality of initial testing scenes according to the initial sampling result; respectively performing simulation testing on the target automatic driving system in the plurality of initial testing scenes to obtain initial simulation testing results, and constructing an evaluation index prediction model according to the initial simulation testing results and the initial sampling results; determining a solving target for obtaining a reference failure sample based on the evaluation index prediction model, and solving the solving target by using an optimization algorithm to obtain a reference failure sample set; and sampling in the proposal distribution space by using the reference failure sample set, and determining a final failure probability according to a final sampling result. By adopting the method and the device for estimating the failure probability of the automatic driving system, the problems of low verification efficiency and inaccurate failure probability estimation of the automatic driving system in the prior art are solved.

The invention provides a new energy equipment system maintenance decision-making method and system based on a fault coupling model, and relates to the technical field of equipment predictive maintenance, and the method comprises the steps: collecting system structure information and system fault data of a complex new energy equipment system, and carrying out the maintenance decision-making of the complex new energy equipment system according to the system structure information and the system fault data; respectively constructing a function model view, a system level fault model view and each flow fault model view; constructing a fault coupling model corresponding to the complex new energy equipment system; probability estimation is carried out on the system fault data according to a kernel density estimation method, the fault probability of each fault node in the complex new energy equipment system is obtained, and key fault nodes in all the fault nodes and key node information corresponding to the key fault nodes are obtained; and mapping the key node information into a risk level corresponding to the key node information, and obtaining an optimal maintenance decision based on a decision tree analysis method and a plurality of maintenance decisions. According to the method, multi-dimensional analysis of a complex system can be realized, and fault modes of different components and subsystems can be identified more clearly, so that the fault detection and diagnosis efficiency is improved.

The invention relates to the technical field of radar remote sensing, discloses a high-order loose body historical activity detection method based on multi-source DEM data, and aims to solve the problem that the historical activity of a high-order loose body in a glacier coverage area is difficult to accurately and quantitatively evaluate in the prior art. The scheme mainly comprises the steps that drainage basin units are automatically divided based on DEM data, and glacier influence areas are screened; multi-source DEM data are fused, and system errors are eliminated through geographic positioning deviation correction, elevation distortion deviation correction and track mode deviation correction; establishing a penetration depth physical model, and correcting the radar penetration depth in the glacier area by using the C / X wave band elevation difference optimization parameter; calculating an elevation change rate by adopting robust regression; and calculating an activity comprehensive index by integrating multiple factors, and identifying a hot spot region and a time period through probability estimation and spatio-temporal clustering. According to the method, automatic and high-precision quantitative evaluation on the historical activity of the high-position loose body is realized, and the method is particularly suitable for geological disaster early warning and risk evaluation in high mountain areas.

The invention discloses a method and a system for cooperatively controlling post-disaster recovery and multi-source resource coordination of three networks, and belongs to the technical field of post-disaster recovery and toughness improvement of a multi-infrastructure system. The method comprises the following steps: obtaining a node importance evaluation function through multi-index fusion, and calculating a recovery priority score of a grey starting power supply; estimating the node state probability to obtain the node state probability; setting boundary conditions of recovery feasibility of the subarea power grid; optimizing a recovery sequence through a grey starting power supply-load-net rack path matching model, and generating a recovery path set; a reinforcement learning algorithm is adopted, node states, resource availability and a path connection matrix are used as state spaces, and an optimal recovery sequence and a resource scheduling scheme are output; and generating a three-network recovery path recommendation, a partition power grid recovery strategy and a multi-element flexible resource scheduling sequence. According to the invention, efficient scheduling and cross-network cooperative control of multi-source flexible resources are realized, and the recovery efficiency and toughness of three networks in a post-disaster scene are effectively improved.

The invention relates to the technical field of structural reliability analysis, in particular to an adaptive structural reliability analysis method based on a Co-kriging proxy model, which comprises the following steps: determining a structural failure mode, acquiring corresponding high-fidelity and low-fidelity performance functions and inputting distribution information of variables; a high-fidelity sample set and a low-fidelity sample set are obtained through sampling; obtaining responses corresponding to the two fidelity samples, and constructing a Co-kriging agent model; carrying out structural reliability analysis by utilizing a Monte Carlo simulation method, and calculating a failure probability of each iteration; judging whether a convergence condition is met; adding new sample points into the training set by using a learning function, constructing a Co-kriging proxy model, and obtaining a final failure probability; according to the method, the Co-kriging proxy model is combined with the learning function MPO (x, m), multi-precision sample data can be fused, and on the premise that it is ensured that the failure probability estimation result has high precision, the calculation process is optimized, unnecessary calculation resource consumption is effectively reduced, and the overall calculation cost is remarkably reduced.

The invention provides a coding and decoding method and electronic equipment. The method comprises the following steps: performing motion estimation on a current frame to obtain a first optical flow; obtaining first optical flow super-prior information based on the feature information of the first optical flow and the prior information of the first optical flow; encoding the first optical flow hyper-prior information to obtain a first code stream; probability estimation is carried out based on the second optical flow hyper-prior information and the prior information of the first optical flow, and optical flow probability distribution is obtained; entropy coding is carried out on the feature information of the first optical flow based on the optical flow probability distribution to obtain a second code stream; performing inter-frame prediction on the current frame to obtain prediction information; determining residual information based on the prediction information and the original image; and encoding the residual information to obtain a third code stream. Compared with the prior art that the determined first optical flow hyper-prior information is used for determining the optical flow probability distribution of all the pixel points in the current frame, the first optical flow hyper-prior information is used for determining the optical flow probability distribution of part of the pixel points in the current frame, and the code rate overhead can be reduced.

The invention discloses a rock mass elastic modulus probability estimation method, which comprises the following steps: S1, acquiring a compression wave velocity Vp of a rock on the basis of field test investigation and statistics for a to-be-estimated hole section area of a rock mass mechanical parameter; s2, determining priori knowledge about the rock elasticity modulus; s3, constructing a Bayesian calculation framework; s4, using an adaptive MH-MCMC algorithm to iterate relevant parameters in the relation equation under a Bayesian calculation framework, generating posterior probability samples corresponding to the rock elasticity moduli, and determining probability distribution of the rock elasticity moduli; and S5, determining the value of the elastic modulus of the actual engineering rock mass based on the H-B criterion, and obtaining the probability distribution of the on-site rock mass parameters. According to the method provided by the invention, the probability range of the rock mass mechanical parameters can be efficiently estimated, the method is used for guiding the formulation of solutions considering various conditions during actual construction, and the method has better economic benefits and has important guiding significance for engineering practice.

The invention relates to probability estimation in multi-symbol entropy coding. Entropy coding, such as multi-symbol arithmetic coding, is used for video compression to encode data into a compressed bitstream for transmission. Some entropy coding techniques are adaptive, which means that the probability distribution is updated instantly based on data. Accuracy of cumulative probability estimation in adaptive multi-symbol arithmetic coding may affect coding efficiency. To solve this problem, a mixture of two or more adaptive cumulative probability estimates calculated using two or more adaptation parameters may be used in place of a single cumulative probability estimate. The two or more adaptation parameters may be unique to the context model. The divergence in the adaptive cumulative probability estimation may represent a sudden change in the probability of the symbol. The divergence may trigger a reset of one or more adaptive cumulative probability estimates.

The invention provides a product storage life evaluation method based on composite sampling inspection success and failure data. An evaluation object is a complex mechanical and electrical product. Comprising the following steps: carrying out point estimation and confidence limit estimation of failure probability on special test data, and carrying out randomization processing on user use data containing fuzzy samples by adopting uniform distribution or truncated normal distribution; carrying out compatibility test on the special test data and the user use data; fusing the special test data meeting the compatibility test and the user use data by adopting a Bayesian method, and obtaining special stage failure probability estimation after updating of different storage periods; and further screening out degradation failure sample data based on the updated special failure probability estimation, and performing storage life evaluation on the degradation failure data by adopting a weighted least square method. The method combines the characteristics of two kinds of sampling data, and can solve the evaluation problems of small evaluation data volume, fuzzy samples and the like.

A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.

Entropy coding a sequence of symbols is described. A first probability model for entropy coding is selected. At least one symbol of the sequence is coded using a probability determined using the first probability model. The probability according to the first probability model is updated with an estimation of a second probability model to entropy code a subsequent symbol. The combination may be a fixed or adaptive combination.

The invention discloses a defect hidden danger data diagnosis method based on a distribution network SVG single line diagram. The method comprises the steps that basic analysis is conducted on the input SVG single line diagram, basic geometric figure elements are extracted and normalized, a topological connection relation is preprocessed, and a multi-level index structure is constructed; a dynamic topology path reasoning mechanism is constructed, and reasoning and self-adaptive optimization of the topological relation of the electrical equipment are realized through path consistency calculation, topological entropy minimization and a dynamic topology updating strategy; a self-adaptive implicit relational knowledge graph is constructed, and semantic association between the electrical equipment is established through explicit topological relation fusion, implicit function dependence reasoning and dynamic semantic embedding optimization; and constructing a multi-modal time sequence feature vector, calculating the hidden danger risk of the equipment by adopting a space-time defect probability estimation model, and giving an alarm through a dynamic risk early warning strategy to realize prediction and early warning of the hidden danger of the defects of the electrical equipment. According to the method, the problems of semantic understanding, topological reasoning, state inference and defect prediction of the SVG single line diagram are effectively solved.

The invention discloses a medical image lossless compression method and system based on transform coding and deep learning, and the method comprises the steps: carrying out the DCT and Round quantization of an input 2D medical image, and dividing the image into a DC coefficient and an AC coefficient; the loss R caused by the DC coefficient and Round quantization is compressed; decomposing the AC coefficient into a structural layer MSB and a texture layer LSB; decomposing the texture layer LSB into a plurality of sub-coefficient blocks; constructing a depth probability estimation model based on a convolutional neural network, carrying out grouping-based autoregression probability density modeling, inputting a plurality of sub-coefficient blocks, and outputting a probability distribution matrix of each sub-coefficient block; and performing lossless coding and decoding on each sub-coefficient block in combination with an entropy arithmetic encoder and a probability distribution matrix. The method has the advantages of high compression efficiency, lossless compression and the like.

The invention relates to a data processing method and device based on a hybrid expert model, computer equipment, a computer readable storage medium and a computer program product. The method comprises: based on a target character in input data, determining a target probability corresponding to the target character, and determining a target probability difference threshold corresponding to the target character, the target probability comprising a probability estimation value of each expert network model in a preset hybrid expert model; screening the expert network models based on the probability estimation values of the expert network models and a target probability difference threshold to obtain a target expert network model; and processing the target character based on each target expert network model to obtain a data processing result. By adopting the method, the models contained in the expert model can be dynamically mixed based on the probability difference threshold, the data processing of the character dimension of the input data is realized, and the adaptation degree between each dynamically determined expert network model and the characters in the input data is ensured.

Systems and methods are configured for accessing data representing video content, the data comprising a set of one or more symbols each associated with a syntax element; performing a probability estimation, for encoding the data, comprising: for each symbol, obtaining, based on the syntax element for that symbol, an adaptivity rate parameter value, the adaptivity rate parameter value being a function of a number of symbols in the set of one or more symbols; updating the adaptivity rate parameter value as a function of an adjustment parameter value; and generating, based on the updated adaptivity rate parameter value, a probability value; generating a probability estimation; and encoding, based on the CDF of the probability estimation, the data comprising the set of one or more symbols for transmission.

A system and method provide artificial intelligence (AI)-based diagnostic guidance for a patient. The system receives captured radiological scan images of the patient, and input components such as results of physical examination of the patient and laboratory data of the patient. The system provides patient imaging information based on the recognized patterns of the received radiological scan images and the three-dimensional reconstructed scans, identifies problems based on predetermined AI criteria and deep-learning based on patient-specific patterns, responses, and the input components, determines risk factors of the patient which cause heath issues, provides probability estimation of diagnoses that predict probability of occurrence of the health issues to the patient based on the risk factors of the patient, prioritize the problems to a hot list based on the probability estimation of diagnoses.

The invention discloses an unmanned aerial vehicle route identification method, and belongs to the technical field of unmanned aerial vehicle route identification, and the technical scheme of the unmanned aerial vehicle route identification method comprises the following steps: S1, route mapping; s2, probability model training; s3, probability estimation and processing; s4, calculating the main direction of the route; s5, Rect area probability data are extracted in the main direction; s6, extracting a route; according to the unmanned aerial vehicle route identification method, the route information is associated with the image, the unmanned aerial vehicle can be guided to fly along the route through image identification, results at all moments are mutually independent, no accumulated error influence exists, and the unmanned aerial vehicle route identification accuracy is improved.

The invention discloses a cooperative fault-tolerant positioning method based on self-adaptive residual model interaction, and the method comprises the steps: enabling single fusion information of each sensor to form a sub-model based on a model mixing thought; residual interaction is carried out on each sub-model by using an interactive multi-model, and the model of the fault sensor is switched, so that a relatively large positioning error is prevented from being introduced in a cooperative positioning process; and finally, obtaining optimal state estimation of cooperative positioning according to model state probability fusion. In order to improve the model switching capability, Markov transition probability estimation of adaptive parameter adjustment is introduced into an interactive multi-model framework, so that the situation that the cooperative fault-tolerant performance is reduced due to model switching failure is avoided. The method can be used for cooperative fault-tolerant positioning of the unmanned aerial vehicle cluster in a limited satellite and unknown complex and easily-interfered environment.

Codebook data compaction using a universal codebook and mismatch probability estimations to improve entropy encoding methods. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A "mismatch codeword" is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook.

The invention relates to a map-free long-time-domain vehicle trajectory prediction method based on end point clustering prior, and the method comprises the steps: constructing a trajectory prediction model, carrying out the quantization processing, and deploying the trajectory prediction model at a vehicle-mounted terminal for vehicle trajectory prediction. Extracting characteristics of interaction between the traffic participants and the surrounding environment; performing coordinate transformation and end point extraction on historical trajectory data, performing end point clustering in different speed domains, and establishing a candidate target point set; probability estimation is carried out on the candidate target points through an attention mechanism, and a multi-modal candidate end point is selected by using a non-maximum suppression algorithm; and on the basis of the multi-modal candidate terminal points, a multi-modal long-time-domain vehicle prediction trajectory is generated by using a trajectory completion module. Compared with the prior art, the method can accurately predict the future long-time-domain motion trail of the traffic participant in a driving scene without high-precision map support, and provides a basis for an automatic driving decision algorithm.

The invention discloses a Bayesian multi-model elliptic maneuvering group target tracking method based on a GRU network, and relates to the technical field of target state estimation methods. The method comprises the following steps: S1, designing and training a neural network: based on a Pytorch development tool box, establishing a GRU neural network-based group target centroid motion sub-model probability estimation network, and based on a group target tracking data set, training the neural network; s2, modeling a Bayesian ellipse maneuvering group target tracking system; and S3, performing Bayesian multi-model maneuvering group target tracking based on neural network model probability estimation. According to the method, the measurement information value in the historical time window is fully grabbed through the GRU neural network, which is different from the previous method that only the current measurement value is utilized to carry out model probability estimation, and the precision is higher; and a measurement noise variance model in the group target tracking model is optimized, so that the measurement noise variance model better conforms to the actual situation, and the estimation precision is further optimized.

The invention discloses a structural reliability updating method based on domain adaptive network integration. The method comprises the following steps: constructing Monte Carlo sample pools of a source domain and a target domain; constructing a proxy model based on deep integration and active learning as a source domain proxy model; generating migration training set sample points; constructing a domain adaptive network integration agent model as a target domain agent model; selecting fine tuning training set sample points based on a learning function; calculating a sample point limit state function value to obtain a fine tuning training set; performing partial fine tuning on the target domain agent model; performing failure probability estimation by adopting a Monte Carlo method, and judging whether the failure probability meets a convergence criterion or not; and outputting a failure probability estimation value updated based on the load variable distribution change. According to the method, updating and solving of the failure probability can be accurately and efficiently completed according to the load variable distribution change condition, the safety degree of continuous service of the structure can be quantified in real time, and therefore more scientific and economical maintenance measures and overhaul plans can be formulated.

Embodiments of a system, a machine-accessible storage medium, and a computer-implemented method are described in which operations are performed. The operations comprising receiving a plurality of image frames associated with a video of an endoscopy procedure, generating a probability estimate for one or more image frames included in the plurality of image frames, and identifying a transition in the video when the endoscopy procedure transitions from a first phase to a second phase based, at least in part, on the probability estimate for the one or more image frames. The probability estimate includes a first probability that one or more image frames are associated with a first phase of the endoscopy procedure.