177 results about "Probability estimation" patented technology

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 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 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.

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.

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 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 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 provides a binocular video compression method based on parallax compensation and deep learning, and the method comprises the steps: firstly obtaining training data, carrying out the preprocessing, and then constructing a binocular video compression network; the binocular video compression network comprises a motion estimation network, a motion information compression network, a motion compensation network, a binocular video frame information compression network and a frame reconstruction network, then carrying out supervised training on the binocular video compression network, and carrying out video compression processing by using the trained binocular video compression network; according to the method, a binocular feature interaction model and a binocular entropy model are arranged in a motion information compression network and a binocular frame information compression network, the binocular feature interaction model aligns binocular visual angle features by using a mixed parallax compensation mechanism in the encoding and decoding process, and the binocular entropy model performs accurate probability distribution modeling through autoregression probability estimation. According to the method, the compression performance can be remarkably improved while the reconstruction quality is guaranteed, and the method is suitable for scenes such as virtual reality and automatic driving needing efficient binocular video processing.

The present application discloses an encoding method, a decoding method and a device, which relate to the field of video or image compression technology based on artificial intelligence (AI), and specifically to an intra-frame prediction mode encoding or decoding method based on a neural network, so as to reduce the hardware implementation complexity of entropy coding of syntax elements related to the intra-frame prediction mode. The method comprises: determining the index of the intra-frame prediction mode syntax element value set of the current image block; performing probability estimation processing on the input data representing the characteristics of the current image block through a neural network to obtain the probability distribution of multiple candidate intra-frame prediction mode syntax element value sets, and the probability distribution represents the probability value of each of the multiple candidate intra-frame prediction mode syntax element value sets; according to the probability distribution of the multiple candidate intra-frame prediction mode syntax element value sets, entropy coding is performed on the probability value related to the index of the intra-frame prediction mode syntax element value set of the current image block to obtain a bitstream.

The invention discloses a sign language recognition model construction method based on uncertainty sample screening and comparative learning, and the method comprises the following steps: S1, obtaining effective motion frames of each sign language vocabulary performed by each sign language performer, constructing an effective motion frame sequence, labeling the effective motion frame sequence to form a data set, and dividing the data set into a training set, a verification set and a test set; s2, inputting the training set into a sign language vocabulary label prediction model for prediction to obtain the sampling probability of each sample; s3, performing sampling probability estimation of a time stage on each sample in the training set to obtain the sampling probability of each sample in the time stage; s4, based on the sampling probability of each sample in the sampling probability time stage based on the sample loss, screening out high-value samples; and S5, constructing a basic recognition model, training the basic recognition model based on calculation results of the steps S2, S3 and S4 by adopting samples in the training set, and obtaining a sign language recognition model.

In one implementation, we propose a bitwise octree coding approach based on deep neural networks and operations on 3D sparse tensors. To encode / decode a certain level of detail (LoD) in an octree, geometric features are first inherited from the previous LoD by upsampling. Then based on the already encoded / decoded voxels, the point cloud geometry is firstly refined by pruning, followed by combining with the known context information. In the end, feature aggregation and probability estimation can be applied to obtain the occupancy probabilities for actual arithmetic encoding / decoding. A corresponding probabilistic training strategy is also proposed for our bitwise octree coding approach.

The invention provides a turbine blade fatigue performance influence factor analysis method and system and a medium, and belongs to the technical field of turbine blade performance prediction.The turbine blade fatigue performance influence factor analysis method includes the steps that a small number of training samples are generated, and a back propagation neural network (BP neural network) describing the relation between the fatigue life of a turbine blade and input variables is constructed; a training sample is added to the sequence, and the BP neural network is updated until the calculated failure probability is converged; based on a failure sample obtained in the process of solving the failure probability, estimating conditional failure probability estimation values at different sample points by using a Bayesian inference theory; and obtaining a fatigue reliability sensitivity estimation value of each input variable according to an average difference between the fatigue failure probability estimation value of the turbine blade and the condition failure probability estimation value of each failure sample point. The method solves the problems that when an existing agent model method is used for solving the reliability and sensitivity of the turbine blade, the sample requirement is large, efficiency is low, and the method cannot be suitable for a high-dimensional problem.

An information leakage possibility evaluation device, an information leakage possibility evaluation method, and a program are provided that can prevent leakage of confidential information when using an externally provided information processing service. [Solution] The terminal device 1 includes a tokenizer 112 that converts the text to be evaluated into a token sequence, a warning target probability estimation unit 116 that uses a warning target probability estimation model to estimate the warning target start probability and warning target end probability for each token included in the token sequence, a warning target token identification unit 117 that identifies the warning target token range based on the warning target start probability and the warning target end probability, and an evaluation result text generation unit 118 that generates evaluation result text information that indicates the information leakage possibility evaluation result based on text information indicating the above-mentioned text and the identified warning target token range.

The invention belongs to the technical field of robot control, and aims to provide a robot environment sensing method and system, electronic equipment and a program product. The method comprises the following steps: acquiring initial environment detection data acquired by various sensors in the robot, and performing space-time alignment processing on the initial environment detection data to obtain aligned environment detection data of each sensor; environment grid occupancy probability estimation models are constructed for the multiple sensors respectively, the aligned environment detection data of the sensors are input into the corresponding environment grid occupancy probability estimation models respectively, initial grid occupancy probabilities of the sensors are obtained, fusion processing is carried out on the initial grid occupancy probabilities, and fused grid occupancy probabilities are obtained; constructing a dynamic occupation grid map of the robot based on the fusion grid occupation probability; and performing path planning processing on the robot based on the state prediction result and the dynamic occupation grid map. According to the method, the sensing precision and the path planning response capability of the robot in a complex dynamic environment can be effectively improved.

A system and method for data compaction with codebook statistical estimates to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted. 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. During encoding, if a mismatch occurs, a secondary encoding process is used to encode the mismatched sourceblock.

The present invention provides encoding methods and apparatuses, decoding methods and apparatuses, devices, storage media, and computer program products. [Solution] When probability estimation is performed in the encoding method, the probability distribution of the unquantized image features is estimated based on the hyperplier features of the unquantized image features via a first probability distribution estimation network, and then the probability distribution of the quantized image features is obtained through quantization. Alternatively, the probability distribution of the quantized image features is directly estimated based on the hyperplier features of the unquantized image features via a second probability distribution estimation network (a network obtained by performing a simple operation on the network parameters of the first probability distribution estimation network).

The application discloses a wireless sensor network distributed guaranteed probability estimation method under a deception attack, and specifically comprises the following steps: a nonlinear system mathematical model under the condition that innovation is deceived is established; a neural network-based distributed state estimator structure model is established according to the nonlinear system mathematical model under the condition that innovation is deceived; a neural network weight matrix adaptive updating strategy is designed; a distributed guaranteed probability state estimator gain is designed; and estimator parameters are optimized to achieve local optimal estimation performance; the application comprehensively considers the nonlinear characteristics and the influence of malicious attacks existing in actual engineering, can be widely applied to military monitoring systems and environmental monitoring systems, estimates the state of a monitoring or monitoring object, is more in line with engineering application, and is high in practicality.

The invention provides a low-complexity image coding and decoding method and device for machine intelligence, and the coding method comprises the steps: carrying out the feature extraction of an input image through a feature extractor, and obtaining an image feature; a forward transformation network is used for the image features to obtain to-be-coded features; using a hyper-prior extraction network for the to-be-coded features to obtain side information, and carrying out quantization and entropy coding on the side information to obtain a side information code stream; carrying out probability estimation on the side information by using a super-prior variance estimation network to obtain an entropy coding probability; estimating the mean value of the to-be-coded features for the side information by using a super-prior mean value estimation module; and on the basis of the entropy coding probability and the estimated mean value, entropy coding is carried out on the quantized to-be-coded feature by using a rans entropy coder, and a coded feature code stream is output. The image encoding and decoding method and device can be used for executing various and not limited to some specific intelligent tasks, one code stream has multiple purposes, and meanwhile, the encoding and decoding time consumption of a model in low-computing-power equipment can be effectively reduced.

This application relates to an encoding method, a decoding method, and an electronic device. An example method includes: performing inter prediction on a current frame, to obtain prediction information of the current frame; determining residual information of the current frame based on the prediction information of the current frame and an original picture of the current frame; determining first residual hyperprior information of the current frame based on the residual information of the current frame and prior information of the residual information of the current frame; encoding the first residual hyperprior information of the current frame, to obtain a first bitstream; and performing probability estimation based on second residual hyperprior information of the current frame and prior information of the residual information of the current frame, to obtain a residual probability distribution of the current frame.

The invention discloses an HRRP correlation assisted broadband radar maneuvering target tracking method and device, and relates to the technical field of radar target tracking, and the method comprises the steps: obtaining the high-resolution range profile correlation of adjacent moments; processing the correlation of the high-resolution range profiles at adjacent moments by adopting a preset auxiliary function to obtain a correlation weighting factor; and weighting the likelihood function of each motion model by adopting a correlation weighting factor so as to quantify the influence of the high-resolution range profile correlation at adjacent moments on the probability estimation of each motion model. The maneuvering target tracking precision can be improved.

The present invention relates to the field of urban road traffic technology, and discloses a generalized estimation method and system for saturated headway at urban road signal intersections, comprising obtaining license plate recognition data and extracting an intersection signal cycle; matching the license plate recognition data with floating vehicle data, extracting saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles; screening out factors influencing the saturated headway; using the saturated headway samples as input, and constructing a probability estimation model of the saturated headway and various saturated headway influencing factors based on the NGBoost model according to the saturated headway influencing factors; using each saturated headway influencing factor as input to the probability estimation model, and outputting a generalized estimation result of the saturated headway. The beneficial effect of the present invention is that it can generalize the saturated headway based on existing and easily obtainable license plate recognition data and floating vehicle data.

The invention relates to a multi-satellite collaborative observation probability estimation method for a window type target. The method comprises the following steps: inputting a statistical time range, an orbital element number and a load observation range of a guiding constellation A, and an orbital element number and a load observation range of a guided constellation B; and determining a to-be-observed area O, based on whether the constellation A is a geostationary orbit satellite, calculating the collaborative observation probability that the constellation A guides the low-orbit constellation B, and outputting a collaborative observation probability result. According to the method, the defect that the time dynamic characteristics of the window type target are not fully considered when the collaborative observation probability of the satellite to the land target is analyzed in the existing method is overcome, and the problem that the window type target collaborative observation success probability is inaccurately estimated in the existing method is solved.

The entropy coding of a current part of a predetermined entropy slice is based on, not only, the respective probability estimations of the predetermined entropy slice as adapted using the previously coded part of the predetermined entropy slice, but also probability estimations as used in the entropy coding of a spatially neighboring, in entropy slice order preceding entropy slice at a neighboring part thereof. Thereby, the probability estimations used in entropy coding are adapted to the actual symbol statistics more closely, thereby lowering the coding efficiency decrease normally caused by lower-delay concepts. Temporal interrelationships are exploited additionally or alternatively.

The invention relates to coding equipment and a micro-processing chip. The coding equipment comprises a first data processing module, an arithmetic coding module, a first storage module and a second storage module, the first data processing module is used for reading the to-be-coded image and acquiring to-be-coded pixels in the to-be-coded image and context information corresponding to the to-be-coded pixels; the first storage module is used for storing a context table; the second storage module is used for storing a probability estimation table; and the arithmetic coding module is used for searching the context index value corresponding to the context information in the context table through the first storage module, searching the probability estimation value corresponding to the context index value in the probability estimation table through the second storage module, and coding the pixel to be coded based on the probability estimation value to obtain coded data. According to the invention, hard coding processing of the image data can be realized through the coding device, the coding efficiency can be improved to a great extent, and the problem of low coding efficiency caused by coding through software in related technologies is avoided.

The invention provides a remote sensing image lossless compression method based on overlapped biorthogonal transformation and Transform, and belongs to the technical field of remote sensing image lossless compression coding, and the method comprises the steps: converting a remote sensing image pixel to a frequency domain through overlapped biorthogonal transformation, and obtaining a direct current (DC), a low frequency (LP) and a high frequency coefficient (HP); partitioning the three coefficients, recombining the three coefficients into coefficient tensors, and performing probability estimation on the coefficients by using a probability modeling network to obtain a coefficient estimation probability; and coding according to the estimation probability of the coefficient to obtain a compressed code stream, thereby realizing the lossless compression task of the remote sensing image. According to the method, an overlapped biorthogonal transformation method is utilized, the spatial redundant information of an original image is reduced, three kinds of frequency domain coefficients in concentrated distribution are obtained, accurate probability estimation is carried out on the frequency domain coefficients through strong feature extraction and parameter fitting capabilities of Transformer, efficient lossless compression of the remote sensing image is realized, and the compression efficiency of the remote sensing image is improved. The performance is obviously superior to that of a traditional lossless compression method.

To more effectively check the medical fee statement. 【Solution means】An information processing apparatus including: a first calculation unit that inputs, as input data, medical fee statement data to be submitted to a review institution into a medical treatment implementation probability estimation model generated by learning, as teacher data, review results of medical fee statement data including medical treatment act information submitted from a medical institution to the review institution, and calculates a probability of being able to perform a medical treatment act to be reviewed included in the medical fee statement data to be submitted to the review institution; and a notification unit that notifies the medical treatment act to be reviewed when the probability of being able to perform the medical treatment act to be reviewed is equal to or less than a first predetermined value.

The present invention belongs to the technical field of 4D millimeter-wave radar perception, and specifically relates to a 4D millimeter-wave radar spatial occupancy probability estimation method based on laser radar supervision, the method comprising constructing an environmental occupancy probability map as a supervision signal for the laser radar; performing feature extraction on the sparse point cloud data of the 4D millimeter-wave radar; performing cross-modal feature alignment of the supervision signal of the laser radar with the radar point cloud features to construct a one-stage occupancy probability model; using the same network and supervision method to obtain a reference occupancy probability model using the laser radar point cloud self-supervision, and using the reference occupancy probability model to supervise and fine-tune the one-stage occupancy probability model, and deploying it to downstream tasks. This application utilizes the geometric prior knowledge of the laser radar to constrain 4D millimeter-wave radar data learning, solves the problem of insufficient modeling accuracy caused by the sparsity, noise and penetration of the 4D millimeter-wave radar point cloud, and realizes low-cost, highly robust environmental geometric perception.

The invention discloses a time-varying thermal failure probability estimation method and system for a deep groove ball bearing, and belongs to the field of bearing design. The method comprises the following steps: constructing a basic parameter model containing a mixed random parameter vector and a limit state function; establishing a transient thermal network model to calculate time-varying temperature response; constructing an agent model to replace high-time-consumption heat generation rate calculation; a self-adaptive time node sampling strategy is adopted, a proxy model and a thermal network model are combined to process large-scale samples, and the time-varying thermal failure probability is estimated. The system comprises a basic parameter modeling module, a transient thermal network simulation module, an agent model construction module and a self-adaptive probability evaluation module which correspond to the system. According to the method, the time-varying and random dual uncertainty is fused, and the proxy model and the adaptive sampling strategy are utilized, so that the defects of conservative property of a traditional static evaluation method and low efficiency of a traditional probability simulation method are effectively overcome, and accurate evaluation of the thermal behavior of the bearing under the dynamic working condition and remarkable improvement of probability calculation efficiency are realized.