16 results about "Probability estimation" patented technology

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.

The application discloses a kind of self-adaptive balance estimation and uncertainty decomposition method of traffic state, it is related to traffic management technical field, mainly includes the following steps: first, construct a double-output neural network that can simultaneously output traffic state mean and variance, and design mixed loss function that fuses data and physical constraints.In training, the variance of each loss gradient is calculated to adaptively adjust its weight, to balance the optimization process.Further, the parameter update is modeled as an underdamped Langevin dynamics process, noise is injected for posterior sampling, and multiple sets of model parameters are obtained.Finally, these parameters are used for multiple inferences, and by aggregating and statistically analyzing the prediction results, the accidental uncertainty from data noise and the cognitive uncertainty from model cognitive deficiency are separated and quantified.The application realizes high-precision probability estimation of traffic state under sparse observation data, providing a quantitative basis for traffic monitoring optimization and risk assessment.

The application belongs to the field of structural reliability analysis and uncertainty quantification, and specifically discloses an active learning reliability analysis method based on spectral normalized Gaussian process, which comprises the following steps: generating a Monte Carlo sample set according to a joint probability density function of random variables; then constructing an initial training data set and a spectral normalized Gaussian process (SNGP) surrogate model of a structural performance function; predicting the Monte Carlo sample set by using the surrogate model, estimating a system failure probability and screening a candidate sample pool; judging whether the model converges and whether the system failure probability meets the accuracy requirement according to the prediction result; if yes, outputting the surrogate model and the system failure probability estimation result, and completing the analysis. The application can significantly reduce the number of calls to the original calculation model in complex high-dimensional nonlinear reliability analysis, greatly improve the analysis efficiency while ensuring the accuracy, and is suitable for reliability analysis and design optimization of high-dimensional strong nonlinear systems in the fields of aerospace, vehicle engineering and the like.

A non-transitory machine-readable medium of an electronic device storing computer-executable instructions for updating a probability estimator value during entropy decoding for a bitstream representing a set of video pictures is provided. The computer-executable instructions, when executed by a processor of the electronic device, update a probability estimator value by (i) performing an initial right bit-shifting operation on the probability estimator value to reduce a length, in bits, of the probability estimator value, (ii) multiplying the right bit-shifted probability estimator value by a range value representing an interval, (iii) performing another right bit-shifting operation on a result of the multiplication generated by multiplying the right bit-shifted probability estimator value by the range value, and (iv) adding a constant value to a result of the other right bit-shifting operation, wherein the probability estimator value is associated with a probability of a bin having a particular value.

This invention provides a multi-source precipitation fusion probability estimation method and system, relating to the fields of hydrological and meteorological data fusion, uncertainty quantification in Earth system science, and probability forecasting. The method includes: acquiring multi-source gridded precipitation characteristic data of a target area; inputting the multi-source gridded precipitation characteristic data into a trained estimation model to obtain mean parameters and discrete parameters; constructing a composite Poisson-Gamma distribution based on the mean parameters, the discrete parameters, and preset or learned power parameters from the estimation model, and generating a precipitation set using a Monte Carlo sampling method to obtain the precipitation probability distribution result. This achieves end-to-end precipitation probability estimation and can further generate distribution curves, zero precipitation probability, arbitrary quantile estimates, interval widths, confidence intervals, and a complete set of uncertainty indicators, meeting the needs of hydrological and meteorological applications for probability input products.

The present application relates to the technical field of image classification, and particularly relates to a training method and device of an image classification model, a computer device and a medium. The method determines a class probability estimation vector and a class probability prediction vector of a to-be-classified image through an image classification model, determines a predicted image class of the to-be-classified image according to the class probability prediction vector, further determines the number of images of each image class, determines a first loss weight parameter of each to-be-classified image in combination with a preset image number threshold, determines a model loss of the image classification model according to the predicted image class, the first loss weight parameter, the class probability estimation vector and the class probability prediction vector of each to-be-classified image, and trains the image classification model. The first loss weight parameter is used as a weight basis of the similarity between the class probability estimation vector and the class probability prediction vector, the influence of the label imbalance problem on the training of the image classification model is reduced, and the accuracy of the image classification model is improved.

This invention proposes a method for identifying fault types in 10kV distribution network lines. First, the amplitude and phase angle features of the initial fault stage are extracted from voltage and current signals to form a preliminary trajectory. Then, wavelet transform decomposition is used to obtain millisecond-scale energy distribution to capture transient characteristic differences, and key components are selected for preliminary classification. Next, fine-grained features from multiple time windows are integrated, and support vector machines are used to calculate the similarity with sample trajectories to obtain probability estimates. Finally, transient trends and steady-state features are combined for final determination, thereby achieving accurate fault type identification. Finally, by clustering and summarizing stable feature patterns and feeding back to optimize the wavelet scale and model, a continuously adaptive identification framework is constructed, significantly improving the accuracy and reliability of fault classification and identification.

The application provides a binary snake optimization algorithm-based antenna sparse array design method, and belongs to the field of antenna array pattern synthesis. The application is based on a snake optimization algorithm, a probability estimation model is added in the algorithm, a new binary coding snake optimization algorithm is provided, and the new binary coding snake optimization algorithm is applied to a sparse planar array layout optimization problem. The new probability estimation model can better map the slight change caused by the update function in the algorithm to the binary coding, maintain the diversity of the population, enhance the global search ability, and also reserve the exploration and development ability of the original algorithm. The method has the global search ability and fast convergence, and improves the sidelobe suppression ability of the sparse antenna.

A computer-implemented method for classifying manoeuvres performed by an aircraft, including: acquiring a data structure including at least one unknown data matrix including a plurality of time series of samples of quantities related to the flight of the aircraft, the samples being relative to a succession of instants of time; applying to the unknown data matrix a neural network generating a corresponding probability matrix including, for each instant of time of the succession of instants of time, a corresponding probability vector including, for each class of a plurality of classes of manoeuvres, a corresponding estimate of the probability that, in the instant of time, the aircraft has performed a manoeuvre belonging to the class; and selecting, for each instant of time of the succession of instants of time, a corresponding class of manoeuvres, based on the probability estimates of the corresponding probability vector.

The application provides an over-the-air reconfigurable metasurface assisted network mobility parameter configuration method. In the method, the fixed parameters of an over-the-air intelligent metasurface network are counted and taken as inputs of an average network switching probability estimation algorithm; the total number of RIS units in a cell and the typical user moving speed are considered, a model is established with the aim of minimizing the average switching probability, and a probability estimation method is obtained through reasonable network modeling. By comparing the switching probability estimation values under different RIS unit numbers N and corresponding deployment densities, the optimal configuration parameters are obtained, so as to minimize the network switching times and reduce the switching overhead. The embodiment of the application solves the problem of how to configure the RIS unit number and the deployment density in the deployment process of the over-the-air reconfigurable metasurface assisted network to reduce the influence of frequent switching on the network performance.

This invention discloses a quantum state detection method and system. The method includes the following steps: obtaining the accuracy requirement corresponding to the target quantum state detection task; performing an initial round of repeated measurements on the target quantum state to obtain preliminary measurement results; dynamically determining the total number of repeated measurements to meet the accuracy requirement based on the preliminary measurement results; and outputting the final probability estimate and its confidence interval based on the corresponding measurement results obtained from the total number of repeated measurements. This invention, when performing measurements on a quantum computer, reduces the number of repeated measurements while ensuring the accuracy of the target detection, thereby reducing the time cost of quantum algorithm operation and quantum simulation, and increasing the output per unit time of the quantum information processor.

This application discloses a method for assessing small failure probabilities based on prior constraint integration and hierarchical correction sampling, relating to the field of uncertainty quantification technology. The method includes: first, constructing an integrated surrogate model incorporating multiple types of prior constraints and performing residual correction using high-fidelity anchor samples; then, calculating soft failure weights based on the corrected surrogate model, and using this weights to perform hierarchical search of candidate samples to obtain a failure sample set; next, clustering the failure samples to identify multiple failure sample clusters and constructing a multi-scale hybrid proposal distribution; then, performing effective sample size-driven bridging sampling, adaptively controlling the transition step size to obtain a stable initial failure probability estimate; finally, using inverse probability weighted unbiased correction, performing high-fidelity verification of the bridging samples prioritizing false negative risk and correcting the initial estimate, outputting the final failure probability. Under the condition of strictly limited high-fidelity evaluation times, this method achieves high-precision, high-stability, and statistically unbiased estimation of small failure probabilities.

Entropy coding a sequence of syntax elements using a selective update of a multi-hypothesis probability estimation is described. A sequence of syntax elements is received, where the sequence of syntax elements is associated with a random variable of multiple random variables to be coded. Whether the sequence of syntax elements is entropy coded using a single hypothesis probability model or a multi-hypothesis probability model is determined based on the random variable. Fewer than all multiple random variables are coded using a respective multi-hypothesis probability model. The method also includes determining a symbol for a syntax element of the sequence and entropy coding, using arithmetic coding, the symbol using the single hypothesis probability model or the multi-hypothesis probability model determined based on the random variable. Thereafter, the single hypothesis probability model or the multi-hypothesis probability model determined based on the random variable is updated.

This application provides a method, apparatus, electronic device, and storage medium for sleep staging detection, comprising: acquiring polysomnography data of a target object within a preset sleep time period, wherein the preset sleep time period is divided into N time segments; inputting the polysomnography data of the i-th time segment into a convolutional network, and outputting the data features corresponding to the i-th time segment; inputting the data features corresponding to the i-th time segment, the data features corresponding to the (i-1)-th time segment, and the staging result of the (i-1)-th time segment into an echo state network, and outputting a probability estimate of the sleep staging of the i-th time segment, wherein the echo state network includes a reserve pool, which is composed of multiple neurons, and different inputs can stimulate different neurons in the reserve pool to change the individual state attributes of the neurons, and the connection weights in the reserve pool are fixed under different inputs. This application improves the accuracy of sleep staging.

This invention discloses a trajectory prediction method and system based on gated attention and probabilistic multimodal analysis, relating to the field of autonomous driving technology. The method includes: acquiring and encoding historical trajectory data of multiple vehicles in a target scene; inputting the encoded features into an interaction enhancement module, reweighting the multi-head self-attention weights by introducing a dynamic relationship-based gating calibration factor to refine the modeling of vehicle interactions; subsequently, inputting the enhanced features into a multimodal prediction head, which is based on a conditional variational autoencoder architecture, generating multiple future trajectories and outputting an independent explicit probability value for each trajectory through a parallel probability estimation branch. This invention, through a gating calibration attention mechanism and a multimodal generation structure capable of outputting explicit probabilities, solves the problems of insufficient fine-grained interaction modeling and ambiguous multimodal prediction probabilities in existing technologies, significantly improving the accuracy, diversity, and interpretability of trajectory prediction.