45 results about "Probit" patented technology

A method for determining a characteristic bit-flip time of a quantum qubit in a superconducting quantum device, comprises the following operations: a) initializing an idling time and a probability density function (P) of bit-flip time testing parameters (θ) according to a first probability distribution (p), said bit-flip testing time parameters (θ) comprising at least a decreasing lifetime rate (Γz) said probability density function (P) of bit-flip time testing parameters (θ) being a Poisson distribution with the probability of observing a total measurement outcome y = ∑i yi   being given by (see formula (I)), where p0 / 1 is a function comprising a component of the type e-Γzt, said probability density function of bit-flip time testing parameters (θ) being stored on a grid of values for the bit-flip time testing parameters (θ), b) preparing a physical qubit in a chosen state, c) idling for a duration derived from the idling time, d) obtaining a bit-flip measurement (y) by reading the state of the physical qubit, e) updating the probability distribution function of bit-flip time testing parameters (p(θ)) using the measurement (y) of operation d), the idling time of operation c), and with the formula p(θ) = p(θ)p(y|θ,t) / p(y,t), f) calculating an estimated information gain for each possible measurement time using the updated probability distribution function of bit-flip time testing parameters (p(θ)) of operation e), from the information gain equal to the difference between the Shannon entropy of the probability distribution function of the decreasing lifetime rate (p(Γz)) and the Shannon entropy of the conditional probability distribution function of the decreasing lifetime rate knowing the measurement of operation d) and the idling time (p(Γz|(y,t)), or an estimated information flow with the information flow which is a function of the information gain and the idling time, g) defining a new idling time as the value of time which maximizes the estimated information gain or the estimated information flow of operation f), h) Returning the bit-flip testing time parameters (θ) if p(θ) satisfies a return condition derived from the evolution of the decreasing lifetime rate (Γz), and / or comparison with a return threshold, and else repeating steps b) to g) with the idling time of operation g) and the probability distribution function (p) of bit-flip time testing parameters (θ) of operation e).

The application provides a cross-modal dataset construction and automatic label annotation method, which comprises the following steps: obtaining an initial cross-modal dataset containing labeled and unlabeled sample pairs, initializing a pseudo label storage, extracting double-modal features from the unlabeled sample pairs through a modal encoder, outputting single-modal class probability distribution by a classifier, fusing the features to obtain cross-modal joint features, outputting joint class probability distribution by a shared classifier, taking the maximum value of the joint probability as the confidence score, taking the L1 distance between the double-single-modal probabilities as the deviation degree between the modes, screening candidate samples that meet the threshold condition, calculating the weight based on the confidence and the deviation, performing exponential moving average on the joint class probability and the smoothed pseudo label of the previous cycle to generate the weighted smoothed pseudo label of the current cycle and update the storage, constructing a hybrid supervision signal with the real label and the pseudo label of the candidate sample, minimizing the total loss function, and iteratively optimizing the modal encoder and the classifier.

This invention relates to the field of financial risk early warning technology, specifically a method for dynamic prediction of repayment ability and overdue early warning based on multi-source time-series data. The method includes: acquiring multi-source time-series data of a target object over a continuous time period; generating a clean time-series data stream through time axis normalization and data quality restoration; constructing a feature set of income stability, expenditure volatility, and debt pressure based on this data stream, forming a multi-dimensional risk feature space; inputting this data into a probabilistic graphical model based on state transitions, outputting a probability distribution of repayment ability states; and calling an inversion inference engine to trace the key characteristic variables and change trajectories of abnormal states through state inversion technology, comparing them with preset risk thresholds to determine the risk critical point and level. This method achieves dynamic prediction of repayment ability and overdue early warning, improving the accuracy and targeting of risk identification.

This invention discloses a method for optimizing the dynamic transmission error distribution characteristics of a harmonic reducer, comprising: establishing a static transmission error probability model to obtain the probability distribution of the overall static transmission error; constructing a dynamic transmission error mathematical model considering static transmission error and dynamic parameters; constructing a high-precision surrogate model of dynamic transmission error including the probability distribution of static transmission error and the range of dynamic parameters; obtaining the probability distribution of dynamic transmission error; and dynamically adjusting the range of dynamic parameters based on a particle swarm optimization strategy to find the parameter range that optimizes the dynamic transmission error distribution. By introducing static transmission error into the dynamic model of the harmonic reducer, considering the influence of the processing and assembly of the harmonic reducer on the transmission error, a probability-range hybrid uncertainty model is used to mathematically describe the probability distribution characteristics of static transmission error and the range of dynamic parameters, and a Chebyshev polynomial method is used to construct an approximate model of dynamic transmission error.

The application discloses a power load prediction method and system based on quantile regression, a medium and equipment, and relates to the technical field of power system load prediction.The method comprises the following steps: acquiring historical power load data and corresponding time characteristic data of coal mine power load; preprocessing the historical power load data to construct a power load feature set; constructing a learning prediction model based on quantile regression; iteratively training the learning prediction model according to a preset quantile and a loss function in a quantile regression output layer based on a training set; inputting corresponding historical power load values and time characteristic data at a to-be-predicted moment into the trained learning prediction model, outputting a load prediction value in the quantile regression output layer, and constructing a probability prediction interval according to multiple quantiles.The application focuses on the key time point that has the greatest influence on the prediction result in the historical power load data, dynamically allocates feature weights, and significantly improves the accuracy of point prediction.

A neural process prediction method for surface reflection characteristics based on micro-surface model guidance is proposed. Current BRDF representations based on analytical models or deterministic neural networks perform poorly in complex material modeling and sparse sampling scenarios. There is a lack of standardized and accurate probabilistic prediction methods for tasks related to quantifying prediction uncertainty and analyzing and controlling error propagation. This invention forms an initial model by training a network on a completed BRDF dataset and applying physical guidance constraints. Based on the initial model, a probabilistic model of the bidirectional reflection distribution function is formed to create a family of BRDF function probability distribution models. The prediction process for reflection characteristics in unobserved directions is then completed based on the established family of BRDF function probability distribution models.

The application discloses a multi-source information fusion method for solving model prediction conflicts, characterized in that a plurality of single-source prediction models are constructed according to multi-source historical data in an industrial scene, basic probability distribution of the single-source prediction models is adaptively corrected in combination with operating conditions, and on this basis, evidence fusion rules are adopted to complete fusion of prediction results.

This invention provides a probabilistic wind speed prediction method based on physical guidance and multi-scale feature fusion, belonging to the interdisciplinary field of meteorological forecasting and computer science. The technical solution includes the following steps: S1: Preprocessing the dataset; S2: Constructing physically guided interactive features; S3: Extracting spatial features of the data at multiple scales using the MSFF-ResNet module; S4: Mining the time-series dynamic patterns of the data using the FAMD-Liquid Time-Constant Networks module; S5: Concatenating features using a gated fusion mechanism, calculating weights, and inputting the results into an MLP for prediction; S6: Designing a Bayesian-optimized composite loss function; S7: Using the trained model to perform multi-task wind speed prediction on a test set. This invention improves the accuracy and physical interpretability of probabilistic wind speed prediction under complex terrain.

This invention belongs to the field of power system demand-side management technology and discloses a probabilistic assessment method for load adjustability potential considering dual uncertainties. The method includes: constructing adjustability potential models for five load types: air conditioning, cold chain, energy storage, electric vehicles, and data centers; determining the random parameters of external influences on each load and quantifying their probability distribution based on historical data; introducing random execution variables for each load type to describe the uncertainty of execution behavior with probability distributions, thus obtaining a more realistic adjustability potential; performing random sampling calculations on the above models and parameters using the Monte Carlo method to aggregate and generate a total adjustability potential sample; and performing fitting analysis on the sample to obtain the probability function of the system's adjustability potential and conduct multi-dimensional evaluation. This invention achieves the quantification of load adjustability potential uncertainty through a three-layer probabilistic modeling of "physical behavior - random influence - random response," providing decision support for optimized power grid operation.

This invention relates to the field of IEGS probabilistic energy flow technology, and particularly to an IEGS probabilistic energy flow monitoring method based on a sparse arbitrary chaotic polynomial model, comprising: S1, establishing a deterministic energy flow model for an integrated energy system (IEGS); S2, modeling random input variables and configuring corresponding non-Gaussian probability density functions; S3, generating P multidimensional orthogonal aPC basis functions; S4, generating M0 initial collocation points; S5, transforming the problem of solving the sparse aPC coefficient vector A into an l1-l2 norm minimization optimization model; solving for the sparse aPC coefficient vector A under the current collocation point set; and constructing a sparse aPC surrogate model; S6, verification; outputting the final sparse aPC surrogate model; S7, analytically calculating the statistical moments of key output quantities and reconstructing the probability density functions of key output quantities; S8, evaluating the safety margin of IEGS operation. This method can effectively reduce model dimensionality and improve computational efficiency by combining sparse recovery techniques while retaining the good adaptability of the aPC method to non-Gaussian inputs.

This fall probability prediction device has a computer function for outputting a numerical value calculated on the basis of an input value. The fall probability prediction device comprises a trained model that has been subjected to machine learning so as to output a fall probability curve, which represents a fall probability as a function of age, using the age of a plurality of individuals and the presence or absence of a fall for each of the plurality of individuals as explanatory variables. Upon input of the age of a subject as an input value, the fall probability prediction device outputs a fall probability curve for predicting the fall probability of the subject at a time later than the time of the input.

This invention discloses a nonlinear control law evolution method based on a large language model and control effect evaluation index, including initializing the control law; converting the weights of the control law operators into sampling probabilities; sampling each control law operator according to the probability distribution of the control law operators in the large language model to generate a control law formula; inputting the set of control law formulas into an aircraft dynamics simulation model to obtain a tracking error sequence and determining the evaluation index matrix of the tracking error sequence; analyzing the relationship between the control law operators and the evaluation index and outputting the update gradient of each control law operator; updating the weights of each control law operator and generating multiple new control law operators, and initializing the weights of the new control law operators; ending the iteration when the number of iterations reaches the maximum limit and outputting the control structure and control parameters of the control law, otherwise resampling and iterating; this invention solves the problem that large language models cannot be embedded in the dynamics simulation environment and evaluate the effect of control laws.

The application discloses a kind of trusted data deletion methods, belong to data security technical field.The method includes: obtaining target data and its storage information to be deleted;According to the probability distribution parameter of preset byte value, generate probability distribution lookup table, wherein the parameter is used to define the appearance probability weight of each byte value;For each byte of target data, by comparing the normalized uniform random number with the cumulative probability interval in the lookup table, determine target byte value and overwrite;Finally, execute delete operation.The application introduces adjustable probability distribution parameter, so that the data after covering can present uniform or non-uniform specific statistical characteristics, thereby effectively interfering data recovery and forensic analysis, and enhances the security and flexibility of data deletion.

This invention discloses a multi-agent collaborative optimization scheduling method for medium- and low-voltage distribution networks under extreme scenarios, relating to the field of energy dispatching technology. The method includes: S1, unified modeling based on probability distribution, which constructs random state vectors and uses probabilistic statistical methods to uniformly characterize the random characteristics of different types of resources; S2, extreme scenario generation based on conditional variational autoencoders, which generates a set of scenario samples satisfying conditional distributions by learning the mapping relationship between historical meteorological data and operating states; S3, collaborative optimization based on Lagrange price signals, which achieves multi-agent collaborative decision-making driven by constructing a revenue function coupled with the scenario; S4, multi-agent coupled modeling based on graph neural networks, which constructs a distribution network topology graph and uses graph convolution operations to aggregate features of node states and extract the interaction relationship between electrical distance and power; and S5, a distributed optimization solution method based on the alternating direction multiplier method, which decomposes the global optimization problem into sub-problems of each agent.

The application relates to the technical field of optimal scheduling of distributed power grids, and specifically discloses a two-stage optimal scheduling method for distributed power grids based on a probabilistic power flow and a WOA-PPSO algorithm, which comprises the following steps: constructing a probabilistic power flow model, calculating the probability distribution characteristics of node voltage and line flow in the power grid containing wind power, photovoltaic power and load randomness based on the semi-invariant method and Gram-Charlier series expansion. The application introduces the probabilistic power flow modeling into the optimization constraint of the scheduling algorithm, and responds to the uncertainty that may exist in the power grid in real time; by constructing a two-stage scheduling algorithm, the global optimal scheduling plan is generated based on the WOA algorithm in the day-ahead scheduling, and the deviation caused by the uncertainty is quickly responded on the basis of the optimal scheduling plan in the real-time scheduling of the day; and the result of the day-ahead scheduling is taken as the reference starting value of the real-time scheduling of the day, and the projection method is combined with the PSO algorithm to construct the PPSO algorithm, so that the solving speed in the real-time scheduling of the day is maximally improved.

The application belongs to the technical field of intelligent monitoring, and particularly relates to a mutual inductor operation state intelligent monitoring method and system, which comprises the following steps: acquiring real-time operation error characteristic data of the mutual inductor and constructing a multi-dimensional feature vector space, calculating the Minkowski distance between each data point and constructing a generalized hybrid distance matrix, determining a near neighbor cutoff distance and calculating the kernel weighted local density of each data point, performing reverse k-nearest neighbor search and calculating a density ratio, combining the reverse neighbor number and the density ratio to generate an isolation coefficient, mapping the isolation coefficient to a statistical distribution model to calculate an outlier probability value, updating an operation abnormality index in combination with the outlier probability mean and fluctuation variance in a sliding time window, and determining that the operation state of the mutual inductor is abnormal when the operation abnormality index exceeds the upper limit of a confidence interval. The application can realize accurate identification and early warning of mutual inductor operation faults and improve the reliability and accuracy of monitoring results.

The application relates to the technical field of optimal scheduling of distributed power grids, and specifically discloses a two-stage optimal scheduling method for distributed power grids based on a probabilistic power flow and a WOA-PPSO algorithm, which comprises the following steps: constructing a probabilistic power flow model, calculating the probability distribution characteristics of node voltage and line flow in the power grid containing wind power, photovoltaic power and load randomness based on the semi-invariant method and Gram-Charlier series expansion. The application introduces the probabilistic power flow modeling into the optimization constraint of the scheduling algorithm, and responds to the uncertainty that may exist in the power grid in real time; by constructing a two-stage scheduling algorithm, the global optimal scheduling plan is generated based on the WOA algorithm in the day-ahead scheduling, and the deviation caused by the uncertainty is quickly responded on the basis of the optimal scheduling plan in the real-time scheduling of the day; and the result of the day-ahead scheduling is taken as the reference starting value of the real-time scheduling of the day, and the projection method is combined with the PSO algorithm to construct the PPSO algorithm, so that the solving speed in the real-time scheduling of the day is maximally improved.

This invention discloses a real-time probabilistic inversion method for TBM (Tunnel Boring Machine) extrusion load based on a surrogate model. First, the extrusion load vector and its prior value range are defined for the TBM shield tunneling environment. A surrogate model is constructed and iteratively updated based on a small number of high-fidelity numerical simulation samples to establish a rapid mapping relationship between the extrusion load and the shield structure response. Then, on-site measured shield monitoring data are acquired, and a likelihood function is constructed by combining sensor observation errors and surrogate model prediction errors. Finally, within a Bayesian probabilistic inversion framework, a Markov chain-Monte Carlo algorithm is used to call the surrogate model for sampling, obtaining the posterior probability density distribution, optimal estimate, and confidence interval of the extrusion load, and calculating the shield structure failure probability accordingly. This invention can meet the real-time requirements of inversion calculations and provide a quantitative assessment of the uncertainty of the inversion results, offering a scientific basis for safety decisions when TBMs traverse strata with large extrusion deformation.

This invention discloses a method, device, and medium for predicting the tail risk of static stability margin in power systems, belonging to the field of risk prediction technology. The method includes: using a quantile regression model to perform multi-quantile prediction of the output of new energy sources such as wind power and photovoltaics, obtaining the cumulative distribution function of the output of each new energy node; further constructing a discrete probability density function of the new energy output through discretization and differencing to avoid modeling errors caused by the assumption of continuous distribution; based on this, combining the thermal power output configuration and the static stability margin based on converter dynamic parameters, establishing a mapping relationship between the random injection of new energy and the static stability margin of the system, realizing the quantitative prediction of the stability margin probability distribution and its tail risk. This invention can accurately reflect the distribution characteristics of the static stability margin of the receiving-end power system in a probabilistic sense, especially the stability margin variation law under low-probability, high-risk operating conditions.

This invention discloses a method for extracting power quality fluctuation periods based on probability statistics. By considering the probability distribution and statistical characteristics of power quality index data, the optimal period division scheme for power quality data is determined. Based on the differences between stable and fluctuating periods, a fluctuation threshold is established and fluctuating periods are extracted. A power quality index fluctuation event database is constructed to assist in the identification of abnormal power quality fluctuations. The proposed fluctuation period division method can be used in the field of responsibility division, which can assist in the division of responsibility for interference sources, and more quickly and accurately trace the source of interference to the user, which is conducive to the proactive prevention and early treatment of power quality disturbances.

The application provides a base number estimation method based on a probability circuit, comprising the following steps: obtaining an original discrete distribution data set, performing discrete value integer coding, dequantization and standardization operation on the discrete distribution data set; training a probability circuit model using the converted standardization data to learn the joint distribution of the standardization data, the probability circuit model comprising sequentially connected sum nodes, product nodes and leaf nodes; obtaining a query condition and converting it into an integral region in a continuous space mapped from an attribute sampling range; integrating the integral region on the leaf node and performing a forward propagation along the network structure to obtain an integral value of the model probability density function on the query region, the integral value being a base number estimation result. The application solves the technical problem of the unavailability of expression and processability of a data-driven machine learning algorithm and the poor robustness of a query-driven algorithm.

This invention discloses an adaptive bouncing order determination method for target scattering simulation based on a probabilistic energy model. Specifically, it includes: acquiring target geometric statistics and material parameters; calculating ray dwell probability and average reflectivity and constructing an energy retention factor; establishing a probabilistic statistical model of relative field truncation error based on the energy retention factor; and determining and outputting the minimum bouncing order that satisfies the constraints according to the upper limit of the allowable error and confidence constraints. This invention is used in the calculation of the radar cross section of complex targets using the bouncing ray method. It establishes a probabilistic statistical model of the truncation error based on the target geometric statistics and material parameters, and determines the minimum bouncing order that satisfies the constraints under a preset upper limit of the allowable error and confidence constraints. This can reduce unnecessary high-order bouncing tracking while satisfying error constraints, providing a priori basis for order selection in scattering simulation.

Techniques for video data processing are disclosed. Probabilities of a probability model for a current frame are initialized based on a probability model associated with a reference frame. Further, counts of the probability model for the current frame are initialized based on prior counts from the probability model associated with a reference frame. The probability model for the current frame is subsequently updated based on these initialized counts. The counts may be initialized by applying a function to the prior counts, such as retaining three-quarters of the prior counts.

This application relates to an overload risk assessment method, apparatus, computer equipment, and storage medium. The method includes: collecting historical load data from multiple users and performing decoupling processing to decompose the total load sequence into multiple independent load subsequences; inputting the total load sequence and the multiple independent load subsequences into corresponding prediction models of decomposed linear probabilities to obtain the distribution data of load conditional probabilities within the prediction window; calculating a first risk index and a second risk index based on the load conditional probability distribution data; constructing a two-dimensional risk decision function based on the first risk index, the second risk index, and a preset thermal inertia time threshold to map the continuous probability space into discrete operating state levels; and determining the core load components causing distribution network overload risk from the multiple independent load subsequences in response to triggering a preset operating state level. This method can improve the accuracy of overload risk assessment.

This invention discloses a method and system for constructing the probabilistic stability domain and inverting parameter uncertainties in milling chatter, belonging to the field of CNC machining and cutting dynamics prediction technology. The method first solves for a given dynamic parameter vector using a fully discrete method. i Stability boundary of the spindle speed-axial depth of cut plane; then use multiple sets i Boundary training agent model to quickly predict arbitrary i The boundary. Given i When distributing parameters, a proxy model is invoked via Monte Carlo sampling to generate a flutter probability distribution map and a preset probability. p The stability boundary is determined. For a limited amount of experimental data, a hierarchical Bayesian model is constructed, with and as hyperparameters as priors. A likelihood function is built using experimental stability / instability labels. The posterior distribution is obtained through Markov chain Monte Carlo sampling, and the probabilistic stability region is updated. Variance is estimated by reducing the likelihood using common random numbers. This invention significantly reduces computational overhead, enabling rapid construction of the probabilistic stability region and parameter distribution inversion, thus aiding in the selection of processing parameters and risk control.

The present application relates to the technical field of computer-aided processing, in particular to a kind of probability characterization method of ceramic matrix composite component mechanical property parameter, comprising: establishing target finite element model;Data set is constructed;Neural network model is constructed, and training is carried out;Error function is constructed;Likelihood function is constructed, and probability distribution model is obtained.The present application can provide accurate parameter basis for the uncertainty quantification, performance dispersion analysis and performance prediction of ceramic matrix composite through the analysis and probability modeling of posterior distribution.

The application discloses a kind of reserve demand quantification method considering net load random fluctuation and equipment failure outage joint occurrence probability, comprising the following steps: step 1) considering that prediction error offsets each other, the total prediction error distribution of system is modeled;Step 2) the discrete fault loss distribution is modeled;Step 3) the cumulative distribution function of total gap random quantity is constructed;Step 4) based on the cumulative distribution function of total gap random quantity, reserve capacity demand target function is constructed;Step 5) the reserve capacity demand target function is standardized, and the reserve capacity demand standardized target function is obtained;Step 6) the reserve capacity demand standardized target function is solved, and the reserve capacity demand of system caused by prediction error is obtained.The application can more accurately depict the correlation and aggregation effect of prediction error, effectively balance the reliability and economy of reserve capacity configuration, especially suitable for system operation and market decision under the condition of high proportion of renewable energy grid connection.

This invention belongs to the field of probability statistics and random variable dependency modeling technology, and specifically discloses a phase modulation-based circular Copula extended modeling method, including the following steps: (1) estimation of the marginal probability distribution of wind speed and wind direction angle, (2) construction of the phase modulation circular Copula model, and (3) acquisition and random sampling of the joint probability distribution model. This invention innovatively introduces a phase modulation mechanism and boundary correction term into the traditional circular Copula, significantly improving the modeling accuracy of the joint distribution for specific site data with statistical characteristics of wind speed-wind direction phase shift. It solves the technical problem that existing models with fixed phases cannot characterize the phase modulation of related structures with wind speed changes under such specific conditions.

A computer-implemented method includes generating a candidate machine learning model. The candidate machine learning model is configured to generate probability scores for members. The method includes training the candidate machine learning model using an initial data set, testing the candidate machine learning model to generate performance metrics indicative of a bias value correlated to the candidate machine learning model, and determining whether the performance metrics are below a threshold. In response to determining that the performance metrics are below the threshold the method includes generating a proxy feature based on the bias value, adding the proxy feature to the candidate machine learning model, calculating weights based on the bias value, updating the initial data set using the weights to generate an updated data set, and retraining the candidate machine learning model using the updated data set.

The video coder may code a block of video data using an intra-prediction mode determined from the most probable mode list. The video coder may build a total most probable mode list including N entries, where the N entries of the total most probable mode list are intra-prediction modes and the planar mode is the first entry in the total most probable mode list, build a first most probable mode list from the first Np entries in the total most probable mode list, where Np is less than N, and build a second most probable mode list from the remaining (N-Np) entries in the total most probable mode list. The video coder may then determine a current intra-prediction mode for a current block of video data using the first most probable mode list or the second most probable mode list.