8 results about "Uncertainty estimate" patented technology

A computer-implemented method utilizing a continuous discrete recurrent Kalman network, wherein the method includes receiving, at an encoder, an input from one or more sensors, wherein the input includes one or more time series data associating data at one or more points in time; outputting, to a Kalman filter, a latent observation and uncertainty estimate in response to the input at the encoder; determining a latent state prior and latent state posterior utilizing the Kalman filter; and outputting, via a decoder, a filtered observation utilizing at least the latent state posterior.

The application provides a rotary kiln energy consumption optimization method and system combined with visual recognition, comprising: preprocessing kiln operation data, and establishing a unified fusion feature representation; adopting a stacked long short-term memory network to process long-term dependence relationship, and simultaneously processing local time sequence mode through a multi-layer expansion convolution, and weighting and fusing the two kinds of features through a self-attention mechanism; in order to improve prediction reliability, training a plurality of basic models with different initializations and architectures, and applying a Bayesian model average technology to obtain a point estimate value, a prediction interval and an uncertainty estimate; based on the prediction model, constructing a graph structure representation of a kiln process parameter space, calculating a predicted coal consumption value of each parameter combination, thereby identifying an optimal process parameter combination, and realizing kiln energy efficiency optimization. The application improves coal consumption prediction accuracy and process parameter optimization efficiency.

This invention relates to the field of drug performance evaluation technology, and more particularly to a drug performance evaluation method and system based on chemical spatial clustering. The method includes: extracting structural features of known drug molecules from a calibration dataset and calculating chemical spatial distances; clustering the calibration dataset using a density-aware clustering strategy, performing statistical calibration within each chemical similarity cluster, and constructing a chemical spatial hierarchical calibration framework; using a graph neural network ensemble model to predict the drug performance of the drug molecule under test, obtaining prediction results and uncertainty estimates, determining the weights of each chemical similarity cluster based on structural features and the chemical spatial hierarchical calibration framework, and generating confidence intervals; adjusting the sensitivity of the confidence intervals according to the safety level corresponding to the drug performance under test, and outputting the prediction results and confidence intervals after a credibility assessment. This invention provides statistically reliable quantitative information for differentiated decision-making in early-stage drug development.

A software tool for predicting a candidate recipe for a food product using: (a) a predictor model trained to output, for a given candidate recipe passed as input to the predictor model, (i) a predicted value for at least one target variable and (ii) a predicted value for a given subset of evaluation variables, and (b) a generator model for: (1) training a base prediction model configured to output (i) a predicted value for at least one target variable of a space of a possible recipe and (ii) an uncertainty estimate for the predicted value, and (2) selecting a candidate recipe from the space of possible recipes based on (i) a balance between a predicted value output by the base prediction model and the uncertainty estimate and (ii) a set of constraints.

This invention discloses an intelligent imputation method and system for continuous missing values ​​in time series based on context-aware generative adversarial networks. The method includes: preprocessing the original time series to generate a mask matrix; constructing a multi-scale dynamic contextual cue generator, extracting multi-scale features through a parallel temporal convolutional network, and dynamically fusing contextual information using a hierarchical attention mechanism to generate an adaptive cue matrix; constructing a time-aware generator, fusing the missing sequence, mask, noise, and cue matrix to generate imputed values ​​and uncertainty estimates; constructing a multi-scale discriminator; jointly training multiple relevant datasets using a multi-task learning framework, and jointly optimizing the model through adversarial, reconstruction, KL divergence, uncertainty calibration, and multi-task consistency loss; and using the trained model to impute missing data, outputting the complete sequence and the uncertainty at each position. This invention significantly improves the accuracy and generalization ability of continuous missing value imputation and provides reliable confidence assessment.

Apparatus and method for uncertainty-aware code generation using LLMs. For example, one embodiment of a method comprises: generating, by a large language model (LLM) code generator, a plurality of RTL code blocks based on a design prompt; determining syntactical similarities and semantic similarities between pairs of the RTL code blocks; arranging the RTL code blocks into a plurality of clusters based on a combination of the syntactical similarities and the semantic similarities; generating uncertainty estimates indicating levels of uncertainty associated with one or more clusters of the plurality of clusters; and determining whether to synthesize an RTL output using one or more of the RTL code blocks based on the uncertainty estimates.

The invention discloses a seismic network multi-neighborhood multi-component cooperative clock error early warning system and method, and belongs to the technical field of seismic network clock errors. According to the method, a reference station set is selected for a target station from a station network neighborhood of the target station, the continuous waveform of each station is subjected to standardization processing, and a multi-component analysis sequence is generated. For each station pair, the relative clock offset and the quality index of the current period are estimated by calculating the similarity between the station pair and the long-term reference feature. And fusing the offsets of all station pairs, and obtaining an absolute clock offset estimation value and uncertainty of the target station based on mass weighting. And analyzing a time sequence formed by historical and current offset values, and automatically identifying a clock error state type. According to a preset early warning rule associated with each state, the current offset value, the uncertainty and the state duration are integrated for judgment, early warning information output of the corresponding grade is triggered, and automatic monitoring and graded early warning of the seismic station clock offset are achieved.

This invention discloses a deep learning-based method for behavioral analysis and risk warning in clinical nursing processes, relating to the field of medical auxiliary diagnostic technology. It includes: S1: Constructing a three-branch deep neural network model through an individualized normal modeling path, a causal risk perception path, and a behavioral variation analysis path; S2: Setting orthogonality constraints and an uncertainty calibration assessment mechanism to obtain corresponding individualized normal vectors, risk feature vectors, style variation vectors, and uncertainty estimates, and combining them to determine the corresponding digital features; S3: Determining the corresponding anomaly confidence level and risk confidence level through the digital features, setting corresponding risk scores, and determining the corresponding risk level based on the risk scores and uncertainty estimates. This invention can effectively assist in the discovery of potential risks and ensure patient safety.