9 results about "Bayesian melding" patented technology

This invention relates to a method for highlighting spike discharge data of intracranial electroencephalogram (EEG) signals. The method involves collecting background noise data from the patient's brain without neuronal discharges, preprocessing the background noise data, automatically selecting the optimal order of an autoregressive (AR) model using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), estimating the AR model coefficients using the Yul-Walker equation, and constructing a background noise model. The intracranial EEG signals to be processed are then subjected to high-pass filtering. A short-time Fourier transform (STFT) and window-based frame-by-frame processing strategy are used to subtract the spectrum of the signal from the noise. By adjusting the parameters of the spectral subtraction, noise removal and preservation of neuronal signal features are achieved.

This invention relates to the field of optical fiber communication testing technology, specifically a method for distinguishing adjacent fault points and estimating the distance between them using an OTDR. The method first acquires the OTDR backscattering curve of the optical fiber link under test, and then locates the starting position of the first fault point through preprocessing. Within a local analysis window, single-fault and double-fault physical models considering the system's finite pulse width and bandwidth characteristics are established. After baseline removal processing of the local observation signal, the optimal parameters of the two models are estimated using ordinary least squares and variable projection methods, respectively. Then, model selection is performed based on the Bayesian information criterion to determine the number of fault points, and when a double fault is identified, the estimated distance between the two fault points is output. This invention solves the problem of accurately distinguishing the number and estimating the distance between adjacent fault points when their responses overlap, significantly improving the OTDR's ability to distinguish and locate nearby fault points. It has high computational efficiency, good robustness, and is suitable for long-distance, large dynamic range optical fiber link testing scenarios.

This disclosure provides an indoor temperature clustering method and apparatus for centralized heating systems, applicable to the field of precise control of centralized heating systems. The method includes: generating initial room temperature sequence data based on room temperature data acquired from multiple room temperature sensors; constructing room temperature observation vectors of a predetermined duration based on target room temperature sequence data, wherein the target room temperature sequence data is obtained by preprocessing the initial room temperature sequence data; determining a Bayesian information criterion value based on the number of room temperature observation vectors of the predetermined duration, and determining the number of model parameters corresponding to the minimum value of the Bayesian information criterion value as the target number of clusters; performing cluster analysis on the room temperature observation vectors of the predetermined duration according to the target number of clusters, and displaying the clustering results obtained when the cluster analysis converges.

The application discloses a tower control visual field efficiency optimization method based on improved gaze point rendering, relates to the technical field of air traffic control simulation, and comprises the following steps: acquiring objects in a current scene, control task data, interactive data of a controller and a simulated tower system, and a current gaze point rendering picture; performing feature extraction on the objects in the current scene and the control task data; inputting the extracted feature data and the interactive data into a preset Bayesian information gain framework to output an attention distribution of a current controller; calculating parameters of a Gabor kernel based on the Gabor kernel function according to the attention distribution of the current controller; generating Gabor noise based on the parameters of the Gabor kernel, superimposing the Gabor noise on the current gaze point rendering picture, and forming a current final rendering image. The application significantly improves the rendering efficiency and enhances the immersion and visual reality of a user.

The application discloses a micro-architecture design space exploration method based on Gaussian mixture regression, adopts a Bayesian optimizer to realize incomplete supervised learning, can reduce the labeling cost of a data set, and accelerates the training of a model by utilizing prior probability, adopts a Gaussian mixture regression model as a proxy model, can simultaneously calculate the predicted mean of a target value and the covariance between multiple targets, compared with other models, can better perform multiple target optimization, uses a Bayesian information optimization criterion to determine the final number of Gaussian components, realizes the function of dynamically optimizing the number of Gaussian components, and makes the model better approximate a real function, collects a function CEIPV in combination with the target value, considers the balance problem between targets while providing the observation points with the highest uncertainty, and realizes the operation of selecting an optimal micro-architecture outside the data set by a conjugate gradient method.

The application discloses a hydrological time series cycle identification method based on spectral peak guidance and iterative waveform matching, which comprises the following steps: obtaining a stationary sequence based on the Akaike information criterion adaptive detrending; iteratively extracting cycles, using residual power spectrum in each round, retaining strong spectral peaks and generating real candidate cycles in the neighborhood to reduce the candidate set capacity; adopting phase binning median interpolation to construct a non-parametric waveform, correcting significance test by multiple comparison, and screening the optimal cycle; correcting the amplitude by joint least squares fitting, determining acceptance according to the Bayesian information criterion drop, and determining continuous extraction by white noise test; and finally outputting the significant cycle and the corrected component. Through the iterative process of spectral peak guidance, waveform matching, residual updating and re-guidance, the application can automatically identify real cycles and complex waveforms, suppress long cycle artifacts and over-extraction, and improve the accuracy and robustness of hydrological cycle identification.

This invention relates to a method for predicting financing demand based on a heterogeneous clustering distributed lag model. The method includes the following steps: constructing a set of predictive and response variables for investors; establishing a heterogeneous clustering distributed lag model and training it using an improved K-means algorithm, determining the number of clusters using the Bayesian information criterion; and performing out-of-sample prediction based on the trained model to predict changes in the financing balance under different financing interest rate adjustment schemes. The method, apparatus, processor, and computer-readable storage medium for predicting financing demand based on a heterogeneous clustering distributed lag model of this invention aim to utilize economic and econometric models, combined with customer credit information and macroeconomic indicators, to predict the likelihood of customers adjusting financing interest rates and the changes in the financing balance after interest rate reductions. By identifying the customer group truly affected by interest rate changes, this helps companies adjust interest rate strategies more accurately and improve the efficiency of financing balance management.