114 results about "Bayes' theorem" patented technology

A method and apparatus that provides an expert system for determining respiratory phase during ventilatory support of a subject. Discrete phase states are partitioned and prior probability functions and observed probability functions for each state are defined. The probability functions are based upon relative duration of each state as well as the flow characteristics of each state. These functions are combined to determine phase probabilities for each state using Bayes' theorem. The calculated probabilities for the states may then be compared to determine which state the subject is experiencing. A ventilator may then conform respiratory support in accordance with the most probable phase. To provide a learning feature, the probability functions may be adjusted during use to provide a more subject specific response that accounts for changing respiratory characteristics.

The invention relates to a method for optimizing residual coding by using residual coefficient distribution features and bayes theorem. The method comprises the following steps of (1) reading each frame of images in a video sequence according to a sequence set in a control file; (2) performing intra-frame prediction and inter-frame prediction on the brightness and the chromatic value of each frame of images so as to obtain a residual coefficient; (3) performing texture judgment or bayes model judgment on a residual block TU (the maximum is 32*32) according to the residual coefficient so as to judge whether block encoding is required to be stopped ahead of time or not; (4) performing DCT (discrete cosine transform) and quantification on the whole residual block TU so as to obtain a quantification parameter; and (5) performing entropy encoding on the quantification coefficient, and finally outputting the quantification coefficient in a bit stream mode. By the method, the video encoding speed is increased under the condition that the loss on the encoding quality can be ignored, and videos can be conveniently acquired in real time.

The invention discloses a probability-based indoor location method. The method comprises the steps of establishing a fingerprint database on the basis of a received signal strength indication (RSSI) sequence and probability distribution of M reference points relative to N access points (AP), and location coordinates of M reference points; acquiring the RSSI sequence and probability distribution of at least one to-be-tested point relative to N AP, wherein Laplace calibration is performed on the probability of at least one to-be-tested point relative to each AP; on the basis of the Bayes theorem, matching the RSSI sequence and probability distribution of the j to-be-tested point of at least one to-be-tested point with the fingerprint database, so as to acquire the probability of the j to-be-tested point on each of the M reference points; and ranking the M probabilities from big to small so as to form a probability list, taking the reference points corresponding to the first k probabilities in the probability list, and computing the oriented coordinate of the j to-be-tested point. Through adoption of the method, the indoor location accuracy can be improved.

The invention discloses a method for recommending context-aware Web service on the basis of weighted time-space effects. The method includes building weighted time decay models so as to find user sets with preference similar to preference of current users under condition that time decay effects are considered; acquiring user sets in contexts similar to current contexts of the users by the aid of location-aware similarity mining algorithms; building relation models of time-space correlation and user preference so as to obtain network service call records conforming to the current preference of the users; predicting QoS (quality of service) values of service by the aid of Bayes theorems on the basis of the obtained user sets with the preference similar to the preference of the current users under the condition that the time decay effects are considered, the user sets in the contexts similar to the current contexts of the users and the network service call records conforming to the current preference of the users so as to obtain the service with the highest degrees of satisfaction; evaluating outcome of the obtained service. The method has the advantage that the recommendation accuracy can be improved by the aid of the method.

The present invention provides a cancellation algorithm of bilayer weighted parallel interference based on the studing of cancellation algorithm of partial parallel interference and based on the weighted parallel interference cancellation algorithm of the Bayes theorem. The algorithm not only has the advantage of weighted algorithm based on the Bayes, theorem which can achieve the minimum cost in bit grade judgement, but also can make up the deviation to the client signal estimation on the statistic means at the advantage possessing in partial weighted algorithm. At the same time, comparing with the cancellation algorithm of weighted parallel interference based on the Bayes theorem, the algorithm has increased the gain a lot at the low signal to noise ratio without mush increasing amount of calculations.

The invention belongs to the technical field of X-ray pulsar autonomous navigation and discloses an X-ray pulsar navigation TOA estimation method based on Bayes estimation. Under the condition that the overall trend of a photon counting rate accords with the Poisson distribution, an X-ray photon arrival time sequence can be modeled into a non-homogeneous Poisson process; the flow characteristics of PSR B0531 + 21 pulsars accord with the Poisson distribution, and a Poisson distribution signal model is established and divided into a time-frequency model and a frequency-stabilizing model; the frequency-stabilizing model of the photon sequence is selected to perform Fourier transform and then the frequency-stabilizing model is converted into a frequency domain to obtain a photon flow probability function expression with time delay estimation parameters; the photon flow probability function expression is converted into a likelihood function capable of calculating a time delay parameter by using a Bayes theorem for solving; and a Tool multi-mode nested sampling algorithm is calculated by Bayes estimation, iteration is carried out, and the parameter estimation value of the likelihood function is further calculated. The invention effectively improves the TOA estimation precision within the observation time and meets the future engineering development requirement of pulsar navigation.

A method for probabilistic fatigue life prediction using nondestructive testing data considering uncertainties from nondestructive examination (NDE) data and fatigue model parameters. The method utilizes uncertainty quantification models for detection, sizing, fatigue model parameters and inputs. A probability of detection model is developed based on a log-linear model coupling an actual flaw size with a nondestructive examination (NDE) reported size. A distribution of the actual flaw size is derived for both NDE data without flaw indications and NDE data with flaw indications by using probabilistic modeling and Bayes theorem. A turbine rotor example with real world NDE inspection data is presented to demonstrate the overall methodology.

A method and apparatus that provides an expert system for determining respiratory phase during ventilatory support of a subject. Discrete phase states are partitioned and prior probability functions and observed probability functions for each state are defined. The probability functions are based upon relative duration of each state as well as the flow characteristics of each state. These functions are combined to determine phase probabilities for each state using Bayes' theorem. The calculated probabilities for the states may then be compared to determine which state the subject is experiencing. A ventilator may then conform respiratory support in accordance with the most probable phase. To provide a learning feature, the probability functions may be adjusted during use to provide a more subject specific response that accounts for changing respiratory characteristics.

In magnetocardiography, an assisting technique for a doctor diagnosing a heart disease will be provided. In the magnetocardiography, magnetism to be generated from a subject's heart is measured by a magnetic sensor, and from this measured data, a position of center of gravity of excitement wave front in a cardiac muscle excitement propagation process and a transition vector (wave front vector) is calculated by an analyzing computer. With this wave front vector as a feature parameter, the Mahalanobis' distance is calculated from a data base obtained by diagnosing in the past, a possibility (probability) of the subject having a heart disease is presumed on the basis of the Bayes' theorem for displaying. Also, by superimposing on a heart picture, the wave front vector position is displayed, and a cluster classification position in the wave front vector is displayed. Also, by the similar retrieval, another subject extremely similar to the subject in the feature parameter will be retrieved from the data base for screen displaying.

The invention provides a commodity price forecasting method and a device, which relate to the technical field of data analysis, and obtain target sample data of commodity price forecasting. Target sample data includes: training sample data; determining a prior distribution function of each parameter in the preset input-output relation function model based on the preset input-output relation function model; according to a priori distribution function, training sample data and Bayesian theorem, the commodity price forecast distribution function model is obtained. The new input parameter data isinputted into the commodity price forecast distribution function model, and the output result of the commodity price forecast distribution function model is calculated as the commodity forecast pricecorresponding to the new input parameter data. The invention establishes a commodity price prediction distribution function model based on the collected sample data of parameters related to commodityprice prediction, predicts the output result of new input data through the distribution function model, and improves the accuracy and credibility of the commodity price prediction result.