33 results about "Bayesian risk" patented technology

The invention discloses an ultrasonic phased array method based on information fusion based on information fusion and belongs to an ultrasonic signal processing technology. The algorithm comprises a bitmap segmenting method and bayes estimation. The bitmap segmenting method comprises the following steps: extracting out medians, upper quartiles and lower quartiles of signals independently collected by each array element of an ultrasonic phased array probe in sequence, obtaining quartile dispersion, solving out elimination points and obtaining effective data fusion sets. Bayes estimation is established by virtue of the following steps: obtaining a characteristic function of each piece of effective data, solving out a decision function, obtaining a risk function, extracting the bayes risk of the decision function and obtaining most effective estimated values of parameters. The method disclosed by the invention can be used for improving the utilization rate of information collected by ultrasonic phased array elements, and obtaining more accurate and reliable description on a real environment.

The invention provides a multi-sensor multi-target joint detection, tracking and classification method, which is characterized in that it includes the following steps: S1: given the initial value of the multi-target state, defining a new Bayesian risk; S2: in the Under the conditions of the category assumptions in the above category hypothesis set, predict the multi-target state to obtain the prior state distribution of multiple targets; S3: under the condition of the category decision set, calculate the multi-target posterior density at time k, and obtain the posterior Check the state distribution of multiple targets; S4: Calculate the multi-target detection loss, state estimation loss and classification loss under different decision-making conditions; S5: According to the detection loss, state estimation loss and classification loss, the multi-target Estimation and Classification Optimal Solutions of Objectives. The method is easy to implement and provides important technical support for multi-sensor network environment perception system.

The invention relates to a voice annotation quality determining method and device, equipment and a computer readable medium. The voice annotation quality determining method comprises the steps that a target audio file is input into a preset voice recognition model to obtain a pre-recognition text and a Bayes risk value of the pre-recognition text; annotation process information of the pre-recognition text in the annotation process by annotation personnel and history annotation information when the annotation personnel annotates a history annotation text are obtained; based on the Bayes risk value, the annotation process information and the history annotation information to determine the text reliability of an annotated text obtained by annotating the pre-recognition text by the annotation personnel; and the annotation quality of the annotation text is determined according to the text reliability. According to the voice annotation quality determining method and device, the equipment and the computer readable medium, inspectors can be assisted to pay attention to annotation texts more likely to be incorrect, and thus the efficiency of the whole voice data annotation quality testing is improved.

The present invention provides a method for quickly selecting SKIP mode based on Bayesian minimum risk decision-making. The specific steps are: firstly, the rate-distortion cost of the SKIP mode of the current coding unit is used as the decision feature, and the first four frames are studied online statistically, and for different quantization parameters and different coding unit depths, the conditional probability distributions of the decision features when the SKIP mode and non-SKIP mode are most preferred are respectively obtained through non-parametric probability density estimation. Then based on the statistical information of online learning, the Bayesian risk minimum model is used to make early judgments on the SKIP mode of the remaining frames. This method can quickly determine the SKIP mode while ensuring the encoding performance, thereby skipping other unnecessary calculations of the predictive encoding mode, which can effectively reduce the encoding complexity of the HEVC encoder, and is conducive to the implementation of the HEVC encoder. real-time applications.

An ultrasonic phased array nondestructive test method based on fractal is disclosed. The method comprises following two parts: Bayesian information fusion algorithm and signal fractal processing. The Bayesian information fusion algorithm includes following steps of: taking each array element of a ultrasonic phased array probe, separately collecting a median, an upper quartile and a lower quartile in signals in sequence to obtain a quartile dispersion, solving an eliminative point to obtain an effective data fusion set, solving characteristic functions and decision functions of effective data to obtain a risk function, and extracting Bayesian risk of the decision functions to obtain optimal estimated values of parameters. The signal fractal processing includes following steps of: analyzing image fractal characteristics through a wavelet decomposition coefficient figure to determine a scale-free zone, calculating a fractal dimension, and determining whether a flaw exists and determining a flaw type according to the fractal dimension. Based on adoption of the Bayesian information fusion algorithm to improve the information utilization rate, the method adopts a fractal technology to process nonlinear modern signals so as to determine whether a flaw exists in a composite material and to determine a flaw type.

The invention relates to the field of water conservancy and hydropower engineering, and discloses a construction method of Bayesian risk network models of a cascade reservoir group. Risk transmission factors of the cascade reservoir group are comprehensively considered, and powerful theoretical and technical support is provided for risk analysis and evaluation of the cascade reservoir group. The method comprises the following steps that firstly, cascade hydropower station Bayesian risk network models of different dam types are generalized by selecting high-level network nodes; secondly, a reservoir inflow flood node and a reservoir outflow flood node on the generalized cascade hydropower station Bayesian risk network model are set to be connection nodes between an upstream hydropower station and a downstream hydropower station which are adjacent to each other; thirdly, the generalized cascade hydropower station Bayesian risk network model serves as a basic unit, the reservoir outflow flood node of an upstream reservoir and the reservoir inflow flood node of a downstream reservoir are connected through a single arrow line according to the combination modes between the cascade reservoirs, and the Bayesian risk network models of the cascade reservoir group in different combination modes are formed.