4 results about "Bayes classifier" patented technology

The embodiment of the application relates to an algorithm and a device of a Bayesian classifier of a non-uniform background, which are applied to an underwater active sonar system, the algorithm comprising: obtaining underwater data to be measured and auxiliary data through the active sonar system; the number K of the auxiliary data is greater than 0; modeling classification of the unknown covariance matrix structure into a binary hypothesis testing problem; hypotheses of the binary hypothesis testing problem comprise H i Wherein i=0, 1, H0 is a case that the unknown covariance matrix is a complex conjugate symmetric matrix; H1 is a case that the unknown covariance matrix is a real symmetric matrix; a Bayesian model is set, the Bayesian model comprising a complex inverse Wishart random matrix and a real inverse Wishart random matrix; a classifier for distinguishing the two hypotheses is obtained by using a minimum error probability criterion.

This disclosure provides a security detection method, apparatus, storage medium, and electronic device based on a large model, relating to the field of network security technology. The method includes: acquiring training sample emails to train a Bayesian classifier, determining the trained Bayesian classifier; using the trained Bayesian classifier to detect emails to be detected, determining a first detection result; if the first detection result is in a first detection interval, determining structured prompt words based on the first detection result; inputting the structured prompt words into a locally deployed large language model, determining the output result of the large language model; and performing either allow or block actions on the emails to be detected based on the first detection result and the output result of the large language model. This achieves accurate email identification.

This invention discloses a method and system for recommending demand response mechanisms at the feeder level in distribution networks based on Bayesian classification. The method includes: Step 1, randomly acquiring hourly output data of distributed photovoltaic (PV) power and electric vehicle (EV) behavior data from each feeder in the distribution network using the Monte Carlo method, and selecting typical scenario data; Step 2, extracting feeder-level feature attribute data from the typical scenario data, using the feature attribute data as input conditions, and having an improved Bayesian classifier output feeder flexibility category labels, recommending feeder-level demand response mechanisms, including price-based demand response and quasi-linear demand response, to the distribution network operator based on the flexibility category labels; Step 3, constructing feeder-level load quasi-linear variables; Step 4, performing PV-energy storage-EV collaborative optimization scheduling. This invention achieves refined management of the distribution network and collaborative optimization of PV-energy storage-charging systems by intelligently assessing feeder flexibility and constructing feeder-level load quasi-linear variables.

The application discloses a water quality evaluation machine learning Bayesian model based on suspended particle matter density, and comprises the following steps: collecting and testing the suspended particle matter density indexes and other multiple water quality indexes of a plurality of water quality samples; establishing an entropy weight Bayesian model for water quality evaluation; calculating entropy weight values; calculating the posterior probability P of the samples being in class I to class V water by using the distance formula with the weight; dividing the adjacent grades between class I to class V water quality grades into ten equal parts, and determining the water quality grade according to the difference between the highest probability value and the second highest probability value in the posterior probability P; matching the water quality grade of each sample with the suspended particle matter density of the sample, selecting a large number of samples as a training set, and generating a water quality evaluation Bayesian classifier based on suspended particle matter; the application simplifies the existing water quality rapid evaluation method, improves the accuracy of the evaluation by improving the calculation formula in the prior art, and realizes the rapid evaluation of the water quality grade based on the suspended particle matter density.