49 results about "Markov transition" patented technology

Methods for dimensionality reduction of large data volumes, in particular hyper-spectral data cubes, include providing a dataset Γ of data points given as vectors, building a weighted graph G on Γ with a weight function wε, wherein wε corresponds to a local coordinate-wise similarity between the coordinates in Γ; obtaining eigenvectors of a matrix derived from graph G and weight function wε, and projecting the data points in Γ onto the eigenvectors to obtain a set of projection values ΓB for each data point, whereby ΓB represents coordinates in a reduced space. In one embodiment, the matrix is constructed through the dividing each element of wε by a square sum of its row multiplied by a square sum of its column. In another embodiment the matrix is constructed through a random walk on graph G via a Markov transition matrix P, which is derived from wε. The reduced space coordinates are advantageously used to rapidly and efficiently perform segmentation and clustering.

Systems and methods for the design or implementation of statistical filters for use in the spectral shaping of transmissions are disclosed. A desired power spectrum may be mapped to find pole locations that approximate the desired spectrum. These pole locations may then be mapped to the edge or inside of an equilateral polygon lying inside a unit circle, the equilateral polygon having the same number of sides as the order of the statistical filter desired and one vertex mapped to unity, to yield a set of eigenvalues. These eigenvalues may be the eigenvalues of a stochastic matrix the elements of which may be the Markov transition probabilities for use in a statistical filter designed to achieve the desired power spectrum. Use of a statistical filter employing these Markov transition values may be utilized to shape UWB or other signals to achieve the desired power spectrum.

The invention discloses a public bicycle system regulation and control method based on a Markov model. The public bicycle system regulation and control method comprises the steps of: (1) acquiring bicycle lending and returning statistics through a public bicycle information center; (2) dividing the acquired bicycle lending and returning statistics into a prior group and a posterior group, and calculating the statistics of the prior group by using a first-order Markov model parameterize method to obtain prior information used for subsequent inference; (3) combining the prior information with data of the posterior group, and utilizing Bayesian reasoning combined with the Markov model to obtain a Markov transition probability matrix; (4) inferring a public bicycle system OD matrix on the basis of the Markov transition probability matrix; (5) and conducting public bicycle system scheduling by using the OD matrix. The public bicycle system regulation and control method based on the Markov model utilizes the OD matrix statistical inference for conducting public bicycle system scheduling in real time, can obtain data easily, avoids solving the unclosed problem, effectively utilizes the prior information and improves accuracy of the obtained data.

The invention discloses a method, device, network equipment and storage medium for extracting a feature string, including: for each candidate feature string, according to the first-order Markov transition probability matrix, determine every two adjacent characters in the candidate feature string transition probability; according to the transition probability of every two adjacent characters and the logarithm of the transition probability, the transition entropy of the candidate feature string is determined; the candidate feature string whose transfer entropy is greater than the preset threshold is marked as the first feature string to be used , using the effective first feature string as the extracted target feature string. In the embodiment of the present invention, according to the first-order Markov transition probability matrix, the transition entropy of the candidate feature string of the data packet can be determined, and the candidate feature string whose transfer entropy meets the requirements is marked as the first access feature string, and the effective The first feature string is used as the extracted target feature string. The method for extracting a feature string provided by the embodiment of the present invention does not require manual intervention, and can completely realize the automatic extraction of the feature string.