16 results about "Conditional entropy" patented technology

The invention provides a target matching and tracking method and device, equipment and a medium. The target matching and tracking method comprises the following steps: constructing a non-uniform grid map under a polar coordinate system; and distributing the point cloud data acquired by the radar to the corresponding grids. And on the basis of the observation result of each grid, the conditional entropy corresponding to the grid is calculated, and the observation result comprises the observed point cloud or the unobserved point cloud. And determining a plurality of non-empty grids with point clouds based on the conditional entropy corresponding to each grid. A plurality of clusters is determined based on the plurality of point clouds in the plurality of non-empty grids. A plurality of matching cost functions between the plurality of cluster clusters and the plurality of predicted trajectories are determined. And based on the plurality of matching cost functions, determining a target cluster and a target prediction trajectory which are matched with each other. The point cloud data is directly processed under the polar coordinate system, so that the calculation error can be reduced, and the accuracy of point cloud matching is improved. And non-homogenization processing is carried out on the grid map, so that redundant calculation of a long-distance area can be reduced.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

This application provides a context optimization method and apparatus for a retrieval-enhanced generation system, relating to the field of information retrieval technology. The method includes: receiving a user query and retrieving a knowledge document set related to the user query through a knowledge base retrieval; dynamically optimizing the knowledge document set by iteratively executing symmetric and asymmetric merging operations using an optimization objective function based on information bottlenecks to generate a compressed context; wherein, the symmetric merging operation is used for document pairs with low relevance scores; and the asymmetric merging operation is used for documents that identify and fuse semantic redundancy based on conditional entropy. The context optimization method and apparatus for a retrieval-enhanced generation system provided by this application, through dynamic intelligent fusion and compression of retrieved documents, can provide larger language models with higher information density and more accurate context at a lower computational cost, thereby obtaining more reliable and complete answers.

The present disclosure provides a privacy protection method of address information, an electronic device and a program product. The privacy protection method of the present disclosure comprises: decomposing address information, determining dimension information corresponding to address dimensions; combining dimension information of different address dimensions to determine several dimension information combinations; determining a conditional entropy value according to the dimension information combinations and the address information, the conditional entropy value being used to represent the difficulty of inferring the complete address information under the premise of knowing part of the dimension information; determining a privacy protection risk level of the address information according to the conditional entropy value; and determining a privacy protection strategy according to the privacy protection risk level.

The application provides a ZDR arc automatic recognition random forest modeling method and system, relates to the meteorological radar data processing technical field, and includes the following steps: acquiring dual-polarization radar observation data, extracting a differential reflectivity field and a reflectivity field, determining an arc candidate area based on a spatial position relationship and extracting multi-dimensional feature parameters, calculating a mutual information matrix and a partial correlation coefficient matrix to screen feature pairs and perform tensor product operation to generate high-order interaction features, constructing a training sample set, initializing a basic random forest, and based on feature conditional entropy, reconstructing a sampling probability distribution to complete decision tree growth to obtain a basic classifier, extracting a decision tree discrimination path sequence, converting it into a topology graph, encoding it into a path embedding vector to train a meta-random forest classifier, inputting to-be-recognized data into the basic classifier and the meta-random forest classifier, and outputting a recognition result. The accuracy and automation degree of ZDR arc recognition are effectively improved.

A method, apparatus or system for processing point cloud information can involve a learned deep entropy model over octrees for lossless compression / decompression of 3D point cloud data, wherein self-supervised compression / decompression involves an adaptive entropy coder operating on a tree-structured conditional entropy model and utilizing information from the local neighborhood as well as the global topology from the tree structure.

The invention discloses a geostatistical modeling method uncertainty quantification method based on a mutual information entropy framework, and relates to the technical field of geostatistical modeling quality control, and the method comprises the steps: collecting the space sample data of an estimation domain, determining target methods, constructing a parameter matrix for each target method, and carrying out the calculation of the parameter matrix; carrying out multiple modeling on each target method according to the parameter matrix on the estimation domain to generate a plurality of modeling results; dispersing each realization modeling result to a unified grid to obtain a realization modeling result set of each grid, counting distribution of a plurality of realization modeling results for each grid, constructing probability distribution of each grid, calculating point entropy of each grid based on the probability distribution, and forming a spatial uncertainty entropy field. According to the method, quantification of spatial uncertainty is achieved by achieving probability distribution and entropy measurement, IMUI indexes are constructed through joint entropy, conditional entropy and mutual information, and evaluation of output convergence and structural consistency of the method is achieved.

This invention relates to a method and system for generating route guidance information for urban rail transit, specifically in the field of traffic flow guidance. The method includes: constructing a utility model for the display format of guidance information and a utility model for the content of guidance information based on information entropy, conditional entropy, and information gain; a route attribute includes multiple display formats, and each display format includes multiple display format levels; determining the perception coefficient of the route's guidance information based on the utility model for the display format and the utility model for the content of guidance information; constructing a passenger route selection model based on the perception coefficient, wherein the passenger route selection model is an improved Logit model; calibrating the model parameters of the passenger route selection model according to different types of passengers; and generating guidance information based on the passenger route selection models calibrated with different model parameters, with the goal of minimizing the uneven distribution of passenger flow. This invention alleviates the problem of uneven passenger flow distribution in the road network.

The present application relates to the technical field of electric data processing, and particularly relates to a lightning arrester test interaction control method based on virtual reality technology, which comprises the following steps: obtaining an initial aliasing sequence in a virtual simulation environment; obtaining a conditional probability distribution of an initial aliasing signal about each interference component, thereby obtaining a conditional entropy and calculating a static contribution degree of each interference component; obtaining a standard physical evolution track of a lightning arrester, calculating a variation rate deviation of the initial aliasing signal and the track to determine a physical tracking operator; determining a suppression weight based on the static contribution degree and the physical tracking operator, and using the suppression weight to weight and combine a characteristic direction matrix of each interference component to construct a characteristic decoupling matrix; and decoupling the initial aliasing signal by using the matrix to restore a response characteristic signal and recover a leakage current value. The present application reduces the masking of test feedback by analog interference through dynamic and static dimensional characteristic decoupling, and improves the evaluation accuracy.

The application provides an information transmission method, system, electronic equipment and readable storage medium. The information transmission method comprises the following steps: acquiring an information block to be transmitted, acquiring a bit position reliability sorting table and a conditional entropy sorting table corresponding to a first component code, wherein the conditional entropy sorting table is used to determine a shaping bit in the bit position, and the number of shaping bits of the first component code is greater than or equal to 1; determining the bit position of each component code according to the bit position reliability sorting table and the conditional entropy sorting table corresponding to the first component code, wherein the bit position comprises an information bit, a frozen bit and / or a shaping bit; and generating a modulation symbol sequence according to the information block to be transmitted and the bit position of each component code. In the application, the probability shaping construction process in the information transmission process determines the bit position by constructing a table, and has low execution difficulty, low complexity, high flexibility and practicability.

Methods and devices for encoding and decoding 3D point clouds. A learned deep entropy model over octrees is proposed for lossless compression of 3D point cloud data. The self-supervised compression consists of an adaptive entropy coder which operates on a tree-structured conditional entropy model. The information from the local neighborhood as well as the global topology is utilized from the octree structure. In an embodiment, the features from the parent level is up-sampled to bring them to the resolution of the current level before further feature aggregation. For processing dense massive point clouds and to facilitate parallel processing, a block-based compression scheme is proposed to reduce the required computation and time resources.

The application relates to a kind of multivariate time series data abnormal root cause identification methods based on space-time causal diagram, belong to abnormal detection technical field.The method, to multivariate time series data, uses multi-window expansion causal convolution, combines mutual information screening, extracts time embedding covering short-term mutation and long-time dependence simultaneously;Non-local spatial correlation is learned using multi-head self-attention, then the directional causal strength is measured by conditional entropy, and a sparse, interpretable space-time causal diagram is generated by normalization-pruning;Causal reinforcement graph attention network is introduced on the space-time causal diagram, and the node embedding is updated by multiple rounds of causal propagation;The root cause score is calculated by combining the abnormal degree and the causal influence, the key source node in the abnormal propagation path is identified, and the precise root cause positioning of system anomaly is realized.The application enhances the adaptability to dynamic behavior patterns and the ability to capture abnormal driving factors, and improves the modeling accuracy and root cause identification ability of the abnormal propagation process.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

Embodiments of the present disclosure disclose a method and a system for training a neural network for generating universal adversarial perturbations. The method includes collecting a plurality of data samples. Each of the plurality of data samples is identified by a label from a finite set of labels. The method includes training a probabilistic neural network for transforming the plurality of data samples into a corresponding plurality of perturbed data samples having a bounded probability of deviation from the plurality of data samples by maximizing a conditional entropy of the finite set of labels of the plurality of data samples conditioned on the plurality of perturbed data samples. The conditional entropy is unknown. The probabilistic neural network is trained based on an iterative estimation of a gradient of the unknown conditional entropy of labels. The method further includes generating the universal adversarial perturbations based on the trained probabilistic neural network.

The invention discloses a sensitivity analysis method for computer numerical calculation software considering an input parameter distribution rule, belongs to the technical field of computer numerical calculation and system sensitivity analysis, and is suitable for scenes such as engineering simulation. The method aims to overcome the defect that an existing gradient method neglects input parameter distribution imbalance, and input-output parameter relevance is quantized through entropy in the information theory. The method comprises the specific steps of determining a parameter combination according to input parameter use frequency, substituting the parameter combination into software to obtain output parameters, counting state frequency distribution of the output parameters, calculating unconditional entropies and conditional entropies of the output parameters so as to obtain uncertainty when the input parameters are removed, and obtaining the input parameters through drawing an uncertainty change trend chart. And judging the input parameter sensitivity according to the slope. The method highlights the influence of high-frequency value points, is accurate in quantification uncertainty, is suitable for a multi-model scene, is visual and easy to understand in result, and can provide technical support for output parameter regulation and control.