20 results about "Probabilistic classification" patented technology

The invention is suitable for the technical field of thunderstorm prediction, and provides a strong thunderstorm potential risk and intensity prediction method based on an artificial neural network, and the method comprises the steps: obtaining the ground-to-ground lightning data of a target region, determining the time distribution characteristics of a thunderstorm event, and recognizing the time valley of the thunderstorm event; collecting multi-source meteorological data in a target area, and screening meteorological prediction factor data; performing space-time alignment processing on the ground-to-ground lightning data and the meteorological prediction factor data, and constructing a grid unit day-by-day sample set based on the aligned data; constructing a probability classification model of a strong thunderstorm event based on the grid unit day-by-day sample set; building a regression neural network model based on the grid units which are judged to be strong thunderstorm high risks by a probability classification model; and a collaborative prediction result is output, a predicted value of a geometric mean value of the ground-to-ground flash frequency and the lightning current amplitude is synchronously output, and a risk assessment basis of classification discrimination and numerical prediction capability is provided for extreme weather early warning and power grid disaster prevention and reduction through double-order modeling.

The invention provides a grid data processing method, system and device of a three-dimensional virtual model and a medium, and belongs to the technical field of three-dimensional virtual model processing, and the method specifically comprises the following steps: receiving original grid data; performing feature extraction and probability distribution on the vertex set; constructing an expansion graph through a k-nearest neighbor algorithm, calculating an edge connection probability, and multiplying the original adjacency matrix by the attention weight matrix to generate a simplified adjacency matrix; and performing feature coding and probability classification on the candidate triangle set, filtering and correcting non-manifold edges to obtain a simplified triangle set, and transmitting the simplified triangle set to target equipment. According to the invention, the lightweight processing of the original grid data is realized. On the premise that key geometric and topological characteristics of the model are not affected, the operation efficiency of the model on the target equipment is improved, the loading time is shortened, the display fluency is improved, and powerful support is provided for application of the three-dimensional model in scenes with high model response speed requirements such as virtual assembly, analogue simulation and real-time rendering.

The invention discloses an enterprise supplier alternative scheme evaluation method and device based on allowable interval approximate estimation, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: building an initial decision matrix based on the evaluation value of each supplier alternative scheme of an enterprise under each evaluation attribute, performing approximate estimation by using an allowable interval approximate estimation method and the evaluation values to determine a first tolerance coefficient, and determining an evaluation value reference scheme based on the first tolerance coefficient and the mean value and the standard deviation of the evaluation values; performing category allocation on the supplier alternative schemes by using a target probability classification method, and determining a boundary contour based on an allocation result; and determining a first close degree of the supplier alternative scheme and a second close degree of the boundary contour based on the boundary contour, the initial decision matrix and the evaluation value reference scheme, and evaluating the supplier alternative scheme by using a preset evaluation rule to obtain an evaluation result. Enterprise vendor objects are efficiently classified and rank inversion present in TOPSIS is prevented.

A control system and a method for controlling search agents to perform search with noisy observations and probabilistic guarantees is provided. The control system collects confidence bounds of a probabilistic classification of at least one region within at least one path of a set of paths. The control system compares aggregations of the confidence bounds of the probabilistic classifications of each path of the set of paths based on the collected confidence bounds, a first path of a set of paths is selected, for visit by a first search agent based on the comparison. The control system commands the first search agent to visit the selected first path to collect measurements associated with each region within the selected first path. The control system updates the confidence bounds of the probabilistic classifications of each region within the selected first path based on the measurements associated with the corresponding regions.

The invention provides a left ventricular hypertrophy classification method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: determining a median waveform of an electrocardiosignal, and extracting an amplitude feature from the median waveform; inputting the amplitude feature and the median waveform into a trained left ventricular hypertrophy classification model to obtain a probability classification result of left ventricular hypertrophy; wherein the left ventricular hypertrophy classification model comprises a plurality of parallel feature extraction modules used for analyzing different electrocardiogram leads; and the feature extraction module comprises a feature fusion module which is used for performing normalization processing on the median waveform and performing element-by-element multiplication operation on the normalized median waveform and the amplitude feature so as to realize amplitude reduction. According to the invention, based on deep learning, efficient and high-precision automatic classification of left ventricular hypertrophy diseases based on electrocardiosignals is realized; the median waveform and the amplitude feature are subjected to element-by-element multiplication operation, so that amplitude reduction is realized, loss of amplitude information is avoided, and the classification accuracy of left ventricular hypertrophy is improved.

The invention relates to a comprehensive quality evaluation method and system for cross-platform learning behaviors and a medium. The method comprises the following steps: preprocessing heterogeneous original data corresponding to each learning platform to obtain a learning behavior event set; creating a dynamic multi-modal event atlas based on the learning behavior event set; inputting the dynamic multi-modal event atlas into a heterogeneous graph neural network to obtain an atlas-level learning behavior embedding vector; a learning intention probability classification result and a cognitive state recognition result are obtained based on the atlas-level learning behavior embedding vector, and a preset probability graph model is triggered to conduct reasoning and conflict resolution on the dynamic multi-mode event atlas to obtain an updated dynamic multi-mode event atlas; and pre-defined comprehensive quality dimension features are extracted from the updated dynamic multi-modal event atlas, and dimension evaluation is carried out to obtain a comprehensive quality evaluation report. By adopting the method, comprehensive quality evaluation can be upgraded to a deep cognitive diagnosis level from surface behavior description.

The present application provides a method and device for predicting energy storage faults, which includes: dividing sample data into positive sample data and negative sample data; classifying the positive sample data, using OVO-SVM classification decision to combine any two categories in pairs, and then inputting the positive sample data to train the SVM model to generate a fault probability classifier; using database language to obtain the type of fault from the negative sample data, filtering the data based on the type of fault, and then using a random forest algorithm to train the negative sample data to generate a fault type classifier; inputting a preset number of data in the sample data into the fault probability classifier and the fault type classifier, respectively, and outputting the predicted fault type and predicted fault probability. The present application improves the accuracy of predicting the failure rate of system operation in the future period and the prediction of the type of fault of system operation in the future period and its corresponding probability of occurrence through bidirectional modeling of positive and negative samples.

A multi-input call panel for controlling an operation of an elevator system, is disclosed. The multi-input call panel includes a touchable interface associated with a plurality of touchable inputs arranged at different locations on the multi-input call panel; a touchless interface including a processor configured to receive readings of a sensor detecting motion in proximity to the touchable interface and executing a probabilistic classifier trained to output a probability of correspondence of the received readings with an intention to touch one or multiple touchable inputs from the plurality of touchable inputs; and a controller configured to control the operation of the elevator system according to a control command associated with a touchable input of the plurality of touchable inputs when the touchable input is touched on the touchable interface, the classifier outputs the probability of the intention to touch the touchable input above a threshold or both.

The present invention discloses a reliability analysis method based on group Monte Carlo and active learning kriging, including: obtaining random variables that affect the reliability and service life of product structures and converting them into standard normal variables; using the Latin hypercube algorithm to generate an initial training point set and constructing a kriging model; using a probability classification function to construct an approximate optimal importance sampling auxiliary sampling function; using the group Monte Carlo algorithm and taking the approximate optimal importance sampling auxiliary sampling function as the goal, iteratively generating an importance sampling auxiliary density function; using the importance sampling auxiliary density function to generate several importance sampling sample points; solving the stopping condition based on the kriging prediction value and the kriging variance; and solving the structural failure probability and coefficient of variation. Through the above scheme, the present invention has the advantages of simple logic, accuracy and reliability, and has high practical value and promotion value in the field of structural reliability analysis and assessment technology.

A control system and method for controlling a search agent to perform a search with noisy observations and probability assurance is provided. The control system collects confidence bounds for probabilistic classification of at least one region within at least one path of the set of paths. The control system compares aggregated results of the confidence boundaries of the probability classification of each path in the set of paths based on the collected confidence boundaries, and selects a first path in the set of paths to be accessed by the first search agent based on the comparison results. The control system commands the first search agent to access the selected first path to collect measurements associated with each region within the selected first path. The control system updates a confidence boundary of the probabilistic classification of each region within the selected first path based on the measurements associated with the corresponding region.

The invention provides an unmanned aerial vehicle inspection terminal data anomaly detection method and system based on behavior coding cooperating with Transform-RVM, and relates to the technical field of artificial intelligence, the method comprises the following steps: preprocessing sensor original data of an unmanned aerial vehicle inspection terminal, and generating a time sequence sample set; the time sequence samples are converted into behavior coding vectors, and space-time fusion features are generated by fusing equipment space topology information through a graph convolutional network; inputting the space-time fusion features into a Transform editor, and extracting deep features through a multi-head self-attention mechanism, residual connection, layer normalization and a feedforward network; constructing a probability classification model by adopting a relevance vector machine RVM, modeling a parameter optimization process by utilizing a neural differential equation, and performing efficient training in combination with a dynamic sparsity control and adjoint sensitivity method to obtain a highly sparse RVM model; and performing anomaly detection on the unmanned aerial vehicle inspection terminal data acquired in real time by using the highly sparse RVM model. According to the scheme, the anomaly detection precision of the unmanned aerial vehicle inspection terminal data can be improved.

Embodiments described herein provide a probabilistic approach using a probabilistic classifier to detect anomalous categorical data, e.g., data points in non-numerical data fields. Specifically, the probabilistic classifier may generate a conditional probability distribution of a data attribute, such as the “country of origin” field in a bond data record, conditioned on various other data attributes of the bond data record obtained from different databases. The generated conditional probability corresponding to the data attribute of the store non-numerical value, e.g., the “country of origin” equals a certain country code as stored, may indicate whether a data anomaly exists with the stored value for this particular data attribute. When an anomaly is detected with a data attribute, the categorical value associated with the highest conditional probability corresponding to the data attribute may be provided as the suggested value for the data attribute.

A multi-input call panel for controlling operation of an elevator system is disclosed. The multi-input call panel includes a touchable interface associated with a plurality of touchable inputs arranged at different locations on the multi-input call panel, a non-touch interface including a processor configured to receive readings of a sensor that detects motion in proximity to the touchable interface, and the non-touch interface executes a probabilistic classifier trained to output a corresponding probability that a received reading is of an intent to touch one or more of the plurality of touchable inputs, and a controller configured to control operation of the elevator system in accordance with a control command associated with a touchable input of the plurality of touchable inputs when the touchable input is touched on the touchable interface, when the classifier outputs a probability that the intent to touch the touchable input is above a threshold, or both.

The invention discloses a Bayesian update-based industrial equipment residual life prediction method and system, and the method comprises the steps: building a prior model of an equipment degradation process through historical failure data, and obtaining an initial parameter through maximum likelihood estimation; collecting state monitoring data of the equipment, and performing feature extraction and normalization processing to form a degradation amount observation value; carrying out recursive updating on posterior distribution of degradation model parameters by utilizing a particle filtering algorithm and a Bayesian theorem; through a Markov chain Monte Carlo sampling technology, outputting a point estimation curve, a confidence interval curve and a failure probability density curve of the residual life; an equipment maintenance plan is dynamically adjusted according to the prediction result, and early warning is triggered when the failure probability exceeds a preset threshold value, so that decision support is provided for predictive maintenance; the system correspondingly comprises a data acquisition and preprocessing module, a prior model construction module and the like. According to the method, through fusion of probability classification and statistical reasoning, prediction accuracy is improved, and a reliable decision basis is provided for predictive maintenance of equipment.

The application discloses a kind of hydraulic ship lift operation fault intelligent diagnosis method and system.The method obtains synchronous shaft torque time sequence in real time, whether system enters abnormal operating state is judged based on pre-configured trigger condition;Synchronous shaft torque time sequence characteristic window corresponding to abnormal period is intercepted;Based on the window, extract the multidimensional time sequence characteristics of the mapping water-machine-compartment coupling physical mechanism;The multidimensional time sequence characteristics are used as the input of the pre-trained probability classification model, and the anisotropic structure parameters in the model are used for calculation to obtain the posterior probability distribution of the to-be-diagnosed sample under each preset fault category;The type of operation fault is identified based on the posterior probability distribution.The application can overcome the problem of fault boundary aliasing in traditional methods under small sample, complex multi-working condition by deep mapping of physical mechanism and feature extraction, and further improve the diagnosis accuracy.

The application discloses a kind of industrial equipment residual life prediction method and system based on bayesian updating, the prior model of equipment degradation process is established by historical failure data, and initial parameters are obtained using maximum likelihood estimation;Collect the condition monitoring data of equipment, after feature extraction and normalization processing, form the observation value of degradation amount;Using particle filtering algorithm and bayes theorem, the posterior distribution of degradation model parameters is recursively updated;Through Markov chain Monte Carlo sampling technology, the point estimate, confidence interval and failure probability density curve of residual life are output;According to the prediction result, dynamically adjust equipment maintenance plan, and trigger early warning when failure probability exceeds preset threshold, provide decision support for predictive maintenance;The system corresponds and contains data acquisition and pretreatment, prior model construction and the like module.The application is fused by probability classification and statistical inference, improves prediction accuracy, and provides reliable decision basis for equipment predictive maintenance.

This invention discloses a multi-feature fusion fault diagnosis method for vibration signals of a vehicle axle assembly, belonging to the field of mechanical fault diagnosis technology. The method includes acquiring and preprocessing the raw vibration signals of the vehicle axle assembly. Time-domain, frequency-domain, and time-frequency-domain features are extracted from the preprocessed signals to form a primary feature set. A subset of sensitive features is selected through redundancy analysis and importance ranking. A kernel function is used to map the sensitive features to a high-dimensional space and perform linear reconstruction oriented towards fault categories, resulting in a more discriminative fusion feature vector. This fusion feature is input into a pre-trained probabilistic classification model to calculate the posterior probability of belonging to each fault mode. The fault mode is determined based on the probability, and a diagnostic result is generated. This invention enhances the discriminative ability of fault features through kernel space feature reconstruction and provides a reliability assessment of the diagnostic results using probability output, thereby improving the accuracy of fault diagnosis and the level of decision support.

The application discloses a photovoltaic power generation power prediction method, which comprises data preprocessing, weather type probability classification, similar day synthesis and multi-view interactive parallel prediction; the method constructs a probability classification model to divide the sample data by weather types, and uses the uncertain information of weather division as a confidence weight to calibrate the model input features; the design uses physical priori to guide feature enhancement, solves the problem that the existing method is prone to feature distortion under extreme weather conditions and the model generalization ability is limited; in addition, the introduction of physical priori to guide feature enhancement enriches the information of photovoltaic data, suppresses the interference of noise or fuzzy samples, significantly improves the utilization efficiency of information, guides the prediction model to pay attention to samples with high confidence, and enhances the generalization ability of the model.

The invention relates to a computer-implemented method (100) for indirectly measuring a probability of the occurrence of a defect in a test object of a type of technical devices, which are preferably batteries (2) or vehicles (3), having the following work steps: a) measuring state data in a plurality of technical devices of the type of technical devices, wherein the state data comprises whether the defect has occurred; b) measuring (101) operating data of the plurality of technical devices of the type and of the test object, wherein the operating data comprises value profiles of time-resolved measurement parameters and characterise an operating behaviour and an environment of a respective technical device from a field operation, wherein the operating data of those technical devices in which the defect has occurred are defect-marked in accordance with the result of the measurement in step a); c) generating (102) features by processing at least some of the operating data by means of mathematical operations and / or by selecting data regions from the operating data; d) selecting (103) features relevant in relation to the defect from the generated features by means of a concatenation of feature selection methods; e) training a probabilistic classification algorithm by means of the selected relevant features and the respectively associated state data, wherein a classifier (1) is generated; and f) generating (203) output data, comprising a probability of the occurrence of the defect in the test object, by applying the classifier (1) to input data based on the operating data of the test object.