13 results about "Sparse data sets" patented technology

The invention provides an urban built-up area air quality space-time evolution and exposure risk prediction method, and relates to the technical field of electrical digital data processing.The space-time evolution method comprises the steps that a to-be-monitored target area is discretized into a cubic sampling domain, an execution flight path of an unmanned aerial vehicle is planned, and a sparse data set containing five-dimensional features is constructed; executing iterative up-sampling operation, and converting the sparse data set into a dense feature point set covering a sampling domain; calculating a pollutant transmission flux vector of each node in the dense feature point set, constructing a weighted reverse tracking vector pointing to a suspected source, and constructing a three-dimensional traceability probability cone in combination with turbulence intensity; extracting a centroid offset coefficient and an axial dispersion, and identifying an emission working condition label of the pollution source through an emission working condition ternary discrimination model; executing multi-source probability field superposition fusion, and determining an estimation coordinate of a pollution source; and executing forward space-time diffusion simulation based on the estimated coordinates, and outputting a three-dimensional risk early warning graph.

A system may access a set of training data and determine a timeframe associated with a positively labeled data item of the training data. A system may generate at least two new positively labeled data items based on the positively labeled data item to generate augmented training data. A system may train a machine learning model by applying the augmented training data as input to a machine learning model, and modifying a weight of the machine learning model.

The invention relates to a multi-beam sounding data quality real-time evaluation method based on a Delaunay triangulation network model, and the method comprises the steps: carrying out the thinning of an original discrete data set, generating a thinned data set, and constructing a Delaunay triangulation network; performing fragmentation processing on the original data set according to the Delaunay triangulation network, and extracting an accurate data range of the fragmented data set; and respectively carrying out gridding processing on each fragmented data set to generate fragmented grids, and carrying out multi-beam sounding data quality real-time evaluation. According to the method, the gridding processing of mass and large-area discrete point data of the multi-beam sounding data can be quickly and accurately realized, the problems of real-time quality evaluation of the mass multi-beam sounding data and the like are solved, and the method can be widely applied to the field of multi-beam sounding data processing.

The invention discloses a multi-process machining process high-temperature intelligent prediction method based on sparse sensing extension, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: constructing a three-dimensional simulation model of a key part of a machine tool, carrying out the finite element thermal analysis, screening nodes in an initial layout network based on a graph node centrality measurement algorithm, and laying sensors, forming a sparse sensing network; acquiring a sparse temperature sensing data set based on the network, and setting a global temperature prediction threshold and an auxiliary anomaly judgment threshold in combination with material characteristics, finite element results and historical data; machine tool numerical control system parameters are collected and analyzed in real time, a process knowledge graph is constructed, and a current process type is judged through a model; inputting the sparse data set and the process type into a space-time extension model to obtain temperature data of all positions; and the process type and the overall data set are input into a high-temperature intelligent prediction model, and the future high-temperature abnormal state is judged in combination with a threshold value, so that the high calculation cost of a mechanism driving method is avoided, and the real-time monitoring and online decision-making requirements are met.

This disclosure provides a data-driven and scalable method to discover cause-and-effect relationships in data from natural systems that include sparse data sets. This technique can learn a causal graph from heterogenous data sources by combining embeddings from real data and embeddings from simulated data generated by process-based models. The causal graph is used for what-if analysis in out-of-distribution settings. One application is understanding the factors that affect soil carbon. A causal model created by these techniques can be used to discover cause-and-effect relationships that affect soil carbon. This model has applications such as forecasting soil carbon for a future time point to help inform farm practices. Farm practices, like tilling, may be modified in response to predictions provided by the model.

This invention provides a method for predicting the spatiotemporal evolution of air quality in urban built-up areas and for forecasting exposure risks. It relates to the field of electronic digital data processing technology. The spatiotemporal evolution method includes: discretizing the target area to be monitored into a cubic sampling domain; planning the flight path of a UAV; and constructing a sparse dataset containing five-dimensional features. Iterative upsampling operations are performed to transform the sparse dataset into a dense set of feature points covering the sampling domain. The pollutant transport flux vector of each node within the dense feature point set is calculated, and a weighted reverse tracing vector pointing to the suspected source is constructed. A three-dimensional source tracing probability cone is then constructed by combining turbulence intensity. The centroid offset coefficient and axial dispersion are extracted, and the emission condition label of the pollution source is identified using a ternary emission condition discrimination model. Multi-source probability field superposition and fusion are performed to determine the estimated coordinates of the pollution source. Based on the estimated coordinates, a forward spatiotemporal diffusion simulation is performed, and a three-dimensional risk warning map is output.

The invention belongs to the technical field of traffic generation, and discloses a diversity-oriented industrial protocol format inference traffic generation method. Designing an adversarial flow generation model oriented to format inference, and isolating learning of flexible payload distribution from rigid syntactic enforcement by adopting a decoupled generation architecture; the generation process is separated from a deterministic rule, so that the traffic generation model can synthesize a high-entropy payload mode exceeding the finite diversity of an original sparse data set; by isolating flexible distributed learning from rigid syntactic enforcement, the FIGAN solves the internal conflict between syntactic effectiveness and semantic diversity, and a new normal form is provided for industrial traffic enhancement.

This disclosure provides a mechanism for the enrichment of sparse datasets using language models. By training language models on the specific distribution of known values in a dataset, missing values can be predicted, and the predicted values added, thereby resulting in a more complete dataset. This method also facilitates the enhancement and augmentation of datasets by predicting values for new properties that were not previously available. The approach proves particularly effective at scale, transforming large sparse datasets into more complete and enhanced datasets. Masking language modeling may be employed to train language models capable of generating representations of technical data. Training data includes corpuses of technical data that may be represented as text strings. These pretrained models are fine-tuned to predict various properties. The resulting models can predict missing values in large technical datasets, providing valuable data for guiding scientific research.

A system may access a set of training data and determine a timeframe associated with a positively labeled data item of the training data. A system may generate at least two new positively labeled data items based on the positively labeled data item to generate augmented training data. A system may train a machine learning model by applying the augmented training data as input to a machine learning model, and modifying a weight of the machine learning model.

This disclosure provides a mechanism for the enrichment of sparse datasets using language models. By training language models on the specific distribution of known values in a dataset, missing values can be predicted, and the predicted values added, thereby resulting in a more complete dataset. This method also facilitates the enhancement and augmentation of datasets by predicting values for new properties that were not previously available. The approach proves particularly effective at scale, transforming large sparse datasets into more complete and enhanced datasets. Masking language modeling may be employed to train language models capable of generating representations of technical data. Training data includes corpuses of technical data that may be represented as text strings. These pretrained models are fine-tuned to predict various properties. The resulting models can predict missing values in large technical datasets, providing valuable data for guiding scientific research.

An ensemble time series prediction system that makes predictions based on observed data. The disclosed ensemble time series prediction system may leverage different types of datasets and information from different resources for making predictions. The disclosed ensemble time series prediction system may extract time dependent features from autoregressive time dependent data, embedding features from sparse datasets, continuous features from continuous dataset, and time lagged features from data that include time-lag information. The disclosed ensemble time series prediction system may then consolidate the features extracted from the different types of datasets and generate a set of consolidated input features for training a neural network, which may include a recurrent neural unit that finds sequential pattern for the sequence of input features and a regression unit that performs regression and predictions. The ensemble time series prediction system may output a set of outputs that include predicted values and associated confidence intervals.

The invention discloses a tooth profile parameter optimization design method based on Gaussian regression. The method comprises the following steps of: 1, performing gear finite element meshing contact simulation setting, including establishing a gear pair model, establishing a contact pair, performing grid division and setting boundary conditions; 2, solving and monitoring a force convergence curve in real time through gear pump meshing contact simulation setting in the step 1, and further obtaining and analyzing the contact stress change of gear dynamic meshing; step 3, constructing a Gaussian regression model by taking the sparse data set as a training set through the gear meshing simulation data set in the step 2, predicting maximum contact stress data under all tooth profile parameter combinations, further obtaining an optimal parameter combination, and carrying out accuracy verification; according to the method, the calculation cost can be remarkably reduced, and the gear tooth profile parameter optimization design efficiency is greatly improved.

The application discloses a kind of unmanned aerial vehicle hyperspectral non-imaging paradigm's oil slick detection method, comprising: parallel push-scan type scanning is carried out to offshore oil spill area, and the hyperspectral original data of measurement sea area is obtained;Spectral mixing matrix pixel splicing method is used to seamlessly splice the hyperspectral original data into image to obtain clean sea area data, other coastline and land non-oil slick area hyperspectral pixel cube;Full-spectral data set obtained in oil slick area and suspected oil slick area is regarded as foreground, and corresponding area relative position information is retained, other clean sea area data, other coastline and land non-oil slick area data are marked as background;The background is carried out hyperspectral cube sparse representation, and spectral segment sparse line compression is carried out using matrix mapping mode to obtain sparse data set, and the feature data set of oil slick area and sparse data set are used as transmission data;Containing relative position information oil slick area is marked to background image, and oil slick area focusing result is marked on map.