86 results about "Synthetic data sets" patented technology

The invention discloses a target detection method based on a synthetic data set. The method includes: adding a real environment picture as a background map to the three-dimensional model of the to-be-detected target in 3ds MAX software so as to establish a three-dimensional scene, rendering and generating a required number of synthetic images, and automatically completing the marking of the imagecategory and the marking box so as to complete the construction of a composite data set; using the synthetic data set as a training set to train the target detection network; and carrying out target detection after training is completed. By using the method, the labeled data set of any target can be quickly obtained at low cost, and the problems that the real data set is high in labeling cost andreal data cannot be obtained in a specific scene are solved. Furthermore, the designed target detection network is added with the SOMConv layer, so that the identification capability of the network onreal data can be improved.

The invention relates to the technical field of data processing, in particular to a CT image data automatic classification method and device based on CNN and GAN, and the method comprises the following steps: S1, obtaining CT image data to be classified; S2, selecting an image of the nodule to carry out data enhancement processing to obtain a public expansion data set; S3, obtaining a generation network and an identification network for the public expansion data set by using the GAN, and performing training at the same time to obtain a GAN synthesis data set; and S4, classifying the GAN synthetic data set by using a CNN network to obtain a final image data set. According to the method, the problem that most of existing researches about lung adenocarcinoma classification focus on radiomicsfeature modeling and other manual marking features, which are based on manual labeling, thus more burden problems are brought to doctors can be solved; according to the invention, the lightweight CNNmodel is also convenient to arrange in a hospital diagnosis system, daily work of radiologists is facilitated, development of precision medical treatment is promoted, and the invention has a very strong market application prospect.

The application provides a scheduling method, a scheduling server and a scheduling system of a combined heat and power generation system of a fire coal thermal power plant. The scheduling method of the combined heat and power generation system of the fire coal thermal power plant is deployed on the scheduling application server in the scheduling system of the combined heat and power generation system of the fire coal thermal power plant, and includes: obtaining initial data needed by building working condition models of all devices in the combined heat and power generation system from a synthetic data integration platform, and building the working condition models of all the devices in the combined heat and power generation system according to the initial data; judging whether preset scheduling conditions are met or not, and if yes, confirming a scheduling policy of the combined heat and power generation system of the fire coal thermal power plant under a current load according to real-time power and heat load data output by the working condition models, and controllable variables and auxiliary variables, which influence outputting of the working condition models. According to the scheduling method, the scheduling server and the scheduling system of the combined heat and power generation system of the fire coal thermal power plant, scheduling problems are considered by integrating blending coal, a boiler, a turbine generator and a temperature and pressure reducer together, and a scheduling scheme can be globally optimized.

The invention discloses an object surface reflection attribute extraction method, and the method proposes a Berlin noise-based synthetic data set, generates textures with low-frequency and high-frequency feature distribution, supports the generation of a data set sample, solves a problem of insufficient training data, and carries out the synthesis of sample data based on Berlin noise. A data set similar to a real sample can be expressed, and the generalization of the network is further improved; besides, the invention also provides an auto-encoder network structure based on VGG-19 and UNet network models, the network performs histogram matching on a feature map, feature extraction is performed through a hole convolution strategy, and the network is trained in combination with weak supervision and rendering technologies so as to obtain an auto-encoder network structure based on VGG-19 and UNet network models. The precision and the quality of the extracted object surface reflection attribute are effectively improved. The invention further provides an object surface reflection attribute extraction device and equipment and a readable storage medium, which have the above beneficial effects.

The invention discloses an end-to-end image defogging method based on multi-feature fusion. The end-to-end image defogging method comprises the steps of 1, acquiring a sample data set; 2, building an end-to-end image defogging network model based on multi-feature fusion, wherein the model comprises a basic network taking a global feature fusion attention module as a core, a prior feature extraction module supporting back propagation and a prior feature adaptive fusion module; the dark channel priori features and the color attenuation priori features enter a priori feature adaptive fusion module to be fused, and then are fused with deep learning features obtained by the basic network; step 3, constructing a loss function; 4, training an end-to-end image defogging network model based on multi-feature fusion; and step 5, carrying out defogging processing on a to-be-processed image by using the trained model to obtain a defogged image. Experimental results of a synthetic data set and a real data set show that the defogging capability and migration capability of the model in a real scene are improved, the parameter quantity is small, and rapid defogging can be realized.

The invention relates to a defogging method for a foggy image in a real scene based on fog migration and feature aggregation, and belongs to the field of image processing. The invention designs a method for realizing image defogging by migrating fog in a foggy image in a real scene to a clear image to generate a data set and then utilizing a defogging network based on feature aggregation. In a fog migration process, a multi-level feature block identification method is designed to migrate fog in a real scene to a clear image to generate a foggy image training data set, and the image in the data set has a style similar to that of a foggy image in the real foggy scene and distribution characteristics of fog in the foggy image. In addition, extraction features are supplemented in a fine-grained detail information and semantic information aggregation mode so as to realize image defogging. According to the method, a good defogging effect is achieved on the foggy image in a real scene, and the problem that a defogging model trained by a synthetic data set is poor in generalization performance on the real foggy image is greatly solved.

Identifying and detecting threats to an enterprise system groups log lines from enterprise data sources and / or from incoming data traffic. The process applies artificial intelligence processing to the statistical outlier in the event of the statistical outliers comprises a sparsely labelled real data set, by receiving the sparsely labelled real data set for identifying malicious data and comprising real labelled feature vectors and generating a synthetic data set comprising a plurality of synthetic feature vectors derived from the real, labelled feature vectors. The process further identifies the sparsely labelled real data set as a local data set and the synthetic data set as a global set. The process further applies a transfer learning framework for mixing the global data set with the local data set for increasing the precision recall area under curve (PR AUC) for reducing false positive indications occurring in analysis of the threats to the enterprise.