8results about How to "Improve classification results" patented technology

This invention discloses a fruit quality detection method and system based on a fruit quality detection model. First, the original hyperspectral image R of the fruit to be detected is acquired and preprocessed. Then, the preprocessed hyperspectral image R2 is input into the fruit quality detection model to obtain the surface defect results and internal quality index values ​​of the fruit. Compared with the prior art, this invention can achieve simultaneous detection of the internal and external quality of the fruit at low cost and high efficiency, and obtain the fruit quality grade result.

The application provides a building fine-grained edge extraction and classification method, device, equipment and medium, relates to the technical field of remote sensing detection, and includes size cropping on the obtained building remote sensing image, and constructing a training sample set, inputting the training set into a plurality of instance segmentation networks selected in advance for training, obtaining a plurality of first building identification models, and obtaining corresponding first identification results output after inputting a test set; the first identification result is used as a label of the test set to expand and optimize the training of the training set, obtain a second building identification model corresponding to each first building identification model, and obtain corresponding second identification results output after inputting a test set; the plurality of second identification results are subjected to fine-grained mask fusion processing to obtain a building category, a confidence score, a building detection frame and a building fine-grained mask. The application can effectively improve the building boundary extraction precision and the classification result.

This invention provides a data cleaning method, comprising: calculating a first distance between the feature vectors of every two data points in an initial dataset, wherein the data in the initial dataset includes feature vectors and original label vectors; constructing a neighborhood set corresponding one-to-one with each data point based on the first distance; training an original classification model based on the initial dataset to obtain an intermediate classification model; inputting each feature vector into the intermediate classification model to obtain a corresponding predicted label vector; constructing a sample set based on a second distance between the original label vector and the predicted label vector; updating the corresponding data based on the sample set and the neighborhood set; determining whether the number of data updates has reached a preset number; updating the data again if the number of data updates has not reached the preset number; and forming the target dataset from the current data when the number of data updates has reached the preset number. This invention effectively cleans the initial dataset, making the data labels more accurate.

The invention provides a special-shaped household garbage classification method and system based on a knowledge graph, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional parameter data of special-shaped household garbage, executing preprocessing and feature extraction to generate visual and material features, integrating and generating structured feature vectors, and converting the structured feature vectors into semantic feature representations, semantic feature representation is generated; constructing a junk knowledge graph, performing feature matching on the semantic feature representation and the knowledge graph, calculating an entity comprehensive matching degree, and screening to generate an instance candidate entity set; based on the instance candidate entity set and the knowledge graph, analyzing and reasoning an association path of the candidate entity and the garbage category, and calculating a credibility score of the association path; according to the method, the entity comprehensive matching degree, the association path and the credibility score are evaluated, and the garbage category is output, so that accurate classification of the special-shaped household garbage can be realized, the limitation of traditional matching based on a shape template is broken through, and the accuracy and adaptability of garbage classification are improved.

The application discloses a text classification model training method, a text generation method and related equipment, the text classification model training method comprises the following steps: converting a question text into an embedding sequence through an embedding layer, effectively extracting coding features in the text through an encoding layer, capturing text features of different granularities by using a multi-scale convolution, enhancing the understanding ability of long text, and introducing a self-attention mechanism into the weighted processing of convolution features and pooling features, effectively improving the weight of important features, reducing the influence of redundant features, optimizing the accuracy of intent classification, finally generating a classification result through an output layer, and realizing model training according to the classification result to obtain a trained text classification model, effectively improving the accuracy of text classification, and the intelligent customer service system can take the classification result output by the trained text classification model as the basis for reply, thereby effectively improving the reply efficiency and reply accuracy of the intelligent customer service system under the condition of limited resources.

The application discloses a kind of pedestrian trajectory classification methods based on semi-supervised random neural network.The method includes constructing pedestrian trajectory data;Pedestrian trajectory data is preprocessed;Different trajectory patterns are established according to pedestrian trajectory data;Finally, the combination algorithm of quantifiable minimum error entropy criterion and semi-supervised random neural network algorithm is used to classify pedestrian behavior.The application uses quantifiable minimum error entropy criterion to replace traditional mean square error criterion, and is combined with semi-supervised random neural network algorithm, which can further improve the classification ability of existing semi-supervised learning model, and also improves the accuracy of pedestrian trajectory classification.

The application discloses a kind of based on feature augmentation aero-engine fault diagnosis method, belong to aero-engine fault diagnosis technical field.The present application is directed to the saliency of aero-engine fault signal feature gradually reduces, it is difficult to extract useful information in engine actual operation and maintenance data, and then influence fault diagnosis accuracy problem.Original sample is carried out high-dimensional feature augmentation, and feature augmented sample is obtained;Again normalization processing is carried out, and then training sample set is constructed from normalized sample;Normal state sample and fault sample in training sample set are set different labels respectively;Training sample set is used to train fault diagnosis network, when reaching preset iteration number, training after fault diagnosis network is obtained;The operation data of aero-engine is collected, and normalized after processing is obtained after diagnosis data;Normalized after diagnosis data is input into training after fault diagnosis network, and aero-engine fault diagnosis result is obtained.The present application is used for aero-engine fault diagnosis.

This application provides a fault prediction model training method, fault prediction method, device, storage medium, and computer program product. The method includes: converting text data samples corresponding to one or more second devices into text semantic vectors through a first model; constructing triple samples based on the text semantic vectors through the first model, and querying a second model based on the triple samples, wherein the second model is deployed on other devices besides the first device; receiving the text semantic evaluation results output by the second model for the triple samples, and adjusting the model parameters of the first model based on the text semantic evaluation results to obtain a trained first model; and determining a fault prediction model based on the trained first model and a classification model obtained after training an initial classification model based on text data samples.