9results about How to "Reduce feature dimension" patented technology

The invention relates to the field of carbon emission prediction, in particular to a subway station construction stage carbon emission prediction method and system. Measuring and calculating carbon emission of the subway integrated station at different stages according to a carbon emission coefficient method, and performing data preprocessing to obtain carbon emission monitoring data; performing feature and target variable separation on the carbon emission monitoring data to obtain a feature matrix and a target variable vector; constructing a carbon emission prediction model to perform feature processing on the carbon emission monitoring data to obtain carbon emission feature data; the method comprises the following steps: constructing a carbon emission prediction integrated model based on LightGBM and XGBoost, identifying carbon emission characteristic data through the carbon emission prediction integrated model, and optimizing parameters in the LightGBM model and the XGBoost model by using grid search to obtain a carbon emission prediction amount in a subway station construction stage.

The application provides a kind of foodborne microorganism identification method based on metal omics coupling XGBoost algorithm, belongs to microorganism identification technical field, the foodborne microorganism identification method includes the following steps: 1) with foodborne microorganism strain as research object, ICP-MS is used to analyze each standard strain metal group characteristics, combined with XGBoost algorithm to construct foodborne microorganism classification model;2) the ICP-MS full quantitative analysis is used to detect the bacterial species, and the detection data of the metal elements of the bacterial species to be detected are obtained;3) the obtained detection data is substituted into the obtained foodborne microorganism classification model for analysis, and the bacterial species to be detected is identified.The foodborne microorganism classification model constructed by the application can quickly and accurately identify 18 kinds of foodborne microorganisms, and lays a good foundation for the broad-spectrum and efficient classification and identification of more kinds of foodborne microorganisms in the future.

The application provides an RGB-D image feature collaborative fusion method based on transfer learning and width learning, and comprises the following steps: obtaining an RGB-D data set, performing preliminary training through a neural network, and performing retraining in the data set after modifying the structure; after feature extraction, performing correlation analysis and fusion on RGB image features and depth image features; and using width learning to classify and identify the fused features. The application can reasonably fuse the features of RGB images and depth images, ensure that the feature information of color images and depth images can complement each other, improve the running speed of the system by using width learning, and finally make the classification result have higher accuracy and reliability.

Embodiments of the present application provide a kind of sentiment recognition method and device of voice information.The method is to a few minority class voice feature set Random oversampling processing, and the minority class voice feature set after processing is merged with majority class voice feature set, obtains target voice feature set;According to inverse order selection algorithm, the voice feature in target voice feature set is selected, and the voice feature selected is constituted candidate feature set;The voice feature in candidate feature set is input into classifier, obtains the voice emotion category of voice information, and the embodiments of the present application avoid the decision rule that model learns from unbalanced data is too much inclined to majority class sample, still can select optimal candidate feature set by the correlation analysis between features, reduce the feature dimension of initial target voice feature set, improve the computing efficiency of model, and improve the detection performance of model.

The invention discloses a ship wake extraction and segmentation method, a storage medium and terminal equipment, and belongs to the technical field of image processing and target monitoring. According to the method, accurate segmentation of different types of wakes is realized by analyzing texture features in an image. Firstly, a plurality of image features including image textures are extracted, then feature dimensions are reduced, and calculation efficiency is improved; clustering the images to realize segmentation of different wake regions; and finally, the segmentation effect is further optimized through an Otsu adaptive threshold method and a heuristic rule, and the connectivity of image regions is ensured. Experimental results show that the method can effectively distinguish the turbulence wake from other types of ship wakes, has high accuracy, and is suitable for the application fields of ship trajectory monitoring, environment monitoring and the like.

The invention provides an indoor CSI fingerprint positioning method based on deep learning, and relates to the technical field of indoor wireless positioning. According to the method, abnormal value detection is carried out through an isolated forest algorithm, noise interference is eliminated by combining Hampel filtering and wavelet denoising, dimension reduction is carried out on amplitude and phase data by utilizing PCA, the data quality is effectively improved, and a foundation is laid for subsequent model training. A hybrid model combining a CNN, a BiLSTM and an attention mechanism is adopted, the CNN is responsible for extracting local spatial features, the BiLSTM captures a time sequence dependency relationship, the attention mechanism dynamically focuses key features, advantages are complementary, and the fingerprint classification precision is remarkably improved. And outputting the probability distribution of each reference point through a softmax function, selecting five points with the highest probability, and carrying out weighted average calculation on the coordinates by taking the probabilities of the five points as weights. According to the method, the spatial correlation of adjacent fingerprints is effectively utilized, the positioning result is smoothed, and the higher positioning precision is realized while the fluctuation is reduced.

The invention relates to a road condition recognition method and device, vehicle end control equipment, a storage medium and a program product. The method comprises the following steps: dividing a road condition video into video segments, and obtaining a visual feature vector, an optical flow feature vector and a brightness feature vector of each video frame in the video segments; obtaining the complexity of each video frame according to the optical flow feature vector and the brightness feature vector of each video frame; performing key frame extraction processing on the video clip according to the complexity of each video frame in the video clip to obtain a key frame in the video clip; obtaining global features of the video clip according to the visual feature vectors, the optical flow feature vectors and the brightness feature vectors of all the key frames of the video clip; and inputting the global feature of the video clip into a pre-trained road condition classification model to obtain a road condition classification result of the video clip. By adopting the method, the road condition recognition precision and recognition efficiency can be effectively balanced.

The invention discloses a river eutrophication prediction method considering climate and landscape pattern changes, and relates to the technical field of river management, and the method comprises the steps: collecting and verifying long-term data in a watershed, carrying out the preprocessing and space matching, and forming a standardized sub-watershed annual data set; based on the sub-basin year data set, extracting landscape indexes of sub-basin scales; calculating a river eutrophication index according to the water quality monitoring data; screening key landscape indexes by using a recursive feature elimination algorithm; constructing a machine learning regression model by taking the key landscape index and the climate data as input variables and the river eutrophication index as an output variable, and obtaining an optimal prediction model; carrying out interpretability analysis, and quantifying the contribution degree and the influence direction of each input feature to the river eutrophication index; and inputting data in different future scenes into the optimal prediction model, and predicting and evaluating river eutrophication risks in different scenes.