11results about How to "Improve classification effect" patented technology

The application provides a video classification method and device, an electronic device and a storage medium, wherein the method comprises: encoding a first video to obtain a plurality of corresponding image frames, wherein the first video is any video in a plurality of videos to be classified; determining the complexity of the first video based on a target image frame in the plurality of image frames, wherein the complexity comprises spatial complexity and temporal complexity; and classifying the plurality of videos based on the complexity. Through the application, the technical problem that the prior art classifies by using the bottom features of a video and needs to rely on strong prior knowledge, resulting in poor video classification effect and weak robustness, is solved.

The application relates to an automatic text classification method based on a car community, which comprises the following steps: obtaining a data set of car community texts; obtaining word vectors and text feature vectors as input values of a double-layer clustering model; performing clustering calculation on the word vectors and the text feature vectors to generate word classification and text classification respectively, so as to form the double-layer clustering model. When a new text enters: calculating the word vectors and the text feature vectors of the new text; calculating the word classification of each word vector of the new text and the word frequency under each classification; calculating the text classification of the new text; performing dynamic analysis according to the word classification, the word frequency and the text classification of each word vector of the new text; when the number of words and the word frequency of the new text generated outside the existing word classification reach a threshold value, the double-layer clustering model is updated. Therefore, the application can realize automatic classification of car community texts in the whole process, improve the classification accuracy and efficiency, and form a closed-loop management.

The invention discloses a multi-extended target joint tracking and classification method based on star convex RHM and LMB filters, and belongs to the field of radar target tracking. According to the method, an LMB parameter set is initialized by using target prior information, and then a sensor measurement set is divided through a mean shift algorithm; then, state prediction is achieved by combining survival target parameter updating and new target parameter sampling, and then measurement updating is completed through LMB-to-GLMB, GLMB updating and GLMB-to-LMB; and then trimming and fusing the LMB parameter set, estimating the number of targets, extracting state information, and circularly executing until observation is finished. According to the method, a star convex RHM modeling expansion state is adopted to reduce dimensions, the low detection probability / high clutter scene performance is improved based on an LMB framework, the tracking classification effect and the real-time performance are both considered, and the engineering application value is high.

The utility model relates to the technical field of sorting devices, and discloses an automatic article sorting device which comprises a sorting device body and two sets of visual inspection devices fixedly connected in the sorting device body, a conveying belt assembly is arranged in the sorting device body, and the conveying belt assembly is connected with the sorting device body. The sorting equipment body is provided with an adjusting mechanism, the adjusting mechanism plays a role in pushing workpieces, the adjusting mechanism comprises a heat dissipation box fixedly connected to one side of the sorting equipment body, and the output end of the heat dissipation box is fixedly connected with a servo motor. Through the arrangement of the servo motor, the bevel gear I, the reciprocating screw rod and the contact block, the effect that the position of a product can be adjusted so as to improve the detection effect of visual detection equipment is achieved, use is convenient, and the problem that when an existing sorting device is used for transporting objects, the sorting efficiency is high is solved. Products are often directly placed on a conveying device, and the problem that the product detection effect is affected due to the fact that the placing positions of the articles are not accurate is prone to occurring.

This application discloses an unsupervised classification method for dedicated power transformer users based on load curves. The method includes: acquiring user load curves; pre-classifying users based on their load curves to determine user types, including horizontal, zero-value regular, annual-cycle, daily-cycle, and random types; and performing secondary classification on annual-cycle and daily-cycle users respectively to obtain the target classification result. This application classifies dedicated power transformer users based on the characteristics of their load curves. This classification method is more suited to the electricity consumption habits of dedicated power transformer users, is highly practical, has good classification results, and can achieve accurate classification.

The invention relates to a multi-source time series data prediction method fusing spatio-temporal characteristics, and belongs to the technical field of Internet of Things data analysis and artificial intelligence. Time domain alignment and resampling are carried out through a unified time reference, and frequency heterogeneity of multi-source data is eliminated; fusing the time-lag mutual information and the dynamic time warping index, mining time-lag and nonlinear dependence among different data sources, and constructing a sparse multi-source spatio-temporal topological graph; the topological graph is used as a structure prior input self-attention mechanism space-time diagram convolutional network for feature extraction and prediction; an improved grey wolf optimization algorithm is adopted to jointly optimize a composition threshold value and a network hyper-parameter, a population is initialized through a hierarchical anchor point and a reverse learning strategy, an adaptive state-time domain double-control convergence factor is used to balance exploration and development, and a mixed gravitation weight strategy is used to update a population position; and performing multi-source time series data prediction by using the optimized model. The problems of asynchronous response and weak coupling of multi-source data are effectively solved, and reliable support is provided for intelligent decision making.

The application discloses a scientific data asset multi-label text classification method based on a capsule network, which comprises the following steps: firstly, preprocessing academic paper data sets to obtain text data; secondly, extracting and fusing features of the text data through a hybrid feature module tAL-HPYP of a multi-level hierarchical capsule network model M-CapsNet; then, transmitting the fused features to a main network M-Caps j Module of the multi-level hierarchical capsule network model, outputting a classification prediction vector to a sub-decoder network, and dynamically adjusting a global loss by using an improved hinge loss; and finally, connecting classification results of the sub-decoder network to a final decoder network, and obtaining a final multi-label prediction classification by summarizing all output labels. The application can extract deep and multi-dimensional multi-label features from scientific data texts, and improve the effect of a multi-label text classification task.

The application discloses a kind of wearable assembled building industry worker emotional intelligent monitoring method and device;Its technical points are in, S100, physiological signal is collected;The physiological signal includes: electrocardiogram signal;S200, the electrocardiogram signal obtained by S100 is carried out denoising processing;S300, extract heart rate variability HRV characteristics from electrocardiogram signal, including time domain feature and frequency domain feature;S400, the extracted HRV characteristics are passed through trained SVM-KNN classifier, to identify emotional category;S500, the identification result is output to handheld end.Using a kind of wearable assembled building industry worker emotional intelligent monitoring method and device, the emotion of industry worker can be effectively identified.

The application provides an adversarial training method, device and equipment based on adaptive group sample perturbation constraint, which comprises the following steps: inputting a training image into an initial network model to obtain a network output vector corresponding to the training image and a predicted category; if it is determined that the classification result of the training image is wrong based on the predicted category and an actual category of the training image, the training image is determined as a natural sample image; if it is determined that the classification result of the training image is correct based on the predicted category and the actual category, a target adaptive group corresponding to the training image is determined based on the network output vector; a target perturbation vector corresponding to the training image is determined based on a target sample perturbation constraint corresponding to the target adaptive group, and a perturbation sample image is generated based on the target perturbation vector and the training image; and the initial network model is trained based on the natural sample image and the perturbation sample image to obtain a target network model. Through the technical scheme of the application, the anti-interference ability of the target network model to attack samples is significantly improved.

The application is a power grid false data injection identification method based on improved CNN-LSTM. It includes the power FDIA feature extraction method based on the stacked autoencoder to solve the problem of large model calculation caused by the feature redundancy of power system operation data. In order to further improve the performance of feature extraction, the attention mechanism module is introduced to improve the model, and the attention mechanism layer is introduced at the input layer of each sub-autoencoder, so as to provide greater weight to the highly relevant features of the attack. The false data injection attack identification model based on CNN-LSTM is constructed, and the sparrow search algorithm is used to optimize the parameters of the CNN-LSTM model to solve the problem that the parameter selection has a great influence on the identification accuracy. The method is scientific and reasonable, has high accuracy, can be applied to the identification of false data injection attacks in power grids, and has certain practical significance for maintaining the safety of power grids.

This invention discloses a Transformer-based intermodal joint encoding method, apparatus, and device, relating to the field of multimodal fusion technology. The technical problem it addresses is "how to fuse intermodal information to achieve better sentiment classification results." The method includes: acquiring a video to be analyzed containing multimodal information; extracting text features, audio features, and video image features from the video; unifying the text features, audio features, and video image features to the same dimension based on fully connected layers and LSTM layers; performing multimodal attention joint encoding on the text features, audio features, and video image features based on a Transformer model; and processing and weighting the representation features based on a multilayer perceptron classification model to obtain the classification result of the video to be analyzed. This method can simultaneously perform joint attention encoding on different modalities using a Transformer model, achieving better classification results.