6results about How to "Improve classification recognition ability" patented technology

The invention discloses a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion, and relates to the field of collaborative robot joint fault diagnosis. In order to solve the problem that an existing harmonic reducer fault diagnosis method is low in diagnosis rate, the invention designs a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting the vibration, torque, temperature and other multi-sensor original data of a harmonic reducer in different fault modes; one-dimensional time-domain signals of a plurality of sensors are converted into two-dimensional time-frequency images through continuous wavelet transform, a fault feature time-frequency graph data set of the harmonic reducer is constructed, feature selection and noise suppression are performed through a residual shrinkage unit in a DRSN-SE algorithm, the feature representation capability is enhanced by using an SE-Net attention mechanism, and the fault feature time-frequency graph data set of the harmonic reducer is constructed. And fusing the features of the multi-source heterogeneous data to obtain final fused features, and outputting a fault diagnosis result through a classification head. According to the invention, the problem of poor reliability of single fault data can be effectively solved, and high-precision fault diagnosis and classification identification of the harmonic reducer are realized.

The invention provides a transformer bushing partial discharge mode identification method and system, and relates to the technical field of power system fault diagnosis. Comprising the steps of obtaining transformer bushing partial discharge signals and extracting original features; carrying out dimension reduction processing on the extracted original features to obtain features after dimension reduction; a radial basis function SVM model is constructed, and parameters of the radial basis function SVM model are optimized through a GTO algorithm optimized by the chimpanzee troops; and inputting the dimensionality-reduced features into a parameter-optimized radial basis function SVM model for classification to obtain a classification identification result. According to the method, the KPCA algorithm and the GTO-SVM algorithm are combined, KPCA dimension reduction processing is carried out on high-frequency signal features, data redundancy is reduced, SVM parameter selection is optimized through the GTO algorithm, the classification and recognition performance is remarkably improved, the high recognition rate is shown in partial discharge fault diagnosis, and particularly the method has remarkable advantages in the aspect of processing complex fault modes.

A data re-labeling classification algorithm based on clustering learning comprises the following steps: after preprocessing the expression image data in the original sample, a feature extraction module is used to extract a feature vector to form sample data; a dynamic clustering module splits a sample data set of a minority class into several sub-class data sets, and a sample data set of a majority class remains unchanged; the extracted feature vector is used as the input of the dynamic clustering module; if the sample data belongs to the sample data set of the minority class, the sample data is assigned to the nearest sub-class data set according to the distance between the sample data and the clustering center, and then a pseudo label is assigned to all sample data; the sub-classes are trained by using the pseudo label; and a label mapping module maps the prediction result of the sub-class data set to which the sample data belongs back to the real label space based on the mapping relationship learned in the training process. The application can improve the classification and recognition performance of an unbalanced expression data set, and fully excavates and utilizes the potential information of the sample data set.

This application provides a sleep monitoring method and system based on electroencephalogram (EEG) signals. The method includes: acquiring EEG signals of a user during sleep; processing the EEG signals to obtain multiple input signals of a set time length, wherein each input signal contains alpha waves, beta waves, delta waves, theta waves, spindle waves, and sawtooth waves of the EEG signal within the same time period; for each input signal: inputting the input signal into a preset sleep monitoring model to obtain the sleep stage category corresponding to the input signal output by the sleep monitoring model, wherein the sleep monitoring model includes a signal feature extraction module and a sleep stage classification module, the signal feature extraction module is used to extract features from the input signal and use the extracted signal features as the input vector of the sleep stage classification module, the sleep stage classification module is used to classify and identify the sleep stage of the input vector to obtain the sleep stage category corresponding to the input signal.

The invention discloses a vehicle fault identification method, device and equipment and a storage medium, and the method comprises the steps: obtaining to-be-detected data which is vehicle operation data; converting the to-be-detected data into a to-be-detected image; inputting the to-be-detected image into a multi-feature information enhanced fault recognition model, extracting fault features from the to-be-detected image by the multi-feature information enhanced fault recognition model, and performing interactive fusion and feature classification recognition on the fault features to obtain a fault recognition result; wherein the fault features comprise local semantic relation features and global semantic relation features. According to the method, the running state of the vehicle is monitored in real time, and the running data of the vehicle is converted into the image, so that the fault identification process is quicker and more accurate. Meanwhile, various fault features including local semantic relation features and global semantic relation features are extracted from the image, the running state of the vehicle can be comprehensively understood, and therefore the accuracy and reliability of fault recognition are improved.

The invention discloses a long-tail feature learning method based on class prior distribution and feature disturbance guidance. A batch of input long-tail distribution image samples are copied into three groups, and basic data enhancement operation is applied to the three groups respectively and then the three groups are input into a network model. Respectively carrying out feature disturbance on the comparison features of the first group and the second group through Gaussian disturbance and a class prior distribution coefficient; and for the third group of classification features, class center features are generated through full connection layer transformation, and class feature samples of each batch are supplemented to participate in training based on Gaussian disturbance frequency in negative correlation with the number of class samples. And finally, updating model parameters by calculating loss. Through the method of the invention, the model can learn the feature distribution with higher discrimination ability on the long-tail distribution data set, thereby remarkably improving the accuracy of long-tail classification. The method is also suitable for visual identification application scenes with long-tail distribution, such as automatic driving scene identification and medical image fine-grained classification.