5results about How to "Efficient discrimination" patented technology

This invention provides a method, system, and storage medium for discriminating surface electromyography (EMG) signals in chronic low back pain based on a Fourier analysis neural network. First, a dataset is acquired, containing several EMG signals and their corresponding ground truth labels. The EMG signals are preprocessed to obtain preprocessed EMG signals. These preprocessed EMG signals are then input into a constructed Fourier analysis-based neural network to obtain a predicted label for each EMG signal. A loss function is constructed based on the ground truth labels and predicted labels, and the Fourier analysis-based neural network is trained to obtain a trained Fourier analysis-based neural network. The EMG signal to be detected is then input into the trained Fourier analysis-based neural network to obtain the corresponding predicted label. This invention, by constructing and training a Fourier analysis-based neural network, aims to extract discriminative latent periodic features, achieving automated discrimination of abnormal patterns in EMG signals of chronic low back pain, thus achieving more robust, generalizable, and efficient discrimination.

The application discloses a measurement method of a brain load positive effect threshold based on a beta high-frequency band time specificity, and is characterized in that a plurality of test tasks of different brain load grades are set from low to high, and original electroencephalogram data of a person to be measured under different brain load grades are collected; the original electroencephalogram data are preprocessed, average band power index values of the person to be measured in a beta high-frequency band under different brain load grades are calculated according to the preprocessed electroencephalogram data; and the minimum time segment serial number of a statistical significant difference between double conditions of different brain load grades is calculated t i and a time segment serial number difference between adjacent brain load grades Adjt i , obtaining Adjt i a brain load grade corresponding to the minimum time i , i *is the brain load positive effect grade threshold of the person to be measured; and the method has the advantages that the beta high-frequency band time specificity change in electroencephalogram signal data is analyzed, and the brain load positive effect threshold of the person to be measured is objectively and accurately evaluated.

A kind of defect detection method of magnetic material, original binary image is obtained by image acquisition and preprocessing;Extract the compactness, bite mark sharpness and four-corner asymmetry of original contour before closing operation, constitute Raw Mask Trident feature, retain the tiny defects covered by morphological operation;At the same time, 16-dimensional macro shape features are extracted by closing operation, combined with 10-dimensional microscopic texture features extracted by uniform pattern LBP, a multi-feature fusion system is constructed;The training of multi-feature fusion system is completed by using weighted XGBoost classifier, and the decision boundary is optimized by dynamic threshold search, and finally the defect is judged.The present application realizes anti-interference detection by the feature extraction method of Raw Mask Trident, balances the precision and recall rate by combining sample weighting and dynamic threshold, can realize high-precision, low-false alarm, interpretable automatic detection of edge collapse, bite mark, internal crack and color point, and realizes quality control in the production process of magnetic components.

The present application belongs to the technical field of road facility equipment layout in traffic engineering, more particularly, relates to a method for monitoring the traffic state of a basic road section of an expressway. The method comprises the following steps: firstly, randomly setting detectors at different intervals on the expressway to obtain original vehicle trajectory data; then, pre-processing the original data; and then, calculating the running speed, speed difference, headway, headway time and vehicle density of each vehicle at each time in the detection interval based on the original data. Based on the fuzzy C-means clustering traffic state clustering analysis, the interval traffic data collected in 1 min is clustered and divided into traffic states, and each category is given a label to provide a training data set for the state discrimination algorithm. The traffic state discrimination model is trained, the precision rate is selected as the machine learning evaluation index, and the traffic state discrimination model based on the random forest is selected. The determination of the detector layout interval based on the traffic state transition.

The invention discloses an emotional disorder detection method based on a spatio-temporal dynamic dependency modeling graph enhanced network, which comprises the following steps of: performing segmentation processing on electroencephalogram signals, constructing a functional connection relationship between electroencephalogram channels through a phase locking value, and mapping the functional connection relationship into a graph structure to extract spatial connection characteristics between the channels; in combination with time sequence feature coding and graph convolution operation, fragment-level feature representation containing dynamic information in the channels and synchronization information between the channels is obtained; a dynamic dependency integration mechanism is introduced, and a local time sequence dependency relationship between adjacent electroencephalogram fragments is modeled; and performing global time sequence modeling on the multi-fragment features by using a sequence modeling structure based on an attention mechanism, extracting long-range dependency features and completing emotional disorder state discrimination. According to the method, the spatial connection characteristics and multi-scale time sequence dynamic information of the electroencephalogram signals can be modeled at the same time, the accuracy, stability and generalization ability of emotional disorder recognition are improved, and the method is suitable for application scenes such as auxiliary diagnosis of emotional disorders and mental health monitoring.