4results about How to "Improve Grading Accuracy" patented technology

This invention provides a method and system for monitoring fatigue status of workers in high-risk industries based on EEG-eye movement fusion, belonging to the field of safety early warning technology. The method includes: real-time acquisition of workers' raw physiological signals using a smart safety helmet; performing bandpass filtering and baseline drift removal processing on the raw physiological signals to obtain multimodal physiological monitoring data containing denoised EEG signals and primary eye movement feature data including blink frequency and gaze duration; extracting blink interval temporal features and gaze point shift features from the primary eye movement feature data in the multimodal physiological monitoring data, and extracting the power spectral density of the EEG feature frequency band within a preset time window. This invention enables accurate classification and reliable early warning of worker fatigue status under complex and high-risk working conditions.

The application relates to a flexible switchable Euphausia superba intelligent sorting method and device, and belongs to the technical field of aquatic product processing and automatic equipment. The application not only realizes high-precision instance segmentation and flexible reference calculation of Euphausia superba through improvement of a deep learning network and a self-adaptive statistical decision model, but also creatively seamlessly converts a dynamic grading signal generated by front-end vision into a physical driving instruction of a servo motor, so that the rear-end spacing of a longitudinal involute sorting pipe is opened and closed in real time and automatically. The application establishes an intelligent linkage closed loop of "visual perception-dynamic decision-physical execution", can automatically and flexibly adjust a physical sorting channel according to real statistical characteristics of batches of materials, and realizes real flexible nondestructive intelligent grading.

The invention discloses an insomnia severity assessment method in combination with a functional near infrared spectrum, and belongs to the technical field of sleep quality. Comprising the following steps: collecting functional near infrared spectrum sequences of a plurality of channels of at least two wavelengths of a prefrontal cortex to obtain three hemoglobin concentration sequences of the plurality of channels; the method comprises the following steps of: mapping a plurality of hemoglobin concentrations into a plurality of predefined brain regions, performing weighted synthesis on channels in the same region to obtain three hemoglobin concentration brain region-level sequences, performing sliding window segmentation to generate a sample set, and calculating a time domain feature vector; obtaining a compact feature vector through feature selection and dimension reduction processing; and constructing a dynamic weight model comprising a plurality of base learners and meta learners, and training the dynamic weight model by using the sample set to obtain a trained dynamic weight model. Compared with equal weight fusion or a single model, the whole grading accuracy and separability are improved by utilizing'brain region entropy weight integration + sliding window feature + dynamic weight Stacking '.