2results about How to "Improve memory efficiency" patented technology

The invention relates to the cross technical field of medical education and mixed reality technology, in particular to a traditional Chinese medicine meridian point visualization system based on mixed reality technology, which comprises a data fusion and personalized calibration module, a virtual-real fusion interaction module, an interaction teaching module, an intelligent evaluation module and a meridian qi and blood simulation module. By fusing medical image data, depth sensing data and a traditional Chinese medicine acupoint selection rule, a personalized three-dimensional human body model is constructed, and acupoints are accurately calibrated; real-time registration and shielding processing of the virtual model and a real scene are achieved through mixed reality equipment; various natural interaction modes such as gestures, voices and sight lines are supported, and an integrated function of learning, training and assessment is provided; and the meridian qi-blood running state can be dynamically simulated based on the midnight-noon ebb-flow theory. The system has the advantages that the problems that traditional Chinese medicine teaching is poor in intuition and interactivity, cannot be personalized, is inconvenient to examine and the like are solved, and immersive, interactive and intelligent teaching of traditional Chinese medicine meridian point knowledge is achieved.

The application relates to a high-resolution range profile (HRRP) increment recognition method based on domain condition feature calibration, which comprises the following steps: collecting an HRRP signal to be recognized; inputting the HRRP signal to be recognized into a trained HRRP increment recognition network to obtain a recognition result of a target category; wherein the HRRP increment recognition network comprises a shared feature extractor, a domain condition feature calibrator, a cross-domain feature aligner and a classifier which are connected in sequence, wherein: the shared feature extractor is used for performing feature extraction on the HRRP signal to be recognized to obtain an HRRP feature vector; the domain condition feature calibrator is used for outputting a domain feature calibration vector; and the cross-domain feature aligner is used for outputting a domain-invariant feature vector which is robust to posture changes, wherein the domain-invariant feature vector is used for recognition by the classifier, so that the recognition result is obtained. The method can significantly improve the generalization ability and memory efficiency in a serialized posture domain.