5results about How to "Improve memory efficiency" patented technology

The invention discloses an English word memorizing system based on a memory curve, and the system comprises a login module which is used for carrying out registration and login operation, obtaining personal information, and recommending learning contents and plans; the learning module is used for executing a learning and memorizing process and recording a learning progress and a memorizing condition; the memory curve updating module is used for generating an initial memory curve and dynamically updating memory curve parameters; the plan generation module is used for automatically generating a personalized review plan; the review module is used for regularly sending review reminding according to the review plan; and the feedback module is used for obtaining feedback and suggestions of the user and carrying out optimization and improvement. According to the method, the review time is intelligently arranged based on the memory curve, the natural law of human memory and forgetting is followed, the learning content and the review plan are customized according to the individual difference of learners, the learning motivation is enhanced, the learners are endowed with more autonomy, the self-management and self-driving capabilities are cultivated, and a foundation is laid for lifelong learning.

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 font generation method comprises the following steps: S1, acquiring original font data to form a target font library; s2, performing feature extraction on the original fonts in the target font library: obtaining boundary feature lines corresponding to a completely enveloped rectangular region along font contour lines, and obtaining feature points corresponding to the writing sequence and strokes of each font through a font extraction feature model; and S3, rewriting the fonts in the target font library based on feature extraction of the original fonts. According to the method, the problem that current computer fonts influence recitation and memory of people is solved, and the negative influence of inertial thinking and reading habits generated due to the fact that the human brain depends on traditional fonts on the memory effect is effectively reduced.

The application provides a resource allocation method and device. The resource allocation method comprises the following steps: setting the number of physical cores of each NUMA node on a physical server to be equal to the maximum value of the bound virtual cores of each NUMA node; reading all cloud desktop virtual machines in a shutdown state when a resource allocation period arrives; dividing all the read cloud desktop virtual machines in the shutdown state into different weight grades; deleting the bound NUMA nodes of all the read cloud desktop virtual machines in the shutdown state; and binding the cloud desktop virtual machines in the shutdown state of each weight grade to the NUMA node currently bound to the minimum number of virtual cores in sequence from high to low according to the weight grades.

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.