4results about How to "Sample requirement is small" patented technology

The application provides an old person medical self-service machine intelligent interaction system and method based on a BP neural network, binocular cameras collect near-infrared and visible light dual-mode image data of a user's face, a first sub-network calculates an old person identity similarity P1; the relationship between the old person identity similarity P1 and a preset threshold T1 is judged, if P1 >= T1, it is considered that an old person is operating, an infrared sensor is triggered to collect hand behavior data; hand behavior features are extracted according to the hand behavior data collected by the infrared sensor, the hand behavior features are used as inputs of a second sub-network, and a behavior compliance degree P2 is calculated; the relationship between the behavior compliance degree P2 and a preset threshold T4 is judged, if P2 < T4, and the hovering duration >= T2 or the click frequency <= T3, it is determined that an operation difficulty state exists; an interaction adaptation and multi-level early warning mechanism is triggered. The application has the characteristics of high intelligence, multi-modal data fusion, active intelligent adaptation, three-level early warning service closed loop, adaptive parameter configuration and small sample learning ability.

The invention discloses a construction method and application of a nucleic acid sequence for cfDNA multiple gene methylation detection. The construction method comprises the following steps: carrying out enzyme digestion on cfDNA to be detected by using methylation sensitive restriction endonuclease to obtain an enzyme digestion fragment, and connecting a joint to a cut end of the enzyme digestion fragment by using ligase to form a joint product; and carrying out PCR amplification by taking the joint product as a template, and constructing a recombinant nucleic acid sequence for detecting the methylation site of the target gene. The construction method disclosed by the invention can be used for methylation qPCR analysis of cfDNA level and quantitative analysis of low-abundance specimens, the demand quantity of samples is small, the coverage range of methylation analysis is wide, and the methylation enrichment efficiency is high.

The present application relates to a kind of physical reinforcement neural network-based fabric constitutive parameter fast identification method, comprising the following steps: 1), the macroscopic tensile, shear and bending test are carried out to the fabric sample to be identified, while collecting microstructure image, obtain multiscale feature dataset;2), physical reinforcement neural network model forward inference: the multiscale feature dataset is input into pre-trained physical reinforcement neural network;3), the complete constitutive parameter vector of target fabric is inferred and output by physical reinforcement neural network model;4), physical consistency verification: with constitutive parameter vector as input, drive finite element simulation, compare quantitatively the mechanical response curve of simulation output and measured curve, if error exceeds threshold value, then trigger adaptive fine-tuning process;5), physical reinforcement neural network model is continuously trained and updated.The present application can realize the constitutive parameter identification of fast, small sample, high precision for new variety fabric.

The application discloses a human posture recognition system based on a wearable sensor and belongs to the technical field of sensor measurement. The human posture recognition system collects foot bottom pressure through a pressure sensor module, filters and denoises and performs smooth processing through a signal processing module, and wirelessly transmits sensor signals to an upper computer through a wireless transmission module. The upper computer adopts a hidden Markov algorithm and a random forest fusion algorithm to recognize postures. The system comprises a pair of flexible pressure sensor arrays, the sensor arrays are worn on the foot bottom to collect foot bottom pressure signals, the signal processing module is completed by the upper computer, the wireless transmission module selects a Bluetooth module, and the upper computer performs real-time recognition after training the model. The system improves the recognition rate, is high in adaptability, and is suitable for medical treatment, human-computer interaction, sports training and other fields.