SCREENING METHOD FOR ADSORBENT IN ENVIRONMENT-FRIENDLY GAS-INSULATING EQUIPMENT

    公开(公告)号:US20230081241A1

    公开(公告)日:2023-03-16

    申请号:US17901841

    申请日:2022-09-01

    Abstract: Disclosed is a screening method for adsorbent in environment-friendly gas-insulating equipment, the method steps include: establishing screening sets, pre-experiment screening, standard gas adsorption experiments screening, mixed gas adsorption experiments screening, establishing mapping relationship between the decomposed gas type set and the third adsorbent type set under different working conditions, and selecting adsorbent combination mode suitable for the working condition type and the mixed gas composition mode based on the mapping relationship. Through adsorption experiments of a single standard gas and a mixed gas under different working conditions, an adsorbent combination mode suitable for adsorbing mixed decomposed gas under different working conditions is obtained; at the same time, in view of the situation that the suitable adsorbent has not been screened through the adsorption experiment, the suitable adsorbent is further screened with the molecular dynamics theory, so that all the adsorbent combinations suitable for different working conditions can be obtained.

    LED light source recognition method, device, apparatus and medium based on deep learning

    公开(公告)号:US12200835B1

    公开(公告)日:2025-01-14

    申请号:US18646707

    申请日:2024-04-25

    Abstract: The disclosure provides an LED light source recognition method, device, apparatus, and medium based on deep learning, belonging to the field of indoor positioning and navigation technology. The method includes the following. In an LED lighting environment, a spline frame is obtained through a CMOS camera, in which the spline frame is an image having dark stripes. The spline frame is input to a target detection model, a dark stripe detection result output by the target detection model is obtained. The multiple rectangular boxes are preprocessed, and based on the predicted classification corresponding to each preprocessed rectangular box, an image feature encoding sequence of the spline frame is determined. The image feature encoding sequence is compared with a light source feature encoding sequence corresponding to each LED light source to determine the light source feature encoding sequence that best matches the spline frame.

    EARLY DETECTION METHOD FOR NETWORK UNRELIABLE INFORMATION BASED ON ENSEMBLE LEARNING

    公开(公告)号:US20240419873A1

    公开(公告)日:2024-12-19

    申请号:US18739362

    申请日:2024-06-11

    Abstract: The invention pertains to an early detection method for network unreliable information using ensemble learning, within the field of early detection technology for unreliable network data. It involves the following steps: (1) converting input text sequences into word vector sequences; (2) inputting these word vectors into three base models—Transformer, Bi-SATT-CAPS, and BiTCN—for classifying unreliable information; (3) training and predicting with these models to generate new training and test data sets; (4) weighting and merging these new data sets to create a new training set for the meta-learner SVM; (5) training the new set with the meta-learner SVM to obtain the final classification result. This method retains the text's grammatical and structural features, using only blog posts and early comments to accurately detect unreliable information. By employing an improved weight fusion strategy, the method leverages the strengths of the three base models to enhance early detection effectiveness.

    Mbp_Argonaute proteins from prokaryotes and applications thereof

    公开(公告)号:US11761001B2

    公开(公告)日:2023-09-19

    申请号:US17854897

    申请日:2022-06-30

    CPC classification number: C12N15/113 C12N9/22

    Abstract: Mbp_Argonaute proteins from prokaryotes and application thereof are provided. The Mbp_Argonaute protein consists of an amino acid sequence as shown in SEQ ID NO: 1 or a sequence with at least 50% or at least 80% of homology with the amino acid sequence as shown in SEQ ID NO: 1. An Argonaute protein gene derived from a cold-resistant prokaryote Mucilaginibacter paaluis is synthesized and named as MbpAgo, which has binding activity to single-stranded guide DNA and has nuclease activity to target RNA and/or target DNA complementarily paired with the single-stranded guide DNA, the MbpAgo can be used for the target RNA editing in vivo and in vitro to achieve site-specific modification of genetic material. The MbpAgo can modify highly-structured RNAs and not affect an endogenous RNAi pathway of animal and plant cells, provides a new and powerful tool for RNA editing with high cleavage activity and good specificity.

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