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18 条记录 (耗时 0.016 秒)

    Multi-component abstract association and fusion method and apparatus in page design
    1.
    发明授权
    Multi-component abstract association and fusion method and apparatus in page design 有权

    公开(公告)号：US12086534B2

    公开(公告)日：2024-09-10

    申请号：US18360794

    申请日：2023-07-27

    申请人： ZHEJIANG LAB

    发明人： Tianshu Zhou ,  Xin Gao ,  Jingsong Li ,  Yu Tian

    IPC分类号： G06F40/14 ,  G10L15/22

    CPC分类号： G06F40/14 ,  G10L15/22 ,  G10L2015/223

    摘要： The present disclosure discloses a multi-component abstract association and fusion method and apparatus in page design. The method includes the following steps: step S1: a construction demand is acquired, and the construction demand is analyzed through a speech recognition method to obtain a natural language text; step S2: an abstract model is constructed by predefining a component library, a rule library and a relationship library, and the abstract model performs components fusion to obtain a JSON structure of a fused component; step S3: the JSON structure of the fused component is escaped into a virtual DOM by using a rendering function, and attributes and events of a virtual DOM node are mapped to obtain a fused component drawing result; and step S4: a real DOM structure is created and interpolated into a real DOM node, so as to realize display of the fused component on a view.

    Method and apparatus for visual construction of knowledge graph system
    2.
    发明授权
    Method and apparatus for visual construction of knowledge graph system 有权

    公开(公告)号：US11907390B2

    公开(公告)日：2024-02-20

    申请号：US18336053

    申请日：2023-06-16

    申请人： ZHEJIANG LAB

    发明人： Jingsong Li ,  Guangyuan Deng ,  Tianshu Zhou ,  Yu Tian

    IPC分类号： H04L29/06 ,  G06F21/62 ,  H04L9/30 ,  G06F21/60 ,  G06F16/901

    CPC分类号： G06F21/6218 ,  G06F16/9024 ,  G06F21/602 ,  H04L9/3006

    摘要： Discloses a method and an apparatus for visual construction of a knowledge graph system. In the present disclosure, data permission of a distributed client is determined through a central server. The central server obtains a master template of a knowledge graph system and sends it to the distributed client. The distributed client receives a natural language inputted by a user and parses to generate an abstract syntax tree. The user completes customization of a subtemplate of the knowledge graph system through visual operation. The distributed client encrypts the subtemplate and then sends it to the central server. When the knowledge graph system is to be used, any knowledge concept is inputted, the central server calls and decrypts the subtemplate and then searches a database, and a tree structure knowledge graph is generated and sent to the distributed client.

    System for the prognostics of the chronic diseases after the medical examination based on the multi-label learning
    3.
    发明授权
    System for the prognostics of the chronic diseases after the medical examination based on the multi-label learning 有权

    公开(公告)号：US11735321B2

    公开(公告)日：2023-08-22

    申请号：US17543736

    申请日：2021-12-07

    申请人： ZHEJIANG LAB

    发明人： Jingsong Li ,  Tianshu Zhou ,  Chengkai Wu ,  Ying Zhang

    IPC分类号： G16H50/20 ,  G16H10/60 ,  G16H40/20 ,  A61B5/00

    CPC分类号： G16H50/20 ,  G16H10/60 ,  A61B5/7267 ,  G16H40/20

    摘要： Provided is a system for the prognostics of the chronic diseases after the medical examination based on the multi-label learning, including a data acquisition module, a data preprocessing module, a basic predicting model constructing module, and a local predicting module. The data acquisition module is configured to acquire physical examination data of a physical examination user. The basic predicting model constructing module is configured to construct a multi-label learning model for a physical examination scenario. The local predicting module includes a local model training unit and a predicting unit. The local model training unit adjusts the basic predicting model into a local predicting model, and solidifies the local predicting model into the local predicting module. The predicting unit outputs a predicted prognostic index for an occurrence of a plurality of chronic diseases, and finally acquires a future expected occurrence time of the chronic diseases.

    Image denoising method and apparatus based on wavelet high-frequency channel synthesis
    4.
    发明授权
    Image denoising method and apparatus based on wavelet high-frequency channel synthesis 有权

    公开(公告)号：US12045961B2

    公开(公告)日：2024-07-23

    申请号：US18489876

    申请日：2023-10-19

    申请人： ZHEJIANG LAB

    发明人： Jingsong Li ,  Jinnan Hu ,  Peijun Hu ,  Yu Tian ,  Tianshu Zhou

    IPC分类号： G06T5/70 ,  G06T5/10

    CPC分类号： G06T5/70 ,  G06T5/10 ,  G06T2207/20064 ,  G06T2207/20081 ,  G06T2207/20084

    摘要： Disclosed is an image denoising method and apparatus based on wavelet high-frequency channel synthesis. Image data are expanded to a plurality of frequency-domain channels, a plurality of “less-noise” channels and a plurality of “more-noise” channels are grouped through a noise-sort algorithm, and a denoising submodule and a synthesis submodule based on style transfer are combined to form a generative network. A discriminative network is established to add a constraint to the global loss function. After iteratively training the GAN model described above, the denoised image data can be obtained through wavelet inverse transformation. The disclosed algorithm can effectively solve the problem of “blurring” and “loss of details” introduced by traditional filtering or CNN-based deep learning methods, which is especially suitable for noise-overwhelmed image data or high dimensional image data.

    Method and system for identifying protein domain based on protein three-dimensional structure image
    5.
    发明授权
    Method and system for identifying protein domain based on protein three-dimensional structure image 有权

    公开(公告)号：US11908140B1

    公开(公告)日：2024-02-20

    申请号：US18364469

    申请日：2023-08-02

    申请人： ZHEJIANG LAB

    发明人： Jingsong Li ,  Jing Ma ,  Yu Wang

    IPC分类号： G06T7/00 ,  G06T7/11

    CPC分类号： G06T7/0014 ,  G06T7/11 ,  G06T2207/10028 ,  G06T2207/20081

    摘要： Disclosed is a method and system for identifying a protein domain based on a protein three-dimensional structure image. According to the present application, the protein domain is identified based on a structure similarity, the identification errors and omissions of the protein domain caused by protein multi-sequence alignment errors when sequence consistency is not high can be effectively solved. According to the present application, the point cloud segmentation model based on the dynamic graph convolutional neural network is constructed, and by integrating global structural features and local structural features, segmentation of the protein domain and acquisition of semantic labels of the protein domain can be completed at the same time.

    Automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network
    6.
    发明授权
    Automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network 有权

    公开(公告)号：US11562491B2

    公开(公告)日：2023-01-24

    申请号：US17541271

    申请日：2021-12-03

    申请人： ZHEJIANG LAB

    发明人： Jingsong Li ,  Peijun Hu ,  Yu Tian ,  Tianshu Zhou

    IPC分类号： G06T7/11 ,  G06V10/82 ,  G06N3/08

    摘要： The present invention discloses an automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network. Under a coarse-to-fine two-step segmentation framework, the method uses a densely connected dilated convolutional neural network as a basis network architecture to obtain multi-scale image feature expression of the target. An initial segmentation probability map of the pancreas is predicted in the coarse segmentation stage. A saliency map is then calculated through saliency transformation based on a geodesic distance transformation. A saliency-aware module is introduced into the feature extraction layer of the densely connected dilated convolutional neural network, and the saliency-aware densely connected dilated convolutional neural network is constructed as the fine segmentation network model. A coarse segmentation model and the fine segmentation model are trained using a training set, respectively.

    System for predicting end-stage renal disease complication risk based on contrastive learning
    9.
    发明授权
    System for predicting end-stage renal disease complication risk based on contrastive learning 有权

    公开(公告)号：US11875882B1

    公开(公告)日：2024-01-16

    申请号：US18352216

    申请日：2023-07-13

    申请人： ZHEJIANG LAB

    发明人： Jingsong Li ,  Feng Wang ,  Shengqiang Chi ,  Yu Tian ,  Tianshu Zhou

    IPC分类号： G16H50/20 ,  G16H50/30

    CPC分类号： G16H50/20 ,  G16H50/30

    摘要： Disclosed is an system for predicting end-stage renal disease complication risk based on contrastive learning, including an end-stage renal disease data preparation module, configured to extract structured data of a patient by using a hospital electronic information system and daily monitoring equipment, and process the structured data to obtain augmented structured data; and a complication risk prediction module, configured to construct a complication representation learning model and a complication risk prediction model, perform training and learning on the augmented structured data through the complication representation learning model to obtain a complication representation, and perform end-stage renal disease complication risk prediction by using the complication representation through the complication risk prediction model.

    Time series deep survival analysis system in combination with active learning
    10.
    发明授权
    Time series deep survival analysis system in combination with active learning 有权

    公开(公告)号：US11461658B2

    公开(公告)日：2022-10-04

    申请号：US17541298

    申请日：2021-12-03

    申请人： ZHEJIANG LAB

    发明人： Jingsong Li ,  Tianshu Zhou ,  Ziyue Yang ,  Shengqiang Chi

    IPC分类号： G06N3/08 ,  G06K9/00 ,  G06K9/62 ,  G06N3/04

    摘要： Provided is a time series deep survival analysis system combined with active learning. The system includes: a data collection module, an active learning module, and a time series deep survival analysis module; the data collection module is used for obtaining survival data of objects to be analyzed; combined with an active learning method, the active learning module selects a part of right censored data to label a survival time; and the time series deep survival analysis module constructs a time series deep survival analysis neural network model, and takes uncensored data and right censored data as model inputs, so as to obtain survival time prediction results of the objects to be analyzed. The present application can make full use of the right censored data in the survival data and time series features.