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    COMPOSITE ARRAY ELECTRODE, PREPARATION METHOD THEREOF AND USE THEREOF
    91.
    发明申请
    COMPOSITE ARRAY ELECTRODE, PREPARATION METHOD THEREOF AND USE THEREOF 有权

    公开(公告)号：US20230133153A1

    公开(公告)日：2023-05-04

    申请号：US17841189

    申请日：2022-06-15

    Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

    Inventor： Qi ZENG ,  Tianzhun WU

    IPC: C25D7/12 ,  A61B5/268 ,  A61B5/1468 ,  B08B3/08 ,  B08B7/02 ,  C25D5/02 ,  C25D5/34 ,  G01N27/327

    Abstract: Provided are composite array electrode, preparation method thereof and use thereof. The composite array electrode comprises a microelectrode array substrate, and a modification layer formed on a surface of a microelectrode of the microelectrode array substrate, wherein the modification layer comprises a plurality of electrically conductive layers arranged at intervals on the surface of the microelectrode, an insulating layer arranged on the surface of the microelectrode except the electrically conductive layers, and wherein material for the electrically conductive layers comprises one or more of nano platinum, nano iridium, conductive polymer and carbon nanotubes. The composite array electrode effectively eliminates the influence of edge effect such that the electric field distributes uniformly on the microelectrode surface of the composite array electrode, significantly improving electrochemical performance and detection capability of the electrode.

    Atrial fibrillation signal recognition method, apparatus and device
    93.
    发明授权
    Atrial fibrillation signal recognition method, apparatus and device 有权

    公开(公告)号：US11538588B2

    公开(公告)日：2022-12-27

    申请号：US16901033

    申请日：2020-06-15

    Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

    Inventor： Ye Li ,  Xiaomao Fan ,  Qihang Yao ,  Liyan Yin

    IPC: G06N3/00 ,  G16H50/20 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08 ,  A61B5/352 ,  A61B5/361

    Abstract: The present disclosure provides an atrial fibrillation signal recognition method, apparatus and device. The method comprises: obtaining an electrocardiogram signal to be recognized; inputting the electrocardiogram signal to be recognized to a pre-established atrial fibrillation signal recognition model, and outputting an atrial fibrillation signal recognition result, where the atrial fibrillation signal recognition model is established in the following way: obtaining a specified number of electrocardiogram sample signals and corresponding identifier information; balancing, according to the number of normal signals, atrial fibrillation signals by means of SMOTE; establishing a network structure of multiple convolutional neural networks, each of the convolutional neural networks being provided with a specific receptive field for recognizing the atrial fibrillation signals of a corresponding granularity; and inputting the normal signals and the balanced atrial fibrillation signals to the network structure for training to generate an atrial fibrillation signal recognition model.

    Method and system for generating composite PET-CT image based on non-attenuation-corrected PET image
    94.
    发明授权
    Method and system for generating composite PET-CT image based on non-attenuation-corrected PET image 有权

    公开(公告)号：US11508048B2

    公开(公告)日：2022-11-22

    申请号：US16976474

    申请日：2020-02-10

    Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

    Inventor： Zhan Li Hu ,  Dong Liang ,  Yong Chang Li ,  Hai Rong Zheng ,  Yong Feng Yang ,  Xin Liu

    IPC: G06T5/50 ,  G06T5/00

    Abstract: The present disclosure discloses a method and a system for generating a composite PET-CT image based on a non-attenuation-corrected PET image. The method includes: constructing a first generative adversarial network and a second generative adversarial network; obtaining a mapping relationship between a non-attenuation-corrected PET image and an attenuation-corrected PET image by training the first generative adversarial network; obtaining a mapping relationship between the attenuation-corrected PET image and a CT image by training the second generative adversarial network; and generating the composite PET-CT image by utilizing the obtained mapping relationships. According to the present disclosure, a high-quality PET-CT image can be directly composited from a non-attenuation-corrected PET image, and medical costs can be reduced for patients, and radiation doses applied to the patients in examination processes can be minimized.

    METHOD AND APPARATUS FOR GENERATING TIME SERIES DATA BASED ON MULTI-CONDITION CONSTRAINTS, AND MEDIUM
    97.
    发明申请
    METHOD AND APPARATUS FOR GENERATING TIME SERIES DATA BASED ON MULTI-CONDITION CONSTRAINTS, AND MEDIUM 有权

    公开(公告)号：US20220253351A1

    公开(公告)日：2022-08-11

    申请号：US17618758

    申请日：2020-03-26

    Applicant: Shenzhen Institutes of Advanced Technology

    Inventor： Lei PENG ,  Junnan ZHANG ,  Huiyun LI

    IPC: G06F11/07

    Abstract: A method and an apparatus for generating time series data based on multi-condition constraints, and a medium are provided. The method include the following: a data repair request from a client is received, the data repair request including to-be-repaired data and condition information; normalization processing is performed on the to-be-repaired data to obtain normalized data, and tensor processing is performed on the condition information to obtain a feature label; a trained data repair model is called to perform repair processing on the normalized data according to the feature label to obtain first repaired data, the data repair model being obtained by training according to sample data, a first sample condition, real sample data and a second sample condition, and the sample data being noise data; and the first repaired data is sent to the client. By adopting embodiments of the application, rich features of the data can be acquired without a large amount of historical data or sample data with high matching degree as training basis, thus ensuring the accuracy and timing of the repaired data, and improving the repair efficiency and quality.

    Parking Guidance Method Based on Temporal and Spatial Features and Its Device, Equipment, and Storage Medium
    98.
    发明申请
    Parking Guidance Method Based on Temporal and Spatial Features and Its Device, Equipment, and Storage Medium 有权

    公开(公告)号：US20220165155A1

    公开(公告)日：2022-05-26

    申请号：US17440626

    申请日：2019-05-08

    Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

    Inventor： Lei PENG ,  Yan NIE

    IPC: G08G1/14 ,  G08G1/01 ,  G06N3/04 ,  G06N3/08

    Abstract: A parking guidance method based on temporal and spatial features and its device, equipment, and storage medium, wherein the said method consists of two steps: accessing the estimated driving information of the targeted vehicles (S101), where the estimated driving information includes the targeted vehicle's planned driving route, destination and estimated time of arrival; inputting the estimated driving information into the pre-trained city-wide parking guidance system to generate recommended parking lot information for the targeted vehicle, where the city-wide parking guidance system is a spatiotemporal classifier trained with the parking events of urban cities in the current city as the training data (S102). The said method eliminates the necessity of relying on parking data from urban parking lots and effectively improves the city-wide parking guidance effect.

    IMAGE FEATURE VISUALIZATION METHOD, IMAGE FEATURE VISUALIZATION APPARATUS, AND ELECTRONIC DEVICE
    99.
    发明申请
    IMAGE FEATURE VISUALIZATION METHOD, IMAGE FEATURE VISUALIZATION APPARATUS, AND ELECTRONIC DEVICE 有权

    公开(公告)号：US20220148293A1

    公开(公告)日：2022-05-12

    申请号：US17283199

    申请日：2020-03-11

    Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

    Inventor： Shuqiang WANG ,  Wen YU ,  Chenchen XIAO ,  Shengye HU ,  Yanyan SHEN

    IPC: G06V10/774 ,  G06V10/82 ,  G06N3/04 ,  G06T7/00

    Abstract: An image feature visualization method and apparatus, and an electronic device during model training, inputs the real training data with positive samples into a mapping generator to obtain fictitious training data with negative samples. The mapping generator includes a mapping module configured to learn a key feature map that distinguishes the real training data with positive samples/negative samples, and the fictitious training data with negative samples is generated based on the real training data with positive samples and the key feature map. The training data with negative samples is input into a discriminator to obtain a discrimination result. An optimizer optimizes the mapping generator and the discriminator until training is completed. During model application, a target image that is to be processed is input into the mapping generator, and the mapper in the mapping generator extracts features of the target image.

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