公开(公告)号：US20250086022A1

公开(公告)日：2025-03-13

申请号：US18569295

申请日：2023-10-11

Applicant: ZHEJIANG LAB

Inventor： Yong LI ,  Laiping ZHAO ,  Jie LI ,  Wen CHENG ,  Guang CHEN ,  Lingfang ZENG

IPC: G06F9/50 ,  G06F9/48

Abstract: A method for data processing is provided, and includes: obtaining each piece of to-be-processed data, determining whether a set amount of the to-be-processed data is capable to be processed under a current processing process by a data processing model, if not, obtaining data processing periods of the data processing model under multiple configuration combinations; for a data processing period of each of the multiple configuration combinations, determining an amount of data that is capable to be processed by the data processing model within the data processing period, as a target data amount; by taking the data processing model to be capable to process the set amount of the to-be-processed data as a target, according to the target data amount for a data processing period of each of the multiple configuration combinations, selecting a target configuration combination from the multiple configuration combinations.

公开(公告)号：US12223650B2

公开(公告)日：2025-02-11

申请号：US18796239

申请日：2024-08-06

Applicant: ZHEJIANG LAB

Inventor： Yu Zhang ,  Chaoliang Sun ,  Zhichao Wang ,  Huan Zhang ,  Haotian Qian ,  Tianzi Jiang

IPC: G06T7/00 ,  G16H30/40

Abstract: A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method. The system can better integrate the cross-modal physiological indexes of multiple brain regions and the correlation between brain regions and improve prediction ability of the model and generalization ability of the model with different diseases.

公开(公告)号：US20250030509A1

公开(公告)日：2025-01-23

申请号：US18549553

申请日：2023-05-25

Applicant: ZHEJIANG LAB ,  ZHEJIANG UNIVERSITY

Inventor： Huizhu HU ,  Zhenhai FU ,  Xiaowen GAO ,  Xingfan CHEN ,  Nan LI ,  Cheng LIU ,  Zhiming CHEN ,  Jinsheng XU ,  Shaochong ZHU ,  Yingying WANG ,  Peitong HE

IPC: H04L1/20 ,  H04L27/00

Abstract: The present disclosure provides a method and device for performing signal communication based on a levitated particle. In one example, the method includes: preparing a levitated state of the particle; regulating and measuring a net charge quantity carried by the levitated particle; calibrating electromagnetic response characteristics of the levitated particle; applying an electromagnetic communication signal; obtaining and demodulating the electromagnetic communication signal. In an example, the device includes: a levitation trapper; a charge measure-regulator; an electromagnetic response calibrator, configured to obtain, in advance, a background noise and an electromagnetic response transfer function of the levitated particle; a communication signal detect-demodulator, configured to detect a motion response of the levitated particle under an electromagnetic communication signal; based on the background noise and the electromagnetic response transfer function of the levitated particle, recover the applied electromagnetic communication signal from the detected motion response, and demodulate symbols of the electromagnetic communication signal.

公开(公告)号：US20250005109A1

公开(公告)日：2025-01-02

申请号：US18689934

申请日：2023-06-30

Applicant: ZHEJIANG LAB

Inventor： Hongyang CHEN ,  Rong CAO

IPC: G06F18/25 ,  G06N3/042 ,  G06N3/045 ,  G06N3/0464

Abstract: Predicting parking space vacancy rate methods and apparatuses, storage media and devices, acquiring parking space vacancy rates of each of parking lots in the area to be predicted at a plurality of moments before the moment to be predicted as historical vacancy rates of each of the parking lots; obtaining a first feature by inputting the historical vacancy rates of each of the parking lots into the feature extraction network, wherein the first feature is used to characterize a relationship between the historical vacancy rates of each of the parking lots and time; obtaining a fusion feature by inputting the spatial relationship diagram and the first feature into the graph fusion network; and obtaining a parking space vacancy rate of each of the parking lots in the area to be predicted at the moment to be predicted by inputting the fusion feature into the result prediction network.

公开(公告)号：US20240422057A1

公开(公告)日：2024-12-19

申请号：US18555805

申请日：2023-07-13

Applicant: ZHEJIANG LAB

Inventor： Peilei WANG ,  Ruyun ZHANG ,  Jun ZHU ,  Tao ZOU ,  Shunbin LI ,  Qi XU

IPC: H04L41/082 ,  H04L41/0894 ,  H04L67/1097

Abstract: A data storage system includes: a server, a client and a control end; the control end is configured to generate a configuration file, and send the configuration file to the client and the server; the client is configured to generate an encapsulation rule based on the configuration file, generate a storage request, perform encapsulation on the storage request to obtain a message packet, and send the message packet to the server; the server is configured to generate an extraction unit and an action unit based on the configuration file, analyze the message packet to obtain the target information, write the target information into each extraction unit, read action information and determine an action unit matching the action information as a target action unit, and execute the storage actions corresponding to the target action unit to store byte stream data of the target information.

公开(公告)号：US20240404642A1

公开(公告)日：2024-12-05

申请号：US18460671

申请日：2023-09-04

Applicant: ZHEJIANG LAB

Inventor： Long ZHENG ,  Yu HUANG ,  Wei ZHOU

IPC: G16B50/30

Abstract: A method, a device and a medium for genome graph analysis based on in-memory computing. The method comprises the following steps: firstly, combining a linear reference genome with genetic variation to construct a genome graph; then, generating indexes for a plurality of vertices of the genome graph, and constructing an index table according to the generated indexes; then dividing the read length into a plurality of substrings with the length of k-mer, and querying the index table to obtain a seed position, generating a reference subgraph according to the seed position, and identifying a candidate mapping position according to the reference subgraph to filter a candidate mapping area; finally, using a PUM mode to run approximate string matching between the read length and all unfiltered candidate mapping positions, so as to complete the optimal alignment of a reference gene sequence and a query gene sequence.

公开(公告)号：US20240398305A1

公开(公告)日：2024-12-05

申请号：US18798861

申请日：2024-08-09

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Haotian QIAN ,  Chaoliang SUN ,  Zhichao WANG ,  Huan ZHANG ,  Tianzi JIANG

IPC: A61B5/245 ,  A61B5/00

Abstract: A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features. The present disclosure integrates the complete analysis pipeline from preprocessing to source reconstruction of the working memory magnetoencephalography data, classifies the working memory task magnetoencephalography data, and is of great significance to the study of working memory decoding and brain memory related mechanisms.

公开(公告)号：US20240395408A1

公开(公告)日：2024-11-28

申请号：US18788009

申请日：2024-07-29

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Peijun HU ,  Yu TIAN ,  Tianshu ZHOU

IPC: G16H50/20 ,  A61B6/00 ,  A61B6/03 ,  A61B6/50 ,  G06T5/20 ,  G06T7/00 ,  G06T7/11 ,  G06T7/13

Abstract: A pancreatic postoperative diabetes prediction system based on supervised deep subspace learning. A deep convolutional neural network and the MITK software are used to obtain postoperative residual pancreas area, so as to taken as the region-of-interest. Traditional image radiomics features and deep semantic features are extracted from the residual pancreas area, and a high-dimensional image feature set is constructed. Clinical factors related to diabetes, including pancreatic excision rate, fat and muscle tissue components, demographic information and living habits are extracted, and a clinical feature set is constructed. Based on a supervised deep subspace learning network, image and clinical features are represented and fused in subspace in dimensionality reduction, while a prediction model is trained to mine sensitive features highly relevant to the prediction risk of a patient suffering postoperative diabetes mellitus with a high degree of automation and discriminative accuracy.

公开(公告)号：US20240388521A1

公开(公告)日：2024-11-21

申请号：US18664368

申请日：2024-05-15

Applicant: ZHEJIANG UNIVERSITY ,  ZHEJIANG LAB

Inventor： Haifeng ZHOU ,  Di WANG ,  Xiang CHEN ,  Chunming WU ,  Wenhai WANG

IPC: H04L43/55 ,  H04L9/40 ,  H04L69/326

Abstract: A method and a system for large-scale traffic generation based on programmable network technology, which are used for the research on network operation and maintenance and defense of attacks such as DDOS. According to the method, the required large-scale traffic is generated as required through the coordination of servers and programmable switches. The method specifically comprises the steps of designing a series of primitives which are based on intentions and are irrelevant to underlying architecture details, and reducing the description difficulty of generating large-scale traffic intentions; completing required configurations on the switch and the server by the designed cooperation mechanism of the server and programmable switch according to intentions expressed by different types of primitives, and achieving large-scale traffic generation by coordinating and utilizing server and switch resources.

公开(公告)号：US12147909B2

公开(公告)日：2024-11-19

申请号：US18491817

申请日：2023-10-23

Applicant: ZHEJIANG LAB

Inventor： Feng Lin ,  Yunhe Pan

IPC: G06N5/022 ,  G06F40/30 ,  G06V10/77 ,  G06V10/80 ,  G06V40/20

Abstract: A method and an apparatus for cross-media corresponding knowledge generation. The method comprises: generating a second knowledge unit of a second medium according to a first knowledge unit of a predefined first medium; generating a first feature parameter vector corresponding to the first knowledge unit and a second feature parameter vector corresponding to the second knowledge unit; mapping the first feature parameter vector and the second feature parameter vector to a corresponding two-dimensional spherical feature surface to obtain a first feature point of the first feature parameter vector on the corresponding two-dimensional spherical feature surface and a second feature point of the second feature parameter vector on the corresponding two-dimensional spherical feature surface; indexing the first feature point and the second feature point to obtain a first index and a second index; and generating a bidirectional index corresponding relationship between the first knowledge unit and the second knowledge unit.