公开(公告)号：US20230047278A1

公开(公告)日：2023-02-16

申请号：US17966917

申请日：2022-10-17

Applicant: ZHEJIANG LAB

Inventor： Congqi SHEN ,  Shaofeng YAO ,  Zhongxia PAN ,  Hanguang LUO ,  Tao ZOU

IPC: H04L45/76 ,  H04L45/745 ,  H04L45/00

Abstract: A geographical identification forwarding method for area-oriented addressing. The geographic location information is used as a transmission identification, and the communication process based on the geographical identification is realized by constructing the SDN-based geographical identification transmission architecture. In this method, a geographical identification is used instead of a traditional IP identification for network transmission, which effectively alleviates the problem of narrow waist of IP single bearing in the current network. At the same time, through a flow table decomposition design, the flow table size of the switch is effectively controlled. The method provided by the present invention can be extended to a plurality of geographical identification areas to realize large-area real-time cross-domain transmission. The method is simple to operate, easy to realize and high in real-time; the method has a wide application range, and can be used to build new networks and improve network performance.

公开(公告)号：US11562491B2

公开(公告)日：2023-01-24

申请号：US17541271

申请日：2021-12-03

Applicant: ZHEJIANG LAB

Inventor： Jingsong Li ,  Peijun Hu ,  Yu Tian ,  Tianshu Zhou

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

Abstract: 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.

公开(公告)号：US11533604B2

公开(公告)日：2022-12-20

申请号：US17727838

申请日：2022-04-25

Applicant: Zhejiang Lab

Inventor： Qi Xu ,  Ruyun Zhang ,  Tao Zou ,  Hanguang Luo

IPC: H04W8/02 ,  H04W8/22 ,  H04W48/14 ,  H04W40/24 ,  H04W84/12

Abstract: The present invention relates to the technical field of network communication, in particular to a method and system for controlling ID identifier network mobility based on a programmable switch. The system includes mobile terminal nodes, mobile access points, programmable switching nodes and control nodes, wherein the control nodes include local control nodes and a global control node, the mobile terminal nodes are connected and communicated with the mobile access points through wireless data links, the mobile access points are connected and communicated with the programmable switching nodes through wired data links, and the programmable switching nodes, the local control nodes and the global control node are connected and communicated in order through control links.

公开(公告)号：US20220345872A1

公开(公告)日：2022-10-27

申请号：US17727838

申请日：2022-04-25

Applicant: Zhejiang Lab

Inventor： Qi XU ,  Ruyun ZHANG ,  Tao ZOU ,  Hanguang LUO

IPC: H04W8/02 ,  H04W8/22 ,  H04W40/24 ,  H04W48/14

Abstract: The present invention relates to the technical field of network communication, in particular to a method and system for controlling ID identifier network mobility based on a programmable switch. The system includes mobile terminal nodes, mobile access points, programmable switching nodes and control nodes, wherein the control nodes include local control nodes and a global control node, the mobile terminal nodes are connected and communicated with the mobile access points through wireless data links, the mobile access points are connected and communicated with the programmable switching nodes through wired data links, and the programmable switching nodes, the local control nodes and the global control node are connected and communicated in order through control links.

公开(公告)号：US11437846B2

公开(公告)日：2022-09-06

申请号：US17566741

申请日：2021-12-31

Applicant: ZHEJIANG LAB

Inventor： Boyang Zhou

IPC: H04L67/12 ,  H04L41/069 ,  H02J13/00

Abstract: Disclosed is a reliable resilient router for a wide-area phasor measurement system of a power grid. The reliable resilient router includes a Data-type data packet processing module, a RetransReq data packet processing module, a RetransReport data packet processing module, a basic data packet processing module, a multi-path forwarding state table module, a content storage queue module and a physical port. The reliable resilient router of the present invention realizes active detection of a lost data packet and a single or batch retransmission mechanism, so that the lost data packet can be directly recovered in the grid from an upstream router through which the lost data packet passes, which improves the recovery time success rate and the high efficiency of the lost data packet, and guarantees the safe and stable operation of the wide-area phasor measurement system of the power grid.

公开(公告)号：US20220255346A1

公开(公告)日：2022-08-11

申请号：US17566741

申请日：2021-12-31

Applicant: ZHEJIANG LAB

Inventor： Boyang ZHOU

IPC: H02J13/00 ,  H04L41/069 ,  H04L67/12

Abstract: Disclosed is a reliable resilient router for a wide-area phasor measurement system of a power grid. The reliable resilient router includes a Data-type data packet processing module, a RetransReq data packet processing module, a RetransReport data packet processing module, a basic data packet processing module, a multi-path forwarding state table module, a content storage queue module and a physical port. The reliable resilient router of the present invention realizes active detection of a lost data packet and a single or batch retransmission mechanism, so that the lost data packet can be directly recovered in the grid from an upstream router through which the lost data packet passes, which improves the recovery time success rate and the high efficiency of the lost data packet, and guarantees the safe and stable operation of the wide-area phasor measurement system of the power grid.

公开(公告)号：US20220222779A1

公开(公告)日：2022-07-14

申请号：US17712080

申请日：2022-04-02

Applicant: ZHEJIANG LAB

Inventor： Duo ZHANG ,  Wentao ZHU ,  Ling CHEN ,  Fan RAO ,  Bao YANG ,  Hui SHEN

IPC: G06T3/60 ,  G06T11/00 ,  G06N3/04

Abstract: Disclosed is an automatic reorientation method from an SPECT three-dimensional reconstructed image to a standard view, wherein a rigid registration parameter P between a SPECT three-dimensional reconstructed image A and a standard SPECT image R is extracted by using a rigid registration algorithm to form a mapping database of A and P; features of the image A are extracted by using a three-layer convolution module, and are converted into a 6-dimensional feature vector T after three times of full connection, and T is applied to A through a spatial transformer network to form an orientation result predicted by the network, thus establishing the automatic reorientation model of the SPECT three-dimensional reconstructed image. The SPECT three-dimensional reconstructed image to be orientated is taken as an input. A standard view can be obtained by using the automatic reorientation model of the SPECT three-dimensional reconstructed image for automatic turning.

公开(公告)号：US20220222529A1

公开(公告)日：2022-07-14

申请号：US17674859

申请日：2022-02-18

Applicant: ZHEJIANG LAB

Inventor： Hongsheng WANG ,  Haijun SHAN ,  Shengjian HU

IPC: G06N3/08 ,  G06F40/20

Abstract: Disclosed is a method for meta-knowledge fine-tuning and platform based on domain-invariant features. According to the method, highly transferable common knowledge, i.e., domain-invariant features, in different data sets of the same kind of tasks is learnt, the common domain features in different domains corresponding to different data sets of the same kind of tasks learnt in the network set are fine-tuned to be quickly adapted to any different domains. According to the present application, the parameter initialization ability and generalization ability of the universal language model of the same kind of tasks are improved, and finally a common compression framework of the universal language model of the same kind of downstream tasks is obtained through fine tuning. In the meta-knowledge fine-tuning network, a loss function of the domain-invariant features is designed in the present application, and domain-independent universal knowledge is learn.

公开(公告)号：US20220093268A1

公开(公告)日：2022-03-24

申请号：US17541301

申请日：2021-12-03

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Yu TIAN ,  Yong SHANG ,  Ran XIN

IPC: G16H50/50 ,  G16H10/60 ,  G16H50/20

Abstract: Provided is a cross-departmental decision support system for early diagnosis of a chronic kidney disease based on knowledge graph, which comprises a patient information model building module, a patient information model library storage module, a knowledge graph association module, a knowledge graph inference module and a decision support feedback module. According to the present application, by constructing a patient information model and utilizing an OMOP CDM standard terminology system, patient electronic medical record data is constructed into a patient information model with unified concept coding and unified semantic structure; making full use the advantages of semantic technology in data interactivity and scalability, so that the system has better adaptability and scalability to heterogeneous data in different hospitals.

公开(公告)号：US20220092789A1

公开(公告)日：2022-03-24

申请号：US17541271

申请日：2021-12-03

Applicant: ZHEJIANG LAB

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

IPC: G06T7/11 ,  G06N3/08

Abstract: 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.