Wu et al., 2024 - Google Patents
Edge computing and few-shot learning featured intelligent framework in digital twin empowered mobile networksWu et al., 2024
- Document ID
- 11975671723033923585
- Author
- Wu Y
- Cao H
- Lai Y
- Zhao L
- Deng X
- Wan S
- Publication year
- Publication venue
- IEEE Transactions on Network and Service Management
External Links
Snippet
Digital twins (DT) and mobile networks have evolved forms of intelligence in Internet of Things (IoT). In this work, we consider a Digital Twin Mobile Network (DTMN) scenario with few multimedia samples. Facing challenges of knowledge extraction with few samples …
- 238000002474 experimental method 0 abstract description 16
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tan et al. | Towards personalized federated learning | |
| Jiang et al. | Mobile traffic prediction in consumer applications: A multimodal deep learning approach | |
| Yang et al. | A joint energy and latency framework for transfer learning over 5G industrial edge networks | |
| Yang et al. | Application of meta-learning in cyberspace security: A survey | |
| CN113822315B (en) | Property graph processing method, device, electronic device and readable storage medium | |
| Xu et al. | A survey on edge intelligence | |
| Qiao et al. | Mp-fedcl: Multiprototype federated contrastive learning for edge intelligence | |
| Zhang et al. | A survey on point-of-interest recommendation: Models, architectures, and security | |
| Park et al. | Future Trends of IoT, 5G Mobile Networks, and AI: Challenges, Opportunities, and Solutions. | |
| Xu et al. | Decentralized and distributed learning for AIoT: A comprehensive review, emerging challenges, and opportunities | |
| Wu et al. | Edge computing and few-shot learning featured intelligent framework in digital twin empowered mobile networks | |
| Nan et al. | MSTL-GLTP: A global–local decomposition and prediction framework for wireless traffic | |
| Zhang et al. | Modeling spatio-temporal mobility across data silos via personalized federated learning | |
| Chen et al. | Global-local feature learning via dynamic spatial-temporal graph neural network in meteorological prediction | |
| Ji et al. | An anomaly event detection method based on GNN algorithm for multi-data sources | |
| Luo et al. | ClusterST: Clustering spatial–temporal network for traffic forecasting | |
| Zhang et al. | Mixture distribution graph network for few shot learning | |
| CN119180299B (en) | A Method for Constructing Heterogeneous Graph Neural Networks for Traffic Prediction | |
| Sun et al. | Confidence-based simple graph convolutional networks for face clustering | |
| Wang et al. | Service matching based on group preference and service representation learning for edge caching | |
| Xiao | Generative adversarial network and its application in energy internet | |
| Feng et al. | A survey of dynamic network link prediction | |
| Jeong et al. | Artificial intelligence for the fourth industrial revolution | |
| Suresh et al. | Federated graph representation learning using self-supervision | |
| Chen et al. | Siamese network based multiscale self-supervised heterogeneous graph representation learning |