Wei Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Wei Wang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bo Wang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication - Ertao Gao
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication - Jianwu Jiang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication - Jingwen Li
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication - Qingyang Wang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication - Tao Yue
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication
Latest Publications
- Open Access
Autonomous Data Association and Intelligent Information Discovery Based on Multimodal Fusion Technology
The effective association of multimodal data is the basis of massive multi-source heterogeneous data sharing in the era of big data. How to realize data autonomous association between massive multimodal databases and the automatic intelligent screening of valuable information from associated data, so as to provide a reliable data source for artificial intelligence (AI), is an urgent problem to be solved. In this paper, a data autonomous association method based on the organizational structure of data cells is proposed, including transaction abstraction based on information nucleuses, symmetric and asymmetric data association based on strategies and data pipes, and information generation based on big data. To screen meaningful data associations, an information-driven intelligent information discovery method and a task-driven intelligent information discovery method are proposed. The former screens meaningful data associations by training the reward and punishment model to simulate the manual scoring of data associations. The latter is task-oriented and screens meaningful data associations by training the reward and punishment model to simulate the manual ranking of data associations related to the task requests. Through the above work, autonomous data association and intelligent information discovery are effectively realized based on multimodal fusion technology, which provides a novel data source mining approach using multimodal data sharing and intelligent information discovery.
Open AccessAutonomous Data Association and Intelligent Information Discovery Based on Multimodal Fusion Technology
The effective association of multimodal data is the basis of massive multi-source heterogeneous data sharing in the era of big data. How to realize data autonomous association between massive multimodal databases and the automatic intelligent screening of valuable information from associated data, so as to provide a reliable data source for artificial intelligence (AI), is an urgent problem to be solved. In this paper, a data autonomous association method based on the organizational structure of data cells is proposed, including transaction abstraction based on information nucleuses, symmetric and asymmetric data association based on strategies and data pipes, and information generation based on big data. To screen meaningful data associations, an information-driven intelligent information discovery method and a task-driven intelligent information discovery method are proposed. The former screens meaningful data associations by training the reward and punishment model to simulate the manual scoring of data associations. The latter is task-oriented and screens meaningful data associations by training the reward and punishment model to simulate the manual ranking of data associations related to the task requests. Through the above work, autonomous data association and intelligent information discovery are effectively realized based on multimodal fusion technology, which provides a novel data source mining approach using multimodal data sharing and intelligent information discovery.