A Novel Method for Intelligent EMC Management Using a “Knowledge Base”

Shi, Dan; Fang, Wei; Zhang, Fangfei; Xue, Mengtao; Yougang, Gao

doi:10.1109/temc.2018.2797053

Cited by 9 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the knowledge data. Which means to process the network learning system to ensure the integrity of the knowledge data and the security of the network learning system [ 6 ]. The experiment proves that the network learning system using the automatic fault monitoring technology can greatly reduce the fault rate of the system itself.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System

Jia

Wang

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

The rapid development of computers has improved the scope of dynamic knowledge network learning applications. Online learning has brought convenience to people in time and place. At the same time, people began to pay attention to the efficiency and quality of online learning. At present, the network knowledge storage system is distributed storage system. The distributed storage system has great performance in terms of capacity, scalability, and parallelism. However, its storage node is inexpensive, and the reliability is not high, and it is prone to fault. Based on the designed fault detection model detection path, relying on building the knowledge data node fault detection mode, constructing the knowledge data link fault detection mode, completing the fault detection model detection mode, and finally realizing the dynamic knowledge network learning system fault intelligent detection technology research. The experiment proves that the dynamic knowledge network learning system fault intelligent detection technology designed in this paper reduces the fault rate of the network learning system by 37.5%.

show abstract

Section: Introductionmentioning

confidence: 99%