Developing Adaptive and Personalized Distributed Learning Systems with Semantic Web Supported Multi Agent Technology

In order to improve the effect of remote ideological and political education, this paper builds a Web ideological and political education system based on Agent technology, and adopts a three-layer abstract system architecture including Web service layer, Agent processing layer and service process layer. Moreover, based on this architecture foundation, this paper proposes an Agent-based Web service integration structure, and illustrates the overall execution process of the system through the execution process of the system integration structure. Then, this paper proposes the organization structure of multi-agent in the Agent processing layer and the organization structure of service process in the service process layer of the system. In addition, this paper uses multi-agent system design to ensure the efficient operation of the entire system, and combines algorithms to implement system resource recommendation modules and practical teaching functions. Finally, this paper designs a control experiment to test the performance of the distance ideological and political education system constructed in this paper. The research results show that the system constructed in this paper has certain practical effects.

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Visual intelligent remote healthcare monitoring system using multi-agent technology

2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) ◽

10.1109/atsip.2016.7523118 ◽

2016 ◽

Author(s):

Afef Ben Jemaa ◽

Hela Ltifi ◽

Mounir Ben Ayed

Keyword(s):

Monitoring System ◽

Agent Technology ◽

Remote Healthcare ◽

Healthcare Monitoring ◽

Multi Agent

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Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Energies ◽

10.3390/en14123654 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3654

Author(s):

Nastaran Gholizadeh ◽

Petr Musilek

Keyword(s):

Machine Learning ◽

Power Systems ◽

Learning Algorithm ◽

Single Point ◽

Distributed Learning ◽

Large Data ◽

Multi Agent Systems ◽

Power Quality Monitoring ◽

Multi Agent ◽

Learning Frameworks

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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