• Journal of Power Electronics
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• v.15 no.2
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• pp.455-468
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• 2015

A microgrid (MG) with integrated renewable energy resources can benefit both utility companies and customers. As a result, they are attracting a great deal of attention. The control of a MG is very important for the stable operation of a MG. The droop-control method is popular since it avoids circulating currents among the converters without using any critical communication between them. Traditional droop control methods have the drawback of an inherent trade-off between power sharing and voltage and frequency regulation. An adaptive droop control method is proposed, which can operate in both the island mode and the grid-connected mode. It can also ensure smooth switching between these two modes. Furthermore, the voltage and frequency of a MG can be restored by using the proposed droop controller. Meanwhile, the active power can be dispatched appropriately in both operating modes based on the capacity or running cost of the Distributed Generators (DGs). The global information (such as the average voltage and output active power of the MG and so on) required by the proposed droop control method to restore the voltage and frequency deviations can be acquired distributedly based on the Multi Agent System (MAS). Simulation studies in PSCAD demonstrate the effectiveness of the proposed control method.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.113-121
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• 2005

Since documents on the Web are naturally partitioned into many document databases, the efficient information retrieval (IR) process requires identifying the document databases that are most likely to provide relevant documents to the query and then querying the identified document databases. We first introduce a neural net agent for such an efficient IR, and then propose the hierarchically organized multi-agent IR system in order to scale our agent with the large number of document databases. In this system, the hierarchical organization of neural net agents reduced the total training cost at an acceptable level without degrading the IR effectiveness in terms of precision and recall. In the experiment, we introduce two neural net IR systems based on single agent approach and multi-agent approach respectively, and evaluate the performance of those systems by comparing their experimental results to those of the conventional statistical systems.

• The Journal of Information Systems
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• v.14 no.3
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• pp.53-58
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• 2005

In this study, we will employ a multi-agent for the search and extraction of data in a distributed environment. We will use an Integrator Agent in the proposed model on the Hierarchical Clustering and Association Rule Discovery(HCARD). The HCARD will address the inadequacy of other data mining tools in processing performance and efficiency when use for knowledge discovery. The Integrator Agent was developed based on CORBA architecture for search and extraction of data from heterogeneous servers in the distributed environment. Our experiment shows that the HCARD generated essential association rules which can be practically explained for decision making purposes. Shorter processing time had been noted in computing for clusters using the HCARD and implying ideal processing period than computing the rules without HCARD.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.101-110
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• 2009

Multi-agent systems are mostly used to integrate the intelligent and distributed approaches to various systems for effective sharing of resources and dynamic system reconfigurations. Object replication is usually used to implement fault tolerance and solve the problem of unexpected failures to the system. This paper presents the intelligent multi-agent distributed platform based on the dynamic object group management and proposes an object search technique based on the proposed filtered k-means (Fk-means). We propose Fk-means for the search mechanism to find alternative objects in the event of object failures and transparently reconnect client to the object. The filtering range of Fk-means value is set only to include relevant objects within the group to perform the search method efficiently. The simulation result shows that the proposed mechanism provides fast and accurate search for the distributed object groups.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1047-1058
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• 2005

In this paper we propose an agent architecture to improve the flexibility of videoconference systems with strategic-INTER knowledge. The proposed architecture achieves more flexibility by selecting dynamically the strategy of QoS (Quality of Service). To select the strategies, the systems have to take into the consideration of the properties of problems occurred on QoS and the status of problem solving process. This architecture is introduced as a part of knowledge base of agent dealing with cooperation between software modules of videoconference systems. We have implemented the architecture and our prototype system shows its capability of flexible problem solving against the QoS degradation, along with other possible problems within the time limitation. We confirmed that the proposed architecture can improve its flexibility of a videoconference system compared to conventional systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.643-648
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• 2003

In order to provide efficient retrieving results for user query on the web environment, the various searching algorithms have developed and considered user's preference and convenience. However, the searching algorithms are developed on the horizontal and non hierarchical web environment in general and could not apply to the complex hierarchical and functional web environments such like the enterprise network. In this paper, we purpose the multi-agent based web mining system which can provide the efficient mining results to the user on the special web environment. For doing this, we suggest the network model with the hierarchical web environment and model the multi agent based web mining system which has four corporation agents and fourteen process modules. Then, we explain the detailed functions of each agent considered the hierarchical environment according to the module. Especially, we purpose the new merging agent and improved ranking algorithm by using the graph theory.

• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.115-123
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• 2007

Agents in Multi-Agent System; (MAS) should make use of a common Agent Communication Language (ACL) in order to negotiate with others, and conform to negotiation protocols thatare designed to reach agreements. Therefore, agents must have suitable architectures that could cover above requirements. In this paper, we define an instructive ACL and compare it with other ACLs such as Foundation for Intelligent Physical Agents (FIPA) ACL and Knowledge Query Manipulation Language(KQML), In particular, we represent agents as logic programs with knowledge base and negotiation library. Finally, we show how the planner, which is in the negotiation library, provides the plan of actions and updates agent's knowledge base.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.281-289
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• 2010

Recently the intelligent agent systems have been emerged as one of key issues for developing the defense M&S systems. However, most conventional agent architecture of M&S systems utilize the script-based models and can only deal with the individual behaviors so that they cannot suitably describe the precise tactical/strategic behavior and/or complex warfare environment. To overcome these problems, we have proposed the HEAP(Hierarchical Encapsulation and Abstraction Principle)-based hierarchical multi-agent system architecture that is able to intelligently cope with the complex missions based on the functional role of each agent on the hierarchy such as an intelligence officer, captain, commander.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.149.5-149
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10b
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• pp.1357-1360
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• 2000

본 논문은 다중 이동 에이전트 시스템을 이용한 웹 지리 정보 시스템 모델을 제안한다. 방대한 지리 정보를 분산 병렬처리하기 위해서 호스트간을 이주하고 네트워크 환경에서 효율적으로 수행하는 이동 에이전트를 사용한다. 제안하는 모델은 사용자와 상호작용하고 에이전트를 생성하여 이주시키며 웹 브라우저에 플러그 인 되는 HAP(home agent platform)과 GIS 서버에서 지리객체를 검색하는 iMAP(internet mobile agent)으로 구성된다. 성능 평가는 클라이언트/서버 모델과 이동 에이전트 모델간의 성능 비교를 통해서 보여준다.