• 한국군사과학기술학회지
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• 제11권5호
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• pp.49-57
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• 2008

In this paper, we suggested a methodology that can be used by an agent to learn models of other agents in a multi-agent system. To construct these model, we used influence diagram as a modeling tool. We present a method for learning models of the other agents at the decision nodes, value nodes, and chance nodes in influence diagram. We concentrated on learning of the other agents at the value node by using neural network learning technique. Furthermore, we treated anti-air units in anti-air defense domain as agents in multi. agent system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3468-3495
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• 2015

In this paper, we present a multi-chain based hierarchical topology control algorithm (MCHTC) for wireless sensor networks. In this algorithm, the topology control process using static clustering is divided into sensing layer that is composed by sensor nodes and multi-hop data forwarding layer that is composed by leader nodes. The communication cost and residual energy of nodes are considered to organize nodes into a chain in each cluster, and leader nodes form a tree topology. Leader nodes are elected based on the residual energy and distance between themselves and the base station. Analysis and simulation results show that MCHTC outperforms LEACH, PEGASIS and IEEPB in terms of network lifetime, energy consumption and network energy balance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 A
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• pp.299-301
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• 2003

In this paper, a protection system using Multi-Agent concept for power distribution network is proposed. Multi agent system consist of Feeder agent, OCR(Over Current Relay) agent, Recloser agent and Switch agent. An agent calculates and corrects its parameter by itself through communication with neighboring agents and its own intelligence algorithm. Simulations in a simple distribution network show the effectiveness of the suggested protection system. Multi-Agent System, protection of distribution network, Communication.

• 지능정보연구
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• 제2권2호
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• pp.69-83
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• 1996

The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.426-439
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• 2021

To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1620-1634
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• 2023

This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.149-154
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• 2023

This paper presents a novel approach to multi-focus image fusion using light field cameras. The proposed neural network, LFFCNN (Light Field Focus Convolutional Neural Network), is composed of three main modules: feature extraction, feature fusion, and feature reconstruction. Specifically, the feature extraction module incorporates SPP (Spatial Pyramid Pooling) to effectively handle images of various scales. Experimental results demonstrate that the proposed model not only effectively fuses a single All-in-Focus image from images with multi focus images but also offers more efficient and robust focus fusion compared to existing methods.

• 청정기술
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• 제17권2호
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• pp.175-180
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• 2011

생태산업단지의 산업 공생망 구성을 위한 다중척도 의사결정 전략을 개발하고 이를 여수산업단지의 산/염기성 폐수 중화망 설계에 적용하였다. 산(또는 염기)성 폐수는 화학산업에서 공통적으로 나오며, 다른 곳에서 나온 염기(또는 산)성 폐수를 자체적으로 중화할 수 있는 원료가 될 수 있다. 따라서 산/염기성 폐수가 대량으로 발생되는 석유화학산업단지에서 대규모의 산업 공생망을 구축할 수 있는 가능성을 지나고 있다. 본 연구에서는 산/염기성 폐수의 상호 중화를 위한 물질 흐름 모델을 구성하고, 여기에 다중척도의사결정 전략을 적용하여 최적이며 대등한 다수의 산업 공생망 후보를 설계하고 이들의 성능을 비교 분석하였다.

• 한국정보통신학회논문지
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• 제21권10호
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• pp.1999-2007
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• 2017

본 논문에서는, 해상에서 가용한 LTE, 초단파(VHF), 단파(HF) 통신 서비스 중에서 네트워크 환경에 따라서 최적의 통신 대역을 결정하는 멀티대역 네트워크 선택기 (MNS: Multi-band Network Selection) 시스템을 리눅스 기반의 소프트웨어로 구현한다. 구현된 소프트웨어는 네트워크 인터페이스, MNS 서버, 사용자 GUI로 구성된다. 2조의 MNS 시스템을 구축하여 구현된 MNS 시스템의 기능을 실내 시험을 통하여 검증한다. 이를 위하여, ITU-R M.1842-1 Annex1과 Annex4를 각각 준수하는 2종의 VHF 통신 링크는 소프트웨어적으로 에뮬레이션하고, HF 통신은 한 MNS 시스템의 송신(수신)을 다른 MNS의 수신(송신)에 직접 연결하여 실내 기능 검증이 가능하도록 한다. LTE, 초단파, 단파 각 통신 링크의 인위적인 단절 또는 재연결에 따른 구현된 MNS의 단절 없는 해상 통신 서비스 기능을 검증한다. 구현된 MNS 시스템은 e-navigation 등과 같은 다양한 해상 통신 서비스에 활용 가능하다.

• 전기학회논문지
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• 제67권11호
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• pp.1506-1511
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• 2018

This study presents a human sensibility evaluation method using neural network and multiple-template method on electroencephalogram(EEG). We used a multi-layer perceptron type neural network as the sensibility classifier using EEG signal. For our research objective, 10-channel EEG signals are collected from the healthy subjects. After the necessary preprocessing is performed on the acquired signals, the various EEG parameters are estimated and their discriminating performance is evaluated in terms of pattern classification capability. In our study, Linear Prediction(LP) coefficients are utilized as the feature parameters extracting the characteristics of EEG signal, and a multi-layer neural network is used for indicating the degree of human sensibility. Also, the estimation for human comfortableness is performed by varying temperature and humidity environment factors and our results showed that the proposed scheme achieved good performances for evaluation of human sensibility.