• 전자공학회논문지B
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• 제29B권1호
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• pp.58-66
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• 1992

This paper proposes a neuro-controller for position control of one-link flexible robot arm. Basically the controller consists of a multi-layer neural network and a conventional PD controller. Two controller are parallelly connected. Neural network is traind by the conventional error back propagation learning rules. During learning period, the weights of neural network are adjusted to minimize the position error between the desired hub angle and the actual one. Finally the effectiveness of the proposed approach will be demonstrated by computer simulation.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2006년도 추계학술발표논문집
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• pp.122-128
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• 2006

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 대한임베디드공학회논문지
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• 제15권6호
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• pp.299-305
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• 2020

We have developed a scheduler that additionally consider network performance by extending the Kubernetes developed to manage lots of containers in cloud computing nodes. The network delay adapt characteristics of the compute nodes were learned during server operation and the learned results were utilized to develop placement algorithm by considering the existing measurement units, CPU, memory, and volume together, and it was confirmed that the low delay network service was provided through placement algorithm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.249-254
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• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.

• 전기전자학회논문지
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• 제14권4호
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• pp.312-316
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• 2010

We describe in this paper how to facilitate ad hoc routing with a particle filter in a hostile radio environment for multi-hop wireless ad hoc networks that connect multi-robots. The proposed scheme increases a connection's throughput by exploiting alternative links without going through the procedure of route discovery when link failure happens among multi-robots' networking. The scheme is implemented by using a particle filter to find strongly connected nodes. The filter estimates the probability distribution function in a sample-based manner with N particles. The particles are associated with a weight which represents the probability of the corresponding node to be the node with the best link. At every step of the estimation, the weights of particles are calculated and particles are resampled based on the weights. Since a node with the strongest link status possesses the largest number of particles, we take this node to forward the packets.

• 조명전기설비학회논문지
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• 제19권8호
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• pp.109-115
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• 2005

이 논문은 자동차에서 제어 네트워크로 이미 신뢰성이 확인된 CAN(Controller Area Network) 필드버스를 산업계 복수전동기 제어에 적용하기 위하여 지능형 제어모듈로 CAN이 내장된 DSP2812 프로세서를 이용하여 제어 및 모니터링기술을 개발하였다. 산업계에 광범위하게 사용되고 있는 유도전동기를 대상으로 여러 대의 유도전동기를 제어하기 위한 제어 알고리즘과 CAN 기반제어네트워크 구축방법을 개발하였다. 이 시스템 성능을 평가하기 위하여 2대의 유도전동기 인버터 구동시스템에 적용하여 CAN 기반 네트워크 제어 실험을 수행하였다. 그 결과 광범위한 속도와 정역회전에서 실시간 네트워크 기반 제어성능을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4041-4059
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• 2019

Key establishment method based on channel reciprocity for time division duplex (TDD) system has earned a vital consideration in the majority of recent research. While most of the cellular systems rely on frequency division duplex (FDD) systems, especially the 5G network, which is not characterized by the channel reciprocity feature. This paper realizes the generation of a group secret key for multi-terminals communicated through a wireless network in FDD mode, by utilizing the nature of the physical layer for the wireless links between them. I consider a new group key generation approach, which using bitwise XOR with a modified pairwise secret key generation approach not based on the channel reciprocity feature. Precisely, this multi-node secret key agreement technique designed for three wireless network topologies: 1) the triangle topology, 2) the multi-terminal star topology, and 3) the multi-node chain topology. Three multi-node secret key agreement protocols suggest for these wireless communication topologies in FDD mode, respectively. I determine the upper bound for the generation rate of the secret key shared among multi-node, for the three multi-terminals topologies, and give numerical cases to expose the achievement of my offered technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.105-107
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• 2009

본 논문에서는 대형 매장 혹은 임의의 공간과 공간사이에 다른 뮤직콘텐츠를 필요로 할 때 이를 해결해 줄 수 있는 멀티 스트리밍 플랫폼에 대해 연구한다. 사용자의 인터넷 사용을 겸하기 위해 NAT를 구성하고, Multi-Channel Connection을 위한 Application 설계방법과 그에 따른 Multi Stream을 위한 Hardware Path를 구현하는 방법을 제안한다.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.203-211
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• 2022

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.