• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.102-107
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• 2021

This paper discusses the containment control problem for multi-agent systems with input saturations. The goal of the containment control is to obtain swarming behavior by driving follower agents into the convex hull which is spanned by multiple leader agents. This paper considers multiple leader agents moving at the same constant speed. Then, to solve the containment problem for moving leaders, we propose a PI-based distributed control algorithm. We next analyze the convergence of follower agents to the desired positions. Specifically, we apply the integral-type Lyapunov function to take into account the saturation nonlinearity. Then, based on Lasalle's Invariance Principle, we show that the asymptotic convergence of error states to zero for any positive constant gains. Finally, numerical examples with the static and moving leaders are provided to validate the theoretical results.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.344-351
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• 2016

Recently, mobile application for english vocabulary learning is being developed actively. However, most mobile English vocabulary learning applications did not effectively connected with the technical advantages of mobile learning. Also,the study of mobile english vocabulary learning app are still insufficient. Therefore, this paper development a english vocabulary context-learning Agent that can practice context learning more reasonably using a location-based service, a character recognition technology and augmented reality technology based on smart phones. In order to evaluate the performance of the proposed agent, we have measured the precision and usability. As results of experiments, the precision of learning vocabulary is 89% and 'Match between system and the real world', 'User control and freedom', 'Recognition rather than recall', 'Aesthetic and minimalist design' appeared to be respectively 3.91, 3.80, 3.85, 4.01 in evaluation of usability. It were obtained significant results.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2771-2773
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• 2005

In network human-robot interaction, human can access services of a robot system through the network The communication is done by interacting with the distributed sensors via voice, gestures or by using user network access device such as computer, PDA. The service organization and exploration is very important for this distributed system. In this paper we propose a new agent-based framework to integrate partners of this distributed system together and help users to explore the service effectively without complicated configuration. Our system consists of several robots. users and distributed sensors. These partners are connected in a decentralized but centralized control system using agent-based technology. Several experiments are conducted successfully using our framework The experiments show that this framework is good in term of increasing the availability of the system, reducing the time users and robots needs to connect to the network at the same time. The framework also provides some coordination methods for the human robot interaction system.

• Journal of information and communication convergence engineering
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• v.17 no.3
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• pp.167-173
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• 2019

This study investigates the problem of leader-following consensus for multi-agent systems with output saturations. This study assumes that the agents are described as a neutrally stable system, and the leader agent generates the bounded trajectory within the saturation level. Then, the objective of the leader-following consensus is to track the trajectory of the leader by exchanging information with neighbors. To solve this problem, we propose an observer-based distributed consensus algorithm. Then, we provide a consensus analysis by applying the Lyapunov stability theorem and LaSalle's invariance principle. The result shows that the agents achieve the leader-following consensus in a global sense. Moreover, we can achieve the consensus by choosing any positive control gain. Finally, we perform a numerical simulation to demonstrate the validity of the proposed algorithm.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.2
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• pp.34-43
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• 2022

In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.808-813
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• 2003

Maintaining agility and responsiveness m designing and manufacturing activities are the key issues for manufacturing companies to cope with global competition. Distributed design and control systems are regarded as an efficient solution for agility and responsiveness. However, distributed nature of a manufacturing system complicates production activities such as design, simulation, scheduling, and execution control. Especially, existing simulation systems have limited external integration capabilities, which make it difficult to implement complex control mechanisms for the distributed manufacturing systems. Moreover, integration and coupling of heterogeneous components and models are commonly required for the simulation of complex distributed systems. In this paper, a collaborative and adaptive simulation architecture is proposed as an open framework for simulation and analysis of the distributed manufacturing enterprises. By incorporating agents with their distributed characteristics of autonomy, intelligence, and goal-driven behavior, the proposed agent-based simulation architecture can be easily adapted to support the agile and distributed manufacturing systems. The architecture supports the coordination and cooperation relations, and provides a communication middleware among the participants in simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.227-232
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• 2013

BCI (Brain Computer Interface) is technology to control external devices by measuring the brain activity, such as electroencephalogram (EEG), so that handicapped people communicate with environment physically using the technology. Among them, EEG is widely used in various fields, especially robot agent control by using several signal response characteristics, such as P300, SSVEP (Steady-State Visually Evoked Potential) and motor imagery. However, in order to control the robot agent without any constraint and precisely, it should take advantage of not only a signal response characteristic, but also combination. In this paper, we try to use the fusion of motor imagery and P300 from EEG for practical use of robot control in BCI environment. The results of experiments are confirmed that the recognition rate decreases compared with the case of using one kind of features, whereas it is able to classify each both characteristics and the practical use technology based on mobile robot and wireless BCI measurement system is implemented.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.91-103
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• 2008

In order to simulate the behavior of automated manufacturing systems, the performance of material handling systems should be measured dynamically. Multi-Agent technology could be well adapted for the development of simulator for distributed and intelligent manufacture systems. A multi-agent system is composed of one coordination agent and multiple application agents. Issues in AGVS simulator can be classified by the set-up and operating problems. Decisions on the number of vehicles, bi- or uni-directional guide-path, etc. are fallen into the set-up problem category, while deadlock tree algorithm and conflict resolution are in operating problem. In this paper, a multi-agent based deadlock-free simulator for automated guided vehicle system(AGVS) are proposed through the use of multi-agent technologies and the development of deadlock-free algorithm. In this AGVS simulator proposed, well-known Floyd algorithm is used to create AGVS Guide path, through which AGVS move. Also, AGVs avoid vehicle conflict and deadlock using check path algorithm. And Moving vehicle agents are operated in real-time control by coordination agent. AGV position is dynamically calculated based on the concept of rolling time horizon. Simulator receives and presents operating information of vehicle in AGVS Gaunt chart. The performance of the proposed algorithm and developed simulator based on multi-agent are validated through set of experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.5
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• pp.109-119
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• 2004

The home network environment can be defined as a network environment, connecting digital home devices such as computer systems, digital appliances, and mobile devices. In this kind of home network environments, there will be numerous local/remote interactions to monitor and control the home network devices and the home gateway. Such an environment may result in communication bottleneck. By applying the mobile agents that can migrate among the computing devices autonomously and work on behalf of the user, remote interactions and network traffics can be reduced enormously. The mobile agent authentication is necessary to apply mobile agent concept to the home network environments, as a prerequisite technology for authorization or access control to the home network devices and resources. The existing mobile agent systems have mainly used the public key based authentication scheme, which is not suitable to the home network environments, composed of digital devices of limited computation capability. In this paper, we propose a shared key based mobile agent authentication scheme for single home domain and expand the scheme to multiple domain environments with the public key based authentication scheme. Application of the shared key encryption scheme to the single domain mobile agent authentication enables to authenticate the mobile agent with less overhead than the public key based authentication scheme.