• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.291-296
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• 2008

This paper presents the dodecagon-based Q-leaning and SVM algorithm for object search with multiple robots. We organized an experimental environment with several mobile robots, obstacles, and an object. Then we sent the robots to a hallway, where some obstacles were tying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and SVM to enhance the fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.59-70
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• 1995

Output performance improvement using fuzzy logic to the conventional control scheme for a magnetic levitation system is presented in this paper, Adverse characteristics of nonlinearity, unstability, system parameter variation, etc, in the levitation system are partially overcome by the general fuzzy control action. Using a PD type compensator, a coarse framework of output performance is provided to the levitation system. Then a fine regulation to the output performance requirement is obtained by the natural description of the control action in the form of fuzzy logic controller. This control action soothes the adverse characteristics of the levitation system. In this way a better output performance can be obtained in a real time experiment.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.819-824
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• 1997

This paper presents a cell-based motion control strategy for soccer playing mobile robots. In the central robot motion planner, the planar ground is divided into rectangular cells with variable sizes and motion indices to which direction the mobile robot should move. At every time the multiple objects-the goal gate, ball, and robots-detected, integer values of motion indices are assigned to the cells occupied by mobile robots. Once the indices being calculated, the most desirable state-action pair is chosen from the state and action sets to achieve successful soccer game strategy. The proposed strategy is computationally simple enough to be used for fast robotic soccer system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.

• International Journal of Railway
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• v.3 no.3
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• pp.106-112
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• 2010

Accidents at level crossing have large portion on train accidents, and causes economical loss by train delay and operational interruption. Various safety equipments are employed to reduce the accident at level crossing, but existing warning device, and crossing barrier are simple train-oriented protection equipments. In this paper, intelligent railway level crossing system is proposed to prevent and reduce accidents. For train driver's prompt action, image of level crossing and obstacle warning message are continuously provided to train driver through wireless communication in level crossing control zone. Obstacle warning messages, which are extracted by computer vision processing of captured image at level crossing, are recognized by train driver through message color, flickering and warning sound. It helps train driver to decide how to take an action. Meanwhile, for vehicle driver's attention, location and speed of approaching train are given to roadside equipments. We identified the effect of proposed system through test installation at Sea train and Airport level crossing of Yeong-dong line.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.471-476
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• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.65-73
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• 2014

An overall responding security-centered framework is necessary required for infringement accidents, failures, and cyber threats. On the other hand, the correspondence structures of existing administrative, technical, physical security have weakness in a system responding to complex attacks because each step is performed independently. This study will recognize all internal and external users as a potentially threatening element. To perform connectivity analysis regarding an action, an intelligent convergence security framework and road map is suggested. A suggested convergence security framework was constructed to be independent of an automatic framework, such as the conventional single solution for the priority defense system of APT of the latest attack type, which makes continuous reputational attacks to achieve its goals. This study suggested the next generation convergence security framework to have preemptive responses, possibly against an APT attack, consisting of the following five hierarchical layers: domain security, domain connection, action visibility, action control, and convergence correspondence. In the domain, the connection layer suggests a security instruction and direction in the domains of administrative, physical and technical security. The domain security layer has consistency of status information among the security domain. A visibility layer of an intelligent attack action consists of data gathering, comparison and decision cycle. The action control layer is a layer that controls the visibility action. Finally, the convergence corresponding layer suggests a corresponding system of before and after an APT attack. The administrative security domain had a security design based on organization, rule, process, and paper information. The physical security domain is designed to separate into a control layer and facility according to the threats of the control impossible and control possible. Each domain action executes visible and control steps, and is designed to have flexibility regarding security environmental changes. In this study, the framework to address an APT attack and load map will be used as an infrastructure corresponding to the next generation security.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.95-105
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• 2005

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.3
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• pp.31-38
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• 2005

This paper proposes an implementation method of intelligent characters that can properly adapt to action patterns of opponent characters in fighting games by using genetic algorithm. For this intelligent characters, past actions patterns of opponent characters should be included in the learning process. To verify the effectiveness of the proposed method, two types of experiments are performed and their results are compared. In first experiment(exp-1), intelligent characters consider current action and its step of an opponent character. In second experiment (exp-2), on the other hands, they take past actions of an opponent characters into account additionally. As a performance index, the ratio of score obtained by an intelligent character to that of an opponent character is adopted. Experimental results shows that even if the performance index of exp-1 is better than that of exp-2 at the beginning of stages, but the performance index of exp-2 outperforms that of exp-1 as stages go on. Moreover, optimum solutions are always found in all experimental cases in exp-2. Futhermore, intelligent characters in exp-2 could learn moving actions (forward and backward) and waiting actions for getting more scores through self evolution.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.406-411
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• 2005

In this paper, we present the area-based Q-learning to search a target object using multiple robot. To search the target in Markovian space, the robots should recognize their surrounding at where they are located and generate some rules to act upon by themselves. Under area-based Q-learning, a robot, first of all, obtains 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to search for a target object while navigating in a unknown hallway where some obstacles were placed. In the end of this paper, we presents the results of three algorithms - a random search, area-based action making (ABAM), and hexagonal area-based Q-teaming.