• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.496-499
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• 2003

Support Vector Machines (SVMs) is applied to a practical problem as one of standard tools for machine learning. The application of Reinforcement Learning (RL) and SVMs in action of mobile robot is investigated. A technique to decide the action of autonomous mobile robot in practice is explained in the paper, The proposed method is to find n basis for good action of the system under unknown environment. In multi-dimensional sensor input, the most reasonable action can be automatically decided in each state by RL. Using SVMs, not only optimal decision policy but also generalized state in unknown environment is obtained.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.197-206
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• 2000

A hybrid control structure for vision-based soccer robot system is considered. The structure is om-posed of four levels such as the role action behavior and execution levels. The control structure which is a combination of hierarchical and behavioral structures can efficiently meet the behavior and design specifications of a soccer robot system Among the four levels only the design of the action level is proposed in the paper and is experimentally evaluated. Design hypothesis and evaluation method are presented to improve the reliability and accomplishment of the robot system. Due to the essential element of soccer robot system design a systematic design procedure of the action level is proposed With the proposed structure and algorithm of the action level the excellent result was shown at the MiroSot'98 held in France.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.57
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• pp.1-10
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• 2000

The purpose of this paper is to propose a method of rest to reduce work load of night shift workers for night shift work. The experiment was carried out 10minutes preparing time, 45minutes first work, 10minutes first rest, 45minutes second work, 10minutes second rest between 2 and 4 o'clock that the lowest physiological function of workers. The methods of rest set up as four patterns (1) non-action rest (2) non-action rest + listening music (3) action rest + non-action rest, (4) action rest + non-action rest + listening music. For the measurements of experiment, heart rates(R-R interval), critical flicker fusion frequency(CFF), blood pressure, oral temperature, reaction time and error rates were considered as criteria for work performance. As a result, action rest + non-action rest and action rest + non-action rest + listening music were more effective to reduce work load additional work than non-action rest and non-action rest + listening music.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying 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 parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.256-261
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• 2017

The notion of System of Systems (SoS), which is composed by many independent systems (i.e., Constituent Systems, CS), has emerged in various domains including social infrastructure. It is widely expected that complex requirements, which cannot be achieved in each CS-level, will be achieved in an SoS-level. While verification of SoS-level goal achievement is one of the most important problems, concrete case studies on SoS modeling and verification are still rare. In this paper, we focus on the fact that each CS performs an action for its own purpose by its own decision-making mechanism. We propose a novel Action-Benefit-Cost (ABC) SoS model which caters to the independent decision-making mechanisms of CSs. Using an abstract SoS example, this proposal provides a case study for the modeling and quantitative verification of the ABC SoS model.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.1
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• pp.39-45
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• 2005

Systems Engineering is the process that controls the technical system development effort with the goal of achieving an optimum balance of all system elements. FRACAS(Failure Reporting an d Corrective Action System) is one of RAMS(Reliability, Availability, Maintainability, Safety) tasks which is conducted from a manufacturing phase. FRACAS is to determine the basic cause of failure resulting from design or manufacture, and to provide a closed-loop method of implementing corrective action. This paper presents the FRACAS established for assessment of the reliability, the analysis and management of the failure data for Korean High Speed Train and application cases.

• International Journal of Human Ecology
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• v.15 no.1
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• pp.23-37
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• 2014

This study critically appraised the accumulated articles based on the critical science perspective on Korea's home economics education. The 55 articles published from 1993 to 2011 were identified and placed into three categories: Critical science paradigm, practical problem-focused curriculum, and systems of action. The common result across categories was that some authors did not fully understand the concepts of practice, a practical problem-focused curriculum, the practical reasoning process, and the systems of action. A positive outcome of including a critical science perspective in the Korean national curriculum will be seen when systems supporting this perspective, such as those providing more specific information on a critical science perspective and the relevant textbook accreditation standards, are available. We present a proposal based on considerations of a national curriculum, teacher education, relevant philosophical perspectives, and classroom practice.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1044-1047
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• 2005

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.457-467
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• 2004

In this paper, a robot is considered as an agent, a structure of robot is presented which consisted by multi-subagents and they have diverse capacity such as perception, intelligence, action etc., required for robot. Also, subagents are consisted by micro-agent($\mu$agent) charged for elementary action required. The structure of robot control have two sub-agents, the one is behavior based reactive controller and action selection sub agent, and action selection sub-agent select a action based on the high label action and high performance, and which have a learning mechanism based on the reinforcement learning. For presented robot structure, it is easy to give intelligence to each element of action and a new approach of multi robot control. Presented robot is simulated for two goals: chaotic exploration and obstacle avoidance, and fabricated by using 8bit microcontroller, and experimented.