• 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.

• 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.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.570-576
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• 2007

This paper presents a hexagon-based Q-leaning algorithm to find a hidden targer object with multiple robots. An experimental environment was designed with five small mobile robots, obstacles, and a target object. Robots went in search of a target object while navigating in a hallway where obstacles were strategically placed. This experiment employed two control algorithms: an area-based action making (ABAM) process to determine the next action of the robots and hexagon-based Q-learning to enhance the area-based action making process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.264-269
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• 2006

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize th ε ir surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 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 research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

• 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.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.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.119-125
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• 2008

This paper presents the polygon-based Q-leaning and Cascade Support Vector Machine algorithm for object search with multiple robots. We organized an experimental environment with ten mobile robots, twenty five obstacles, and an object, and 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 Cascade SVM to enhance the fusion model with DBAM and ABAM process.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1039-1044
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• 2005

This paper presents a robotic agent design and application in the ubiquitous intelligent space. We set up an experimental environment with Bluetooth host, Bluetooth client, furniture and home appliance, and robotic agents. First, the agents basically performed patrol guard to detect unexpected penetration, and to keep home safely from gas-leakage, electric leakage, and so on. They were out to patrol fur a robbery while navigating in a living room and a private room. In this task, we used an area-based action making and a hexagon-based Q-learning to control the agents. Second, the agents communicate with Bluetooth host device to access and control a home appliance. The Bluetooth host offers a manual control to person by inquiring a client robot when one would like to check some place especially. In this exercise, we organize asynchronous connection less (ACL) between the host and the client robots and control the robot maneuver by Bluetooth host controller interface (HCI).

• Korean Institute of Interior Design Journal
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• v.22 no.6
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• pp.98-107
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• 2013

Space evolves from the concept of deterministic static location to a dynamic, connected area through the interference of the user. While this does incorporate physical changes of the space, it also reflects the changes of the program or characteristics of the space through the actions and changes of the user. Therefore, in this study we plan to review the characteristic of time appearing within space, thereby discussing the impact of changing of condition in time-space to the decision making of the user. Further, we plan to analyze the specific causes, and subsequently introduce a new perspective over space. In order to achieve this, we need to first understand the reason why the attribute of time needs to be discussed in space, and perform a fundamental analysis of factors for the changes of the users' actions following changes in space-time condition. This means that space is not limited to merely satisfying its innate objective as an area, but may have a basis for modifying its role to help the decision making of the users caused by changes in space-time conditions. Accordingly, we analyze the factors for change of environment that can appear in space following the flow of time caused by correlation in space-time, as well as psychological factors and variables for decision making by the users. Based on this, we analyze cases to study the influence of condition changes in time-space on the action decision judgment of the users. Through this, we propose that the actions of the users can be determined following changes in time-space conditions, and discuss the need for changes in our perspective of space.

• Journal of Information Technology Applications and Management
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• v.30 no.6
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• pp.1-15
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• 2023

With the advent of the Fourth Industrial Revolution, propelled by digital technology, we are transitioning into an era of hyperconnectivity, where everything and objects are becoming interconnected. A smart supply chain refers to a supply chain system where various sensors and RFID tags are attached to objects such as machinery and products used in the manufacturing and transportation of goods. These sensors and tags collect and analyze process data related to the products, providing meaningful information for operational use and decision-making in the supply chain. Before the spread of COVID-19, the fundamental principles of supply chain management were centered around 'cost minimization' and 'high efficiency.' A smart supply chain overcomes the linear delayed action-reaction processes of traditional supply chains by adopting real-time data for better decision-making based on information, providing greater transparency, and enabling enhanced collaboration across the entire supply chain. Therefore, in this study, a hierarchical model for building a smart supply chain was constructed to systematically derive the importance of key factors that should be strategically considered in the construction of a smart supply chain, based on the major factors identified in previous research. We applied AHP (Analytical Hierarchy Process) techniques to identify urgent improvement areas in smart SCM initiatives. The analysis results showed that the external supply chain integration is the most urgent area to be improved in smart SCM initiatives.