• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• Journal of Information Science Theory and Practice
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• v.1 no.4
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• pp.48-66
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• 2013

The aim of this study is to examine the user's online searching behavior in IR interfaces of e-journal database systems. The study is purely based on survey methods and tries to analyse the online searching behavior of respondents of social science disciplines who were doing research in three target central universities of Delhi (i.e. DU, JMI, and JNU). For measuring the responses of the respondents in IR interfaces of e-journal database systems, a total of 396 questionnaires were distributed among the students and out of all, 305 responses were used for the study. The findings of the study reveal that most of the students were not using all the facilities offered in IR interfaces of e-journal database systems for their retrieval process and also encourages menu based searches rather than command based searching.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.314-320
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• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.903-908
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• 2003

In this paper, we present an observer-based robust controller which achieves not only robust stability but also an performance robustness for linear uncertain discrete-time systems. The performance robustness means that comparing the transient behavior of the uncertain system with a desired one generated by the nominal system, the deterioration of control performance (i.e. the error between the real response and the desired one) is suppressed without excessive control input. The control law consists of a state feedback law for the nominal system and a compensation input given by a feedback form of an estimated error signal. In this paper, we show that conditions for the existence of the observer-based controller are given in terms of linear matrix inequalities (LMIs). Finally, a numerical example is given to illustrate the proposed technique.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.532-532
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• 2000

Conventional artificial intelligence systems are not properly responding under dynamically changing environments. To overcome this problem, reactive planning systems implementing new Al principles, called behavior-based Al or emergent computation, have been proposed and confirmed their usefulness. As another alternative, biological information processing systems may provide many feasible ideas to these problems. Immune system, among these systems, plays important roles to maintain its own system against dynamically changing environments. Therefore, immune system would provide a new paradigm suitable for dynamic problem dealing with unknown environments. In this paper, a new approach to behavior-based Al by paying attention to biological immune system is investigated. The feasibility of this method is confirmed by applying to behavior control of an autonomous mobile robot in cluttered environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.591-597
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• 2001

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control school is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.760-768
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• 2007

This paper introduces an emotional behavior decision model based on linear system for intelligent service robots. An emotional model should make different behavior decisions according to the purpose of the robots. We propose an emotional behavior decision model which can change the character of intelligent service robots and make different behavior decisions although the situation and environment remain the same. We defined each emotional element such as reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics by state dynamic equations. The proposed system model is a linear dynamic system. If you want to add one external stimulus or behavior, you need to add just one dimensional vector to the matrix of external stimulus or behavior dynamics. The case of removing is same. The change of reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics also follows the same procedure. We implemented a cyber robot and an emotional head robot using 3D character for verifying the performance of the proposed emotional behavior decision model.

• Steel and Composite Structures
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• v.22 no.3
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• pp.589-611
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• 2016

The primary objectives of this research are to investigate the energy factor response of steel moment resisting frame (MRF) systems equipped with fuses subject to ground motions and to develop an energy-based evaluation approach for evaluating the damage-control behavior of the system. First, the energy factor of steel MRF systems with fuses below the resilience threshold is derived utilizing the energy balance equation considering bilinear oscillators with significant post-yielding stiffness ratio, and the effect of structural nonlinearity on the energy factor is investigated by conducting a parametric study covering a wide range of parameters. A practical transformation approach is also proposed to associate the energy factor of steel MRF systems with fuses with classic design spectra based on elasto-plastic systems. Then, the energy balance is extended to structural systems, and an energy-based procedure for damage-control evaluation is proposed and a damage-control index is also derived. The approach is then applied to two types of steel MRF systems with fuses to explore the applicability for quantifying the damage-control behavior. The rationality of the proposed approach and the accuracy for identifying the damage-control behavior are demonstrated by nonlinear static analyses and incremental dynamic analyses utilizing prototype structures.