• 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.

• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.183-195
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• 2014

Kinetically constrained models (KCM) have attracted interest as models that assign dynamic origins to the interesting dynamic properties of supercooled liquid. Signs of dynamic heterogeneity in the crossover model that linearly interpolates between the FA-like symmetric constraint and the East model constraint by asymmetric parameter b were investigated using Monte Carlo technique. When the asymmetry parameter was decreased sufficiently, smooth fragile-to-strong dynamic transition was observed in terms of the relaxation time, diffusion constant, Stokes-Einstein violation, and dynamic length scale. Competition between energetically favored symmetric relaxation mechanism and entropically favored asymmetric relaxation mechanism is behind such transition.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1177-1180
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• 1996

In this paper, we propose an algorithm that realizes cooperative behavior by construction of autonomous mobile robot system. Each robot is able to sense other robots and obstacles, and it has the rule of behavior to achieve the goal of the system. In this paper, to improve performance of the whole system, we use Genetic Programming based on Natural Selection. Genetic Programming's chromosome is a program of tree structure and it's major operators are crossover and mutation. We verify the effectiveness of the proposed scheme from the several examples.

• Information Systems Review
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• v.19 no.3
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• pp.179-199
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• 2017

As users' cross-over shopping behaviors become more popular, many studies have attempted to describe a theoretical mechanism in multichannel environments. Apart from explaining a simplified multichannel user behavior, relevant researchers must deeply understand the mechanism of users' cross-over shopping behavior, which cannot be discovered by employing either existing theories or traditional research methods. Thus, this study explores why, how, and when users conduct cross-over shopping behaviors in multichannel environments by employing a grounded theory approach. In this study, we have interviewed 25 participants who have prior experiences in cross-over shopping. By analyzing the interview manuscripts using the grounded theory approach, we have extracted 118 codes in the coding steps and ultimately presented 28 categories by incorporating similar concepts from those codes. In this qualitative grounded theory study, we have discussed why, how, and when users do cross-over shopping behavior based on our selected codes and categories as well as by listening to the stories of our interviewees. By grounding our proposed framework, which can capture both dynamic information search and purchasing behavior, this study provides an alternative research approach to explain user behavior, thereby bolstering our current understanding of the cross-over shopping behavior of users in multichannel environments.

• Journal of Dental Anesthesia and Pain Medicine
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• v.19 no.3
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• pp.143-149
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• 2019

Background: Uncooperative behavior of children due to dental anxiety may interfere with the effective delivery of dental care and compromise the quality of treatment provided. Injection of local anesthesia is one of the most anxiety-inducing stimuli in pediatric dentistry. This study aimed to compare the efficacy of a child-friendly device, having a combined effect of vibration and distraction, with the conventional method of injection on pain, anxiety, and behavior of pediatric patients aged 6-11 years. Methods: This randomized, crossover, split-mouth study included 30 children requiring a bilateral inferior alveolar nerve block. The children were equally divided into two groups: group 1, aged 6-8 and group 2, aged 9-11 years. All children were injected with anesthesia using the conventional and device method in two separate sessions. They were assessed for anxiety by measuring the pulse rate before and during the administration of local anesthesia. Behavior was assessed using Faces, Legs, Activity, Cry, Consolability (FLACC) scale, and the child's experience while receiving anesthesia was assessed using the Wong Bakers Pain Rating Scale. Results: Results showed that the children who received local anesthesia using the device method had a lower mean pulse rate, FLACC scores, and pain rating scores than those who received local anesthesia using the conventional method. Conclusion: The device method was more effective than the conventional method in managing pain, anxiety, and behavior of patients aged 6-11 years. The device is a cost effective, simple, and child-friendly product for administrating local anesthesia in pediatric patients.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.56-64
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• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Proceedings of the Korean Magnestics Society Conference
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• 2008.06a
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• pp.108-108
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• 2008

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.20-25
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• 2002

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy for evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.08a
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• pp.132-132
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• 2011

The sheet resistance (Rs) of undoped GaN films on AlN/c-plane sapphire substrate was investigated in which the AlN films were grown by R. F. magetron sputtering method. The Rs was strongly dependent on the AlN layer thickness and semi-insulating behavior was observed. To clarify the effect of crystalline property on Rs, the crystal structure of the GaN films has been studied using x-ray scattering and transmission electron microscopy. A compressive strain was introduced by the presence of AlN nucleation layer (NL) and was gradually relaxed as increasing AlN NL thickness. This relaxation produced more threading dislocations (TD) of edge-type. Moreover, the surface morphology of the GaN film was changed at thicker AlN layer condition, which was originated by the crossover from planar to island grains of AlN. Thus, rough surface might produce more dislocations. The edge and mixed dislocations propagating from the interface between the GaN film and the AlN buffer layer affected the electric resistance of GaN film.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.624-636
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• 2006

This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.