• Korean Journal of Cognitive Science
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• v.6 no.2
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• pp.47-79
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• 1995

Since the early age of computer graphics, facial animation has been applied to various fields, and nowadays it has found several novel applications such as virtual reality(for representing virtual agents), teleconference, and man-machine interface.When we want to apply facial animation to the system with multiple participants connected via network, it is hard to animate facial expression as we desire in real-time because of the size of information to maintain an efficient communication.This paper's major contribution is to adapt 'Level-of-Detail'to the facial animation in order to solve the above problem.Level-of-Detail has been studied in the field of computer graphics to reperesent the appearance of complicated objects in efficient and adaptive way, but until now no attempt has mode in the field of facial animation. In this paper, we present a systematic scheme which enables this kind of adaptive control using Level-of-Detail.The implemented system can generate speech synchronized facial expressions with various types of user input such as text, voice, GUI, head motion, etc.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.35-45
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• 2012

Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.

• Journal of Microbiology and Biotechnology
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• v.27 no.4
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• pp.709-717
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• 2017

Mucosal tissues are the initial site through which most pathogens invade. As such, vaccines and adjuvants that modulate mucosal immune functions have emerged as important agents for disease prevention. Herein, we investigated the immunomodulatory mechanisms of the B subunit of Escherichia coli heat-labile enterotoxin type IIa ($LT-IIa-B_5$), a potent non-toxic mucosal adjuvant. Alternations in gene expression in response to $LT-IIa-B_5$ were identified using a genome-wide transcriptional microarray that focused on dendritic cells (DC), a type of cell that broadly orchestrates adaptive and innate immune responses. We found that $LT-IIa-B_5$ enhanced the homing capacity of DC into the lymph nodes and selectively regulated transcription of pro-inflammatory cytokines, chemokines, and cytokine receptors. These data are consistent with a model in which directional activation and differentiation of immune cells by $LT-IIa-B_5$ serve as a critical mechanism whereby this potent adjuvant amplifies mucosal immunity to co-administered antigens.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.109-123
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• 2003

The relationship between suppliers and customers is treated importantly not only in the traditional business-to-business (BtoB) commerce but also in today's Internet environments. On the one hand, most of Internet-based BtoB commerce services like customer-centric e-procurement, supplier-centric e-sales or intermediary-centric e-marketplace focus mainly on the selection of partners according to bidding, auction, etc. This point may result in the problem of overlooking the relationships between suppliers and customers. To overcome this problem in this paper, an intelligent agents-based supply chain operation architecture is proposed and appraised considering the relationship and its adaptation.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10c
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• pp.568-570
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• 2001

최근에 들어와 그 응용분야가 확대되어 가고 있는 에이전트 소프트웨어는 점점 그 크기와 복잡성이 커져 감에 따라 소프트웨어 개발치 효율성을 필요로 하는 추세이다. 이에 따라 네트워크에 분산되고 이질적인 시스템에 존재하는 에이전트들은 자신의 정보를 효율적으로 교환할 수 있도록 하기 위해 통신 프레임웍을 필요로 하게 되었다. 본 논문에서는 네트워크에 존재하고 동일한 환경 또는 서로 다른 환경에 존재하는 에이전트들 간의 의사 소통을 위한 통신 프레임웍의 설계 및 구현을 제시한다. 기본적으로 에이전트 소프트웨어 아키텍처는 KRIL(KQML- Router Interface Labrary), Router, Facilitator로 구성되어 진다. 같은 호스트에 존재하는 에이전트들은 같은 주소를 가지기 때문에 각기 다른 Router를 가질 필요 없이 하나의 Router를 공유한다. Router는 로컬 호스트에 존재하는 에이전트들의 리스트를 유지 관리하며, 로컬 에이전트가 보낸 메시지의 수신자가 자신의 리스트에 없을 경우 Facilitator를 통해 다른 호스트에 존재하는 에이전트에 메시지를 전달한다. 서로 다른 환경에 존재하는 에이전트들 간의 통신과 스레드 관리, 병행처리와 동기화 등을 위해 KRIL은 ACE(The Adaptive Communication Environment) 라이브러리를 사용하였으며, Router와 Facilitator는 Java로 구현하였다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.12
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• pp.71-82
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• 2003

In this paper, we present a new method to calculate reinforcement value in QoS routing algorithm targeted for real-time applications based on Ant algorithm to efficiently and effectively reinforce ant-like mobile agents to find the best route toward destination in a network regarding necessary QoS metrics. Simulation results show that the proposed method realizes QoS routing more efficiently and more adaptively than those of the existing method thereby providing better solutions for the best route selection for real-time application that has high priority on delay jitter and bandwidth.

• Journal of Korea Game Society
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• v.8 no.1
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• pp.79-90
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• 2008

This paper describes a motivation-based hierarchical behavior planning framework to allow autonomous agents to select adaptive actions in simulation game environments. The combined behavior planning system is formed by four levels of specification, which are motivation extraction, goal list generation, action list determination and optimization. Our model increases the complexity of virtual human behavior planning by adding motivation with sudden and cumulative attributes. The motivation selection by probability distribution allows NPC to make multiple decisions in certain situations in order to embody realistic virtual humans. Hierarchical goal tree enhances the effective reactivity. Optimizing for potential actions provides NPC with safe and satisfying actions to adapt to the virtual environment. A restaurant simulation game was used to elucidate the mechanism of the framework.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.129-134
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• 2003

This paper describes an application level composition mechanism called Generic Modeling Environment(GME) for web caching on an Application Level Active Network(ALAN). Web caching on an ALAN requires the application level composition mechanism and a service composition to support adaptability for self-organization. ALAN was developed to solve the problems of the network level Active Network(AN) ALAN has the features of both AN as well as mobile agents. The efficient composition mechanism for the existing AN Projects has been supported primarily for the network level AN. Conversely, ALAN lacks support for the application level AN The existing web caching technology is inter-connected in a manually configured hierarchical tree. Since a self-organization system is intended to be adaptive, web caching for self-organization does not involve a manual configuration or any low-level tuning of the individual nodes of the entire system but requires service composition to support adapting intelligence and fault-tolerance to enable self-organization.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1265-1275
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• 2002

In the network management system, there are two approaches; the centralized approach based on SNMP and the distributed approach based on mobile agent. Some information changes with time and the manager needs to monitor its value in real time. In such a case, the polling is generally used in SNMP because the manager can query agents periodically. However, the polling scheme needs both request and response messages for management information every time, which results in network traffic increase. In this paper, we suggest an adaptive network monitoring method to reduce the network traffic for SNMP-based network management. In the proposed strategy, each agent first decides its on monitoring period. Then, the manager collects them and approves each agent's period without modification or adjusts it based on the total traffic generated by monitoring messages. After receiving response message containing monitoring period from the manager, each agent sends management information periodically without the request of manager. To evaluate performance of the proposed method, we implemented it and compared the network traffic and monitoring quality of the proposed scheme with the general polling method.