• Proceedings of the Korea Association of Information Systems Conference
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• 2004.11a
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• pp.409-416
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• 2004

동적 공급사슬망은 복잡하고 다양한 이해관계를 가진 기업들로 구성되어 있다. 다수의 구매자로부터 주문 의뢰가 동시다발적으로 발생하므로 하위 구성원들은 경쟁적 관계에 놓이게 된다. 그러므로 최적의 공급사슬구성을 위해서는 수평적 경쟁 관계를 고려하여 구성주체들간의 협력관계를 통해 이를 해결하여야 한다. 지금까지의 스케줄링 문제에서는 상위의 구성원이 하위 구성원들을 일방적으로 선택하는 의사결정이 이루어졌으나 본 문제에서는 구성원간의 협력관계에서 에이전트를 통한 다자간 협상을 통해 공급사슬 전체의 최적화를 구성하는 방법론을 제시한다. 본 협상방법론은 단일기계에서 상이한 납기일, 조기생산(earliness), 지연생산(tardiness)을 동시에 고려하였으며 전체 공급사슬의 평균절대편차(Mean Absolute Deviation)의 최소화를 목적으로 하고 있다. 본 협상방법론의 효과성을 증명하기 위해 분지한계법(Branch & Bound)과 비교하고, 알고리즘 구현을 통해 구매자 협상방법론의 최적화 여부를 실험을 통해 증명하였다.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.11-25
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• 2005

In MIPv6 environment, an important design consideration for public key based binding update protocols is to minimize asymmetric cryptographic operations in mobile nodes with constraint computational power, such as PDAs and cellular phones, For that, public key based protocols such as CAM-DH. SUCV and Deng-Zhou-Bao's approach provides an optimization to offload asymmetric cryptographic operations of a mobile node to its home agent. However, such protocols have some problems in providing the optimization. Especially, CAM-DH with this optimization does not unload all asymmetric cryptographic operations from the mobile node, while resulting in the home agent's vulnerability to denial of service attacks. In this paper, we improve the drawbacks of CAM-DH. Furthermore, we adopt Aura's two hash-based CGA scheme to increase the cost of brute-force attacks searching for hash collisions in the CGA method. The comparison of our protocol with other public key based protocols shows that our protocol can minimize the MN's computation overhead, in addition to providing better manageability and stronger security than other protocols.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.1-13
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• 2018

Earthwork planning is one of the critical issues in a construction process management. For the construction process management, there are some different approaches such as optimizing construction with either mathematical methodologies or heuristics with simulations. This paper propose a simulated earthwork scenario and an optimal path for the simulation using a reinforcement learning. For reinforcement learning, we use two different Markov decision process, or MDP, formulations with interacting excavator agent and truck agent, sequenced learning, and independent learning. The simulation result shows that two different formulations can reach the optimal planning for a simulated earthwork scenario. This planning could be a basis for an automatic construction management.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.3
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• pp.215-221
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• 2012

In this paper, we introduce an optimization method to improve pursuit performance of a pursuer in a pursuit-evasion game (PEG). Pursuers build a probability map and employ a hybrid pursuit policy which combines the merits of local-max and global-max pursuit policies to search and capture evaders as soon as possible in a 2-dimensional space. We propose an episodic parameter optimization (EPO) algorithm to learn good values for the weighting parameters of a hybrid pursuit policy. The EPO algorithm is performed while many episodes of the PEG are run repeatedly and the reward of each episode is accumulated using reinforcement learning, and the candidate weighting parameter is selected in a way that maximizes the total averaged reward by using the golden section search method. We found the best pursuit policy in various situations which are the different number of evaders and the different size of spaces and analyzed results.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.193-199
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• 2005

One of the important fields for heuristics algorithm is how to balance between Intensificationand Diversification. Ant Colony System(ACS) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem(TSP). In this paper, we propose Multi Colony Interaction Ant Model that achieves positive negative interaction through elite strategy divided by intensification strategy and diversification strategy to improve the performance of original ACS. And, we apply multi colony interaction ant model by this proposed elite strategy to TSP and compares with original ACS method for the performance.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.447-451
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• 2010

Monitoring and analyzing the status of smart objects, the ubiquitous smart monitoring system provides several information such as user-state index, service states and system operation among the services in real time. It also provides self-optimization and self-management for enhancing the performance of services. In order to expand the application scope of this real-time monitoring system, it is indispensible to process huge amount of stream data. In this paper, we propose a load balancing scheme to solve the overload of the monitoring agents. Our proposed scheme reduces deadline miss ratio of entire data by more than 80%.

• Journal of the Korea Society for Simulation
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• v.23 no.2
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• pp.25-33
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• 2014

In this study, the border security problem of the ROK Army is examined by applying the agent-based modeling and simulation (ABMS) concept as well as its platform, MANA. Based on the approximately optimized behavior of the infiltrator obtained using genetic algorithm (GA), we evaluate the GOP border security system which consists of human resources, surveillance, as well as command and control (C2) systems. We use four measures of effectiveness (MOEs) to evaluate its performance, and we apply a near optimal latin hypercube (NOLH) design to deal with the large number of factors of interest in our model. By using a NOLH design, our simulation runs are implemented efficiently. We hope the results of this study provide valuable data for deciding the configuration of the border security system structure and the number of soldiers assigned in the platoon.

• The Journal of Korean Association of Computer Education
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• v.3 no.1
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• pp.169-176
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• 2000

This paper presents ENIG Agent can gather distributed educational newspaper information in the web as well as provide teachers and student those information for the NIE. ENIG Agent gleans newspaper headline of appropriate educational news portal site for real-time provision of those information. The optimized extraction of headline is performed through the pre-process of educational news site, information noise filtering, pattern matching. The educational newspaper headline information that is gotten through previous process will be shown to students by web-browser. To increase the usage of those information, intelligent education methods and visualized classification techniques are used. By experiment, the performance of this ENIG Agent was evaluated.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.376-378
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• 2005

사용자의 요구사항을 실시간으로 반영하고 연구비 예산정책과 관련된 의사결정 과정을 최대한 지원할수 있는 시스템이 절대적으로 필요하게 되었다. 이러한 현실적인 상황을 반영하여 연구비의 예산편성 및 예산집행의 효율성을 높이고자 J2ME 기반 모바일 연구비 통합관리 시스템을 개발하였다. J2ME 기반 모바일 연구비 통합관리 시스템은 설계단계에서 정보검색 에이전트와 정보통합 에이전트를 이용하였다. 이러한 결과 연구비 계획단계에서부터 예산편성 및 예산집행, 예산정산까지 독립적으로 관리하고 있는 운영 시스템의 데이터베이스들을 최적화하였다. 또한 각 시스템간의 이질성을 최소화하여 연구비 집행업무의 투명성을 향상시키고 상호간의 유기적인 정보교환과 조직의 계획수립 및 분석적 업무를 효과적으로 지원할 수 있었다. 이로 인해 최종 사용자가 원하는 분석정보에 신속하게 접근하여 단편적인 관점보다는 종합적인 관점에서 다양한 자료를 제공받을 수 있었다.