• 한국산업정보학회논문지
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• 제3권1호
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• pp.49-65
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• 1998

The trend toward globalization and competitive advantage is increasing the importance of international data communication . The more companies attempt to be globalized and competitive, the more critical the problems of the computer networks are. To improve computer networks in the business world, it is believed that the network shoud be approached from the viewpoint of service quality : we need to understand what network quality means to network users, those who use the network. The purpose of this study is to develop a dimension that captures the aspects of network quality that are important to network users. Many articles show that speed is very important factor in network systems. But the more important thing is that speed is just one of the factors contributing to network quality. It is suggested that a quality network meet nine dimensions : (1) speed (2) accessibility (3) connectivity (4) security (5) compatibility, (6) government restrictions (7) complexity (8) sustainability and (9) sharability.

• 산업경영시스템학회지
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• 제33권4호
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• pp.122-129
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• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• Korean Chemical Engineering Research
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• 제46권2호
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• pp.369-375
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• 2008

생태산업단지의 주요 목표인 무배출(zero-emission)을 달성하기 위해서는, 기업간의 효과적인 용수 재이용망의 구축이 요구된다. 다양한 서브시스템과 의사결정과정을 포함한 복잡한 망 구조를 이루기 때문에 생기는 모델링 부분에서의 문제점을 해결하기 위하여, 본 연구에서는 새로운 모델링 기법인 에이전트기반 모델링(agent based modeling, ABM)을 도입하였다. 에이전트기반 모델링 기법을 이용해 물질교환망 내의 기업을 에이전트화 하고 이를 토대로 전체 시스템을 모사하는 방법론을 제시하였으며, 이를 이용해 실제 산업단지 내 기업에 대한 간단한 용수교환망 설계 모델을 구축하였다. 에이전트기반 모델에 기초한 용수재이용망은 용수공급에서 35% 감소를 보이며 폐수 배출량은 약 50%가 감소하였다. 실제 산업단지 모델링 결과 에이전트기반 모델은 시스템 변동시에도 전체 시스템을 수정할 필요가 없으며, 시스템이 확장하는 경우에도 그 적용이 용이하기 때문에 생태산업단지와 같은 복잡한 시스템의 모델링과 설계시 유용한 기술로 사용될 수 있음을 보였다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제2권2호
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• pp.111-123
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• 1998

Performance index is a measure of telecommunications network integrating reliability and capacity simultaneously. This paper suggests a computerized algorithm evaluating a performance index for telecommunications network and compares this computerized algorithm with the algorithm[1] by experimenting on several benchmarks. A computerized algorithm proposed by this paper is superior to the algorithm[1] with respect to the computation time for most of the benchmarks.

• 대한산업공학회지
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• 제34권2호
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• pp.181-189
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• 2008

Survivability of a network is one of the most important issues in designing present-day communication networks. For the past few decades, a lot of researches have proposed the mathematical models to evaluate the survivability of networks. In this paper, we attempt to survey such researches and classify them based on how these researches measure the survivability of a network.

• 산업기술연구
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• 제16권
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• pp.197-205
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• 1996

In this paper, we propose a new neural architecture. We synthesize the architecture from a combination of structures known as MRCCN (Multi-resolution Radial-basis Competitive and Cooperative Network) and BPN (Backpropagation Network). The proposed neural network is able to improve the learning speed of MRCCN and the mapping capability of BPN. The ability and effectiveness of identifying a ninlinear dynamic system using the proposed architecture will be demonstrated by computer simulation.

• 산업경영시스템학회지
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• 제13권21호
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• pp.51-60
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• 1990

In this paper we deal with the miniaml cost network flow problem with uncertain arc capacity constraints. When the arc capacities are fuzzy with linear L-R type membership function, using parametric programming procedure, we reduced it to the deterministic minimal cost network flow problem which can be solved by various typical network flow algorithms. A modified Algorithm using the Out-of-kilter algorithm is developed.

• 산업경영시스템학회지
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• 제18권34호
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• pp.99-106
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• 1995

Recently, neural network is recognized as a new approach to solve jobshop scheduling problems in manufacturing system. Scheduling problem is known to be a difficult combinational explosive problem with domain-dependence variations in general. In addition, the needs to achieve a good performance in flexible manufacturing system increase the dimensions of decision complexity. Therefore, mathematical approach to solve realistic problems could be failed to find optimal or optimal-trending. In this paper a technique with neural network for jobs grouping by job-attributes and Gaussian machine network for generating to near-optimal sequence is presented.

• 산업기술연구
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• 제11권
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• pp.99-106
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• 1991

In this paper the functional requirements of a B-ISDN access network based on MAN are investigated. Firstly, criteria of designing access network for the B-ISDN is studied. Secondly, the types of subscribers and their service requirements are defined. Thirdly, various architectures of CPNs(Customer Premises Networks) are considered and their characteristics are studied, Finally, the interworking functions of a DQDB MAN as an access network are investigated.

• 한국산업정보학회논문지
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• 제2권1호
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• pp.73-87
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• 1997

Multilayer neural network using a modified beackpropagation learning algorithm was introduced to achieve automatic identification of different types of aircraft in a variety of 3-D orientations. A 3-D shape of an aircraft can be described by a library of 2-D images corresponding to the projected views of an aircraft. From each 2-D binary aircraft image we extracted 2-D invariant (L, Φ) feature vector to be used for training neural network aircraft classifier. Simulations concerning the neural network classification rate was compared using nearest-neighbor classfier (NNC) which has been widely served as a performance benchmark. And we also introduced reliability measure of the designed neural network classifier.